Pipeline defect intelligent identification system and method based on pipe network microorganisms

Abstract

The invention discloses an intelligent pipeline defect recognition system and method based on pipe network microorganisms, and belongs to the technical field of pipeline monitoring. The system comprises a marker microorganism putting module for putting marker microorganisms into a pipeline; the water sample collection module is used for collecting a water sample at an appointed position of a pipeline; the electrostatic field separation module is used for separating particles with different sizes and properties in the water sample by virtue of an electrostatic field separation technology aiming at each water sample collection position; carrying out photographing and image segmentation on the separated particles to obtain a target area image; the microorganism identification module is used for performing feature extraction on each target area image and identifying microorganism species so as to obtain the mark microorganism concentration of each sampling position; and the defect identification module is used for identifying whether the to-be-monitored pipeline has defects and identifying the positions and severity of the defects. Defects are indicated by utilizing the concentration change of the marking microorganisms in the pipeline, the operation is simple, the cost is low, the detection period is short, and comprehensive real-time monitoring can be realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of pipeline monitoring, and in particular relates to a pipeline defect intelligent identification system and method based on pipeline network microorganisms. Background Art

[0002] As an important infrastructure, pipelines undertake the key task of fluid transportation and play an irreplaceable role in the transportation of resources such as oil, natural gas, and water. However, due to long-term use, external environmental influences, and the corrosion of internal fluids, various defects such as corrosion, cracks, deformation, and deposition may occur inside and outside the pipelines. These defects will not only affect the normal operation of the pipelines and reduce the transportation efficiency, but may also cause serious safety accidents and threaten people's lives and property.

[0003] Traditional pipeline defect detection methods mainly include ultrasonic testing, X-ray testing, magnetic particle testing, etc. Although these methods have high detection accuracy, they have limitations such as complex operation, high cost, and long detection cycle. Especially for large and complex pipeline systems, traditional detection methods are difficult to achieve comprehensive and real-time monitoring. In addition, these methods often require professional inspection personnel and equipment, and the detection difficulty and cost will increase significantly for remote areas or difficult-to-access pipeline sections. Therefore, it is particularly important to develop an efficient, low-cost detection method that can comprehensively monitor the pipeline status. Summary of the invention

[0004] The present invention proposes a pipeline defect intelligent identification system and method based on pipeline network microorganisms. The system and method use the concentration change of marker microorganisms in the pipeline to indicate pipeline defects. By placing marker microorganisms at fixed points and in fixed quantities, and using automated detection means to monitor their concentration distribution in the pipeline, combined with image processing and artificial intelligence recognition technology, intelligent identification of pipeline defects is achieved, solving the technical problems of existing pipeline defect detection, which are complex operation, high cost, long detection cycle, and difficulty in achieving comprehensive real-time monitoring.

[0005] The present invention provides the following technical solutions:

[0006] In a first aspect, a pipeline defect intelligent identification system based on pipeline network microorganisms is provided, comprising:

[0007] A marker microorganism delivery module is used to deliver marker microorganisms at fixed points and in fixed quantities in the pipeline to be monitored;

[0008] A water sample collection module is used to collect water samples from a designated location of the pipeline to be tested after a preset period of time after the marker microorganisms are placed;

[0009] An electrostatic field separation module is used to separate particles of different sizes and properties in each water sample collection location by electrostatic field separation technology, wherein the particles include marker microorganisms, other microorganisms and impurities;

[0010] An imaging module is used to take pictures and segment the separated particles to obtain a target area image containing the microbial morphology or a set required part;

[0011] The microorganism identification module is used to extract features from each target area image and identify the type of microorganisms to obtain the concentration of the marker microorganisms at each sampling location;

[0012] The defect recognition module is used to identify whether there are defects in the pipeline to be monitored, as well as the location and severity of the defects based on the preset concentration gradient threshold of the marker microorganisms and the concentration of the marker microorganisms at different locations of the pipeline to be monitored.

[0013] Optionally, the electrostatic field separation module adopts a multi-stage electrostatic separator structure, and each stage of the electrostatic separator can adjust the parameters of the electrostatic field to adjust the intensity and direction of the electrostatic field;

[0014] The electrostatic field separation technology is used to separate particles of different sizes and properties in the water sample. Specifically, when the collected water sample passes through the electrostatic separator, the electrostatic separator applies different electric field forces to all microorganisms and impurities in the water sample, and separates them by utilizing the charge differences of different microorganisms and impurities in the water sample.

[0015] Optionally, the imaging module includes a high-resolution camera and a segmentation unit; the high-resolution camera takes a picture of the separated particles to obtain an original photographed image; the segmentation unit segments the original photographed image by a fully automatic segmentation method to segment out a target area image containing the microbial morphology or a set required part;

[0016] The fully automatic segmentation method specifically comprises:

[0017] Input the original captured image; convert the original captured image into a grayscale image; enhance the contrast through point operation and perform Gaussian filtering to remove noise; use the Canny edge detection operator to detect the boundary of each microorganism; close the edge image through morphological processing and obtain the final segmentation result.

[0018] Optionally, the microorganism identification module includes a feature extraction unit; the feature extraction unit is used to extract image features from each target area image;

[0019] The extracted image features include geometric features, internal structure histogram features, Fourier descriptors, gray-level co-occurrence matrices, and rotation-invariant local binary patterns;

[0020] The geometric features include: the area of ​​the microorganism, the perimeter of the microorganism boundary contour, the roundness of the microorganism, and the major axis length and minor axis length of an ellipse having the same normalized second-order central moment as the microorganism area;

[0021] The internal structure histogram feature is used to describe the internal spatial structure of the image and reflect the distribution of pixel intensity in the image;

[0022] The Fourier descriptor is used to distinguish different contours through frequency domain information, and then identify objects, and is used to reflect the contour characteristics of the image;

[0023] The gray level co-occurrence matrix is ​​used to reflect the spatial correlation of the gray values ​​of any two pixels in the image;

[0024] The rotation-invariant local binary pattern is a robust feature operator that is invariant to any monotonic transformation of a grayscale image, has grayscale invariance and rotation invariance, and is used to reflect the texture characteristics of an image.

[0025] Optionally, the microorganism identification module also includes an identification unit and a marker microorganism concentration acquisition unit; the identification unit uses a particle swarm optimization algorithm to optimize the parameters of the SVM model, and uses the optimized and trained SVM model to classify the features of the extracted target area image to identify the type of each microorganism; the marker microorganism concentration acquisition unit counts the marker microorganisms at each sampling position, and obtains the concentration of the marker microorganism based on the number of marker microorganisms and the volume of the collected water sample.

[0026] Optionally, the particle swarm optimization algorithm is used to optimize the parameters of the SVM model, and the specific process is:

[0027] According to the preset particle swarm size, the position and velocity of each particle are randomly initialized; each particle represents a parameter combination of the SVM model;

[0028] The SVM model is trained using the SVM parameter combination represented by each particle, and the classification accuracy of the SVM model is calculated using the test set of the training process. The classification accuracy of the SVM model is used as the fitness value of the particle.

[0029] According to the fitness value of the particle, update the individual historical optimal position and global optimal position of the particle swarm;

[0030] Update the speed and position of each particle according to the updated individual optimal position and global optimal position of the particle swarm;

[0031] Repeatedly update the individual optimal position and global optimal position of the particle until the termination condition is met, output the global optimal position and the corresponding global optimal fitness value, the parameter combination of the SVM model corresponding to the particle at the global optimal position is the optimal parameter combination of the SVM model; the optimal parameter combination of the SVM model includes the penalty factor C and the kernel parameter .

[0032] Optionally, the objective function of the SVM model during training is for:

[0033]

[0034] The constraints are:

[0035]

[0036]

[0037] in, and are the Lagrange multipliers of training samples i and j respectively, , N is the number of samples used for model training, and are the feature data of training samples i and j respectively; and are the category labels of training samples i and j respectively, is the RBF kernel function; C is the penalty factor;

[0038] The decision function of the SVM model for:

[0039]

[0040]

[0041] Among them, b is a constant bias term, x is the feature data of the target area image to be classified, is the kernel parameter.

[0042] Optionally, the defect recognition module is used to identify whether there is a defect in the pipeline to be monitored, as well as the location and severity of the defect based on a preset concentration gradient threshold of the marker microorganism and the concentration of the marker microorganism at different locations of the pipeline to be monitored. The specific process is as follows:

[0043] A first concentration threshold range, a second concentration threshold range and a third concentration threshold range are set; the first concentration threshold range is located between the second concentration threshold range; the second concentration threshold range is located between the third concentration threshold range;

[0044] If the concentration of the marker microorganism at the current position is within the first concentration threshold range, then the current position of the pipeline to be monitored has no defects;

[0045] If the marker microorganism concentration at the current position exceeds the upper limit of the first concentration threshold range, then the current position of the pipeline to be monitored has leakage, corrosion or other defects that cause the microorganism concentration to increase; if the marker microorganism concentration at the current position is lower than the lower limit of the first concentration threshold range, then the current position of the pipeline to be monitored has poor water flow, sediment accumulation or other defects that cause the microorganism concentration to decrease;

[0046] If the marker microorganism concentration at the current location is outside the first concentration threshold range and within the second concentration threshold range, the severity of the defect is primary; if the marker microorganism concentration at the current location is outside the second concentration threshold range and within the third concentration threshold range, the severity of the defect is intermediate; if the marker microorganism concentration at the current location is outside the third concentration threshold range, the severity of the defect is severe.

[0047] Optionally, the marker microorganism delivery module is used to deliver the marker microorganisms at fixed points and in fixed quantities in the pipeline to be monitored, and the delivery positions of the marker microorganisms are located at the connection points, branches and turns of the pipeline to be monitored, and the delivery positions of the marker microorganisms are assigned unique coordinate values;

[0048] The water sample collection module is used to collect water samples from designated positions of the pipeline to be detected after the marker is placed, after a preset time period. The designated positions for collecting water samples are the inlet, outlet, connection point, branch and turn of the pipeline to be monitored.

[0049] In a second aspect, a pipeline defect intelligent identification method based on pipeline network microorganisms is provided, comprising:

[0050] Place marker microorganisms at fixed points and in fixed quantities in the pipeline to be monitored;

[0051] After the marker microorganisms are placed, water samples are collected from designated locations of the pipeline to be tested after a preset period of time;

[0052] For each water sample collection location, particles of different sizes and properties in the water sample are separated by electrostatic field separation technology, and the particles include marker microorganisms, other microorganisms and impurities;

[0053] The separated particles are photographed and image segmented to obtain target area images containing microbial morphology or set required parts;

[0054] Perform feature extraction on each target area image and identify the microbial species to obtain the marker microbial concentration at each sampling location;

[0055] Based on the preset concentration gradient threshold of the marker microorganism and the concentration of the marker microorganism at different positions of the pipeline to be monitored, it is possible to identify whether there are defects in the pipeline to be monitored, as well as the location and severity of the defects.

[0056] Compared with the prior art, the present invention has the following beneficial effects:

[0057] The present invention utilizes the concentration changes and distribution characteristics of marker microorganisms in the pipeline to realize intelligent identification of pipeline defects. It is not only easy to operate, but also has high accuracy and sensitivity. It can timely discover tiny defects inside the pipeline, providing an important basis for pipeline maintenance and safe operation. In addition, the present invention can realize comprehensive real-time monitoring of pipeline status by combining automated monitoring means with image processing technology, which can improve the efficiency and accuracy of pipeline maintenance, reduce errors and omissions caused by manual monitoring, and reduce maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 It is a structural framework diagram of the pipeline defect intelligent identification system based on pipeline network microorganisms of the present invention;

[0059] Figure 2 It is a work flow chart of the identification unit of the pipeline defect intelligent identification system based on pipeline network microorganisms of the present invention;

[0060] Figure 3 It is a flow chart of the pipeline defect intelligent identification method based on pipeline network microorganisms of the present invention. DETAILED DESCRIPTION

[0061] The present invention will be further described below in conjunction with the accompanying drawings. The following examples are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the scope of protection of the present invention. It should be noted that the term "comprising" and any variation thereof in the specification and claims of the present invention are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0062] Example 1

[0063] like Figure 1 As shown, a pipeline defect intelligent identification system based on pipeline network microorganisms is provided, including: a marker microorganism delivery module, a water sample collection module, an electrostatic field separation module, an imaging module, a microorganism identification module and a defect identification module.

[0064] As an option, the marker microorganism delivery module, the water sample collection module and the electrostatic field separation module are deployed on the terminal; the imaging module, the microorganism identification module and the defect identification module are deployed on the server side.

[0065] 1. Marker microorganism delivery module

[0066] Used to place marker microorganisms at fixed points and in fixed quantities in the pipeline to be monitored.

[0067] When releasing the marker microorganisms, the positioning method based on the pipeline drawings and the geographic information system is used to achieve the fixed-point release of the marker microorganisms, and the coordinates of the release points are recorded in the geographic information system to indicate the location of the release points. The release points are selected at the connection points, branches, and turns of the pipeline to be monitored.

[0068] The marker microorganisms are added once, and the concentration of the marker microorganism suspension is adjusted according to the size of the pipeline and the water flow rate. The flow meter is used to ensure the accuracy of the number of microorganisms added each time. The marker microorganisms are usually genetically modified and have special biological characteristics. They can effectively grow, reproduce and distribute in the pipeline. In addition, they can show strong unidirectional movement characteristics under the electric field. The selection of marker microorganisms can be determined by expert experience. As an option, the marker microorganism can be selected from Escherichia coli, Pseudomonas or yeast. Usually only one marker microorganism is used for defect monitoring.

[0069] It should be noted that the pipeline drawings to be monitored record in detail the structure, size, material and connection relationship of the pipeline network. By digitizing the pipeline drawings, the location and attributes of each part of the pipeline network can be accurately determined. The Geographic Information System (GIS) is a computer system used to store, edit, query, analyze and display geographic data. Integrating pipeline drawings with GIS can achieve precise positioning and visual management of the pipeline network. In GIS, you can intuitively see the layout, direction and surrounding environment of the pipeline network, providing accurate spatial reference for fixed-point delivery.

[0070] In GIS, each delivery point is assigned a unique coordinate value. The coordinate value can be used to accurately locate the location of each delivery point to ensure the accuracy of delivery. In addition to coordinate records, the location of delivery points can also be indicated in GIS through labels, icons or colors, which helps operators quickly identify and locate delivery points and improve delivery efficiency.

[0071] The connection points, branches and bends of the pipeline are areas in the pipeline network where defects are prone to occur. Placing marker microorganisms at these locations can more effectively monitor the health of the pipeline. There may be welding defects or sealing problems at the connection points, scouring or deposition may occur at the branches due to changes in the direction of water flow, and eddies or scouring may occur at the bends due to changes in the water flow velocity.

[0072] Single-time delivery of marker microorganisms can simplify the operating process, reduce the number of deliveries and costs, and through precise concentration adjustment and delivery volume control, it can ensure that the number of microorganisms delivered each time is accurate and meets monitoring needs. A flow meter is an instrument used to measure fluid flow. During the process of microbial delivery, the use of a flow meter can accurately control the delivery volume to ensure that the number of microorganisms delivered each time is accurate.

[0073] Genetic modification technology can give marker microorganisms special biological characteristics, allowing them to grow, reproduce and distribute more effectively in pipelines, thereby improving the sensitivity and accuracy of monitoring. The strong unidirectional movement characteristics under the electric field are a special manifestation of the passage of marker microorganisms, which enables the marker microorganisms to move in a directed manner along the direction of the electric field in the pipeline, making them easier to detect and track.

[0074] 2. Water Sample Collection Module

[0075] It is used to collect water samples from a designated location of the pipeline to be tested after a preset period of time after the marker microorganisms are released.

[0076] The water sample collection module usually includes a water sample extraction device and a water sample storage container. The water sample extraction device is used to extract water samples from the pipeline, and the water sample storage container is used to store the extracted water samples. The water sample extraction device uses a sterile container to collect water samples, and automatically and regularly collects water samples from the inlet, outlet, connection points, branches, and turns of the pipeline. The water sample storage container uses a sterile container to store water samples, and ensures the integrity of the water samples through refrigeration.

[0077] It should be noted that the water sample extraction device includes an extraction mechanism and a sterile container. The extraction mechanism includes a pump, a pipe and a valve, which is used to extract water samples from the pipe; the sterile container is used to directly receive the extracted water samples and must undergo strict sterilization before use to ensure that the interior is sterile and to ensure that the water samples are not contaminated by the outside world during the collection process.

[0078] The water sample storage container has aseptic storage function and refrigeration processing function. The aseptic storage function is to use a sterile container for storage to prevent the water sample from being contaminated during the storage process; the refrigeration processing function can reduce the activity of microorganisms, extend the shelf life of water samples, help maintain the physical and chemical properties of water samples stable, and ensure that the water sample is not affected by the external environment during storage and transportation, maintaining its integrity.

[0079] It is worth noting that the microbial placement and water sample collection of this application are fully automatically implemented based on pipeline drawings and geographic information; Marker placement time requirements: The placement of marker microorganisms is carried out regularly according to the use of the pipeline and the maintenance plan. For example, it can be placed once a month or quarterly, and the specific frequency depends on the frequency of use of the pipeline to be monitored and the expected probability of defects. Water sample collection time requirements: Water samples need to be collected within a period of time after the placement of marker microorganisms to ensure that the distribution of marker microorganisms in the pipeline can be detected. The specific time interval can be determined based on the growth and diffusion characteristics of the marker microorganisms.

[0080] 3. Electrostatic Field Separation Module

[0081] It is used to separate particles of different sizes and properties in each water sample collection location through electrostatic field separation technology. The particles include marker microorganisms, other microorganisms and impurities.

[0082] The electrostatic separation module adopts a multi-stage structural design, which can achieve multiple separation functions. Each stage of the electrostatic separator can adjust the parameters of the electrostatic field to accurately separate microorganisms and impurities of different types and sizes. It should be noted that the marker microorganisms and other microorganisms and impurities will show different behavioral characteristics in charge distribution, polarization degree, and migration speed in the electric field, which determines the response mode and separation effect of microorganisms and impurities in the electric field. Based on the differences in behavioral characteristics, by constructing a specific electric field environment, microorganisms and impurities are subjected to different forces in the electric field, thereby achieving effective separation.

[0083] Electrostatic field separation technology is based on the different charge properties of particles under the action of an external electric field, and achieves separation through the generated electrostatic force. When particles enter the electrostatic field, they will be subjected to electric field forces in different directions according to their own charge properties, thereby producing an offset movement. This offset movement causes different particles to be subjected to different forces in the electric field, thereby achieving separation.

[0084] The multi-stage structure design has three characteristics: step-by-step separation, adjustable parameters and high efficiency and energy saving. The multi-stage structure design enables the particles to be separated step by step in multiple electric fields. Each level of the electric field can separate particles of different sizes and properties, thereby achieving a more refined separation effect; in each level of the electric field, the electric field strength and direction can be adjusted according to actual needs to meet the separation requirements of different particles; the multi-stage structure design can improve the separation efficiency and reduce energy consumption. By optimizing the parameters of each level of the electric field, higher separation efficiency can be achieved while reducing energy consumption.

[0085] 4. Imaging Module

[0086] The separated particles are photographed and image segmented to obtain target area images containing microbial morphology or set required parts.

[0087] The imaging module includes a high-resolution camera and a segmentation unit. The high-resolution camera takes pictures of the separated particles to obtain the original image; the segmentation unit segments the original image through a fully automatic segmentation method to segment the target area image containing the microbial morphology or the set required part.

[0088] The structure of the high-resolution camera can refer to the existing technology, and is usually equipped with adjustable light control and autofocus functions, which can capture the fine structure and dynamic changes of microorganisms. As an option, the high-resolution camera is a Canon EOS 80D camera, the ISO speed of the camera is 800, and the exposure time is 1 / 200s. These parameters ensure high clarity and low noise of the image, and the image resolution is 4000×6000, providing rich detail information, and the image size is 600×900, which helps to reduce the amount of calculation and improve the subsequent processing rate.

[0089] Microbial morphology: The target microbial region can be the complete morphology of a marker microorganism, including its cell structure, shape, etc. For example, if the type and concentration of the marker microorganism need to be identified, extracting the entire morphology can provide more comprehensive information.

[0090] Set the required part: The target microbial area can also be the set required part of the marker microorganism, such as the cell membrane, cell nucleus, specific organelles, etc. For example, if it is necessary to detect a specific physiological state or metabolic activity of the marker microorganism, extracting the set required part may be more helpful to achieve this goal.

[0091] The fully automatic segmentation method specifically includes:

[0092] Step a: input the original captured image;

[0093] Step b: convert the original captured image into a grayscale image;

[0094] Step c: Enhance the contrast by point operation and perform Gaussian filtering to remove noise; the specific steps of enhancing the contrast by point operation can refer to the prior art.

[0095] Step d: Use the Canny edge detection operator to detect the boundary of each microorganism.

[0096] The steps of Canny edge detection include gradient calculation: given the gray value function f(x,y) in the microbial image, calculate the gradient of the image at each pixel (x, y) , the magnitude of the gradient and the direction of the gradient at that coordinate. The specific formula is:

[0097]

[0098]

[0099]

[0100] in, and They are the gradient components in the horizontal x direction and the vertical y direction, and the gradient amplitude represents the strength of the gradient, Indicates the direction of the gradient.

[0101] The steps of Canny edge detection include non-maximum suppression: non-maximum suppression is performed on the gradient amplitude in the four gradient directions of 0°, 45°, 90° and 135°. For each pixel point (x, y), two intersection points are found in its 8-neighborhood according to the gradient direction. The intersection points are the two endpoints where the straight line where the gradient direction is located intersects the neighborhood. Since the image is discrete, the intersection point may not exist on the integer coordinates, so interpolation is required to estimate the gradient amplitude at the intersection point. If the gradient amplitude of the center point is the maximum value among the three points, the point is retained; otherwise, it is set to 0.

[0102] The steps of Canny edge detection include double threshold detection: set two thresholds: low threshold and high threshold. If the gradient amplitude of a pixel point is less than the low threshold, it is discarded; if the gradient amplitude is greater than the high threshold, the point is retained as a strong edge point; if the gradient amplitude is between the two, check the pixels in its 8-neighborhood; if the gradient amplitude of any point in the neighborhood is greater than the high threshold, the point is retained as a weak edge point; otherwise, it is discarded.

[0103] Step e: Close the edge image through morphological processing and obtain the final segmentation result.

[0104] 5. Microbial Identification Module

[0105] It is used to extract features from each target area image, identify the type of microorganism, and obtain the concentration of marker microorganisms at each sampling location.

[0106] The microorganism identification module includes a feature extraction unit, an identification unit and a marker microorganism concentration acquisition unit; the feature extraction unit is used to extract image features from each target area image; the identification unit uses a particle swarm optimization algorithm to optimize the parameters of the SVM model, and uses the optimized and trained SVM model to classify the features of the extracted target area image to identify the type of each microorganism; the marker microorganism concentration acquisition unit counts the marker microorganisms at each sampling position, and obtains the concentration of the marker microorganism according to the number of marker microorganisms and the volume of the collected water sample.

[0107] The image features extracted by the feature extraction unit include: geometric features, internal structure histogram features, Fourier descriptors, gray-level co-occurrence matrices and rotationally invariant local binary patterns.

[0108] The geometric features include: the area of ​​the microorganism, the perimeter of the microorganism boundary contour, the roundness of the microorganism, and the major and minor axis lengths of the ellipse with the same normalized second-order central moment as the microorganism area.

[0109] In this embodiment, the microorganism area is A, and the total number of pixels in the microorganism area in the segmented image is calculated; the perimeter of the microorganism boundary contour is P, which is obtained by using Canny edge detection to extract the boundary of the microorganism and then calculating the pixel length of the boundary; the roundness of the microorganism is calculated using the formula 4πA / Calculate, where A is the area and P is the perimeter; the length of the major axis of the ellipse is and the length of the minor axis is the major axis and minor axis of the ellipse that has the same normalized second-order central moment as the microbial area.

[0110] The internal structure histogram feature is used to describe the internal spatial structure of the image and reflect the distribution of pixel intensity in the image. In this embodiment, the steps of calculating the internal structure histogram include (step G1-step G3):

[0111] Step G1: Sampling point marking: Mark K sampling points with equal spacing on the microbial boundary contour;

[0112] Step G2: internal structure angle calculation: for any three sampling points, calculate the inscribed angle between them;

[0113] Step G3: Histogram statistics: Count all available inscribed angles within a certain angle range to obtain the ISH (Internal Structure Histogram) feature.

[0114] The Fourier descriptor is used to distinguish different contours through frequency domain information, thereby identifying objects, and is used to reflect the contour features of the image. In this embodiment, the steps of calculating the Fourier descriptor include (step N1-step N4):

[0115] Step N1: Sampling point marking: Mark M sampling points at equal intervals on the microbial boundary contour;

[0116] Step N2: Euclidean distance calculation: Calculate the Euclidean distance from each sampling point to the center point and arrange them in order as { , , ..., };

[0117] Step N3: Discrete Fourier Transform: Perform discrete Fourier transform on the distance sequence to extract the frequency domain component. The specific formula is:

[0118]

[0119] in, represents the sth complex coefficient, s=0, 1, ..., M-1; j represents the unit imaginary part; Represents the Euclidean distance from the tth sampling point to the center point.

[0120] Step N4: Standardization: Perform internal standardization on the Fourier coefficients to reduce the effects of noise, translation and rotation. The specific formula is: , is the eigenvector corresponding to the sth complex coefficient; since the microbial boundary contour can be expressed as a real function, There is conjugate symmetry, and finally we get a set The eigenvector of dimension ( , ..., ).

[0121] The gray level co-occurrence matrix is ​​used to reflect the spatial correlation of the gray values ​​of any two pixels in the image. In this embodiment, the steps of calculating the gray level co-occurrence matrix include (step O1-step O4):

[0122] Step O1: Grayscale compression: compress and quantize the grayscale of the original image into 16 levels;

[0123] Step O2: Calculate the co-occurrence matrix: Calculate the co-occurrence matrix in four directions of 0°, 45°, 90° and 135°;

[0124] Step O3: Normalization processing: normalizing the co-occurrence matrix;

[0125] Step O4: Statistic calculation: Calculate six statistics including energy, entropy, contrast, moment of inertia, correlation and inverse difference moment to obtain a 24-dimensional feature vector.

[0126] The rotation-invariant local binary pattern is a robust feature operator that is invariant to any monotonic transformation of a grayscale image, has grayscale invariance and rotation invariance, and is used to reflect the texture characteristics of an image. In this embodiment, the steps of the rotation-invariant local binary pattern include (steps K1 to K5):

[0127] Step K1: Block processing: divide the grayscale image into blocks;

[0128] Step K2: Sampling and encoding: Encoding sampling is performed in the circular neighborhood of each pixel point in each block;

[0129] Step K3: Circular shift operation: performing a circular shift operation on the sampled LBP (local binary pattern) value to obtain several different values;

[0130] Step K4: Take the minimum value: take the smallest one as the final LBP value;

[0131] Step K5: Histogram statistics: Perform histogram statistics on the LBP value of each block to obtain the rotation-invariant LBP (local binary pattern) feature.

[0132] The recognition unit uses a particle swarm optimization algorithm to optimize the parameters of the SVM model, and uses the optimized and trained SVM model to classify the features of the extracted target area image to identify the type of each microorganism.

[0133] Specifically, the particle swarm optimization algorithm is used to optimize the parameters of the SVM model. The specific process is (step U1-step U5):

[0134] Step U1: According to the preset particle swarm size, the position and velocity of each particle are randomly initialized; each particle represents a parameter combination of the SVM model;

[0135] Step U2: Use the SVM parameter combination represented by each particle to train the SVM model, and use the test set of the training process to calculate the classification accuracy of the SVM model, and use the classification accuracy of the SVM model as the fitness value of the particle;

[0136] Step U3: Update the individual historical optimal position and global optimal position of the particle swarm according to the fitness value of the particle;

[0137] Step U4: Update the speed and position of each particle according to the updated individual optimal position and global optimal position of the particle swarm;

[0138] Step U5: Repeatedly update the individual optimal position and the global optimal position of the particle until the termination condition is met, output the global optimal position and the corresponding global optimal fitness value, and the parameter combination of the SVM model corresponding to the particle at the global optimal position is the optimal parameter combination of the SVM model; the optimal parameter combination of the SVM model includes the penalty factor C and the kernel parameter .

[0139] The classification accuracy of the SVM model is calculated by using the SVM model parameters C and Establish an SVM model and evaluate the recognition accuracy of the test set.

[0140] In this embodiment, the particle swarm optimization algorithm searches for the optimal solution by randomly initializing particles and then iteratively updating the speed and position of the particles. Each particle moves along the direction of individual optimality and global optimality. In the process of searching in multidimensional space, the formulas for each particle to update its own speed and position are:

[0141]

[0142]

[0143] Among them, id represents the particle i in the d-dimensional space, k is the number of iterations, and w is the inertia weight. and are two acceleration constants, and are two random numbers uniformly distributed in (0, 1), and Represent the individual optimal position and the global optimal position respectively; is the velocity of particle i in d-dimensional space in the k-th iteration; The position of particle i in d-dimensional space at the kth iteration;

[0144] In this embodiment, the objective function of the SVM model during training is for:

[0145]

[0146] The constraints are:

[0147]

[0148]

[0149] in, and are the Lagrange multipliers of training samples i and j respectively, , N is the number of samples used for model training, and are the feature data of training samples i and j respectively; and are the category labels of training samples i and j respectively, is the RBF kernel function; C is the penalty factor;

[0150] Decision function of the SVM model for:

[0151]

[0152]

[0153] Among them, b is a constant bias term, x is the feature data of the target area image to be classified, is the kernel parameter.

[0154] The optimal parameter combination C and , thereby obtaining the optimal decision function of the SVM model.

[0155] The objective function is used in the SVM model to guide the model training process and find the optimal Lagrange multiplier; the decision function uses these optimal Lagrange multipliers to classify new samples.

[0156] That is Figure 2 As shown, the specific process steps of the recognition unit for recognition are:

[0157] Step P1: Input the characteristic data set of microorganisms;

[0158] Step P2: PSO algorithm parameter setting, including the total number of particles in the particle swarm, the maximum number of iterations, the inertia weight, the local and global learning factors, and the limit range of the parameters to be optimized C and γ;

[0159] Step P3: Initialize the particle swarm, randomly initialize the particle speed and position, and set the number of iterations to zero;

[0160] Step P4: loop through steps P6 to P9, with the number of iterations increasing by 1 each time the loop is repeated;

[0161] Step P5: Use the SVM parameters C and γ optimized by PSO to establish the SVM classification model of microbial image features, and evaluate the recognition accuracy of the test set;

[0162] Step P6: Calculate the fitness of each particle;

[0163] Step P7: Calculate the individual and global optimization fitness of the particles, and update the speed and position of each particle;

[0164] Step P8: Determine whether the loop is terminated. If the minimum error between two generations or the maximum number of iterations is reached, the loop is terminated. Otherwise, return to step P5.

[0165] Step P9: output the optimal parameters C and γ;

[0166] Step P10: Establish an optimized SVM model;

[0167] Step P11: Output the microbial classification and identification results.

[0168] After determining the number of marker microorganisms, the sample volume of the collected water sample can be determined: by cutting a specific length of fluid in the pipeline and measuring its volume, the concentration of marker microorganisms can be calculated. That is, the number of marker microorganisms is divided by the sample volume to obtain the concentration of marker microorganisms.

[0169] 6. Defect Identification Module

[0170] Based on the preset concentration gradient threshold of the marker microorganism and the concentration of the marker microorganism at different positions of the pipeline to be monitored, it is possible to identify whether there are defects in the pipeline to be monitored, as well as the location and severity of the defects.

[0171] The specific process is: setting a first concentration threshold range, a second concentration threshold range and a third concentration threshold range; the first concentration threshold range is located between the second concentration threshold range; the second concentration threshold range is located between the third concentration threshold range.

[0172] As an optional option, the upper limit values ​​of the first concentration threshold range, the second concentration threshold range and the third concentration threshold range are 2 times, 3 times and 4 times the concentration of the marker microorganism; the lower limit values ​​of the first concentration threshold range, the second concentration threshold range and the third concentration threshold range are 0.5 times, 0.2 times and 0.01 times the concentration of the marker microorganism. The upper and lower thresholds set in the first concentration threshold range, the second concentration threshold range and the third concentration threshold range are based on in-depth research and analysis of the relationship between pipeline defects and changes in microbial concentrations. Although specific multiples are initially set as thresholds (such as 2 times, 3 times, and 4 times the upper threshold, and 0.5 times, 0.2 times, and 0.01 times the lower threshold), in actual applications, the thresholds are not constant. With the changes in pipeline operating conditions, the succession of microbial populations, and the fluctuations of environmental factors, the thresholds should be dynamically adjusted according to actual conditions to ensure the accuracy and reliability of the judgment results.

[0173] If the marker microorganism concentration at the current position is within the first concentration threshold range, then the current position of the pipeline to be monitored has no defects.

[0174] If the marker microorganism concentration at the current location exceeds the upper limit of the first concentration threshold range, there is leakage, corrosion or other defects at the current location of the monitored pipeline that cause increased microbial concentration; these defects destroy the integrity of the pipeline, allowing external microorganisms to enter the pipeline and multiply in large numbers.

[0175] If the marker microbial concentration at the current location is lower than the lower limit of the first concentration threshold range, the current location of the monitored pipeline has poor water flow, sediment accumulation or other defects that lead to a decrease in microbial concentration. These defects need to be given sufficient attention and necessary measures should be taken to investigate and deal with them.

[0176] If the marker microorganism concentration at the current location is outside the first concentration threshold range and within the second concentration threshold range, the severity of the defect is primary; if the marker microorganism concentration at the current location is outside the second concentration threshold range and within the third concentration threshold range, the severity of the defect is intermediate; if the marker microorganism concentration at the current location is outside the third concentration threshold range, the severity of the defect is severe.

[0177] Example 2

[0178] like Figure 3 As shown, a pipeline defect intelligent identification method based on pipeline network microorganisms includes the following steps:

[0179] Step S1: placing marker microorganisms at fixed points and in fixed quantities in the pipeline to be monitored.

[0180] When placing marker microorganisms, it is necessary to consider the layout of the pipeline, water flow rate, selection of placement points and placement amount. A reasonable placement strategy can ensure that the marker microorganisms are evenly distributed in the pipeline and improve the accuracy and reliability of the test results.

[0181] Step S2: After the marker microorganisms are introduced, water samples are collected from a designated location of the pipeline to be monitored after a preset period of time.

[0182] When collecting water samples, professional sampling tools and methods are needed to ensure that the collected water samples are representative and can truly reflect the distribution of microorganisms inside the pipeline. Care must also be taken to avoid contamination during the sampling process.

[0183] Step S3: For each water sample collection location, particles of different sizes and properties in the water sample are separated by electrostatic field separation technology, wherein the particles include marker microorganisms, other microorganisms and impurities.

[0184] Through electrostatic field separation technology, microorganisms in water samples can be effectively separated from other impurities, providing clear microbial samples for subsequent image processing and feature extraction.

[0185] Step S4: photographing and segmenting the separated particles to obtain a target area image containing the microbial morphology or a set required part.

[0186] The photographic equipment should have high resolution and high sensitivity, and be able to clearly capture the morphology and characteristics of microorganisms. The equipment should also have a stable light source and shooting environment to ensure that the quality of the captured images is stable and reliable.

[0187] Image segmentation is a key technology in image processing, which aims to effectively separate the target area from the background area in the image. For captured images, edge detection method is used for segmentation.

[0188] Step S5: Extract features from each target area image and identify the type of microorganisms to obtain the concentration of marker microorganisms at each sampling location.

[0189] The extracted features refer to the information that can reflect the essential attributes of the target object in the image, such as shape, texture, and color. For microbial images, shape features, texture features, and color features can be selected as the basis for classification and recognition.

[0190] Step S6: Based on the preset concentration gradient threshold of the marker microorganism and the concentration of the marker microorganism at different positions of the pipeline to be monitored, identify whether there is a defect in the pipeline to be monitored, as well as the location and severity of the defect.

[0191] According to the classification results of the SVM model, the concentration of the marker microorganisms can be obtained, and the concentration can be used to determine whether the pipeline has defects, the location of the defects, and the severity of the defects.

[0192] For more specific processes of the above method, please refer to the corresponding contents disclosed in the aforementioned embodiments, which will not be repeated here.

[0193] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the method disclosed in the embodiment, since it corresponds to the system disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the system part description.

[0194] Those skilled in the art can clearly understand that the technology in the embodiments of the present invention can be implemented by means of software plus a necessary general hardware platform. Based on this understanding, the technical solution in the embodiments of the present invention can be essentially or partly contributed to the prior art in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in the various embodiments of the present invention or certain parts of the embodiments.

[0195] The above are only preferred embodiments of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should be regarded as the protection scope of the present invention.

Claims

1. An intelligent pipeline defect identification system based on pipeline network microorganisms, characterized in that: include: A marker microorganism delivery module is used to deliver marker microorganisms at fixed points and in fixed quantities in the pipeline to be monitored; A water sample collection module is used to collect water samples from a designated location of the pipeline to be tested after a preset period of time after the marker microorganisms are placed; An electrostatic field separation module is used to separate particles of different sizes and properties in each water sample collection location by electrostatic field separation technology, wherein the particles include marker microorganisms, other microorganisms and impurities; An imaging module is used to take pictures and segment the separated particles to obtain a target area image containing the microbial morphology or a set required part; The microorganism identification module is used to extract features from each target area image and identify the type of microorganisms to obtain the concentration of the marker microorganisms at each sampling location; The defect recognition module is used to identify whether there are defects in the pipeline to be monitored, as well as the location and severity of the defects based on the preset concentration gradient threshold of the marker microorganisms and the concentration of the marker microorganisms at different locations of the pipeline to be monitored.

2. The pipeline defect intelligent identification system based on pipeline network microorganisms according to claim 1 is characterized in that: The electrostatic field separation module adopts a multi-stage electrostatic separator structure, and each stage of the electrostatic separator can adjust the parameters of the electrostatic field to adjust the intensity and direction of the electrostatic field; The electrostatic field separation technology is used to separate particles of different sizes and properties in the water sample. Specifically, when the collected water sample passes through the electrostatic separator, the electrostatic separator applies different electric field forces to all microorganisms and impurities in the water sample, and separates them by utilizing the charge differences of different microorganisms and impurities in the water sample.

3. The pipeline defect intelligent identification system based on pipeline network microorganisms according to claim 1 is characterized in that: The imaging module includes a high-resolution camera and a segmentation unit; the high-resolution camera takes pictures of the separated particles to obtain an original photographed image; the segmentation unit segments the original photographed image by a fully automatic segmentation method to segment out a target area image containing the microbial morphology or a set required part; The fully automatic segmentation method specifically comprises: Input the original captured image; convert the original captured image into a grayscale image; enhance the contrast through point operation and perform Gaussian filtering to remove noise; use the Canny edge detection operator to detect the boundary of each microorganism; close the edge image through morphological processing and obtain the final segmentation result.

4. The pipeline defect intelligent identification system based on pipeline network microorganisms according to claim 1 is characterized in that: The microorganism identification module includes a feature extraction unit; The feature extraction unit is used to extract image features from each target area image; The extracted image features include geometric features, internal structure histogram features, Fourier descriptors, gray-level co-occurrence matrices, and rotation-invariant local binary patterns; The geometric features include: the area of ​​the microorganism, the perimeter of the microorganism boundary contour, the roundness of the microorganism, and the major axis length and minor axis length of an ellipse having the same normalized second-order central moment as the microorganism area; The internal structure histogram feature is used to describe the internal spatial structure of the image and reflect the distribution of pixel intensity in the image; The Fourier descriptor is used to distinguish different contours through frequency domain information, and then identify objects, and is used to reflect the contour characteristics of the image; The gray level co-occurrence matrix is ​​used to reflect the spatial correlation of the gray values ​​of any two pixels in the image; The rotation-invariant local binary pattern is a robust feature operator that is invariant to any monotonic transformation of a grayscale image, has grayscale invariance and rotation invariance, and is used to reflect the texture characteristics of an image.

5. The pipeline defect intelligent identification system based on pipeline network microorganisms according to claim 1 is characterized in that: The microorganism identification module also includes an identification unit and a marker microorganism concentration acquisition unit; the identification unit uses a particle swarm optimization algorithm to optimize the parameters of the SVM model, and uses the optimized and trained SVM model to classify the features of the extracted target area image to identify the type of each microorganism; the marker microorganism concentration acquisition unit counts the marker microorganisms at each sampling position, and obtains the concentration of the marker microorganism based on the number of marker microorganisms and the volume of the collected water sample.

6. The pipeline defect intelligent identification system based on pipeline network microorganisms according to claim 5 is characterized in that: The particle swarm optimization algorithm is used to optimize the parameters of the SVM model. The specific process is as follows: According to the preset particle swarm size, the position and velocity of each particle are randomly initialized; each particle represents a parameter combination of the SVM model; The SVM model is trained using the SVM parameter combination represented by each particle, and the classification accuracy of the SVM model is calculated using the test set of the training process. The classification accuracy of the SVM model is used as the fitness value of the particle. According to the fitness value of the particle, update the individual historical optimal position and global optimal position of the particle swarm; Update the speed and position of each particle according to the updated individual optimal position and global optimal position of the particle swarm; Repeatedly update the individual optimal position and global optimal position of the particle until the termination condition is met, output the global optimal position and the corresponding global optimal fitness value, the parameter combination of the SVM model corresponding to the particle at the global optimal position is the optimal parameter combination of the SVM model; the optimal parameter combination of the SVM model includes the penalty factor C and the kernel parameter .

7. The pipeline defect intelligent identification system based on pipeline network microorganisms according to claim 5 is characterized in that: The objective function of the SVM model during training is for: ； The constraints are: ； ； in, and are the Lagrange multipliers of training samples i and j respectively, , N is the number of samples used for model training, and are the feature data of training samples i and j respectively; and are the category labels of training samples i and j respectively, is the RBF kernel function; C is the penalty factor; The decision function of the SVM model for: ； ； Among them, b is a constant bias term, x is the feature data of the target area image to be classified, is the kernel parameter.

8. The pipeline defect intelligent identification system based on pipeline network microorganisms according to claim 1 is characterized in that: The defect recognition module is used to identify whether there is a defect in the pipeline to be monitored, as well as the location and severity of the defect based on the preset concentration gradient threshold of the marker microorganism and the concentration of the marker microorganism at different positions of the pipeline to be monitored. The specific process is as follows: A first concentration threshold range, a second concentration threshold range and a third concentration threshold range are set; the first concentration threshold range is located between the second concentration threshold range; the second concentration threshold range is located between the third concentration threshold range; If the concentration of the marker microorganism at the current position is within the first concentration threshold range, then the current position of the pipeline to be monitored has no defects; If the marker microorganism concentration at the current position exceeds the upper limit of the first concentration threshold range, then the current position of the pipeline to be monitored has leakage, corrosion or other defects that cause the microorganism concentration to increase; if the marker microorganism concentration at the current position is lower than the lower limit of the first concentration threshold range, then the current position of the pipeline to be monitored has poor water flow, sediment accumulation or other defects that cause the microorganism concentration to decrease; If the marker microorganism concentration at the current location is outside the first concentration threshold range and within the second concentration threshold range, the severity of the defect is primary; if the marker microorganism concentration at the current location is outside the second concentration threshold range and within the third concentration threshold range, the severity of the defect is intermediate; if the marker microorganism concentration at the current location is outside the third concentration threshold range, the severity of the defect is severe.

9. The pipeline defect intelligent identification system based on pipeline network microorganisms according to claim 1 is characterized in that: The marker microorganism delivery module is used to deliver the marker microorganisms at fixed points and in fixed quantities in the pipeline to be monitored. The delivery locations of the marker microorganisms are located at the connection points, branches and turns of the pipeline to be monitored, and the delivery locations of the marker microorganisms are assigned unique coordinate values; The water sample collection module is used to collect water samples from designated positions of the pipeline to be detected after the marker is placed, after a preset time period. The designated positions for collecting water samples are the inlet, outlet, connection point, branch and turn of the pipeline to be monitored.

10. A pipeline defect intelligent identification method based on pipeline network microorganisms, characterized in that: include: Place marker microorganisms at fixed points and in fixed quantities in the pipeline to be monitored; After the marker microorganisms are placed, water samples are collected from designated locations of the pipeline to be tested after a preset period of time; For each water sample collection location, particles of different sizes and properties in the water sample are separated by electrostatic field separation technology, and the particles include marker microorganisms, other microorganisms and impurities; The separated particles are photographed and image segmented to obtain target area images containing microbial morphology or set required parts; Perform feature extraction on each target area image and identify the microbial species to obtain the marker microbial concentration at each sampling location; Based on the preset concentration gradient threshold of the marker microorganism and the concentration of the marker microorganism at different positions of the pipeline to be monitored, it is possible to identify whether there are defects in the pipeline to be monitored, as well as the location and severity of the defects.

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