Concrete production process quality remote monitoring system and method

Abstract

The present invention relates to a remote monitoring system and method for the quality of a concrete production process. The system includes: an image acquisition module for acquiring image data of concrete to be inspected for quality in a production site; a first calculation module for determining the average grayscale value of pixels of the image data based on the image data; a second calculation module for determining the comprehensive grayscale value of the image data based on the average grayscale value; the comprehensive grayscale value is used to characterize the overall grayscale distribution of the image data after combining the weights of each pixel; a first damage module for determining the damaged area of ​​the concrete and the damage data corresponding to the damaged area based on the comprehensive grayscale value; a second damage module for determining the degree of damage of the concrete based on the damage data and the comprehensive grayscale value; and a quality monitoring module for determining the quality of the concrete based on the degree of damage. The present invention can improve the accuracy and reliability of quality monitoring of internal damage to concrete.

Description

Technical Field

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

[0002] As a low-cost, easy-to-produce engineering material, concrete is widely used in various harsh environments. Due to the influence of the external environment, concrete often suffers damage, which reduces its mechanical properties and durability, thereby shortening the service life of concrete components. In order to quantitatively analyze the pore structure and microcracks inside the damaged concrete, traditional quality monitoring methods usually use a microscope to observe the microstructure of concrete samples. By collecting images of the surface to be tested, objects that meet certain conditions are identified as holes, and those that exceed the conditions are identified as microcracks. Then, the holes and microcracks are statistically calculated to evaluate the degree of damage inside the concrete.

[0003] However, when there is a combination of microcracks and pores in concrete samples, traditional quality monitoring methods cannot accurately identify this combination, resulting in inaccurate quantitative analysis results of pores and microcracks. This inaccuracy in turn affects the accurate evaluation of concrete quality and may lead to misjudgment and unnecessary repair or replacement work.

[0004] Therefore, the limitations of existing quality monitoring methods for concrete production need to be overcome to improve the accuracy and reliability of quality monitoring of concrete internal damage. Summary of the invention

[0005] The present invention aims at the technical problems existing in the prior art and provides a remote monitoring system and method for the quality of a concrete production process, which can improve the accuracy and reliability of monitoring the quality of internal damage of concrete.

[0006] The technical solution of the present invention to solve the above technical problems is as follows:

[0007] The present invention provides a remote monitoring system for the quality of a concrete production process, the system comprising:

[0008] An image acquisition module, used to acquire image data of concrete to be inspected for quality in a production site;

[0009] A first calculation module, used for determining an average grayscale value of pixels of the image data based on the image data;

[0010] A second calculation module is used to determine a comprehensive grayscale value of the image data based on the average grayscale value; the comprehensive grayscale value is used to characterize the overall grayscale distribution of the image data after combining the weights of each pixel;

[0011] A first damage module, used for determining a damaged area of ​​the concrete and damage data corresponding to the damaged area based on the comprehensive gray value;

[0012] A second damage module, used for determining the degree of damage of the concrete based on the damage data and the comprehensive gray value;

[0013] A quality monitoring module is used to determine the quality of the concrete based on the damage degree.

[0014] Optionally, the second calculation module is further specifically configured to:

[0015] Based on the average gray value, construct a gray value processing function;

[0016] Based on the gray value processing function, gray value processing is performed on the gray value of the pixel of the image data to obtain first image data after gray value processing;

[0017] Based on the image data and the first image data, a comprehensive grayscale value of the image data is determined.

[0018] Optionally, the gray value processing function is expressed as:

[0019]

[0020] Among them, f(I j ) is the gray value processing function, its function value is equal to the gray value after processing, P is the average gray value, j is the pixel number, I j is the gray value of the jth pixel.

[0021] Optionally, the second calculation module is further specifically configured to:

[0022] Acquire a first weight and a first grayscale value of each pixel of the image data, and a total number of the pixels;

[0023] Acquire a second weight and a second grayscale value of a pixel of the first image data; the total number of pixels of the first image data is equal to the total number of the pixels;

[0024] Based on the first grayscale value, the first weight, the second grayscale value, the second weight and the total number, a comprehensive grayscale value of the image data is determined.

[0025] Optionally, the comprehensive gray value is expressed as:

[0026]

[0027] Wherein, T is the comprehensive gray value, N is the total number of pixels in the image data and the first image data, i, j are the numbers of the pixels, I is the first gray value, Ii is the gray value of the i-th pixel, I j is the gray value of the jth pixel, f(I j ) is the second gray value, ω i is the first weight of the i-th pixel, v i is the second weight of the i-th processed pixel.

[0028] Optionally, the first damage module is further specifically used for:

[0029] Obtaining a first threshold value;

[0030] Determining the damaged area based on a comparison result between the integrated gray value and the first threshold value includes: determining an area of ​​pixels having an integrated gray value greater than the first threshold value as the damaged area;

[0031] The total area of ​​the pore structure in the damaged region and the total length of the microcracks are determined to obtain the damage data.

[0032] Optionally, the damage degree is expressed as:

[0033]

[0034] Wherein, D is the degree of damage of concrete, A is the total area of ​​pore structure, L is the total length of microcracks, T is the comprehensive gray value, α, β, γ are all adjustment parameters, and the adjustment parameters are used to control the sensitivity and accuracy of calculating the degree of damage.

[0035] Optionally, the quality monitoring module is further specifically used for:

[0036] Obtaining a second threshold value;

[0037] Determining the quality of the concrete based on a comparison result between the damage degree and the second threshold includes: determining the quality of concrete with a damage degree greater than or equal to the second threshold as unqualified, and determining the quality of concrete with a damage degree less than the second threshold as qualified.

[0038] Optionally, the system further comprises a preprocessing module, wherein the preprocessing module is used to:

[0039] Before the gray value calculation module calculates the gray value, the image data is preprocessed, and the preprocessing process includes:

[0040] Performing grayscale processing on the image data to obtain a corresponding grayscale image;

[0041] Performing denoising on the grayscale image to obtain a corresponding denoised image;

[0042] The denoised image is enhanced to obtain processed image data.

[0043] The present invention also provides a method for remote monitoring of quality of a concrete production process, the method comprising:

[0044] Acquire image data of concrete to be inspected for quality in a production site;

[0045] Based on the image data, determining an average grayscale value of pixels of the image data;

[0046] Based on the average grayscale value, determining a comprehensive grayscale value of the image data; the comprehensive grayscale value is used to characterize the overall grayscale distribution of the image data after combining the weights of each pixel;

[0047] Based on the comprehensive gray value, determining the damaged area of ​​the concrete and the damage data corresponding to the damaged area;

[0048] Determining the degree of damage of the concrete based on the damage data and the comprehensive gray value;

[0049] Based on the degree of damage, the quality of the concrete is determined.

[0050] In addition, to achieve the above objectives, the present invention also proposes an electronic device, comprising: a memory for storing a computer software program; a processor for reading and executing the computer software program, thereby realizing a remote monitoring method for the quality of a concrete production process as described above.

[0051] In addition, to achieve the above-mentioned purpose, the present invention also proposes a non-transitory computer-readable storage medium, in which a computer software program is stored. When the computer software program is executed by a processor, a remote monitoring method for the quality of a concrete production process as described above is implemented.

[0052] The beneficial effects of the present invention are:

[0053] (1) By introducing machine learning algorithms and image processing technology, the present invention can more accurately identify the damaged area inside the concrete in a remote monitoring manner, avoiding the subjectivity and errors of human judgment.

[0054] (2) The present invention can reduce labor costs and improve the efficiency of concrete quality monitoring. At the same time, the faster processing speed of the machine learning algorithm is applied to image processing and remote quality monitoring, which can respond to concrete quality problems in a timely manner.

[0055] (3) The present invention obtains more accurate concrete quality assessment results by quantitatively analyzing the damaged area of ​​concrete, which helps manufacturers better understand the actual quality status of concrete components and helps improve product quality.

[0056] (4) The present invention can timely discover potential problems by real-time monitoring of the damage inside the concrete, take preventive maintenance measures, extend the service life of the concrete components, and reduce maintenance costs.

[0057] In summary, the present invention can realize remote monitoring and quality assessment of the concrete production process, which provides beneficial effects for improving the quality of concrete products, reducing production costs, and ensuring project safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 A scene diagram of a method for remote monitoring of quality of a concrete production process provided by the present invention;

[0059] Figure 2 A flow chart of a method for remote monitoring of quality of a concrete production process provided by the present invention;

[0060] Figure 3 A structural schematic diagram of a remote monitoring system for the quality of a concrete production process provided by the present invention;

[0061] Figure 4 A schematic diagram of the hardware structure of a possible electronic device provided by the present invention;

[0062] Figure 5 A schematic diagram of the hardware structure of a possible computer-readable storage medium provided by the present invention. DETAILED DESCRIPTION

[0063] 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 those skilled in the art without creative work are within the scope of protection of the present invention.

[0064] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0065] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration or explanation". Any embodiment described as "for example" in the present invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present invention.

[0066] See also Figure 1 , Figure 1 This is a scene diagram of a method for remote monitoring of the quality of a concrete production process provided by the present invention. Figure 1 As shown, the terminal and the server are connected via a network, such as a wired or wireless network connection. The terminal may include but is not limited to portable terminals such as mobile phones and tablets installed with various network platform applications, as well as fixed terminals such as computers, query machines, and advertising machines. The server provides users with various business services, including service push servers, user recommendation servers, etc.

[0067] It should be noted that Figure 1 The scenario diagram of a method for remote monitoring of the quality of a concrete production process shown is only an example. The terminal, server, and application scenario described in the embodiment of the present invention are intended to more clearly illustrate the technical solution of the embodiment of the present invention, and do not generate limitations on the technical solution provided by the embodiment of the present invention. A person of ordinary skill in the art can know that with the evolution of the system and the emergence of new business scenarios, the technical solution provided by the embodiment of the present invention is also applicable to similar technical problems.

[0068] Among them, the terminal can be used for:

[0069] Acquire image data of concrete to be inspected for quality in a production site;

[0070] Based on the image data, determining an average grayscale value of pixels of the image data;

[0071] Based on the average grayscale value, determining a comprehensive grayscale value of the image data; the comprehensive grayscale value is used to characterize the overall grayscale distribution of the image data after combining the weights of each pixel;

[0072] Based on the comprehensive gray value, determining the damaged area of ​​the concrete and the damage data corresponding to the damaged area;

[0073] Determining the degree of damage of the concrete based on the damage data and the comprehensive gray value;

[0074] Based on the degree of damage, the quality of the concrete is determined.

[0075] It should also be noted that the present invention strictly follows the principles of user authorization and legal provisions. For example, before collecting relevant data from users, it is possible to ensure that the user's explicit authorization is obtained, and the data is processed strictly in accordance with the scope and purpose of the user's authorization. At the same time, it also strictly follows applicable privacy laws and regulations, protects the security and privacy of user data by formulating strict data protection policies and security measures, and regularly reviews and updates the data processing process to ensure compliance with the latest legal requirements and best practices.

[0076] See also Figure 2 , Figure 2 A structural schematic diagram of a remote monitoring system for concrete production process quality provided by the present invention.

[0077] like Figure 2 As shown, a remote monitoring system for the quality of a concrete production process proposed in an embodiment of the present invention includes: an image acquisition module 201, a first calculation module 202, a second calculation module 203, a first damage module 204, a second damage module 205 and a quality monitoring module 206, which are specifically as follows:

[0078] The image acquisition module 201 is used to acquire image data of concrete to be quality inspected in a production site.

[0079] The image data may be an image of the matrix structure of concrete. In the scenario of the present invention, the image data of concrete to be inspected in the production site can be obtained, which means that during the concrete production process, an imaging device (such as a camera or a sensor) is used to obtain image information of the surface of the concrete component. The image data may include a grayscale image, a color image, or other forms of image data of the concrete surface.

[0080] In some embodiments, a camera or other imaging device may be installed in the production site to capture images of the surface of the concrete component. The image data of the concrete component may be captured by the imaging device, for example, shooting at different angles and perspectives may be involved to ensure that comprehensive and accurate image information is obtained.

[0081] In some embodiments, the collected image data can be saved to a computer or other storage device for subsequent processing and analysis. If remote monitoring or real-time analysis is required, the image data can also be transmitted to a remote server or cloud platform for remote access and processing.

[0082] In some embodiments, the image data may be marked and annotated, for example, including marking the location of the concrete component, timestamp, and other information to facilitate subsequent data analysis and quality assessment.

[0083] By acquiring image data of concrete to be inspected for quality in the production site in the above manner, remote monitoring and quality assessment of the concrete production process can be achieved, providing important data support for improving the quality and production efficiency of concrete products.

[0084] The first calculation module 202 is used to determine the average grayscale value of the pixels of the image data based on the image data.

[0085] The average gray value refers to the average value calculated from the gray values ​​of all pixels in the image data. Specifically, during the image processing process, the image data of the concrete surface can be converted into a gray image, and then the entire image area is analyzed to extract the pixels of the concrete matrix area and the gray value of each pixel, and the average gray value of these pixels is calculated based on the gray value of each pixel, that is, the average gray value is obtained.

[0086] In some embodiments, the system of the present invention further comprises a preprocessing module, wherein the preprocessing module is used to:

[0087] Before the first calculation module calculates the grayscale value, the image data is preprocessed, and the preprocessing process includes:

[0088] Performing grayscale processing on the image data to obtain a corresponding grayscale image;

[0089] Performing denoising on the grayscale image to obtain a corresponding denoised image;

[0090] The denoised image is enhanced to obtain processed image data.

[0091] In some embodiments, the image data may be grayed, which means converting the acquired color image of the concrete component into a gray image, with the purpose of simplifying the image data, removing color information, and retaining only gray information. Graying can reduce the complexity of processing and speed up subsequent image processing and analysis.

[0092] Next, the grayscale image can be denoised to eliminate the noise that may exist in the grayscale image. For example, noise can be caused by factors such as imaging equipment and ambient light, which will affect the subsequent image analysis and processing results. Common denoising methods include mean filtering, median filtering, Gaussian filtering, etc. Through denoising, the quality and clarity of the image can be improved and the errors in the subsequent processing process can be reduced.

[0093] Finally, the denoised image can be enhanced, which means adjusting and optimizing the image to highlight specific information in the image and improve the contrast and clarity of the image. Enhancement can be done using methods such as histogram equalization, contrast stretching, and edge enhancement. Through enhancement, the image can be made clearer and easier to analyze, which helps to accurately identify the damaged areas and quality characteristics of the concrete surface.

[0094] Through the above methods, the present invention can perform grayscale processing, denoising processing and enhancement processing on image data, and can obtain processed image data, providing a clear and accurate image basis for subsequent concrete quality monitoring and damage identification.

[0095] The second calculation module 203 is used to determine the comprehensive grayscale value of the image data based on the average grayscale value.

[0096] The comprehensive grayscale value is used to characterize the overall grayscale distribution of the image data after combining the weights of each pixel. It can be understood that in the process of concrete quality monitoring and damage identification, the image data contains the grayscale information of the concrete surface. By weighting each pixel in the image, the comprehensive grayscale value T of the entire image can be obtained.

[0097] The calculation of the comprehensive gray value T is achieved by combining the gray value and weight of each pixel. The weight can be adjusted according to the specific application requirements and image characteristics to highlight the important features or areas in the image. For example, the weight of the pixel can be determined according to the importance of different areas in the image, the degree of damage, etc.

[0098] By calculating the comprehensive grayscale value T, the overall grayscale distribution of the image data can be obtained. T reflects the overall grayscale characteristics of the concrete surface, including the grayscale values ​​of different regions and their weight relationships. Therefore, T can be used as an important parameter for evaluating the quality and damage degree of concrete, providing an important reference for subsequent quality assessment and damage identification.

[0099] In some embodiments, the second calculation module 203 is further specifically configured to:

[0100] Based on the average gray value, construct a gray value processing function;

[0101] Based on the gray value processing function, gray value processing is performed on the gray value of the pixel of the image data to obtain first image data after gray value processing;

[0102] Based on the image data and the first image data, a comprehensive grayscale value of the image data is determined.

[0103] In some embodiments, the gray value processing function is expressed as:

[0104]

[0105] Among them, f(I j ) is the gray value processing function, its function value is equal to the gray value after processing, P is the average gray value, j is the pixel number, I j is the gray value of the jth pixel.

[0106] In a specific implementation, the grayscale value of a specific pixel, that is, the pixel of the first image data, can be weighted to highlight the characteristics of the concrete matrix area. By combining the grayscale value of the specific pixel with the average grayscale value of the entire matrix area and the square root of the product of the grayscale value and the average grayscale value, the damaged area and the matrix area can be better distinguished.

[0107] First, the gray value of a specific pixel I can be calculated j The product of the average gray value P of the entire matrix area and taking its square root. The purpose of this is to introduce the correlation between a specific pixel and the entire matrix area in order to better highlight the characteristics of the specific pixel.

[0108] Then, I j Add the square root of the product with P, add P, and divide the result by 3. The purpose of this is to average the gray value of a specific pixel with the average gray value of the overall matrix area, while taking into account the square root of its product with the average gray value, to better highlight the characteristics of the specific pixel.

[0109] The purpose of this is to introduce the correlation between a specific pixel and the overall matrix area in order to better highlight the features of the specific pixel.

[0110] Then, I j Add the square root of the product with P, add P, and divide the result by 3. The purpose of this is to average the gray value of a specific pixel with the average gray value of the overall matrix area, while taking into account the square root of its product with the average gray value, to better highlight the characteristics of the specific pixel.

[0111] In the above way, a more comprehensive and balanced grayscale value of a specific pixel can be obtained, which helps to better identify the damaged area in the concrete.

[0112] In some embodiments, the second calculation module 203 is further specifically configured to:

[0113] Acquire a first weight and a first grayscale value of each pixel of the image data, and a total number of the pixels;

[0114] Acquire a second weight and a second grayscale value of a pixel of the first image data; the total number of pixels of the first image data is equal to the total number of the pixels;

[0115] Based on the first grayscale value, the first weight, the second grayscale value, the second weight and the total number, a comprehensive grayscale value of the image data is determined.

[0116] In some embodiments, the integrated grayscale value is represented by:

[0117]

[0118] Wherein, T is the comprehensive gray value, N is the total number of pixels in the image data and the first image data, i, j are the numbers of the pixels, I is the first gray value, I i is the gray value of the i-th pixel, I j is the gray value of the jth pixel, f(I j ) is the second gray value, ω i is the first weight of the i-th pixel, v i is the second weight of the i-th processed pixel.

[0119] In the specific implementation, in the formula for calculating the comprehensive gray value T, the first part of the formula The first part is to calculate the weighted average gray value of all pixels, and the second part is to calculate the weighted gray value of a specific pixel after being processed by the gray value processing function.

[0120] For the first part, the weighted average grayscale value of all pixels is calculated, and the first weight ω i This can be adjusted based on the location and importance of the pixel. For example, pixels located at the edge of a concrete component can be given a lower weight, while pixels located in the center can be given a higher weight.

[0121] For the second part It is used to calculate the weighted gray value of some specific pixels, which may be very important for the identification and analysis of the damaged area. By using the function f(I j ) Processing the grayscale values ​​of specific pixels can emphasize the characteristics of these pixels to better distinguish the damaged areas.

[0122] The first damage module 204 is used to determine the damaged area of ​​the concrete and the damage data corresponding to the damaged area based on the comprehensive gray value.

[0123] In some embodiments, the first damage module 204 is further configured to:

[0124] Obtaining a first threshold value;

[0125] Determining the damaged area based on a comparison result between the integrated gray value and the first threshold value includes: determining an area of ​​pixels having an integrated gray value greater than the first threshold value as the damaged area;

[0126] The total area of ​​the pore structure in the damaged region and the total length of the microcracks are determined to obtain the damage data.

[0127] In some embodiments, obtaining the first threshold refers to determining a threshold during image processing to segment the image into different regions. The first threshold can usually be determined based on the grayscale characteristics of the image and specific application requirements. The purpose of obtaining the first threshold is to segment the image into a background region and a foreground region, providing a basis for subsequent damage region identification and data extraction.

[0128] In some embodiments, determining the damaged area based on the comparison result between the integrated gray value and the first threshold value means that, based on the relationship between the integrated gray value T and the first threshold value, the area where the pixels in the image with gray values ​​greater than the first threshold value are located is determined as the damaged area. In the image processing process, the pixels with gray values ​​greater than the first threshold value usually represent the damaged area or other key features, so the area where the above pixels are located is marked and identified as the damaged area.

[0129] In some embodiments, determining the total area of ​​the pore structure and the total length of the microcracks in the damaged area means that after the damaged area is identified, the damaged area is further analyzed and quantified to calculate the total area of ​​the pore structure and the total length of the microcracks. The above damage data can be used to evaluate the degree of damage and quality status of the concrete component, providing a basis for subsequent repair and maintenance.

[0130] It can be understood that the comparison of the comprehensive gray value with the first threshold value and the determination of the damaged area are one of the key steps in concrete quality monitoring and damage identification in the present invention, which can help to timely discover the damage on the surface of the concrete component and conduct quantitative analysis of the damaged area, providing important data support for subsequent maintenance and management.

[0131] The second damage module 205 is used to determine the degree of damage of the concrete based on the damage data and the comprehensive gray value.

[0132] In some embodiments, the degree of impairment is expressed as:

[0133]

[0134] Wherein, D is the degree of damage of concrete, A is the total area of ​​pore structure, L is the total length of microcracks, T is the comprehensive gray value, α, β, γ are all adjustment parameters, and the adjustment parameters are used to control the sensitivity and accuracy of calculating the degree of damage.

[0135] In the specific implementation, the adjustment parameters α, β, and γ can be used to control the sensitivity and accuracy of the damage calculation. The numerator A×α+L×(1-α) represents the total volume of the damaged area. By weighting the total area A of the pore structure and the total length L of the microcracks, a comprehensive index of the damaged area is obtained. The denominator T 2 ×(1-β) γ Reflecting the grayscale characteristics of the overall concrete and the influence of the adjustment parameters, the denominator is used to normalize and adjust the damage degree to ensure that the damage degree assessment is more accurate and reliable. Therefore, by comparing the total volume of the damaged area with the grayscale characteristics of the overall concrete, a comprehensive damage degree assessment index D can be obtained.

[0136] The quality monitoring module 206 is used to determine the quality of the concrete based on the damage degree.

[0137] In some embodiments, the quality monitoring module 206 is further specifically configured to:

[0138] Obtaining a second threshold value;

[0139] Determining the quality of the concrete based on a comparison result between the damage degree and the second threshold includes: determining the quality of concrete with a damage degree greater than or equal to the second threshold as unqualified, and determining the quality of concrete with a damage degree less than the second threshold as qualified.

[0140] In some embodiments, obtaining the second threshold value refers to determining a corresponding threshold value in the process of concrete quality monitoring and damage identification, which is used to judge the quality status of the concrete component. The second threshold value can usually be determined based on the calculation result of the damage degree and the specific application requirements. The purpose of obtaining the second threshold value is to classify the quality of the concrete component into two categories: qualified and unqualified, so as to provide a basis for quality assessment and management.

[0141] In some embodiments, determining the quality of the concrete based on the comparison result between the degree of damage and the second threshold value means determining the quality of the concrete component as qualified or unqualified based on the relationship between the degree of damage and the second threshold value. Specifically, if the degree of damage is greater than or equal to the second threshold value, the quality of the concrete is determined to be unqualified; if the degree of damage is less than the second threshold value, the quality of the concrete is determined to be qualified.

[0142] It can be understood that by comparing the damage degree with the second threshold, the quality status of the concrete component can be judged in time, so that corresponding maintenance or replacement measures can be taken to ensure the safety and reliability of the project. This step is one of the key links in the concrete quality assessment and management of the present invention, which can effectively improve the accuracy and reliability of the concrete quality and provide important support for project management.

[0143] See also Figure 3 , provides a flow chart of a method for remote monitoring quality of a concrete production process of the present invention, comprising the following steps:

[0144] Step 301, obtaining image data of concrete to be quality inspected in a production site;

[0145] Step 302: Based on the image data, determine the average grayscale value of the pixels of the image data;

[0146] Step 303: Determine a comprehensive grayscale value of the image data based on the average grayscale value; the comprehensive grayscale value is used to characterize the overall grayscale distribution of the image data after combining the weights of each pixel;

[0147] Step 304: determining the damaged area of ​​the concrete and the damage data corresponding to the damaged area based on the comprehensive gray value;

[0148] Step 305: determining the degree of damage of the concrete based on the damage data and the comprehensive gray value;

[0149] Step 306: Determine the quality of the concrete based on the degree of damage.

[0150] See also Figure 4 , Figure 4 Schematic diagram of an electronic device provided by an embodiment of the present invention. Figure 4 As shown, an embodiment of the present invention provides an electronic device 400, including a memory 410, a processor 420, and a computer program 411 stored in the memory 410 and executable on the processor 420. When the processor 420 executes the computer program 411, the following steps are implemented:

[0151] Acquire image data of concrete to be inspected for quality in a production site;

[0152] Based on the image data, determining an average grayscale value of pixels of the image data;

[0153] Based on the average grayscale value, determining a comprehensive grayscale value of the image data; the comprehensive grayscale value is used to characterize the overall grayscale distribution of the image data after combining the weights of each pixel;

[0154] Based on the comprehensive gray value, determining the damaged area of ​​the concrete and the damage data corresponding to the damaged area;

[0155] Determining the degree of damage of the concrete based on the damage data and the comprehensive gray value;

[0156] Based on the degree of damage, the quality of the concrete is determined.

[0157] See also Figure 5 , Figure 5 A schematic diagram of an embodiment of a computer-readable storage medium provided in an embodiment of the present invention. Figure 5 As shown, this embodiment provides a computer-readable storage medium 500, on which a computer program 411 is stored. When the computer program 411 is executed by a processor, the following steps are implemented:

[0158] Acquire image data of concrete to be inspected for quality in a production site;

[0159] Based on the image data, determining an average grayscale value of pixels of the image data;

[0160] Based on the average grayscale value, determining a comprehensive grayscale value of the image data; the comprehensive grayscale value is used to characterize the overall grayscale distribution of the image data after combining the weights of each pixel;

[0161] Based on the comprehensive gray value, determining the damaged area of ​​the concrete and the damage data corresponding to the damaged area;

[0162] Determining the degree of damage of the concrete based on the damage data and the comprehensive gray value;

[0163] Based on the degree of damage, the quality of the concrete is determined.

[0164] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0165] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as systems, methods or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0166] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A system that specifies the functions of a box or multiple boxes.

[0167] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction system, which is implemented in the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0168] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0169] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0170] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A remote monitoring system for the quality of concrete production process, characterized in that: The system comprises: An image acquisition module, used to acquire image data of concrete to be inspected for quality in a production site; A first calculation module, used for determining an average grayscale value of pixels of the image data based on the image data; A second calculation module is used to determine a comprehensive grayscale value of the image data based on the average grayscale value; the comprehensive grayscale value is used to characterize the overall grayscale distribution of the image data after combining the weights of each pixel; A first damage module, used for determining a damaged area of ​​the concrete and damage data corresponding to the damaged area based on the comprehensive gray value; A second damage module, used for determining the degree of damage of the concrete based on the damage data and the comprehensive gray value; A quality monitoring module, used for determining the quality of the concrete based on the damage degree; The second calculation module is further specifically used for: Based on the average gray value, construct a gray value processing function; Based on the gray value processing function, gray value processing is performed on the gray value of the pixel of the image data to obtain first image data after gray value processing; Based on the image data and the first image data, a comprehensive grayscale value of the image data is determined.

2. The remote monitoring system for concrete production process quality according to claim 1 is characterized in that: The gray value processing function is expressed as: Among them, f(I j ) is the gray value processing function, its function value is equal to the gray value after processing, P is the average gray value, j is the pixel number, I j is the gray value of the jth pixel.

3. The remote monitoring system for concrete production process quality according to claim 2 is characterized in that: The second calculation module is further specifically used for: Acquire a first weight and a first grayscale value of each pixel of the image data, and a total number of the pixels; Acquire a second weight and a second grayscale value of a pixel of the first image data; the total number of pixels of the first image data is equal to the total number of the pixels; Based on the first grayscale value, the first weight, the second grayscale value, the second weight and the total number, a comprehensive grayscale value of the image data is determined.

4. The remote monitoring system for concrete production process quality according to claim 3 is characterized in that: The comprehensive gray value is expressed as: Wherein, T is the comprehensive gray value, N is the total number of pixels in the image data and the first image data, i, j are the numbers of the pixels, I is the first gray value, I i is the gray value of the i-th pixel, I j is the gray value of the jth pixel, f(I j ) is the second gray value, ω i is the first weight of the i-th pixel, v i is the second weight of the i-th processed pixel.

5. The remote monitoring system for the quality of concrete production process according to claim 4 is characterized in that: The first damage module is further specifically used for: Obtaining a first threshold value; Determining the damaged area based on a comparison result between the integrated gray value and the first threshold value includes: determining an area of ​​pixels having an integrated gray value greater than the first threshold value as the damaged area; The total area of ​​the pore structure in the damaged region and the total length of the microcracks are determined to obtain the damage data.

6. The remote monitoring system for the quality of concrete production process according to claim 5 is characterized in that: The damage degree is expressed as: Wherein, D is the degree of damage of concrete, A is the total area of ​​pore structure, L is the total length of microcracks, T is the comprehensive gray value, α, β, γ are all adjustment parameters, and the adjustment parameters are used to control the sensitivity and accuracy of calculating the degree of damage.

7. The remote monitoring system for the quality of concrete production process according to any one of claims 1 to 6, characterized in that: The quality monitoring module is also specifically used for: Obtaining a second threshold value; Determining the quality of the concrete based on a comparison result between the damage degree and the second threshold includes: determining the quality of concrete with a damage degree greater than or equal to the second threshold as unqualified, and determining the quality of concrete with a damage degree less than the second threshold as qualified.

8. The remote monitoring system for concrete production process quality according to claim 1 is characterized in that: The system further comprises a preprocessing module, wherein the preprocessing module is used for: Before the first calculation module calculates the grayscale value, the image data is preprocessed, and the preprocessing process includes: Performing grayscale processing on the image data to obtain a corresponding grayscale image; Performing denoising on the grayscale image to obtain a corresponding denoised image; The denoised image is enhanced to obtain processed image data.

9. A method for remote monitoring of the quality of a concrete production process, characterized in that: The method comprises: Acquire image data of concrete to be inspected for quality in a production site; Based on the image data, determining an average grayscale value of pixels of the image data; Based on the average grayscale value, determining a comprehensive grayscale value of the image data; the comprehensive grayscale value is used to characterize the overall grayscale distribution of the image data after combining the weights of each pixel; Based on the comprehensive gray value, determining the damaged area of ​​the concrete and the damage data corresponding to the damaged area; Determining the degree of damage of the concrete based on the damage data and the comprehensive gray value; determining the quality of the concrete based on the degree of damage; Based on the average gray value, construct a gray value processing function; Based on the gray value processing function, gray value processing is performed on the gray value of the pixel of the image data to obtain first image data after gray value processing; Based on the image data and the first image data, a comprehensive grayscale value of the image data is determined.

Images