CN117173130A - Pipeline detection method, system, terminal equipment and storage medium - Google Patents
Pipeline detection method, system, terminal equipment and storage medium Download PDFInfo
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Abstract
The application relates to the technical field of pipeline detection, in particular to a pipeline detection method, a pipeline detection system, terminal equipment and a storage medium, wherein the method comprises the following steps: if the drainage pipeline has defects, identifying a pipeline detection image picture to acquire defect detection characteristic data corresponding to the drainage pipeline; analyzing the defect detection characteristic data according to a preset pipeline defect classification standard, and determining the defect type corresponding to the drainage pipeline, wherein the defect type comprises structural defects and functional defects; evaluating the defect detection characteristic data according to the pipeline defect evaluation standard corresponding to the defect type, and outputting the defect type corresponding to the drainage pipeline and the defect evaluation grade corresponding to the defect type; and combining the defect types and the defect evaluation grades to generate a pipeline detection report corresponding to the drainage pipeline. The pipeline detection method, the system, the terminal equipment and the storage medium provided by the application can improve the detection analysis effect of pipeline detection work.
Description
Technical Field
The present application relates to the field of pipeline detection technologies, and in particular, to a pipeline detection method, a system, a terminal device, and a storage medium.
Background
The urban drainage pipeline is an indispensable important infrastructure of a modern city, is a backbone engineering for urban water pollution control and urban water drainage, flood drainage and flood control, is one of important marks for measuring the level of the modern city, and is an important link for improving urban investment environment. In the same way, the investigation, carding and detection management of sewage, wastewater, rainwater and the like (hereinafter referred to as drainage) discharged into urban drainage facilities are performed, so that the urban drainage facilities are guaranteed to be normally maintained and safely operated, the paid use management of the urban drainage facilities is enhanced, and necessary and effective means for improving urban water environment are promoted.
In practical application, the traditional pipeline detection method generally controls a capsule robot or a CCTV pipeline endoscopic television camera detection system to camera the internal environment of the sewage pipeline, a detection person analyzes the current state of the sewage pipeline by observing a video file and judges whether the sewage pipeline has defects, and because the judgment of whether the sewage pipeline has defects by manpower needs to be manually judged, certain subjective errors exist, and further the analysis of pipeline detection data is not in place, so that the detection and analysis effect of the pipeline is poor.
Disclosure of Invention
In order to improve the detection and analysis effects of pipeline detection work, the application provides a pipeline detection method, a system, terminal equipment and a storage medium.
The application provides a pipeline detection method, which comprises the following steps:
acquiring a pipeline detection image picture corresponding to a drainage pipeline;
the pipeline detection image picture is guided into a preset pipeline defect identification model, and whether the drainage pipeline has defects or not is judged according to an identification result corresponding to the preset pipeline defect identification model;
if the drainage pipeline has defects, identifying the pipeline detection image picture to acquire defect detection characteristic data corresponding to the drainage pipeline;
analyzing the defect detection characteristic data according to a preset pipeline defect classification standard, and determining a defect type corresponding to the drainage pipeline, wherein the defect type comprises structural defects and functional defects;
evaluating the defect detection characteristic data according to the pipeline defect evaluation standard corresponding to the defect type, and outputting the defect type corresponding to the drainage pipeline and the defect evaluation grade corresponding to the defect type;
and generating a pipeline detection report corresponding to the drainage pipeline by combining the defect type and the defect evaluation grade.
By adopting the technical scheme, the pipeline detection image picture is led into the preset pipeline defect identification model, initial judgment can be effectively and accurately carried out on the shallow defects of the current pipeline, after the drainage pipeline with the defects is further identified, corresponding defect detection characteristic data can be effectively extracted from the pipeline detection image picture, accurate and objective qualitative analysis can be effectively carried out on the pipeline defects, then the extracted defect detection characteristic data is analyzed according to the preset pipeline defect classification standard, so that the defect type corresponding to the drainage pipeline is determined, the misjudgment condition of artificial subjective experience analysis is reduced, and in order to further carry out deep analysis on the defects existing in the pipeline, the defect detection characteristic data is evaluated according to the pipeline defect evaluation standard so as to determine the defect type and the corresponding defect evaluation grade of the drainage pipeline, and the pipeline detection report corresponding to the drainage pipeline is generated by combining the defect type and the defect evaluation grade. The report comprises basic information, defect types and evaluation grades of the drainage pipeline, specific description of the defects, suggested repairing measures and the like, so that the report is provided for related personnel to carry out subsequent maintenance and improvement work.
Optionally, the step of importing the pipeline detection image frame into a preset pipeline defect identification model, and judging whether the drainage pipeline has defects according to an identification result corresponding to the preset pipeline defect identification model includes the following steps:
the pipeline detection image picture is imported into a preset pipeline defect identification model, and structural data corresponding to the drainage pipeline are obtained, wherein the structural data comprise the size, the shape and the pipeline surface integrity of the drainage pipeline;
comparing the physical structure data with a preset structure data index to generate a corresponding identification result;
and judging whether the drainage pipeline has defects according to the identification result.
By adopting the technical scheme, the structural data is extracted by adopting the image processing technology, so that the interference of human factors can be reduced, and the accuracy of defect identification is improved.
Optionally, after judging whether the drainage pipeline has a defect according to the identification result, the method further includes the following steps:
if the drainage pipeline has defects, acquiring abnormal structure data corresponding to the drainage pipeline and an abnormal identification image corresponding to the abnormal structure data in the identification result;
And associating the abnormal structure data with the abnormal identification image to form a defect detection mark corresponding to the drainage pipeline.
By adopting the technical scheme, the defect detection mark is formed by associating the abnormal structure data with the abnormal identification image, so that more comprehensive defect information can be provided, and the subsequent analysis and processing are convenient.
Optionally, the step of evaluating the defect detection feature data according to a pipeline defect evaluation criterion corresponding to the defect type, and outputting a defect type corresponding to the drainage pipeline and a defect evaluation grade corresponding to the defect type includes the following steps:
analyzing the structural defect characteristic data to generate defect characteristics corresponding to the drainage pipeline and defect parameters corresponding to the defect characteristics;
combining the defect characteristics and the defect parameters, and determining the corresponding defect types in the pipeline defect evaluation standard and the defect descriptions corresponding to the defect types;
and matching the defect evaluation grade corresponding to the defect type according to the defect evaluation value corresponding to the defect description in the pipeline defect evaluation standard.
By adopting the technical scheme, the unified evaluation standard can be established according to the defect characteristics and the defect description matching defect evaluation grade, so that the accuracy of judging the pipeline defects is improved.
Optionally, after determining the defect type corresponding to the defect evaluation standard and the defect description corresponding to the defect type according to the defect characteristics and the defect parameters, the method further includes the following steps:
performing association analysis by combining the defect types and pipeline external force data corresponding to the drainage pipeline to obtain induction factors corresponding to the defect types;
if the induction factors are multiple, respectively acquiring correlation coefficients between the induction factors and the defect types;
and setting target weights corresponding to the induction factors according to the correlation coefficients, and generating a defect induction prediction ranking table corresponding to the defect types.
By adopting the technical scheme, the induction factors corresponding to different defect types can be clarified by carrying out correlation analysis according to the pipeline external force data corresponding to the drainage pipeline, and the method is helpful for in-depth understanding of the reasons and mechanisms of defect formation.
Optionally, after evaluating the defect detection feature data according to the pipeline defect evaluation criterion corresponding to the defect type, outputting the defect type corresponding to the drainage pipeline and the defect evaluation grade corresponding to the defect type, the method further includes the following steps:
If the defect evaluation level meets a preset pipeline damage standard, obtaining a predicted pipeline damage result corresponding to the defect evaluation level;
if the predicted pipeline damage results are multiple, setting priority orders corresponding to the predicted pipeline damage results according to the occurrence probability corresponding to each predicted pipeline damage result;
and combining the predicted pipeline damage result and the priority order corresponding to the predicted pipeline damage result to generate a predicted pipeline damage analysis table corresponding to the defect type.
By adopting the technical scheme, the priority order of the predicted pipeline damage results is set according to the occurrence probability, the importance and the urgency of different results can be determined, and the resource allocation and the work arrangement can be optimized.
Optionally, after analyzing the defect detection feature data according to a preset pipeline defect classification standard and determining the defect type corresponding to the drainage pipeline, the method further includes the following steps:
if the defect type has a corresponding historical defect record, acquiring a historical defect type in the historical defect record and time distribution data corresponding to the historical defect type;
and generating a defect type trend analysis report corresponding to the drainage pipeline by combining the historical defect types and the time distribution data.
By adopting the technical scheme, the occurrence condition of different defect types in different time periods can be known according to the historical defect types corresponding to the pipeline and the corresponding time distribution data, and the seasonal or periodic characteristics of the defects and possible influencing factors can be determined.
In a second aspect, the present application provides a pipeline inspection system comprising:
the image acquisition module is used for acquiring a pipeline detection image picture corresponding to the drainage pipeline;
the defect identification module is used for guiding the pipeline detection image picture into a preset pipeline defect identification model and judging whether the drainage pipeline has defects according to an identification result corresponding to the preset pipeline defect identification model;
the feature recognition module is used for recognizing the pipeline detection image picture to acquire defect detection feature data corresponding to the drainage pipeline if the drainage pipeline has defects;
the defect analysis module is used for analyzing the defect detection characteristic data according to a preset pipeline defect classification standard and determining a defect type corresponding to the drainage pipeline, wherein the defect type comprises structural defects and functional defects;
The defect evaluation module is used for evaluating the defect detection characteristic data according to the pipeline defect evaluation standard corresponding to the defect type and outputting the defect type corresponding to the drainage pipeline and the defect evaluation grade corresponding to the defect type;
and the detection report generation module is used for combining the defect types and the defect evaluation grades to generate a pipeline detection report corresponding to the drainage pipeline.
By adopting the technical scheme, the pipeline detection image picture acquired by the image acquisition module is guided into the preset pipeline defect identification model through the defect identification module, initial judgment can be effectively and accurately carried out on the shallow defect of the current pipeline, after the defect-containing drainage pipeline is further identified, corresponding defect detection characteristic data are extracted from the pipeline detection image picture through the characteristic identification module, accurate and objective qualitative can be effectively carried out on the pipeline defect, then the extracted defect detection characteristic data are analyzed according to the pipeline defect classification standard preset in the defect analysis module, so that the defect type corresponding to the drainage pipeline is determined, the occurrence of misjudgment condition of artificial subjective experience analysis is reduced, and in order to further carry out deep analysis on the defect existing in the pipeline, the defect detection characteristic data are evaluated according to the pipeline defect evaluation standard in the cut-in line evaluation module so as to determine the defect type and the corresponding defect evaluation grade of the drainage pipeline, and the pipeline detection report corresponding to the drainage pipeline is generated through the detection report generation module. The report comprises basic information, defect types and evaluation grades of the drainage pipeline, specific description of the defects, suggested repairing measures and the like, so that the report is provided for related personnel to carry out subsequent maintenance and improvement work.
In a third aspect, the present application provides a terminal device, which adopts the following technical scheme:
the terminal equipment comprises a memory and a processor, wherein the memory stores computer instructions capable of running on the processor, and the processor adopts the pipeline detection method when loading and executing the computer instructions.
By adopting the technical scheme, the computer instruction is generated by the pipeline detection method and is stored in the memory to be loaded and executed by the processor, so that the terminal equipment is manufactured according to the memory and the processor, and the use is convenient.
In a fourth aspect, the present application provides a computer readable storage medium, which adopts the following technical scheme:
a computer readable storage medium having stored therein computer instructions which, when loaded and executed by a processor, employ a pipeline detection method as described above.
By adopting the technical scheme, the pipeline detection method generates the computer instructions, stores the computer instructions in the computer readable storage medium to be loaded and executed by the processor, and facilitates the reading and storage of the computer instructions through the computer readable storage medium.
In summary, the present application includes at least one of the following beneficial technical effects: the pipeline detection image picture is led into a preset pipeline defect identification model, initial judgment can be effectively and accurately carried out on the shallow defects of the current pipeline, after the defect-containing drainage pipeline is further identified, corresponding defect detection characteristic data are extracted from the pipeline detection image picture, accurate and objective qualitative can be effectively carried out on the pipeline defects, then the extracted defect detection characteristic data are analyzed according to preset pipeline defect classification standards, so that the defect types corresponding to the drainage pipeline are determined, the occurrence of misjudgment of artificial subjective experience analysis is reduced, and in order to further carry out deep analysis on the defects existing in the pipeline, the defect detection characteristic data are evaluated according to pipeline defect evaluation standards so as to determine the defect types and the corresponding defect evaluation grades of the drainage pipeline, and pipeline detection reports corresponding to the drainage pipeline are generated by combining the defect types and the defect evaluation grades. The report comprises basic information, defect types and evaluation grades of the drainage pipeline, specific description of the defects, suggested repairing measures and the like, so that the report is provided for related personnel to carry out subsequent maintenance and improvement work.
Drawings
Fig. 1 is a schematic flow chart of steps S101 to S106 in the pipeline detection method provided by the present application.
Fig. 2 is a schematic flow chart of steps S201 to S203 in the pipeline detection method provided by the present application.
Fig. 3 is a schematic flow chart of steps S301 to S302 in the pipeline detection method provided by the present application.
Fig. 4 is a schematic flow chart of steps S401 to S403 in the pipeline detection method provided by the present application.
Fig. 5 is a schematic flow chart of steps S501 to S503 in the pipeline detection method provided by the present application.
Fig. 6 is a schematic flow chart of steps S601 to S603 in the pipeline detection method provided by the present application.
Fig. 7 is a schematic flow chart of steps S701 to S702 in the pipeline detection method provided by the present application.
Fig. 8 is a schematic block diagram of a pipeline inspection system according to the present application.
Reference numerals illustrate:
1. an image acquisition module; 2. a defect identification module; 3. a feature recognition module; 4. a defect analysis module; 5. a defect evaluation module; 6. and a detection report generation module.
Detailed Description
The application is described in further detail below with reference to fig. 1-8.
The embodiment of the application discloses a pipeline detection method, as shown in fig. 1, comprising the following steps:
S101, acquiring a pipeline detection image picture corresponding to a drainage pipeline;
s102, importing a pipeline detection image picture into a preset pipeline defect identification model, and judging whether a drainage pipeline has defects according to an identification result corresponding to the preset pipeline defect identification model;
s103, if the drainage pipeline has defects, identifying pipeline detection image pictures to acquire defect detection characteristic data corresponding to the drainage pipeline;
s104, analyzing defect detection characteristic data according to a preset pipeline defect classification standard, and determining defect types corresponding to the drainage pipeline, wherein the defect types comprise structural defects and functional defects;
s105, evaluating the defect detection characteristic data according to a pipeline defect evaluation standard corresponding to the defect type, and outputting a defect type corresponding to the drainage pipeline and a defect evaluation grade corresponding to the defect type;
s106, combining the defect types and the defect evaluation grades to generate a pipeline detection report corresponding to the drainage pipeline.
In step S101, the pipe detection image screen refers to an image acquired when the drainage pipe is internally detected by using a specific detection device (such as a camera, a laser scanner, or the like). These images may show the internal structure of the pipe, the condition of the pipe wall, and possible defects or problems. Where the pipeline inspection image frames are typically presented in digital form, either as static images or as a continuous video stream. These images may be viewed and analyzed on a computer screen or other display device to help engineers, technicians, or maintenance personnel better understand the condition of the pipeline.
In step S102, the preset pipeline defect recognition model may be a classifier constructed based on a machine learning algorithm (such as a support vector machine, random forest, etc.) or a deep learning algorithm (such as a convolutional neural network). The recognition result is obtained by analyzing and judging the imported pipeline detection image based on a preset pipeline defect recognition model. These results may provide information about whether the drain line is defective. If the model judges that the defect exists, namely the identification result shows that the pipeline is defective, further analysis and evaluation can be carried out according to the result output by the model.
Specifically, if the identification result determines that the pipe has a defect, the method includes the following steps: the defect type, for example, can discern the crack, corrode, root invade, block up etc. different types of defects; the defect location, the recognition result may provide information about the location of the defect in the drain line. This may help to determine the specific location of the defect for more accurate repair or maintenance operations; the defect level, the recognition result may indicate the level or severity of the defect. This may help assess the impact and urgency of the defect to prioritize or schedule appropriate maintenance; the identification result may be binary (defective/non-defective) or multiple (multi-category defective), the binary judgment result indicating whether there is a defect, and the multiple judgment result may distinguish between different categories.
In step S103, the defect detection feature data refers to information related to a defect extracted from the pipeline detection image. These feature data may include, but are not limited to, the following: texture features, which are used for describing texture information of the pipeline surface, such as roughness, direction, frequency and the like of textures; shape characteristics for describing shape information of the pipe defect, such as length, width, depth, etc. of the defect; color features for describing color information of the pipe defect region, such as color distribution, color difference, etc. of the defect region; edge features for describing edge information of defects and surrounding environments, such as edge density of defect areas, edge continuity, and the like; statistical features describing statistical information of defective areas such as mean, variance, histogram, etc. of gray distribution.
The above-mentioned method for obtaining the defect detection feature data corresponding to the drainage pipeline may be performed by a feature extraction method, that is, by using image processing and computer vision technology, various feature extraction methods may be used to extract the feature data from the pipeline detection image, where these methods may include conventional computer vision algorithms, such as filtering, edge detection, region segmentation, and the like, and may also use deep learning technologies, such as Convolutional Neural Network (CNN), and the like.
Second, the extracted feature data may be represented in a vector, matrix, or other form. Each defect region may be represented as a feature vector that contains values for a plurality of feature dimensions. These feature data may be used for subsequent tasks such as defect classification, localization or level assessment.
And if the drainage pipeline has no defect, the acquired pipeline detection image picture is backed up for analysis and comparison of later pipeline detection data.
In step S104, the preset pipeline defect classification criterion refers to a system or specification for classifying and categorizing pipeline defects. By formulating defect classification criteria, different types of defects can be defined and distinguished clearly, which is helpful for better understanding and handling of defect problems.
The structural defect refers to the situation that the physical structure or the structure of the drainage pipeline is problematic or damaged, and the problems of pipeline rupture, water leakage, collapse and the like can be caused. For example, cracking: cracking or breaking phenomena occurring on the surface or inside of the pipe, corrosion: the pipeline material is damaged or corroded by chemical substance erosion, and collapses: the physical structure of the pipeline is severely deformed or collapsed, and the interface is detached: the interface part of the connecting pipeline is separated or loosened.
Secondly, the functional defect refers to the problem that the normal function or performance of the drainage pipeline is affected in the use process. For example, occlusion: the inside obstruction thing that exists of pipeline leads to the drainage to be blocked or unable normal circulation, leaks: the leakage phenomenon of the pipeline leads to the leakage of water or sewage, and the gradient problem is that: the inclination of pipeline does not accord with the standard requirement and leads to drainage unsmooth or problem of flowing backwards, design defect: the design of the pipe has problems such as unreasonable bends, too small a diameter, etc.
For example, according to the preset pipeline defect classification standard, the defect detection characteristic data is analyzed to obtain: the defect detection characteristic data show that the pipeline is longitudinally broken, and the pipeline defect classification standard is preset to classify the pipeline as structural defects.
For another example, the defect detection feature data is analyzed according to a preset pipeline defect classification standard to obtain: the defect detection characteristic data show that the deposition exists at the bottom of the pipeline, and the pipeline defect classification standard is preset to classify the pipeline defect into functional defects.
In step S105, the pipeline defect evaluation criteria corresponding to the defect type refers to a set of evaluation criteria or guidelines formulated for different types of pipeline defects, for determining the severity and urgency of the defects, and providing guidance and decision basis for subsequent repair, maintenance and management work. These evaluation criteria typically classify defects into different types of structural defects, such as cracks, corrosion, collapse, interface drop, and functional defects, such as plugging, leakage, grade problems, design defects, and the like, based on the structural and functional characteristics of the pipe.
Wherein for each defect type, the evaluation criteria defines a different evaluation level for describing the severity and urgency of the defect. These assessment grades are typically classified by factors such as the extent of the defect, the extent of the spread, the impact on the functioning of the pipeline, etc., e.g., mild, moderate, severe grades.
For example, the defect detection characteristic data shows that the pipeline is broken, and the specific characteristic is that obvious gaps are formed at the broken positions, but the shape of the pipeline is unaffected and the broken positions are not separated, the defect is obtained through judging through the preset pipeline defect classification standard, the defect is structural defect, the defect is obtained according to the pipeline defect evaluation standard corresponding to the structural defect, the pipeline is broken into 4 grade defect evaluation grades, the specific defect evaluation grade comprises that the crack of the pipeline is 1 grade defect evaluation grade, the crack of the pipeline is 2 grade defect evaluation grade, the broken pipeline is 3 grade defect evaluation grade, the collapse of the pipeline is 4 grade defect evaluation grade, and the higher the defect evaluation grade is, the more serious the structural defect of the pipeline is indicated. The defects corresponding to the specific characteristics are described as pipeline cracks, the corresponding defect evaluation level is 2, and then evaluation information of the cracks of the pipeline and the 2-level defect evaluation level is output.
For another example, the defect detection characteristic data shows that the pipeline deposits, which is specifically characterized in that the thickness of the deposit is 20% -30% of the pipe diameter, the defect is obtained by judging through the preset pipeline defect classification standard, and then the defect is obtained according to the pipeline defect evaluation standard corresponding to the structural defect, the pipeline deposits are classified into 4 grade defect evaluation grades, specifically comprising 20% -30% of the pipe diameter as 1 grade defect evaluation grade, 30% -40% of the pipe diameter as 2 grade defect evaluation grade, 40% -50% of the pipe diameter as 3 grade defect evaluation grade, the thickness of the deposit is greater than 50% of the pipe diameter as 4 grade defect evaluation grade, and the higher the defect evaluation grade is, the more serious the functional defect of the pipeline is indicated. The defect description corresponding to the specific characteristics is pipeline deposition, the corresponding defect evaluation grade is 1 grade, and then the evaluation information of the pipeline deposition and the 1 grade defect evaluation grade is output.
In step S106, a pipeline inspection report corresponding to the drainage pipeline may be further generated in combination with the obtained defect type and the defect evaluation level corresponding to the defect type. The pipeline inspection report also includes a specific explanation of the current occurrence of a defect in the pipeline. For example, a current pipe section A is split, a defect rating of 2, etc. indicates that the pipe is broken to a lesser extent, and the pipe structure is less affected, but requires repair to prevent further propagation and exacerbate damage to the pipe.
According to the pipeline detection method, the pipeline detection image picture is led into the preset pipeline defect identification model, initial judgment can be effectively and accurately carried out on the shallow defects of the current pipeline, after the defect-containing drainage pipeline is further identified, corresponding defect detection characteristic data can be effectively extracted from the pipeline detection image picture, accurate objective qualitative detection can be effectively carried out on the pipeline defects, then the extracted defect detection characteristic data is analyzed according to the preset pipeline defect classification standard to determine the defect type corresponding to the drainage pipeline, misjudgment situations of artificial subjective experience analysis are reduced, in order to further carry out deep analysis on the defects existing in the pipeline, defect detection characteristic data are evaluated according to the pipeline defect evaluation standard to determine defect types and corresponding defect evaluation grades of the drainage pipeline, and a pipeline detection report corresponding to the drainage pipeline is generated by combining the defect types and the defect evaluation grades. The report comprises basic information, defect types and evaluation grades of the drainage pipeline, specific description of the defects, suggested repairing measures and the like, so that the report is provided for related personnel to carry out subsequent maintenance and improvement work.
In one implementation manner of the present embodiment, as shown in fig. 2, step S102 includes the steps of introducing a pipeline detection image frame into a preset pipeline defect recognition model, and determining whether a defect exists in the drainage pipeline according to a recognition result corresponding to the preset pipeline defect recognition model, where the step includes the following steps:
s201, importing a pipeline detection image picture into a preset pipeline defect identification model to obtain structural data corresponding to a drainage pipeline, wherein the structural data comprise the size, the shape and the pipeline surface integrity of the drainage pipeline;
s202, comparing physical structure data with preset structure data indexes to generate corresponding identification results;
s203, judging whether the drainage pipeline has defects according to the identification result.
In step S201, through model analysis, size information of the drainage pipeline, including diameter, length, etc., may be obtained, and according to a preset size index, whether the size of the drainage pipeline meets the requirement may be determined. If the dimensional deviation is large, improper installation or wear of the pipeline may occur; through model analysis, shape information of the drainage pipeline, including bending degree, flatness and the like, can be obtained. According to a preset shape index, whether the shape of the drainage pipeline meets the requirement or not can be judged, and if the shape is abnormal, the problems of pipeline deformation, breakage or blockage and the like can exist; through model analysis, can acquire the integrality information on drainage pipe surface, including damage degree, the corruption condition etc. of pipeline wall, according to the surface integrality index of predetermineeing, can judge whether there is damage or corruption on drainage pipe's surface, if there is obvious damage or corruption on surface, can influence the normal operating and the drainage effect of pipeline.
In steps 202 to 203, the preset structural data index is an index for evaluating the structural quality of the pipe, which is set in the design or specification of the drain pipe. They are formulated according to engineering requirements, industry standards or related specifications, aimed at ensuring good performance and reliability of the drainage pipeline.
Wherein, if the physical structure data completely accords with the preset structure data index, the defect-free drainage pipeline can be judged. The size, the shape and the surface integrity of the drainage pipeline are in good working state according to the preset requirements. If the physical structure data and the preset structure data index have small-range deviation, the slight defect of the drainage pipeline can be judged. It is indicated that the size, shape or surface of the drainage pipeline may be slightly abnormal, but the influence on the drainage function is not great, and the drainage pipeline can still work normally. Periodic inspection and maintenance is recommended to prevent further defect development. If the physical structure data has obvious deviation from the preset structure data index, the drainage pipeline can be judged to have serious defects. Indicating that obvious anomalies exist in the size, shape or surface of the drainage pipeline, which can cause the blockage of the drainage function or potential safety hazard. It is recommended to immediately take maintenance or replacement measures to restore the normal working state of the drain pipe.
According to the pipeline detection method provided by the embodiment, the image processing technology is adopted to extract the structural data, so that the interference of human factors can be reduced, and the accuracy of defect identification is improved.
In one implementation manner of this embodiment, as shown in fig. 3, in step S203, after determining whether the drainage pipeline has a defect according to the identification result, the method further includes the following steps:
s301, if the drainage pipeline has defects, acquiring abnormal structure data corresponding to the drainage pipeline and an abnormal identification image corresponding to the abnormal structure data in the identification result;
s302, associating the abnormal structure data with the abnormal identification image to form a defect detection mark corresponding to the drainage pipeline.
In steps S301 to S302, the abnormal structure data describes an abnormal condition of the drain pipe, and may include a dimensional change, a shape deformation, a surface damage, and the like of the pipe. By comparing the abnormal structural data with the preset structural data index, a specific defect type and severity can be determined. The abnormal identification image is obtained through an image identification technology, and shows the abnormal part or defect detail of the drainage pipeline. The image can intuitively display the problems of damage, corrosion, blockage and the like of the pipeline, and is helpful for more accurately judging the position and the property of the defect.
And secondly, correlating the abnormal structure data with the abnormal identification image to form a defect detection mark corresponding to the drainage pipeline. The identification may be a comprehensive report, table or file containing information about the defect description, defect location, defect type, defect severity, etc. of the drain line for further analysis, evaluation and processing decisions.
In the pipeline detection method provided in this embodiment, step S105 is to evaluate the defect detection feature data according to the pipeline defect evaluation criteria corresponding to the defect type, and output the defect type corresponding to the drainage pipeline and the defect evaluation level corresponding to the defect type includes the following steps:
s401, analyzing structural defect characteristic data to generate defect characteristics corresponding to a drainage pipeline and defect parameters corresponding to the defect characteristics;
s402, combining the defect characteristics and defect parameters, and determining the corresponding defect types in the pipeline defect evaluation standard and defect descriptions corresponding to the defect types;
s403, matching the defect evaluation grade corresponding to the defect type according to the defect evaluation value corresponding to the defect description in the pipeline defect evaluation standard.
In step S401, a defect characteristic of the drain pipe may be determined based on the analysis of the structural defect characteristic data. Such features may include, but are not limited to, cracking, breakage, corrosion, plugging, deformation, etc. of the pipe. Defect features describe the type and morphology of defects and can help people identify and classify defects. The defect parameters are specific values or metrics for each defect feature that describe the nature and extent of the defect. For example, for a crack defect, the defect parameters may include the length, width, depth, etc. of the crack; for corrosion defects, the defect parameters may include the area, thickness, etc. of the corrosion. Through analysis and comparison of defect parameters, the severity and extent of impact of defects can be assessed.
In steps S402 to S403, according to the defect characteristics and defect parameters described above, the corresponding defect type and defect description corresponding to the defect type in the pipeline defect evaluation criteria may be determined. Then, according to the defect evaluation values corresponding to the defect descriptions in the defect evaluation criteria, the defect evaluation levels corresponding to the defect types may be matched.
For example, the defect is characterized by broken pipe, the corresponding defect parameter is that the circumferential coverage of the residual fragments at the broken pipe wall is not more than 60 degrees, then the corresponding defect evaluation score is 5 minutes according to the corresponding defect type and the defect description corresponding to the defect type in the defect evaluation standard of the pipe, and the specific corresponding defect evaluation grade is 3 grades.
According to the pipeline detection method provided by the embodiment, the unified evaluation standard can be established according to the defect characteristics and the defect description matching defect evaluation grade, so that the accuracy of judging the pipeline defects is improved.
In one implementation manner of this embodiment, as shown in fig. 5, after determining the defect type corresponding to the defect evaluation standard and the defect description corresponding to the defect type in the pipeline defect evaluation standard in step S402, that is, by combining the defect characteristics and the defect parameters, the method further includes the following steps:
S501, carrying out association analysis by combining defect types and pipeline external force data corresponding to a drainage pipeline to obtain induction factors corresponding to the defect types;
s502, if the induction factors are multiple, respectively acquiring correlation coefficients between each induction factor and the defect type;
s503, setting target weights corresponding to the induction factors according to the correlation coefficients, and generating a defect induction prediction ranking table corresponding to the defect types.
In step S501, the correlation analysis of the pipeline external force data refers to the statistics and analysis of the external force applied to the drainage pipeline to determine the relationship between the influence degree of the external force on the pipeline defect and the induction factor. This analysis can help us to understand the external forces experienced by the drain pipe during use and how much these external forces contribute to pipe defects.
Wherein, when carrying out the correlation analysis, can collect and record the external pressure data that the drain pipe receives, such as pressure, striking, friction etc.. Then, by counting and analyzing these data, the correlation between different external force factors and the types of defects of the pipeline, namely the induction degree and possible influence mechanism of the external force factors on the different types of defects, can be determined.
In steps S502 to S503, if the evoked factors are multiple, the correlation coefficient between each defect type and the evoked factor can be calculated. The correlation coefficient has a value ranging from-1 to 1, wherein positive values represent positive correlations, negative values represent negative correlations, and an absolute value closer to 1 represents stronger correlations.
Next, a target weight is set according to the correlation coefficient. That is, the closer the correlation coefficient is to 1 or-1, the higher the correlation degree between the inducing factor and the defect type is. Based on this degree of correlation, a target weight may be set for each evoked factor. The weight can be set according to actual requirements and professional judgment, and factors such as the size of a correlation coefficient, the importance of factors and the like can be considered.
Then, a defect induction prediction ordering table is generated. Namely, according to the set target weight, weighting calculation is carried out on the induction factors of each defect type to obtain a comprehensive score. And sorting the defect types according to the comprehensive score, and arranging the defect types from high to low. This results in a defect induction prediction ranking table indicating the likelihood that each defect type will be affected by different induction factors. Through the steps, a defect induction prediction sequencing table can be obtained and used for guiding maintenance and repair work of the drainage pipeline. From this table, it is possible to understand the degree of influence of different inducing factors on each defect type, and to prioritize the factors that affect the most so as to reduce the risk of occurrence of defects. Meanwhile, the design and improvement of the drainage pipeline can be provided with references, so that the external force resistance of the drainage pipeline is enhanced, and the occurrence of defects is reduced.
According to the pipeline detection method provided by the embodiment, the correlation analysis is carried out according to the pipeline external force data corresponding to the drainage pipeline, so that the induction factors corresponding to different defect types can be clarified, and the deep understanding of the cause and mechanism of defect formation is facilitated.
In one implementation manner of this embodiment, as shown in fig. 6, in step S105, the defect detection feature data is evaluated according to the pipeline defect evaluation criteria corresponding to the defect type, and the following steps are further included after outputting the defect type corresponding to the drainage pipeline and the defect evaluation level corresponding to the defect type:
s601, if the defect evaluation level meets a preset pipeline damage standard, obtaining a predicted pipeline damage result corresponding to the defect evaluation level;
s602, if a plurality of predicted pipeline damage results are provided, setting priority orders of the predicted pipeline damage results according to the occurrence probability corresponding to each predicted pipeline damage result;
s603, combining the predicted pipeline damage results and the priority orders corresponding to the predicted pipeline damage results, and generating a predicted pipeline damage analysis table corresponding to the defect types.
In step S601, the predicted pipe damage outcome corresponding to each defect evaluation level is determined according to the preset pipe damage criteria. These criteria may be set according to actual conditions and related specifications and typically include information on the type, extent and possible extent of damage at different assessment levels. And obtaining corresponding predicted pipeline damage results according to the defect evaluation grade. The consequences of pipe damage, such as crack growth, leakage, cracking, etc., that may occur at different evaluation levels can be determined according to preset criteria. The predicted lesion consequences will also vary depending on the level of the assessment.
Second, predicted damage consequences include possible impact ranges, possible environmental impact, impact on drain pipe function, and the like. This can help decision makers and maintenance personnel to better understand the meaning and importance of the assessment level, and to take corresponding maintenance and repair strategies.
In steps S602 to S603, the probability of occurrence corresponding to the predicted pipe damage result refers to the probability of occurrence of the predicted pipe damage result under a specific condition. The probability of occurrence is estimated based on historical data of the pipeline, experience or expert judgment and the like, and is used for evaluating and quantifying the probability of the damage event of the pipeline. Specific considerations include the age and maintenance of the pipeline, environmental factors, design and construction quality, and pipeline operating conditions.
Second, the predicted pipe damage results are ranked according to the probability values described above to determine which damage results are more likely to occur. The predicted pipe impairment results are then ranked from high to low according to the prioritization described above. And correlating each damage result with the corresponding defect type to generate a predicted pipeline damage analysis table. The table includes defect types, corresponding predicted pipe damage results, and their prioritization.
Wherein the significance and importance of each predicted pipe damage outcome can be explained for the generated predicted pipe damage resolution table. It is possible to account for the severe impact and urgency that may be brought about by the high priority lesion consequences, as well as the potential risk and long term impact that may be brought about by the low priority lesion consequences. This can help decision makers and maintenance personnel to better understand the importance of different defect categories and the strategy of handling to formulate corresponding maintenance and repair plans.
According to the pipeline detection method provided by the embodiment, the priority order for predicting the damage results of the pipeline is set according to the occurrence probability, the importance and the urgency of different results can be determined, and the resource allocation and the work arrangement can be optimized.
In one implementation manner of this embodiment, as shown in fig. 7, in step S104, the defect detection feature data is analyzed according to a preset pipeline defect classification standard, and after determining the defect type corresponding to the drainage pipeline, the method further includes the following steps:
s701, if the corresponding historical defect record exists in the defect type, acquiring the type of the historical defect in the historical defect record and time distribution data corresponding to the type of the historical defect;
S702, combining the historical defect types and the time distribution data to generate a defect type trend analysis report corresponding to the drainage pipeline.
In steps S701 to S702, by referring to the history defect record of the system with respect to the pipe, the history defect type of the drain pipe and the time distribution data of occurrence of each defect type, that is, the occurrence number or occurrence probability of each defect type in different time periods, etc., can be obtained. And then, according to the historical defect type and the time distribution data, carrying out defect type trend analysis. By counting and analyzing the time variation trend of different defect types, whether the occurrence condition of the different defect types shows the trend of rising, falling or stabilizing can be obtained. The method is helpful for understanding the evolution and trend of different defect types, and provides basis for developing corresponding maintenance and repair plans.
Second, for the generated defect type trend analysis report, the cause of the increase or decrease in the occurrence frequency of some defect types may be described, such as being possibly related to the service life of the pipeline, environmental factors, design and construction quality, etc. Meanwhile, the defect types with larger influence on the operation safety of the pipeline can be pointed out, and important attention and treatment are needed. Thus, decision makers and maintenance staff can be helped to better understand the development trend of the defect types, adjust maintenance strategies and strengthen risk management.
According to the pipeline detection method provided by the embodiment, according to the historical defect types corresponding to the pipeline and the corresponding time distribution data, the occurrence condition of different defect types in different time periods can be known, and the seasonal or periodic characteristics of the defects and possible influencing factors can be determined.
The embodiment also discloses a pipeline detection system, as shown in fig. 8, including:
the image acquisition module 1 is used for acquiring pipeline detection image pictures corresponding to the drainage pipelines;
the defect recognition module 2 is used for guiding the pipeline detection image picture into a preset pipeline defect recognition model and judging whether the drainage pipeline has defects according to a recognition result corresponding to the preset pipeline defect recognition model;
the feature recognition module 3 is used for recognizing a pipeline detection image picture to acquire defect detection feature data corresponding to the drainage pipeline if the drainage pipeline has defects;
the defect analysis module 4 is used for analyzing the defect detection characteristic data according to a preset pipeline defect classification standard and determining the defect type corresponding to the drainage pipeline, wherein the defect type comprises structural defects and functional defects;
the defect evaluation module 5 is used for evaluating the defect detection characteristic data according to the pipeline defect evaluation standard corresponding to the defect type and outputting the defect type corresponding to the drainage pipeline and the defect evaluation grade corresponding to the defect type;
And the detection report generation module 6 is used for combining the defect type and the defect evaluation grade to generate a pipeline detection report corresponding to the drainage pipeline.
According to the pipeline detection system provided by the embodiment, the pipeline detection image picture acquired by the image acquisition module 1 is led into the preset pipeline defect identification model through the defect identification module 2, initial judgment can be effectively and accurately carried out on the shallow defect of the current pipeline, after the defect-containing drainage pipeline is further identified, corresponding defect detection characteristic data are extracted from the pipeline detection image picture through the characteristic identification module 3, accurate and objective qualitative can be effectively carried out on the pipeline defect, then the extracted defect detection characteristic data are analyzed according to the pipeline defect classification standard preset in the defect analysis module 4, so that the defect type corresponding to the drainage pipeline is determined, the occurrence of misjudgment of artificial subjective experience analysis is reduced, and in order to further carry out deep analysis on the defect existing in the pipeline, the defect detection characteristic data are evaluated according to the pipeline defect evaluation standard in the cut-in line evaluation module, so that the defect type and the corresponding defect evaluation grade of the drainage pipeline are determined, and the pipeline detection report corresponding to the drainage pipeline is generated through the detection report generation module 6 in combination with the defect type and the defect evaluation grade. The report comprises basic information, defect types and evaluation grades of the drainage pipeline, specific description of the defects, suggested repairing measures and the like, so that the report is provided for related personnel to carry out subsequent maintenance and improvement work.
It should be noted that, the pipeline detection system provided by the embodiment of the present application further includes each module and/or the corresponding sub-module corresponding to the logic function or the logic step of any one of the above pipeline detection methods, so that the same effects as each logic function or logic step are achieved, and detailed descriptions thereof are omitted herein.
The embodiment of the application also discloses a terminal device which comprises a memory, a processor and computer instructions stored in the memory and capable of running on the processor, wherein when the processor executes the computer instructions, any pipeline detection method in the embodiment is adopted.
The terminal device may be a computer device such as a desktop computer, a notebook computer, or a cloud server, and the terminal device includes, but is not limited to, a processor and a memory, for example, the terminal device may further include an input/output device, a network access device, a bus, and the like.
The processor may be a Central Processing Unit (CPU), or of course, according to actual use, other general purpose processors, digital Signal Processors (DSP), application Specific Integrated Circuits (ASIC), ready-made programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., and the general purpose processor may be a microprocessor or any conventional processor, etc., which is not limited in this respect.
The memory may be an internal storage unit of the terminal device, for example, a hard disk or a memory of the terminal device, or an external storage device of the terminal device, for example, a plug-in hard disk, a Smart Memory Card (SMC), a secure digital card (SD), or a flash memory card (FC) provided on the terminal device, or the like, and may be a combination of the internal storage unit of the terminal device and the external storage device, where the memory is used to store computer instructions and other instructions and data required by the terminal device, and the memory may be used to temporarily store data that has been output or is to be output, which is not limited by the present application.
Any one of the pipeline detection methods in the embodiments is stored in the memory of the terminal device through the terminal device, and is loaded and executed on the processor of the terminal device, so that the use is convenient.
The embodiment of the application also discloses a computer readable storage medium, and the computer readable storage medium stores computer instructions, wherein when the computer instructions are executed by a processor, any pipeline detection method in the embodiment is adopted.
The computer instructions may be stored in a computer readable medium, where the computer instructions include computer instruction codes, where the computer instruction codes may be in a source code form, an object code form, an executable file form, or some middleware form, etc., and the computer readable medium includes any entity or device capable of carrying the computer instruction codes, a recording medium, a usb disk, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a Random Access Memory (RAM), an electrical carrier signal, a telecommunication signal, a software distribution medium, etc., where the computer readable medium includes but is not limited to the above components.
Any one of the pipeline detection methods in the above embodiments is stored in the computer readable storage medium through the present computer readable storage medium, and is loaded and executed on a processor, so as to facilitate the storage and application of the method.
The above embodiments are not intended to limit the scope of the present application, so: all equivalent changes in structure, shape and principle of the application should be covered in the scope of protection of the application.
Claims (10)
1. A method of pipeline inspection comprising the steps of:
acquiring a pipeline detection image picture corresponding to a drainage pipeline;
the pipeline detection image picture is guided into a preset pipeline defect identification model, and whether the drainage pipeline has defects or not is judged according to an identification result corresponding to the preset pipeline defect identification model;
if the drainage pipeline has defects, identifying the pipeline detection image picture to acquire defect detection characteristic data corresponding to the drainage pipeline;
analyzing the defect detection characteristic data according to a preset pipeline defect classification standard, and determining a defect type corresponding to the drainage pipeline, wherein the defect type comprises structural defects and functional defects;
Evaluating the defect detection characteristic data according to the pipeline defect evaluation standard corresponding to the defect type, and outputting the defect type corresponding to the drainage pipeline and the defect evaluation grade corresponding to the defect type;
and generating a pipeline detection report corresponding to the drainage pipeline by combining the defect type and the defect evaluation grade.
2. The pipe inspection method according to claim 1, wherein the step of importing the pipe inspection image frame into a predetermined pipe defect recognition model and judging whether the drain pipe has a defect according to a recognition result corresponding to the predetermined pipe defect recognition model comprises the steps of:
the pipeline detection image picture is imported into a preset pipeline defect identification model, and structural data corresponding to the drainage pipeline are obtained, wherein the structural data comprise the size, the shape and the pipeline surface integrity of the drainage pipeline;
comparing the physical structure data with a preset structure data index to generate a corresponding identification result;
and judging whether the drainage pipeline has defects according to the identification result.
3. The pipe inspection method according to claim 2, further comprising the steps of, after said judging whether said drain pipe is defective according to said identification result:
If the drainage pipeline has defects, acquiring abnormal structure data corresponding to the drainage pipeline and an abnormal identification image corresponding to the abnormal structure data in the identification result;
and associating the abnormal structure data with the abnormal identification image to form a defect detection mark corresponding to the drainage pipeline.
4. The pipe inspection method according to claim 1, wherein the evaluating the defect inspection characteristic data according to the pipe defect evaluation criteria corresponding to the defect type, and outputting the defect type corresponding to the drain pipe and the defect evaluation level corresponding to the defect type, comprises the steps of:
analyzing the structural defect characteristic data to generate defect characteristics corresponding to the drainage pipeline and defect parameters corresponding to the defect characteristics;
combining the defect characteristics and the defect parameters, and determining the corresponding defect types in the pipeline defect evaluation standard and the defect descriptions corresponding to the defect types;
and matching the defect evaluation grade corresponding to the defect type according to the defect evaluation value corresponding to the defect description in the pipeline defect evaluation standard.
5. The pipe inspection method of claim 4, further comprising, after determining a corresponding defect type in the pipe defect review criteria and a defect description corresponding to the defect type in combination with the defect signature and the defect parameters, the steps of:
performing association analysis by combining the defect types and pipeline external force data corresponding to the drainage pipeline to obtain induction factors corresponding to the defect types;
if the induction factors are multiple, respectively acquiring correlation coefficients between the induction factors and the defect types;
and setting target weights corresponding to the induction factors according to the correlation coefficients, and generating a defect induction prediction ranking table corresponding to the defect types.
6. The pipe inspection method according to claim 1, wherein after evaluating the defect inspection characteristic data according to the pipe defect evaluation criteria corresponding to the defect type, outputting the defect type corresponding to the drain pipe and the defect evaluation level corresponding to the defect type, further comprising the steps of:
if the defect evaluation level meets a preset pipeline damage standard, obtaining a predicted pipeline damage result corresponding to the defect evaluation level;
If the predicted pipeline damage results are multiple, setting priority orders corresponding to the predicted pipeline damage results according to the occurrence probability corresponding to each predicted pipeline damage result;
and combining the predicted pipeline damage result and the priority order corresponding to the predicted pipeline damage result to generate a predicted pipeline damage analysis table corresponding to the defect type.
7. The pipe inspection method according to claim 1, wherein after analyzing the defect inspection characteristic data according to a preset pipe defect classification standard and determining a defect type corresponding to the drainage pipe, further comprises the steps of:
if the defect type has a corresponding historical defect record, acquiring a historical defect type in the historical defect record and time distribution data corresponding to the historical defect type;
and generating a defect type trend analysis report corresponding to the drainage pipeline by combining the historical defect types and the time distribution data.
8. A pipeline inspection system, comprising:
the image acquisition module (1) is used for acquiring pipeline detection image pictures corresponding to the drainage pipelines;
the defect identification module (2) is used for guiding the pipeline detection image picture into a preset pipeline defect identification model and judging whether the drainage pipeline has defects according to an identification result corresponding to the preset pipeline defect identification model;
The feature recognition module (3) is used for recognizing the pipeline detection image picture to acquire defect detection feature data corresponding to the drainage pipeline if the drainage pipeline has defects;
the defect analysis module (4) is used for analyzing the defect detection characteristic data according to a preset pipeline defect classification standard and determining a defect type corresponding to the drainage pipeline, wherein the defect type comprises a structural defect and a functional defect;
the defect evaluation module (5) is used for evaluating the defect detection characteristic data according to the pipeline defect evaluation standard corresponding to the defect type and outputting the defect type corresponding to the drainage pipeline and the defect evaluation grade corresponding to the defect type;
and the detection report generation module (6) is used for combining the defect type and the defect evaluation grade to generate a pipeline detection report corresponding to the drainage pipeline.
9. A terminal device comprising a memory and a processor, wherein the memory has stored therein computer instructions executable on the processor, and wherein the processor, when loaded and executing the computer instructions, employs the pipeline detection method of any one of claims 1 to 7.
10. A computer readable storage medium having stored therein computer instructions, which when loaded and executed by a processor, employ the pipeline detection method of any one of claims 1 to 7.
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| CN117523501B (en) * | 2024-01-04 | 2024-03-15 | 四川省铁路建设有限公司 | Control method and system for pipeline inspection robot |
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