Sludge settling ratio measuring method and device for sewage treatment

Abstract

The invention relates to the technical field of sewage treatment, and discloses a sludge sedimentation ratio measuring method and device for sewage treatment, and the method comprises the following steps: carrying out image processing on sludge sedimentation image video data to obtain observation bottle pattern data containing an observation water sample; dividing a bottle body area in the observation bottle pattern data containing the observation water sample to obtain a plurality of bottle body segmented areas; pixel brightness of the multiple bottle body segmented areas is obtained, clustering segmentation is carried out on the pixel brightness of the multiple bottle body segmented areas, and water body segmented areas are determined; pixel brightness of the water body segmented areas is obtained, clustering analysis is carried out on the pixel brightness of the water body segmented areas, and a sludge sediment area is determined; and determining a sludge sedimentation ratio measurement result based on the multi-section bottle body segmentation area, the water body segmentation area and the sludge sediment area. According to the invention, human intervention is reduced, the intelligent level of the sludge settling ratio measurement process is improved, and the measurement precision and reliability of the sludge settling ratio are improved.

Description

Technical Field

[0001] The invention relates to the technical field of sewage treatment, and in particular to a sludge settling ratio measuring method and device for sewage treatment. Background Art

[0002] With the acceleration of urbanization and the development of industrialization, the amount of sewage discharge has increased significantly, and the problem of water pollution has become increasingly serious. In order to effectively treat sewage and ensure that the discharged water quality meets environmental protection standards, various sewage treatment technologies have emerged. In sewage treatment technology, the measurement of sludge settling ratio (SSR) is one of the key steps and is of great significance to the optimization and control of the entire sewage treatment process. Sludge settling ratio refers to the settling rate and settling effect of sludge in a settling tank within a certain period of time.

[0003] By measuring the sludge settling ratio, the settling performance of the sludge can be evaluated, and then the relevant parameters in the sewage treatment process can be adjusted to improve the treatment efficiency and effluent quality. However, the relevant sludge settling ratio measurement methods mainly rely on manual operation, such as the use of instruments such as measuring cylinders for manual measurement, which is not only time-consuming and labor-intensive, but also has low accuracy and is greatly affected by subjective factors. It is difficult to meet the needs of modern sewage treatment plants for efficient, accurate and automated measurement. Summary of the invention

[0004] In view of this, the present invention provides a sludge settling ratio measurement method and device for sewage treatment, so as to solve the problem that the sludge settling ratio measurement method relies on manual operation and the sludge settling ratio measurement accuracy is low.

[0005] In a first aspect, the present invention provides a method for measuring sludge settling ratio for sewage treatment, the method comprising:

[0006] Acquire sludge settling image video data in the sewage treatment process, perform image processing on the sludge settling image video data, and obtain observation bottle pattern data containing the observed water sample;

[0007] Dividing the bottle body area in the observation bottle pattern data containing the observed water sample to obtain a plurality of bottle body segmented areas;

[0008] Obtain pixel brightness of multiple bottle body segmented areas, perform clustering segmentation on the pixel brightness of multiple bottle body segmented areas, and determine water body segmented areas;

[0009] Obtain pixel brightness of the water body segmented area, perform cluster analysis on the pixel brightness of the water body segmented area, and determine the sludge sediment area;

[0010] The sludge settling ratio measurement result is determined based on the multi-segment bottle body segmented areas, the water body segmented areas and the sludge sedimentation areas.

[0011] The present embodiment provides a sludge settling ratio measurement method for sewage treatment, which obtains sludge settling image video data in the sewage treatment process, divides the bottle body area in the observation bottle pattern data containing the observed water sample, and then clusters and segments the pixel brightness of the multiple bottle body segmented areas obtained by the division, determines the water body segmented area, and clusters and analyzes the pixel brightness of the water body segmented area to determine the sludge sediment area, and finally determines the sludge settling ratio measurement result based on the multiple bottle body segmented areas, the water body segmented areas and the sludge sediment area, thereby realizing the motion tracking of the sludge sediment between consecutive frame images, reducing human intervention, avoiding human operation errors, improving the intelligence level of the sludge settling ratio measurement process, and improving the measurement accuracy and reliability of the sludge settling ratio.

[0012] In an optional embodiment, the sludge settling image video data is image processed to obtain observation bottle pattern data containing the observed water sample, including:

[0013] Grayscale processing is performed on the sludge sedimentation image video data to obtain grayscale image data;

[0014] The grayscale image data is semantically segmented to obtain observation bottle pattern data containing the observed water sample.

[0015] The present embodiment provides a sludge settling ratio measurement method for sewage treatment, which realizes separation of an observation bottle from a background by gray-scaling and semantically segmenting sludge settling image video data. In addition, image segmentation is used to obtain observation bottle pattern data containing an observed water sample, thereby realizing effective segmentation of gray-scale image data and improving the measurement accuracy of the sludge settling ratio to a certain extent.

[0016] In an optional implementation, clustering and segmenting the pixel brightness of multiple bottle body segment regions to determine the water body segment regions includes:

[0017] Based on the pixel brightness of the multiple bottle body segmented areas, the average brightness values ​​of the multiple bottle body segmented areas are calculated respectively;

[0018] Based on the average brightness values ​​of the multiple bottle body segmented areas, the average brightness differences between the bottle top area and other bottle body segmented areas are calculated respectively; wherein the other bottle body segmented areas are the remaining bottle body segmented areas except the bottle top area;

[0019] The average brightness difference between the bottle top area and other bottle body segment areas is compared with the first threshold, and the multiple bottle body segment areas are segmented based on the comparison result to obtain the water body segment areas.

[0020] The present embodiment provides a sludge settling ratio measurement method for sewage treatment, which calculates the average brightness difference between the bottle top area and other bottle body segmented areas, and compares the average brightness difference between the bottle top area and other bottle body segmented areas with a first threshold value, thereby determining the water body segmented area. It utilizes the characteristic that the average brightness difference becomes larger when transitioning from non-water body to water body, thereby achieving effective segmentation of non-water body area and water body area in the bottle body segmented area, thereby improving the accuracy and intelligence of sludge settling ratio measurement.

[0021] In an optional embodiment, cluster analysis is performed on the pixel brightness of the water body segmented area to determine the sludge sediment area, including:

[0022] Based on the pixel brightness of the water body segmented area, the average brightness difference between each water body segmented area is calculated;

[0023] The average brightness difference between each water body segment area is compared with the second threshold value, and the water body segment area is segmented based on the comparison result to obtain the sludge sediment area.

[0024] The present embodiment provides a sludge settling ratio measurement method for sewage treatment. The method compares the average brightness difference between each water body segment area with a second threshold value, and segments the water body segment area based on the comparison result to obtain the sludge sedimentation area. This achieves accurate segmentation of the supernatant area and the sludge sedimentation area, reduces human intervention, and improves the intelligence level of the sludge settling ratio measurement process.

[0025] In an optional embodiment, determining the sludge settling ratio measurement result based on the multiple bottle body segmented areas, water body segmented areas and sludge sediment areas includes:

[0026] The sludge settling ratio is calculated based on the row values ​​of the bottom area in the multi-segmented bottle body segmentation area, the row values ​​of the upper interface in the water body segmentation area, and the row values ​​of the upper interface in the sludge sedimentation area. The calculation formula of the sludge settling ratio is as follows:

[0027]

[0028] Among them, SV(t) represents the sludge settling ratio, R s Represents the upper interface value of the sludge sedimentation area, R w Represents the upper interface value of the water segment area, R b Indicates the row value of the bottle bottom area.

[0029] In an optional implementation, it also includes:

[0030] The sludge settling ratio corresponding to each frame of image data in the observation bottle pattern data containing the observed water sample is obtained. If the sludge settling ratio corresponding to the image data of a preset number of frames does not change, the sludge settling ratio summary analysis result is determined based on the sludge settling ratio corresponding to each frame of image data.

[0031] This embodiment provides a sludge settling ratio measurement method for sewage treatment, which realizes the motion tracking of sludge sediment between consecutive frame images, can accurately measure the sludge settling ratio in each frame image, and provides detailed real-time data of the settling process, helping users to timely understand the dynamic changes of the sewage treatment process.

[0032] In a second aspect, the present invention provides a sludge settling ratio measuring device for sewage treatment, the device comprising:

[0033] An image processing module is used to obtain sludge settling image video data in the sewage treatment process, and perform image processing on the sludge settling image video data to obtain observation bottle pattern data containing the observed water sample;

[0034] A division module, used for dividing the bottle body area in the observation bottle pattern data containing the observed water sample to obtain multiple bottle body segmented areas;

[0035] The first determination module is used to obtain the pixel brightness of multiple bottle body segmented areas, perform clustering segmentation on the pixel brightness of multiple bottle body segmented areas, and determine the water body segmented areas;

[0036] The second determination module is used to obtain the pixel brightness of the water body segmented area, perform cluster analysis on the pixel brightness of the water body segmented area, and determine the sludge sediment area;

[0037] The third determination module is used to determine the sludge settling ratio measurement result based on the multiple bottle body segmented areas, water body segmented areas and sludge sediment areas.

[0038] In a third aspect, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the sludge settling ratio measurement method for sewage treatment according to the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0039] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the sludge settling ratio measurement method for sewage treatment according to the first aspect or any corresponding embodiment thereof.

[0040] In a fifth aspect, the present invention provides a computer program product, comprising computer instructions for causing a computer to execute the sludge settling ratio measurement method for sewage treatment according to the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0042] Figure 1 is a schematic flow chart of a sludge settling ratio measurement method for sewage treatment according to an embodiment of the present invention;

[0043] Figure 2 is a structural schematic diagram of a sewage video image acquisition model according to an embodiment of the present invention;

[0044] Figure 3 is a schematic diagram of the bottle body segmentation and layering interface according to an embodiment of the present invention;

[0045] Figure 4 is a schematic flow chart of another method for measuring sludge settling ratio for sewage treatment according to an embodiment of the present invention;

[0046] Figure 5 is a schematic flow chart of another method for measuring sludge settling ratio for sewage treatment according to an embodiment of the present invention;

[0047] Figure 6 is a schematic diagram of a flow chart of a visual measurement method for sludge settling ratio for sewage treatment according to an embodiment of the present invention;

[0048] Figure 7 is a structural block diagram of a sludge settling ratio measuring device for sewage treatment according to an embodiment of the present invention;

[0049] Figure 8 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0051] With the rapid development of computer technology and image processing technology, measurement methods based on computer vision have gradually gained attention and application. Computer vision technology obtains image data through cameras or other imaging devices, analyzes and processes images using image processing algorithms, and extracts useful information. This technology has the advantages of high degree of automation, high measurement accuracy, and good real-time performance. It is suitable for measuring sludge settling ratio in sewage treatment processes.

[0052] The sludge settling ratio measurement method based on computer vision mainly includes the following steps: first, the image sequence of the sludge settling process is obtained through the camera; second, the image is pre-processed by the image processing algorithm, such as denoising, contrast enhancement, etc.; then, the height of the extracted sludge and clear liquid is used; finally, the sludge settling velocity and settling ratio are obtained.

[0053] However, the above-mentioned sludge settling ratio measurement method still has some problems in practical application. For example, the physical and chemical properties of different sludges may affect the effect of image processing, such as color, transparency, particle size, etc. The system needs to have strong adaptability to handle different types of sludge; in addition, different sludges have different settling characteristics, and the relevant algorithms are difficult to adapt to the sludge settling ratio detection situations with different settling characteristics.

[0054] The embodiment of the present invention provides a method for measuring a sludge settling ratio for sewage treatment. It should be noted that the method for measuring a sludge settling ratio for sewage treatment provided by the embodiment of the present invention may be implemented as a device for measuring a sludge settling ratio for sewage treatment. The device for measuring a sludge settling ratio for sewage treatment may be implemented as part or all of an electronic device through software, hardware, or a combination of software and hardware. The electronic device may be a server or a terminal. The server in the embodiment of the present application may be a single server or a server cluster composed of multiple servers. The terminal in the embodiment of the present application may be a smart phone, a personal computer, a tablet computer, an intelligent robot, or other intelligent hardware devices. In the following method embodiments, the execution subject is an electronic device as an example for explanation.

[0055] According to an embodiment of the present invention, an embodiment of a sludge settling ratio measurement method for sewage treatment is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0056] In this embodiment, a sludge settling ratio measurement method for sewage treatment is provided, which can be used in the above-mentioned electronic device. Figure 1 FIG. 1 is a flow chart of a method for measuring sludge settling ratio for sewage treatment according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0057] Step S101, acquiring sludge sedimentation image video data in the sewage treatment process, performing image processing on the sludge sedimentation image video data, and obtaining observation bottle pattern data containing observed water samples.

[0058] Specifically, Figure 2 As shown, the sewage video image acquisition model includes an LED strong focusing light source, a high-resolution camera, an observation bottle, a pumping valve and a sedimentation tank, wherein, in order to avoid the pumping valve blocking the line of sight, the LED strong focusing light source and the high-resolution camera are arranged on one side of the observation bottle, the pumping valve is arranged on the other side of the observation bottle, and the LED strong focusing light source is arranged at a position about 45 degrees above one side of the observation bottle to ensure that the light emitted by the LED strong focusing light source can cover the entire observation bottle, and then the LED strong focusing light source and the high-resolution camera are used to continuously shoot the observation bottle in the sewage treatment process to obtain sludge sedimentation image video data.

[0059] Furthermore, a high-resolution camera with a resolution of 1080p or higher is used to ensure clear image details; a strong focused LED light source is used to ensure that the light is concentrated and evenly irradiated on the observation bottle to avoid shadows and reflections; a transparent observation bottle made of glass or highly transparent plastic with uniform wall thickness is selected to ensure that light can pass through without obvious refraction.

[0060] Furthermore, the focus, aperture and shutter speed of the high-resolution camera are adjusted to ensure that the image is clear and properly exposed, and the continuous shooting mode of the high-resolution camera is set. According to the length of the sewage sedimentation process, an appropriate shooting frequency (such as 30 frames per second) and total shooting time (such as continuous shooting for 1 hour) are set.

[0061] Step S102 , dividing the bottle body region in the observation bottle pattern data containing the observed water sample to obtain a plurality of bottle body segment regions.

[0062] Specifically, Figure 3As shown, according to the height of the bottle body, a series of small grids with k rows and l columns of pixels are divided from the top to the bottom of the bottle. The bottle is divided into n bottle body segment areas, and the n bottle body segment areas are numbered 0, 1, 2----n-1 in the order from the top to the bottom of the bottle.

[0063] Furthermore, obtain the total number of rows and columns of the observation bottles, and record the row number where the bottle bottom is located as R b .

[0064] Step S103, obtaining pixel brightness of multiple bottle body segmented areas, performing cluster segmentation on the pixel brightness of multiple bottle body segmented areas, and determining water body segmented areas.

[0065] Specifically, the brightness clustering segmentation method is used to segment the bottle body segment area into two types of areas: a water-free segment area and a water body segment area.

[0066] Step S104, obtaining the pixel brightness of the water body segmented area, performing cluster analysis on the pixel brightness of the water body segmented area, and determining the sludge sediment area.

[0067] Specifically, the brightness clustering segmentation method is used to segment the water body segment area into two areas: the supernatant area and the sludge sediment area.

[0068] Step S105 , determining a sludge settling ratio measurement result based on the multiple bottle body segmented areas, water body segmented areas and sludge sediment areas.

[0069] Specifically, the movement of sludge sediment between consecutive frames in the observation bottle pattern data containing the observed water sample is tracked, the heights of the supernatant and the sludge sediment in the observation bottle are calculated, and the sewage settling ratio measurement of each frame image is achieved; and the above steps S103-S105 are repeated until the tracked supernatant and sludge sediment heights in the observation bottle no longer change, completing the measurement of the sludge settling ratio that changes with time during the sewage sampling observation, and obtaining the sludge settling ratio measurement result.

[0070] Furthermore, the sludge settling ratio corresponding to each frame of image data in the observation bottle pattern data containing the observed water sample is obtained. If the sludge settling ratio corresponding to the image data of a preset number of frames does not change, the sludge settling ratio summary analysis result is determined based on the sludge settling ratio corresponding to each frame of image data.

[0071] Furthermore, observe and compare the sludge settling ratio corresponding to each frame of image data, and determine whether the sludge sedimentation height (i.e., the upper interface line value of the sludge sedimentation area) has changed. If the sludge sedimentation height is still changing, repeat the above steps S103-S105. When the sludge settling ratio recorded for t+x (x=10) consecutive times no longer changes, t represents the current number of records, then the sedimentation process is considered to be stable. According to the data analysis results, the recorded data are summarized to obtain the sludge settling ratio measurement results at key time points.

[0072] The present embodiment provides a sludge settling ratio measurement method for sewage treatment, which obtains sludge settling image video data in the sewage treatment process, divides the bottle body area in the observation bottle pattern data containing the observed water sample, and then clusters and segments the pixel brightness of the multiple bottle body segmented areas obtained by the division, determines the water body segmented area, and clusters and analyzes the pixel brightness of the water body segmented area to determine the sludge sediment area, and finally determines the sludge settling ratio measurement result based on the multiple bottle body segmented areas, the water body segmented areas and the sludge sediment area, thereby realizing the motion tracking of the sludge sediment between consecutive frame images, reducing human intervention, avoiding human operation errors, improving the intelligence level of the sludge settling ratio measurement process, and improving the measurement accuracy and reliability of the sludge settling ratio.

[0073] In this embodiment, a sludge settling ratio measurement method for sewage treatment is provided, which can be used in the above-mentioned electronic device. Figure 4 FIG. 1 is a flow chart of a method for measuring sludge settling ratio for sewage treatment according to an embodiment of the present invention. Figure 4 As shown, the process includes the following steps:

[0074] Step S401, acquiring sludge sedimentation image video data in the sewage treatment process, performing image processing on the sludge sedimentation image video data, and obtaining observation bottle pattern data containing the observed water sample.

[0075] Step S4011, grayscale processing is performed on the sludge sedimentation image video data to obtain grayscale image data.

[0076] Specifically, before grayscale processing, the sludge sedimentation image video data is first subjected to preprocessing operations such as denoising and contrast enhancement to improve image quality and ensure the accuracy of subsequent processing.

[0077] Furthermore, the sludge sedimentation image video data is a color image, which consists of three channels: red, green, and blue (RGB), while the grayscale image has only one channel, which represents brightness; therefore, the color image is converted into a grayscale image by calculating the weighted sum of the values ​​of each RGB channel. The specific steps include: Assume that I grayRepresents the brightness value of each pixel in the grayscale image, R, G and B are the color values ​​of each pixel in the three channels of the color image, and the grayscale processing can be expressed as;

[0078] I gray =0.299*R+0.587*G+0.114*B (1)

[0079] Step S4012, semantically segmenting the grayscale image data to obtain observation bottle pattern data containing the observed water sample.

[0080] Specifically, semantic segmentation is to divide the grayscale image data into different regions and mark the category to which each pixel belongs. After segmentation, two types of segmentation results are obtained: the observation bottle and the water body in the bottle.

[0081] Furthermore, a series of image data (no less than 100) containing the observation bottle and the water in the bottle are collected, and the images are annotated with two types of targets at the pixel level using semi-automatic annotation tools or manual annotation tools (such as Labelbox, LabelMe, etc.), and the corresponding observation bottle and bottle water segmentation mask training data set is generated. This training data set is used to evaluate and adjust the effect of the segmentation model.

[0082] Furthermore, the Segment Anything Model (SAM) is installed and configured, the above-mentioned observation bottle and bottle water segmentation mask training data set is loaded, and the average brightness value of the observation bottle and bottle water mask annotated in the training set is calculated as a feature to help distinguish the observation bottle and the bottle water. The SAM and the average brightness value of the two types of segmentation target pixel masks are used to perform inference segmentation on the grayscale image data, and the two types of segmentation results of the observation bottle and the bottle water are obtained, and the observation bottle pattern data containing the observed water sample is obtained.

[0083] Furthermore, any two points on the observation bottle with and without water can be selected and set as the observation bottle key points and the water body key points. The selected key points and the collected images can be input into a pre-trained segmentation model for inference to obtain pixel coordinate masks corresponding to the observation bottle and the water body in the image. The original pixels in the images corresponding to the observation bottle and the water body can be taken out from the image, and the interval of interest can be cut out to obtain the observation bottle pattern data containing the observed water sample.

[0084] Step S402, dividing the bottle body area in the observation bottle pattern data containing the observed water sample to obtain multiple bottle body segmented areas. Figure 1 Step S102 of the illustrated embodiment will not be described in detail here.

[0085] Step S403, obtaining the pixel brightness of multiple bottle body segmented areas, clustering and segmenting the pixel brightness of multiple bottle body segmented areas, and determining the water body segmented areas. Figure 1 Step S103 of the illustrated embodiment will not be described in detail here.

[0086] Step S404, obtain the pixel brightness of the water body segmented area, perform cluster analysis on the pixel brightness of the water body segmented area, and determine the sludge sediment area. Figure 1 Step S104 of the illustrated embodiment will not be described in detail here.

[0087] Step S405, determining the sludge settling ratio measurement result based on the multiple bottle body segmented areas, water body segmented areas and sludge sediment areas. Figure 1 Step S105 of the illustrated embodiment will not be described in detail here.

[0088] The present embodiment provides a sludge settling ratio measurement method for sewage treatment, which realizes separation of an observation bottle from a background by gray-scaling and semantically segmenting sludge settling image video data. In addition, image segmentation is used to obtain observation bottle pattern data containing an observed water sample, thereby realizing effective segmentation of gray-scale image data and improving the measurement accuracy of the sludge settling ratio to a certain extent.

[0089] In this embodiment, a sludge settling ratio measurement method for sewage treatment is provided, which can be used in the above-mentioned electronic device. Figure 5 FIG. 1 is a flow chart of a method for measuring sludge settling ratio for sewage treatment according to an embodiment of the present invention. Figure 5 As shown, the process includes the following steps:

[0090] Step S501, obtaining sludge settling image video data in the sewage treatment process, performing image processing on the sludge settling image video data, and obtaining observation bottle pattern data containing the observed water sample. Figure 4 Step S401 of the illustrated embodiment will not be described in detail here.

[0091] Step S502, dividing the bottle body area in the observation bottle pattern data containing the observation water sample to obtain multiple bottle body segmented areas. Figure 4 Step S402 of the illustrated embodiment will not be described in detail here.

[0092] Step S503, obtaining pixel brightness of multiple bottle body segmented areas, performing cluster segmentation on the pixel brightness of multiple bottle body segmented areas, and determining water body segmented areas.

[0093] Specifically, the above step S503 includes:

[0094] Step S5031, based on the pixel brightness of the multiple bottle body segmented areas, respectively calculate the average brightness values ​​of the multiple bottle body segmented areas.

[0095] Specifically, since each bottle body segment contains a total of k×l pixels, it is assumed that the brightness of each pixel is I ab , then the average brightness value of each bottle segment area is:

[0096]

[0097] in, Represents the average brightness value of the i-th bottle body segment area.

[0098] Furthermore, an index table with n rows and 2 columns is created, and the numbers and average brightness values ​​of the bottle body segmented regions are written into the index table; wherein the number of rows in the index table is the same as the number of the bottle body segmented regions.

[0099] Step S5032, based on the average brightness values ​​of the multiple bottle body segmented areas, respectively calculate the average brightness difference between the bottle top area and other bottle body segmented areas; wherein the other bottle body segmented areas are the remaining bottle body segmented areas except the bottle top area.

[0100] Step S5033: compare the average brightness difference between the bottle top area and other bottle body segment areas with a first threshold, and segment multiple bottle body segment areas based on the comparison result to obtain water body segment areas.

[0101] Specifically, since the average brightness value of the grid in the transparent observation bottle without water is large, the average brightness value of the grid in the observation bottle with water is small, and the upper boundary of the water body is lower than the top of the observation bottle, it is assumed that the first threshold is th1, and the average brightness value of the bottle top area is The average brightness of the i-th bottle segment area is i∈[1,...,n-1], and then judge from the top area to the bottom area in the following way:

[0102]

[0103] The first bottle segment area that satisfies the above formula (3) is taken as the segment area where the upper interface of the water body is located, and the upper interface row value R of the water body segment area is extracted. w , and determine the water body segment area based on the number of the segment area that is greater than the upper interface of the water body.

[0104] Step S504, obtaining the pixel brightness of the water body segmented area, performing cluster analysis on the pixel brightness of the water body segmented area, and determining the sludge sediment area.

[0105] Specifically, the above step S504 includes:

[0106] Step S5041, calculating the average brightness difference between each water body segment area based on the pixel brightness of the water body segment area.

[0107] Step S5042, comparing the average brightness difference between each water body segment area with the second threshold, and segmenting the water body segment area based on the comparison result to obtain the sludge sediment area.

[0108] Specifically, since the average brightness value of the water segment area without sludge sediment is large, the average brightness value of the water segment area with sludge sediment is small, and the sludge sediment area number is greater than the segment area number of the upper interface of the water segment area, it is assumed that the segment area number of the upper interface of the water segment area is, the second threshold is th2, and the average brightness value of the i+1th water segment area is The average brightness value of the jth water segment from the i+1th water segment to the bottle bottom is j∈[i+2,...,n-1], judge in the following ways:

[0109]

[0110] The water segmented area that satisfies the above formula (4) is taken as the sludge sedimentation area, and the other areas in the water segmented area are taken as the supernatant area. The minimum number corresponding to the sludge sedimentation area is taken as the segmented area where the upper interface of the sludge sedimentation is located, and the upper interface row value R of the sludge sedimentation area is extracted. s .

[0111] Step S505 , determining a sludge settling ratio measurement result based on the multiple bottle body segmented areas, water body segmented areas and sludge sediment areas.

[0112] Specifically, obtain the total number of rows and columns of the observed bottles, and record the row value R of the bottle bottom area. b ; Then, the sludge settling ratio is calculated based on the row values ​​of the bottle bottom area in the multi-segmented bottle body segmented area, the row values ​​of the upper interface of the water body segmented area, and the row values ​​of the upper interface of the sludge sediment area. The calculation formula of the sludge settling ratio is as follows:

[0113]

[0114] Among them, SV(t) represents the sludge settling ratio, R s Represents the upper interface value of the sludge sedimentation area, R w Represents the upper interface value of the water segment area, R b Indicates the row value of the bottle bottom area.

[0115] The present embodiment provides a sludge settling ratio measurement method for sewage treatment, which calculates the average brightness difference between the bottle top area and other bottle body segmented areas, and compares the average brightness difference between the bottle top area and other bottle body segmented areas with a first threshold value, thereby determining the water body segmented area, and utilizing the characteristic that the average brightness difference becomes larger when transitioning from the non-water body to the water body, thereby achieving effective segmentation of the non-water body area and the water body area in the bottle body segmented area; and, by comparing the average brightness difference between each water body segmented area with a second threshold value, the water body segmented area is segmented based on the comparison result to obtain the sludge sedimentation area, thereby achieving accurate segmentation of the supernatant area and the sludge sedimentation area, reducing human intervention, and improving the intelligence level of the sludge settling ratio measurement process.

[0116] The following is a specific example to illustrate the specific steps of a method for measuring sludge settling ratio for sewage treatment.

[0117] Embodiment 1:

[0118] like Figure 6 As shown, a visual measurement method for sludge settling ratio for sewage treatment includes the following steps:

[0119] S1. Use a high-resolution camera and a strong focused light source to continuously shoot the observation bottle of the sewage treatment to obtain the sludge sedimentation image video data during the sewage treatment process;

[0120] S2, converting the image video into a grayscale image, and performing semantic segmentation on the acquired observation bottle and sewage extraction process images to obtain two types of segmentation results of the observation bottle and the water body in the bottle;

[0121] S3, divide the bottle body into n segments evenly according to the height, number each segment from the top to the bottom of the bottle 0, 1, 2----n-1 in sequence, and calculate the average brightness value of each segment area of ​​the bottle body;

[0122] S4, writing the numbers and average brightness values ​​of the segmented areas of the bottle body into an index table, and determining the segmented areas with water bodies inside the bottle body, i.e., the water body segmented areas;

[0123] S5, using the average brightness ranking value of the water body segmented area, finding the water body segmented area where the supernatant and the sludge sediment are located, performing cluster analysis on the brightness of all pixels in the water body segmented area, and using the clustered brightness results to segment the supernatant and the sludge sediment in the water body segmented area;

[0124] S6, tracking the movement of the sludge sediment between consecutive frames, calculating the height of the supernatant and the sludge sediment in the observation bottle, and realizing the measurement of the sludge settling ratio corresponding to each frame image;

[0125] S7. Repeat the process of S5-S6 until the height of the supernatant and sludge sediment tracked in the observation bottle no longer changes, completing the measurement of the sedimentation ratio that changes with time during the sewage sampling observation period.

[0126] Through the above steps S1-S7, the visual measurement of the sludge settling ratio of sewage treatment can be achieved. When applied to industrial water treatment in coal chemical enterprises, the visual-based sludge settling ratio detection results obtained through water sampling are shown in Table 1 below.

[0127] Table 1:

[0128]

[0129] In the above-mentioned embodiment 1, by using a high-resolution camera and a strong focusing light source, a clear and detailed image of the sludge sedimentation process can be obtained, thereby improving the accuracy and reliability of the sludge sedimentation ratio measurement; in combination with image processing technology, the observation bottle and the background are separated, thereby ensuring the accuracy of data processing; by adopting the brightness mean sorting method of the bottle body segmented area, the number of segmented areas of the bottle body segmented area can be effectively determined, and the supernatant and sludge sediment areas can be accurately segmented through the pixel brightness clustering segmentation method, thereby reducing human intervention, improving the intelligence level of the measurement process, realizing the motion tracking of the sludge sediment between consecutive frames, being able to accurately measure the sewage sedimentation ratio in each frame image, providing detailed real-time data of the sedimentation process, and helping users to timely understand the dynamic changes of the sewage treatment process; the above-mentioned method is applicable to various types of sewage, avoids human operation errors, has a broad application prospect, and can provide an effective measurement means for different types of sewage treatment.

[0130] In the present embodiment, a sludge settling ratio measuring device for sewage treatment is also provided, which is used to implement the above-mentioned embodiments and preferred embodiments, and the descriptions thereof will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware of a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0131] This embodiment provides a sludge settling ratio measuring device for sewage treatment, such as Figure 7 As shown, including:

[0132] The image processing module 701 is used to obtain the sludge settling image video data in the sewage treatment process, and perform image processing on the sludge settling image video data to obtain the observation bottle pattern data containing the observed water sample;

[0133] A division module 702 is used to divide the bottle body area in the observation bottle pattern data containing the observation water sample to obtain multiple bottle body segmented areas;

[0134] The first determination module 703 is used to obtain the pixel brightness of multiple bottle body segmented areas, perform clustering segmentation on the pixel brightness of multiple bottle body segmented areas, and determine the water body segmented areas;

[0135] The second determination module 704 is used to obtain the pixel brightness of the water body segmented area, perform cluster analysis on the pixel brightness of the water body segmented area, and determine the sludge sediment area;

[0136] The third determination module 705 is used to determine the sludge settling ratio measurement result based on the multiple bottle body segment areas, water body segment areas and sludge sediment areas.

[0137] In some optional implementations, the image processing module 701 includes:

[0138] A grayscale processing unit, used for grayscale processing the sludge settling image video data to obtain grayscale image data;

[0139] The semantic segmentation unit is used to perform semantic segmentation on the grayscale image data to obtain observation bottle pattern data containing the observed water sample.

[0140] In some optional implementations, the first determining module 703 includes:

[0141] A first calculation unit, configured to calculate average brightness values ​​of the plurality of bottle body segmented regions respectively based on pixel brightness of the plurality of bottle body segmented regions;

[0142] A second calculation unit is used to calculate the average brightness difference between the bottle top area and other bottle body segment areas based on the average brightness value of the multiple bottle body segment areas; wherein the other bottle body segment areas are the remaining bottle body segment areas except the bottle top area;

[0143] The first segmentation unit is used to compare the average brightness difference between the bottle top area and other bottle body segment areas with a first threshold value, and segment multiple bottle body segment areas based on the comparison result to obtain water body segment areas.

[0144] In some optional implementations, the second determining module 704 includes:

[0145] A third calculation unit is used to calculate the average brightness difference between each water body segment area based on the pixel brightness of the water body segment area;

[0146] The second segmentation unit is used to compare the average brightness difference between each water body segment area with the second threshold value, and segment the water body segment area based on the comparison result to obtain the sludge sediment area.

[0147] In some optional implementations, the third determination module 705 is specifically used to calculate the sludge settling ratio based on the row values ​​of the bottle bottom area in the multi-segmented bottle body segmented areas, the row values ​​of the upper interface of the water body segmented area, and the row values ​​of the upper interface of the sludge sediment area. The calculation formula of the sludge settling ratio is as follows:

[0148]

[0149] Among them, SV(t) represents the sludge settling ratio, R s Represents the upper interface value of the sludge sedimentation area, R w Represents the upper interface value of the water segment area, R b Indicates the row value of the bottle bottom area.

[0150] In some optional embodiments, the method further comprises:

[0151] The summary analysis module is used to obtain the sludge settling ratio corresponding to each frame of image data in the observation bottle pattern data containing the observed water sample. If the sludge settling ratio corresponding to the image data of a preset number of frames does not change, the sludge settling ratio summary analysis result is determined based on the sludge settling ratio corresponding to each frame of image data.

[0152] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0153] In this embodiment, a sludge settling ratio measuring device for sewage treatment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0154] The embodiment of the present invention also provides a computer device having the above Figure 7 A sludge settling ratio measuring device for sewage treatment is shown.

[0155] See also Figure 8 , Figure 8 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Figure 8As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 8 A processor 10 is taken as an example.

[0156] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0157] The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0158] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0159] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0160] The computer device also includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30 and the output device 40 may be connected via a bus or other means. Figure 8 The example of connecting through bus is taken in the following.

[0161] The input device 30 can receive input digital or character information, and generate key signal input related to the user settings and function control of the computer device, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, an indicator bar, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 may include a display device, an auxiliary lighting device (e.g., an LED) and a tactile feedback device (e.g., a vibration motor), etc. The above-mentioned display device includes but is not limited to a liquid crystal display, a light emitting diode, a display and a plasma display. In some optional embodiments, the display device can be a touch screen.

[0162] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0163] A part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the existence of the computer program instruction in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc., and accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium accessible to the computer.

[0164] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for measuring sludge settling ratio for sewage treatment, characterized in that: The method comprises: Acquire sludge settling image video data in the sewage treatment process, perform image processing on the sludge settling image video data, and obtain observation bottle pattern data containing the observed water sample; Dividing the bottle body area in the observation bottle pattern data containing the observed water sample to obtain a plurality of bottle body segmented areas; Acquire the pixel brightness of the multiple bottle body segmented areas, perform clustering segmentation on the pixel brightness of the multiple bottle body segmented areas, and determine the water body segmented areas; Acquiring pixel brightness of the water body segmented area, performing cluster analysis on the pixel brightness of the water body segmented area, and determining the sludge sediment area; A sludge settling ratio measurement result is determined based on the multiple bottle body segment areas, the water body segment areas and the sludge sediment area.

2. The method according to claim 1, characterized in that The sludge settling image video data is subjected to image processing to obtain observation bottle pattern data containing the observed water sample, including: Grayscale processing is performed on the sludge settling image video data to obtain grayscale image data; The grayscale image data is semantically segmented to obtain the observation bottle pattern data containing the observed water sample.

3. The method according to claim 1, characterized in that Clustering and segmenting the pixel brightness of the multiple bottle body segmented areas to determine the water body segmented areas includes: Based on the pixel brightness of the multiple bottle body segmented areas, respectively calculate the average brightness values ​​of the multiple bottle body segmented areas; Based on the average brightness values ​​of the multiple bottle body segmented areas, respectively calculating the average brightness difference between the bottle top area and other bottle body segmented areas; wherein the other bottle body segmented areas are the remaining bottle body segmented areas except the bottle top area; The average brightness difference between the bottle top area and other bottle body segment areas is compared with a first threshold, and the multiple bottle body segment areas are segmented based on the comparison result to obtain the water body segment area.

4. The method according to claim 3, characterized in that Cluster analysis is performed on the pixel brightness of the water body segmented area to determine the sludge sediment area, including: Calculate the average brightness difference between each water body segment area based on the pixel brightness of the water body segment area; The average brightness difference between the water body segmented areas is compared with a second threshold value, and the water body segmented areas are segmented based on the comparison result to obtain the sludge sediment area.

5. The method according to claim 1, characterized in that Determining a sludge settling ratio measurement result based on the multiple bottle body segmented areas, the water body segmented areas, and the sludge sediment area includes: The sludge settling ratio is calculated based on the row values ​​of the bottle bottom area in the multi-segmented bottle body segmented areas, the row values ​​of the upper interface of the water body segmented area, and the row values ​​of the upper interface of the sludge sediment area. The calculation formula of the sludge settling ratio is as follows: Among them, SV(t) represents the sludge settling ratio, R s Represents the upper interface value of the sludge sedimentation area, R w Represents the upper interface value of the water segment area, R b Indicates the row value of the bottle bottom area.

6. The method according to claim 1, characterized in that Also includes: The sludge settling ratio corresponding to each frame of image data in the observation bottle pattern data containing the observed water sample is obtained. If the sludge settling ratio corresponding to the image data of a preset number of frames does not change, the sludge settling ratio summary analysis result is determined based on the sludge settling ratio corresponding to each frame of image data.

7. A sludge settling ratio measuring device for sewage treatment, characterized in that: The device comprises: An image processing module is used to obtain sludge settling image video data in the sewage treatment process, and perform image processing on the sludge settling image video data to obtain observation bottle pattern data containing the observed water sample; A division module, used for dividing the bottle body area in the observation bottle pattern data containing the observation water sample to obtain a plurality of bottle body segmented areas; A first determination module is used to obtain the pixel brightness of the multiple bottle body segmented areas, perform clustering segmentation on the pixel brightness of the multiple bottle body segmented areas, and determine the water body segmented areas; A second determination module is used to obtain the pixel brightness of the water body segmented area, perform cluster analysis on the pixel brightness of the water body segmented area, and determine the sludge sediment area; The third determination module is used to determine the sludge settling ratio measurement result based on the multiple bottle body segmented areas, the water body segmented areas and the sludge sediment area.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the sludge settling ratio measurement method for sewage treatment according to any one of claims 1 to 6 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the sludge settling ratio measurement method for sewage treatment according to any one of claims 1 to 6.

10. A computer program product, characterized in that The method comprises computer instructions for causing a computer to execute the sludge settling ratio measurement method for sewage treatment according to any one of claims 1 to 6.

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