Concrete block quality control method and system based on production data analysis

Abstract

The invention discloses a concrete block quality control method and system based on production data analysis, and belongs to the technical field of general control systems.The method comprises the steps that pictures in the concrete block manufacturing process are collected, and the pictures are imported into a pore comprehensive anomaly analysis strategy to conduct pore comprehensive anomaly condition analysis on concrete blocks; importing the picture into a crack anomaly analysis strategy to analyze the crack anomaly condition of the concrete block; performing comprehensive abnormal condition analysis on the concrete block according to a block comprehensive abnormal analysis strategy, obtaining the optimal temperature according to a temperature adjustment model, calculating a comprehensive abnormal value again, comparing the difference value between the two times with a preset difference threshold value, and judging whether the temperature needs to be adjusted again according to a comparison result; according to the method, the quality of the building block is evaluated through comprehensive analysis of air holes and cracks of the concrete building block, and the temperature in the manufacturing process is adjusted according to the quality condition, so that the manufacturing temperature of the building block is maintained in the optimal temperature range, and the production quality of the concrete building block is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of general control or regulation systems, and more specifically to a method and system for controlling the quality of concrete blocks based on production data analysis. Background Art

[0002] The existing Chinese patent with authorization announcement number CN105045307B discloses a large-volume concrete full-process intelligent temperature control system and method. The application can realize the automatic perception, transmission, interconnection, sharing and control of temperature control information of the whole process of concrete raw materials, mixing, transportation, warehousing, unloading, vibration, maintenance, water cooling and joint grouting, and realize the full-factor and full-process management of temperature control and crack prevention based on Internet and Internet of Things technology, so as to effectively prevent the occurrence of large-volume concrete cracks and improve the construction quality of concrete. The system includes an aggregate intelligent pre-cooling temperature control unit, a concrete intelligent mixing control unit, and a temperature control unit. A temperature unit, an intelligent micro-environment unit in a concrete pouring bin, an intelligent water cooling unit for concrete, an intelligent maintenance unit for exposed concrete surfaces, an intelligent insulation unit for exposed concrete surfaces, an intelligent joint grouting unit, and a control center are also provided. A control method is also provided. However, the prior art mostly controls the temperature through automatic sensing of the material temperature to prevent cracks in the concrete and manage the construction quality. The prior art cannot obtain the temperature that needs to be adjusted through a comprehensive assessment of the pores and cracks in the concrete blocks, and thus cannot control and accurately analyze the quality of the concrete blocks in real time, which leads to a reduction in the quality of the concrete blocks.

[0003] In order to solve the above problems, the present invention designs a concrete block quality control method and system based on production data analysis. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention proposes a concrete block quality control method and system based on production data analysis. The present invention collects pictures during the production process of concrete blocks, and imports the pictures into a comprehensive pore abnormality analysis strategy to perform a comprehensive pore abnormality analysis on the concrete blocks; imports the pictures into a crack abnormality analysis strategy to perform a crack abnormality analysis on the concrete blocks; performs a comprehensive abnormality analysis on the concrete blocks according to the comprehensive abnormality analysis strategy of the blocks, obtains the optimal temperature according to the temperature adjustment model, calculates the comprehensive abnormality value again after adjustment, and compares the difference between the two with a preset difference threshold, and determines whether the temperature needs to be adjusted again according to the comparison result. The present invention evaluates the quality of the concrete blocks through a comprehensive analysis of the pores and cracks of the concrete blocks, adjusts the temperature during the production process according to the quality conditions, and maintains the block production temperature within the optimal temperature range, thereby improving the production quality of the concrete blocks.

[0005] To achieve the above object, the present invention provides the following technical solutions: The concrete block quality control method based on production data analysis includes the following specific steps: S1, collecting pictures of the concrete block production process and preprocessing the pictures; S2, importing the preprocessed images into the pore comprehensive abnormality analysis strategy to perform pore comprehensive abnormality analysis on the concrete block; S3, importing the pre-processed image into the crack anomaly analysis strategy to analyze the abnormal crack conditions of the concrete block; S4, importing the calculated pore comprehensive abnormal value and crack abnormal value into the block comprehensive abnormal analysis strategy to perform comprehensive abnormal situation analysis of the concrete block, comparing the calculated comprehensive abnormal value with the preset comprehensive abnormal threshold, and obtaining the abnormal block marked by the judgment module; S5. Import the comprehensive abnormal value of the abnormal block into the temperature adjustment model to obtain the optimal temperature, adjust the current temperature to the optimal temperature and then calculate the comprehensive abnormal value of the concrete block again, obtain the difference between the two calculated comprehensive abnormal values ​​and compare it with the preset difference threshold, and determine whether the temperature needs to be adjusted again based on the comparison result.

[0006] Preferably, S1 comprises the following specific steps: S101, collecting pictures during the production process of concrete blocks; S102, preprocessing the image, wherein the preprocessing includes image segmentation and feature extraction; S103: storing the preprocessed image in an image storage module.

[0007] Preferably, S2 comprises the following specific steps: S201, dividing the preprocessed image into a plurality of image slices, and extracting pore information according to the image slices, wherein the pore information includes pore area, pore spacing, and the number of pores in each image slice; S202, introducing the pore area into a pore area abnormal value calculation formula to calculate the pore area abnormal value, the pore area abnormal value calculation formula is: ,in, Indicates the abnormal value of pore area, Indicates the first The area of ​​the pores, Indicates the number of pore areas obtained, Indicates the preset pore area abnormal threshold. hour, The value is 0; S203, introducing the pore spacing into a pore spacing abnormal value calculation formula to calculate the pore spacing abnormal value, wherein the pore spacing abnormal value calculation formula is: ,in, Indicates the abnormal value of pore spacing, Indicates the first The air hole spacing, Indicates the number of pore spacings obtained, Indicates the preset pore spacing abnormal threshold. hour, The value is 0; S204, importing the number of pores in each image slice into a calculation formula for pore distribution abnormality to calculate the pore distribution abnormality, the calculation formula for pore distribution abnormality is: ,in, Indicates the abnormal value of pore distribution, Indicates the first The number of pores in each image slice, Indicates the number of image slices obtained; S205, importing the preprocessed image into the pore comprehensive abnormality analysis strategy to perform pore comprehensive abnormality analysis on the concrete block, wherein the pore comprehensive abnormality analysis strategy includes a pore comprehensive abnormality value calculation formula, and the pore comprehensive abnormality value calculation formula is: ,in, represents the first proportion coefficient, represents the second proportion coefficient, and are positive numbers and .

[0008] Preferably, S3 comprises the following specific steps: The pre-processed image is imported into the crack anomaly analysis strategy to analyze the crack anomaly of the concrete block. The crack anomaly analysis strategy includes a crack anomaly value calculation formula, which is: ,in, represents the crack outlier value, Indicates The length of the crack, Indicates The average width of the cracks, Indicates the number of cracks obtained, Indicates the preset abnormal threshold of crack area.

[0009] Preferably, S4 comprises the following specific steps: S401, importing the calculated pore comprehensive abnormal value and crack abnormal value into the block comprehensive abnormal analysis strategy to perform comprehensive abnormal situation analysis of the concrete block, wherein the block comprehensive abnormal analysis strategy includes a block comprehensive abnormal value calculation formula, and the block comprehensive abnormal value calculation formula is: ,in, represents the coefficient of comprehensive stomatal anomaly ratio, represents the crack anomaly ratio, and are positive numbers and ; S402. Compare the calculated comprehensive abnormality value with the preset comprehensive abnormality threshold. If the calculated comprehensive abnormality value is less than the preset comprehensive abnormality threshold, the building block is judged to be a normal building block. If the calculated comprehensive abnormality value is greater than or equal to the preset comprehensive abnormality threshold, the building block is judged to be an abnormal building block, and the abnormal building block marked by the judgment module is obtained.

[0010] Preferably, S5 comprises the following specific steps: S501, importing the comprehensive abnormal value of the abnormal block into the temperature adjustment model to obtain the optimal temperature, wherein the temperature adjustment model includes an optimal temperature calculation formula, and the optimal temperature calculation formula is: ,in, Indicates the optimal temperature, Indicates the current temperature. represents the temperature conversion coefficient, Indicates standard temperature, Indicates the comprehensive standard value of the block; S502. After adjusting the current temperature to the optimal temperature, the comprehensive abnormal value of the concrete block is calculated again, the difference between the two calculated comprehensive abnormal values ​​is obtained and compared with the preset difference threshold. If the difference between the two calculated comprehensive abnormal values ​​is less than the preset difference threshold, it is determined that there is no need to adjust the temperature again. If the difference between the two calculated comprehensive abnormal values ​​is greater than or equal to the preset difference threshold, it is determined that the temperature needs to be adjusted again.

[0011] A concrete block quality control system based on production data analysis, implemented based on the above-mentioned concrete block quality control method based on production data analysis, comprises an image acquisition module, an image processing module, an information extraction module, a pore comprehensive abnormality analysis module, a crack abnormality analysis module, a comprehensive abnormality analysis module, a comparison judgment module, a temperature adjustment module and a control module; Preferably, the control module is used to control the operation of the image acquisition module, the image processing module, the information extraction module, the pore comprehensive abnormality analysis module, the crack abnormality analysis module, the comprehensive abnormality analysis module, the comparison and judgment module and the temperature adjustment module; Preferably, the image acquisition module is used to collect images during the production process of concrete blocks, the image processing module is used to preprocess the collected images during the production process of concrete blocks, the information extraction module is used to extract pore information according to image slices, the pore comprehensive abnormality analysis module is used to import the preprocessed images into the pore comprehensive abnormality analysis strategy to perform pore comprehensive abnormality analysis on concrete blocks, the crack abnormality analysis module is used to import the preprocessed images into the crack abnormality analysis strategy to perform crack abnormality analysis on concrete blocks, the comprehensive abnormality analysis module is used to import the calculated pore comprehensive abnormality value and crack abnormality value into the block comprehensive abnormality analysis strategy to perform comprehensive abnormality analysis on concrete blocks, the comparison and judgment module is used to compare the calculated comprehensive abnormality value with a preset comprehensive abnormality threshold, obtain the abnormal blocks marked by the judgment module, and obtain the difference between the two calculated comprehensive abnormality values ​​and compare it with the preset difference threshold, and the temperature adjustment module is used to import the comprehensive abnormality value of the abnormal blocks into the temperature adjustment model to obtain the optimal temperature.

[0012] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention collects pictures during the production process of concrete blocks, and imports the pictures into the comprehensive pore abnormality analysis strategy to perform comprehensive pore abnormality analysis of the concrete blocks; imports the pictures into the crack abnormality analysis strategy to perform crack abnormality analysis of the concrete blocks; performs comprehensive abnormality analysis of the concrete blocks according to the comprehensive abnormality analysis strategy of the blocks, obtains the optimal temperature according to the temperature adjustment model, calculates the comprehensive abnormality value again after adjustment, and compares the difference between the two with the preset difference threshold, and determines whether the temperature needs to be adjusted again according to the comparison result. The present invention evaluates the quality of the concrete blocks through a comprehensive analysis of the pores and cracks of the concrete blocks, adjusts the temperature during the production process according to the quality conditions, and maintains the block production temperature within the optimal temperature range, thereby improving the production quality of the concrete blocks. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for describing the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative labor, among which: Figure 1 It is a flow chart of the concrete block quality control method based on production data analysis of the present invention; Figure 2 It is a schematic diagram of step S1 in the concrete block quality control method based on production data analysis of the present invention; Figure 3It is a schematic diagram of the overall framework of the concrete block quality control system based on production data analysis of the present invention; Figure 4 Schematic diagram of image segmentation steps. DETAILED DESCRIPTION

[0014] Example embodiments will now be described more fully with reference to the accompanying drawings; however, the example embodiments may be implemented in various forms and should not be construed as being limited to the examples set forth herein; rather, these embodiments are provided so that the present invention will be more comprehensive and complete and will fully convey the concepts of the example embodiments to those skilled in the art, and the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0015] Example 1. Please refer to Figure 1 The present invention provides an embodiment: a concrete block quality control method based on production data analysis, which includes the following specific steps: S1, collecting pictures of the concrete block production process and preprocessing the pictures; In this embodiment, if Figure 2 As shown, S1 includes the following specific steps: S101, collecting pictures during the production process of concrete blocks; S102, preprocessing the image, the preprocessing including image segmentation and feature extraction; S103: storing the preprocessed image in an image storage module.

[0016] What needs to be further explained in this embodiment is that Figure 4 As shown, the steps of image segmentation are: Step 1. Read image: load image file from the image storage module; Step 2, Image denoising: Use filters (such as Gaussian filtering, median filtering) to remove noise from the image; Step 3, Image enhancement: Adjust the contrast and brightness of the image to make the pores and cracks clearer; Step 4, Grayscale conversion: Convert the image into a grayscale image for subsequent processing; Step 5, threshold segmentation: select the appropriate threshold for segmentation according to the grayscale distribution to separate the pores and cracks from the background; Step 6, edge detection: Use edge detection algorithms (such as Canny algorithm) to detect the edges of pores and cracks; Step 7, morphological operation: use morphological operation to improve the segmentation effect of pore and crack areas; Step 8, region filling: fill the segmented pore and crack regions to facilitate feature extraction.

[0017] The steps of feature extraction are: Step 1, region marking: mark the segmented image to distinguish different pore and crack regions, and use OpenCV's "findContours" method to find the contours of pores and cracks; Step 2, shape feature extraction: calculate the shape features of each area, such as area, circumference, roundness, etc.

[0018] S2, importing the preprocessed images into the pore comprehensive abnormality analysis strategy to perform pore comprehensive abnormality analysis on the concrete block; In this embodiment, S2 includes the following specific steps: S201, dividing the preprocessed image into a plurality of image slices, and extracting stomata information according to the image slices, where the stomata information includes stomata area, stomata spacing, and the number of stomata in each image slice; It should be further explained in this embodiment that the method for extracting pore information is: Step 1. Read the image: Use functions in an image processing library (such as OpenCV) to read the image, including specifying the path or URL of the image file and using an appropriate function (such as imread) to load the image; Step 2, segment the image into slices: Segmenting the image into multiple slices can be achieved through thresholding, morphological operations, sliding windows, etc. First, determine the size and resolution of the image, then define the slice size and range, and finally use the functions or algorithms in the library to segment the image into multiple slices; Step 3, identify stomata and calculate their areas: For each image slice, use the algorithms in the image processing library to identify stomata and calculate their areas. Morphological operations, connected component analysis and other methods can be used to find the boundaries or contours of stomata, determine the location of stomata in each slice, and then use appropriate algorithms (such as integral graph, area measurement, etc.) to calculate the boundary area of ​​stomata; Step 4. Find the distance between stomata: For each image slice, this can be achieved by finding the pixel distance between the stomata boundary points to determine the positional relationship between adjacent stomata; Step 5. Count stomata: For each image slice, use the functions or algorithms in the image processing library to count the number of stomata. This can be done by finding the locations of all boundary pixels and using the counting function or algorithm to calculate the number of stomata in each slice. Step 6. Save the results: Finally, save the pore area, pore distance and pore number of all slices into the corresponding data structure (such as array, list) or file, which can be a text file, database record or data set on a cloud storage platform.

[0019] S202, the pore area is introduced into the pore area abnormal value calculation formula to calculate the pore area abnormal value, and the pore area abnormal value calculation formula is: ,in, Indicates the abnormal value of pore area, Indicates the first The area of ​​the pores, Indicates the number of pore areas obtained, Indicates the preset pore area abnormal threshold. hour, The value is 0; S203, the pore spacing is introduced into the pore spacing abnormal value calculation formula to calculate the pore spacing abnormal value, and the pore spacing abnormal value calculation formula is: ,in, Indicates the abnormal value of pore spacing, Indicates the first The air hole spacing, Indicates the number of pore spacings obtained, Indicates the preset pore spacing abnormal threshold. hour, The value is 0; S204, importing the number of pores in each image slice into a calculation formula for pore distribution abnormality to calculate the pore distribution abnormality, the calculation formula for pore distribution abnormality is: ,in, Indicates the abnormal value of pore distribution, Indicates the first The number of pores in each image slice, Indicates the number of image slices obtained; S205, importing the preprocessed image into the pore comprehensive abnormality analysis strategy to perform pore comprehensive abnormality analysis on the concrete block, the pore comprehensive abnormality analysis strategy includes a pore comprehensive abnormality value calculation formula, and the pore comprehensive abnormality value calculation formula is: ,in, represents the first proportion coefficient, represents the second proportion coefficient, and are positive numbers and .

[0020] S3, importing the pre-processed image into the crack anomaly analysis strategy to analyze the abnormal crack conditions of the concrete block; In this embodiment, S3 includes the following specific steps: The pre-processed images are imported into the crack anomaly analysis strategy to analyze the abnormal crack conditions of concrete blocks. The crack anomaly analysis strategy includes a crack anomaly value calculation formula, which is: ,in, represents the crack outlier value, Indicates The length of the crack, Indicates The average width of the cracks, Indicates the number of cracks obtained, Indicates the preset abnormal threshold of crack area.

[0021] It should be further explained in this embodiment that when calculating the crack abnormal value in S3, the crack is regarded as a rectangle, and the area of ​​the crack is obtained by crack length*average width.

[0022] S4, importing the calculated pore comprehensive abnormal value and crack abnormal value into the block comprehensive abnormal analysis strategy to perform comprehensive abnormal situation analysis of the concrete block, comparing the calculated comprehensive abnormal value with the preset comprehensive abnormal threshold, and obtaining the abnormal block marked by the judgment module; In this embodiment, S4 includes the following specific steps: S401, importing the calculated pore comprehensive abnormal value and crack abnormal value into the block comprehensive abnormal analysis strategy to perform comprehensive abnormal situation analysis of the concrete block, the block comprehensive abnormal analysis strategy includes a block comprehensive abnormal value calculation formula, and the block comprehensive abnormal value calculation formula is: ,in, represents the coefficient of comprehensive stomatal anomaly ratio, represents the crack anomaly ratio, and are positive numbers and ; S402. Compare the calculated comprehensive abnormality value with the preset comprehensive abnormality threshold. If the calculated comprehensive abnormality value is less than the preset comprehensive abnormality threshold, the building block is judged to be a normal building block. If the calculated comprehensive abnormality value is greater than or equal to the preset comprehensive abnormality threshold, the building block is judged to be an abnormal building block, and the abnormal building block marked by the judgment module is obtained.

[0023] S5. Import the comprehensive abnormal value of the abnormal block into the temperature adjustment model to obtain the optimal temperature, adjust the current temperature to the optimal temperature and then calculate the comprehensive abnormal value of the concrete block again, obtain the difference between the two calculated comprehensive abnormal values ​​and compare it with the preset difference threshold, and determine whether the temperature needs to be adjusted again based on the comparison result.

[0024] In this embodiment, S5 includes the following specific steps: S501, importing the comprehensive abnormal value of the abnormal block into the temperature adjustment model to obtain the optimal temperature, wherein the temperature adjustment model includes an optimal temperature calculation formula, and the optimal temperature calculation formula is: ,in, Indicates the optimal temperature, Indicates the current temperature. represents the temperature conversion coefficient, Indicates standard temperature, Indicates the comprehensive standard value of the block; It should be further explained in this embodiment that the current temperature is monitored in real time by a temperature sensor, and the data is transmitted to the control system.

[0025] S502. After adjusting the current temperature to the optimal temperature, the comprehensive abnormal value of the concrete block is calculated again, the difference between the two calculated comprehensive abnormal values ​​is obtained and compared with the preset difference threshold. If the difference between the two calculated comprehensive abnormal values ​​is less than the preset difference threshold, it is determined that there is no need to adjust the temperature again. If the difference between the two calculated comprehensive abnormal values ​​is greater than or equal to the preset difference threshold, it is determined that the temperature needs to be adjusted again.

[0026] It should be further explained in this embodiment that the pore area abnormal threshold , Stomatal spacing abnormal threshold , abnormal crack area threshold , the first proportion coefficient , the second proportion coefficient , Comprehensive anomaly ratio of pores , Crack abnormality ratio , Temperature conversion coefficient , Standard temperature , comprehensive standard value of blocks The comprehensive anomaly threshold and difference threshold are taken as follows: 1,000 concrete blocks to be manufactured are selected, pictures of the concrete block manufacturing process are collected, and the pictures are imported into the pore comprehensive anomaly analysis strategy to conduct comprehensive pore anomaly analysis of the concrete blocks; the pictures are imported into the crack anomaly analysis strategy to conduct crack anomaly analysis of the concrete blocks; the calculated pore comprehensive anomaly values ​​and crack anomaly values ​​are imported into the block comprehensive anomaly analysis strategy to conduct comprehensive anomaly analysis of the concrete blocks, 50 concrete industry experts are invited to conduct quality assessment on these concrete blocks, and the expert assessment results and the calculated comprehensive anomaly values ​​are imported into the fitting software to output a set of pore area anomaly thresholds that are closest to the expert assessment results. , Stomatal spacing abnormal threshold , abnormal crack area threshold , the first proportion coefficient , the second proportion coefficient , Comprehensive anomaly ratio of pores , Crack abnormality ratio coefficient , Temperature conversion coefficient , Standard temperature , comprehensive standard value of blocks , the values ​​of the comprehensive anomaly threshold and the difference threshold.

[0027] Example 2. Please refer to Figure 3 , a concrete block quality control system based on production data analysis is implemented based on the above-mentioned concrete block quality control method based on production data analysis, including an image acquisition module, an image processing module, an information extraction module, a pore comprehensive abnormality analysis module, a crack abnormality analysis module, a comprehensive abnormality analysis module, a comparison and judgment module, a temperature adjustment module and a control module.

[0028] In this embodiment, the image acquisition module is used to collect images during the production process of concrete blocks, the image processing module is used to preprocess the collected images during the production process of concrete blocks, the information extraction module is used to extract pore information based on image slices, the pore comprehensive abnormality analysis module is used to import the preprocessed images into the pore comprehensive abnormality analysis strategy to perform pore comprehensive abnormality analysis on concrete blocks, the crack abnormality analysis module is used to import the preprocessed images into the crack abnormality analysis strategy to perform crack abnormality analysis on concrete blocks, the comprehensive abnormality analysis module is used to import the calculated pore comprehensive abnormality value and crack abnormality value into the block comprehensive abnormality analysis strategy to perform comprehensive abnormality analysis on concrete blocks, the comparison and judgment module is used to compare the calculated comprehensive abnormality value with the preset comprehensive abnormality threshold, obtain the abnormal blocks marked by the judgment module, and obtain the difference between the two calculated comprehensive abnormality values ​​and compare it with the preset difference threshold, and the temperature adjustment module is used to import the comprehensive abnormality value of the abnormal blocks into the temperature adjustment model to obtain the optimal temperature.

[0029] In this embodiment, the control module is used to control the operation of the image acquisition module, the image processing module, the information extraction module, the pore comprehensive abnormality analysis module, the crack abnormality analysis module, the comprehensive abnormality analysis module, the comparison and judgment module and the temperature adjustment module.

[0030] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function according to the embodiment of the present invention is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server or data center to another website, computer, server or data center via a wired network or / and a wireless network. The computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media sets. The available media may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium may be a solid-state hard disk.

[0031] In the description of this specification, the descriptions with reference to the terms "one embodiment", "example", "specific example", etc. mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example, and the specific features, structures, materials or characteristics described may be combined in an appropriate manner in any one or more embodiments or examples.

[0032] The above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for controlling the quality of concrete blocks based on production data analysis, characterized in that: The specific steps include: S1, collecting pictures of the concrete block production process and preprocessing the pictures; S2, importing the preprocessed images into the pore comprehensive abnormality analysis strategy to perform pore comprehensive abnormality analysis on the concrete block; S3, importing the pre-processed image into the crack anomaly analysis strategy to analyze the abnormal crack conditions of the concrete block; S4, importing the calculated pore comprehensive abnormal value and crack abnormal value into the block comprehensive abnormal analysis strategy to perform comprehensive abnormal situation analysis of the concrete block, comparing the calculated comprehensive abnormal value with the preset comprehensive abnormal threshold, and obtaining the abnormal block marked by the judgment module; S5. Import the comprehensive abnormal value of the abnormal block into the temperature adjustment model to obtain the optimal temperature, adjust the current temperature to the optimal temperature and then calculate the comprehensive abnormal value of the concrete block again, obtain the difference between the two calculated comprehensive abnormal values ​​and compare it with the preset difference threshold, and determine whether the temperature needs to be adjusted again based on the comparison result.

2. The method for controlling the quality of concrete blocks based on production data analysis according to claim 1, characterized in that: The S2 comprises the following specific steps: S201, dividing the preprocessed image into a plurality of image slices, and extracting pore information according to the image slices, wherein the pore information includes pore area, pore spacing, and the number of pores in each image slice; S202, introducing the pore area into a pore area abnormal value calculation formula to calculate the pore area abnormal value, the pore area abnormal value calculation formula is: ,in, Indicates the abnormal value of pore area, Indicates the first The area of ​​the pores, Indicates the number of pore areas obtained, Indicates the preset pore area abnormal threshold. hour, The value is 0; S203, introducing the pore spacing into a pore spacing abnormal value calculation formula to calculate the pore spacing abnormal value, wherein the pore spacing abnormal value calculation formula is: ,in, Indicates the abnormal value of pore spacing, Indicates the first The air hole spacing, Indicates the number of pore spacings obtained, Indicates the preset pore spacing abnormal threshold. hour, The value is 0.

3. The method for controlling the quality of concrete blocks based on production data analysis according to claim 2, characterized in that: The S2 further comprises the following specific steps: S204, importing the number of pores in each image slice into a calculation formula for pore distribution abnormality to calculate the pore distribution abnormality, the calculation formula for pore distribution abnormality is: ,in, Indicates the abnormal value of pore distribution, Indicates the first The number of pores in each image slice, Indicates the number of image slices obtained; S205, importing the preprocessed image into the pore comprehensive abnormality analysis strategy to perform pore comprehensive abnormality analysis on the concrete block, wherein the pore comprehensive abnormality analysis strategy includes a pore comprehensive abnormality value calculation formula, and the pore comprehensive abnormality value calculation formula is: ,in, represents the first proportion coefficient, represents the second proportion coefficient, and are positive numbers and .

4. The method for controlling the quality of concrete blocks based on production data analysis according to claim 3, characterized in that: The S3 includes the following specific steps: The pre-processed image is imported into the crack anomaly analysis strategy to analyze the crack anomaly of the concrete block. The crack anomaly analysis strategy includes a crack anomaly value calculation formula, which is: ,in, represents the crack outlier value, Indicates The length of the crack, Indicates The average width of the cracks, Indicates the number of cracks obtained, Indicates the preset abnormal threshold of crack area.

5. The method for controlling the quality of concrete blocks based on production data analysis according to claim 4, characterized in that: The S4 comprises the following specific steps: S401, importing the calculated pore comprehensive abnormal value and crack abnormal value into the block comprehensive abnormal analysis strategy to perform comprehensive abnormal situation analysis of the concrete block, wherein the block comprehensive abnormal analysis strategy includes a block comprehensive abnormal value calculation formula, and the block comprehensive abnormal value calculation formula is: ,in, represents the coefficient of comprehensive stomatal anomaly ratio, represents the crack anomaly ratio, and are positive numbers and ; S402. Compare the calculated comprehensive abnormality value with the preset comprehensive abnormality threshold. If the calculated comprehensive abnormality value is less than the preset comprehensive abnormality threshold, the building block is judged to be a normal building block. If the calculated comprehensive abnormality value is greater than or equal to the preset comprehensive abnormality threshold, the building block is judged to be an abnormal building block, and the abnormal building block marked by the judgment module is obtained.

6. The method for controlling the quality of concrete blocks based on production data analysis according to claim 5, characterized in that: The S5 comprises the following specific steps: S501, importing the comprehensive abnormal value of the abnormal block into the temperature adjustment model to obtain the optimal temperature, wherein the temperature adjustment model includes an optimal temperature calculation formula, and the optimal temperature calculation formula is: ,in, Indicates the optimum temperature, Indicates the current temperature. represents the temperature conversion coefficient, Indicates standard temperature, Indicates the comprehensive standard value of the block; S502. After adjusting the current temperature to the optimal temperature, the comprehensive abnormal value of the concrete block is calculated again, the difference between the two calculated comprehensive abnormal values ​​is obtained and compared with the preset difference threshold. If the difference between the two calculated comprehensive abnormal values ​​is less than the preset difference threshold, it is determined that there is no need to adjust the temperature again. If the difference between the two calculated comprehensive abnormal values ​​is greater than or equal to the preset difference threshold, it is determined that the temperature needs to be adjusted again.

7. The method for controlling the quality of concrete blocks based on production data analysis according to claim 6, characterized in that: The S1 comprises the following specific steps: S101, collecting pictures during the production process of concrete blocks; S102, preprocessing the image, wherein the preprocessing includes image segmentation and feature extraction; S103: storing the preprocessed image in an image storage module.

8. A concrete block quality control system based on production data analysis, which is implemented based on the concrete block quality control method based on production data analysis as claimed in any one of claims 1 to 7, characterized in that: It includes an image acquisition module, an image processing module, an information extraction module, a pore comprehensive abnormality analysis module, a crack abnormality analysis module, a comprehensive abnormality analysis module, a comparison and judgment module, a temperature adjustment module and a control module. The control module is used to control the operation of the image acquisition module, the image processing module, the information extraction module, the pore comprehensive abnormality analysis module, the crack abnormality analysis module, the comprehensive abnormality analysis module, the comparison and judgment module and the temperature adjustment module.

9. The concrete block quality control system based on production data analysis according to claim 8, characterized in that: The image acquisition module is used to collect images during the production process of concrete blocks, the image processing module is used to preprocess the collected images during the production process of concrete blocks, the information extraction module is used to extract pore information according to image slices, the pore comprehensive abnormality analysis module is used to import the preprocessed images into the pore comprehensive abnormality analysis strategy to perform pore comprehensive abnormality analysis on concrete blocks, the crack abnormality analysis module is used to import the preprocessed images into the crack abnormality analysis strategy to perform crack abnormality analysis on concrete blocks, the comprehensive abnormality analysis module is used to import the calculated pore comprehensive abnormality value and crack abnormality value into the block comprehensive abnormality analysis strategy to perform comprehensive abnormality analysis on concrete blocks, the comparison and judgment module is used to compare the calculated comprehensive abnormality value with the preset comprehensive abnormality threshold, obtain the abnormal blocks marked by the judgment module, and obtain the difference between the two calculated comprehensive abnormality values ​​and compare it with the preset difference threshold, and the temperature adjustment module is used to import the comprehensive abnormality value of the abnormal blocks into the temperature adjustment model to obtain the optimal temperature.

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