A sedimentation effect monitoring system and method for a mud-water separation tank

Abstract

The present invention provides a sedimentation effect monitoring system and method for a mud-water separation tank, relating to the technical field of mud-water separation tank operation monitoring technology. The system comprises: multiple sets of image acquisition devices, each disposed in a mud-water separation tank and distributed along the water flow direction of the mud-water separation tank, for respectively acquiring and outputting real-time underwater images within the corresponding acquisition area; and an image processing module, connected to each of the image acquisition devices, for obtaining, based on the real-time processing of each underwater image, a clear-turbidity dividing line of the mud-water separation tank along the water flow direction, so as to monitor the sedimentation effect of the mud-water separation tank in real time based on the clear-turbidity dividing line. The system achieves a visual, accurate, and scientific monitoring of the sedimentation effect, effectively saving labor costs while significantly improving the timeliness of sedimentation effect monitoring.

Description

Technical Field

[0001] The present invention relates to the technical field of operation monitoring of a mud-water separation tank, and in particular to a sedimentation effect monitoring system and method of a mud-water separation tank. Background Art

[0002] Mud-water separation tanks primarily use sedimentation to separate mud and water. The effectiveness of these sedimentation processes directly impacts the quality of the effluent. As a type of mud-water separation tank, sedimentation tanks are widely used in water plant processes. Generally, sedimentation tanks are the first step in a water plant's process flow. Due to fluctuations in influent quality, the operational stability of these tanks has always been a key focus for water plant managers. The sedimentation performance of a sedimentation tank directly impacts the quality of its effluent. Currently, the primary methods for assessing sedimentation effectiveness include testing the effluent quality and conducting manual inspections.

[0003] Among them, the detection of effluent water quality is usually carried out by measuring water quality indicators such as turbidity or suspended solids through instruments. It only targets the water quality of the inlet and outlet water, and there is a certain time difference, which is approximately equal to the residence time of the water in the sedimentation tank (t hours). When the effluent water quality of the sedimentation tank is abnormal, the sedimentation tank t hours ago has already been abnormal. For example, the residence time of a horizontal flow sedimentation tank is usually around 2 hours, resulting in a long interval between the water quality evaluation of the inlet and outlet water, which makes it difficult to provide real-time feedback on the sedimentation tank. In order to improve the operational stability of the sedimentation tank, the usual practice is to increase the hydraulic retention time during the design phase and increase the coagulant dosage during the operation phase. However, increasing the hydraulic retention time means a larger floor space and higher construction costs, while increasing the coagulant dosage means greater chemical consumption and increased aluminum ion content in the effluent. Manual inspections can only see the water on the surface of the sedimentation tank, and cannot observe the state of the deep water tank, let alone judge the rate of floc descent, and therefore cannot measure the sedimentation effect of the sedimentation tank.

[0004] To this end, technicians have developed several image recognition technologies to assist water plant managers. For example, image recognition technology for coagulant dosing systems uses the image characteristics of alum flocs during the flocculation phase to automatically identify the coagulation effect, thereby automatically identifying and determining the amount of coagulant added, and ultimately automatically adjusting the coagulant dosage. However, due to technical bottlenecks, there is still no clear correlation between the image characteristics of alum flocs and the amount of coagulant added. Furthermore, high-precision image acquisition devices are expensive, and the number of acquisition points is limited. Summary of the Invention

[0005] In view of the problems existing in the prior art, the present invention provides a sedimentation effect monitoring system for a mud-water separation tank, comprising:

[0006] Multiple groups of image acquisition devices are respectively arranged in a mud-water separation tank and distributed along the water flow direction of the mud-water separation tank, and are used to respectively acquire and output underwater images in corresponding acquisition areas in real time;

[0007] The image processing module is connected to each of the image acquisition devices, and is used to obtain the clear and turbid dividing line of the mud-water separation tank along the water flow direction according to each of the underwater images in real time, so as to monitor the sedimentation effect of the mud-water separation tank in real time according to the clear and turbid dividing line.

[0008] Preferably, the image processing module includes:

[0009] a first processing unit, configured to obtain the clear-to-turbidity boundary line according to each of the underwater images;

[0010] The second processing unit is connected to the first processing unit and is used to obtain a sedimentation efficiency according to the clear-turbidity dividing line processing, so as to monitor the sedimentation effect according to the sedimentation efficiency.

[0011] Preferably, the first processing unit includes:

[0012] A first processing subunit is configured to process the underwater image according to a pre-trained machine learning model for each group of image acquisition devices to obtain a corresponding sub-dividing line;

[0013] The second processing sub-unit is connected to the first processing sub-unit and is used to sequentially splice the sub-dividing lines according to the setting order of each group of the image acquisition devices to obtain the clear-to-turbid dividing line.

[0014] Preferably, the second processing unit includes:

[0015] a third processing sub-unit, configured to sequentially extract a plurality of coordinate points from the clear-voided dividing line and process the extracted coordinate points to obtain a tangent slope corresponding to each of the coordinate points;

[0016] a fourth processing subunit, connected to the third processing subunit, configured to obtain a key slope based on each of the tangent slopes, and obtain a clear-turbidity separation sedimentation velocity based on the key slope;

[0017] The fifth processing subunit is connected to the fourth processing subunit and is used to process and obtain the ratio between the clear and turbid separation sedimentation velocity and a pre-acquired theoretical interception sedimentation velocity as the sedimentation efficiency.

[0018] Preferably, it further includes a monitoring feedback module connected to the image processing module, and the monitoring feedback module includes:

[0019] a storage unit, configured to store at least one pre-configured efficiency interval and a feedback measure associated with the efficiency interval;

[0020] A feedback unit is connected to the storage unit and is used to output the feedback measure when it is determined that the sedimentation efficiency is within the efficiency range, so as to prompt the water plant management personnel to respond in time.

[0021] Preferably, the efficiency range includes:

[0022] A first efficiency interval, wherein the feedback measure associated with the first efficiency interval is to reduce the dosage or increase the water intake; and / or

[0023] A second efficiency interval, wherein the feedback measure associated with the second efficiency interval is a measure to maintain current precipitation; and / or

[0024] A third efficiency interval, wherein the feedback measure associated with the third efficiency interval is to increase the dosage or reduce the water intake; and / or

[0025] A fourth efficiency interval, wherein the feedback measure associated with the fourth efficiency interval is suspending water intake.

[0026] Preferably, the mud-water separation tank includes a plurality of compartments, and at least one set of the image acquisition devices is provided in each compartment.

[0027] Preferably, each group of the image acquisition devices includes at least one image acquisition device, and is distributed perpendicular to the water flow direction.

[0028] The present application also provides a sedimentation effect monitoring method for a mud-water separation tank, which is applied to the above-mentioned sedimentation effect monitoring system. The sedimentation effect monitoring method includes:

[0029] Step S1, the sedimentation effect monitoring system collects multiple underwater images of the mud-water separation tank along the water flow direction in real time;

[0030] In step S2, the sedimentation effect monitoring system obtains a clear-turbidity dividing line of the mud-water separation tank along the water flow direction according to each of the underwater images, so as to monitor the sedimentation effect of the mud-water separation tank in real time according to the clear-turbidity dividing line.

[0031] Preferably, the step S2 includes:

[0032] Step S21, the sedimentation effect monitoring system processes each of the underwater images to obtain the clear-turbidity dividing line;

[0033] In step S22, the sedimentation effect monitoring system obtains a sedimentation efficiency according to the clear-turbidity dividing line, and monitors the sedimentation effect according to the sedimentation efficiency.

[0034] Preferably, the step S21 includes:

[0035] Step S211: the sedimentation effect monitoring system processes the underwater image according to a pre-trained machine learning model for each group of image acquisition devices to obtain a corresponding sub-dividing line;

[0036] In step S212, the sedimentation effect monitoring system sequentially splices the sub-dividing lines according to the setting order of each group of the image acquisition devices to obtain the clear-turbidity dividing line.

[0037] Preferably, the step S22 includes:

[0038] Step S221, the sedimentation effect monitoring system sequentially extracts a plurality of coordinate points on the clear-turbidity dividing line and processes them to obtain the tangent slope corresponding to each of the coordinate points;

[0039] Step S222, the sedimentation effect monitoring system obtains a key slope based on each of the tangent slopes, and obtains a clear-turbid separation sedimentation velocity based on the key slope;

[0040] In step S223, the sedimentation effect monitoring system processes and obtains a ratio between the clear and turbid separation sedimentation velocity and a pre-acquired theoretical interception sedimentation velocity as the sedimentation efficiency.

[0041] Preferably, the precipitation effect monitoring system is pre-configured with at least one efficiency interval and feedback measures associated with the efficiency interval; then after executing step S2, the system further includes:

[0042] The sedimentation effect monitoring system outputs the feedback measure when determining that the sedimentation efficiency is within the efficiency range, so as to prompt the water plant management personnel to respond in time.

[0043] Preferably, the efficiency range includes:

[0044] A first efficiency interval, wherein the feedback measure associated with the first efficiency interval is to reduce the dosage or increase the water intake; and / or

[0045] A second efficiency interval, wherein the feedback measure associated with the second efficiency interval is a measure to maintain current precipitation; and / or

[0046] A third efficiency interval, wherein the feedback measure associated with the third efficiency interval is to increase the dosage or reduce the water intake; and / or

[0047] A fourth efficiency interval, wherein the feedback measure associated with the fourth efficiency interval is suspending water intake.

[0048] The above technical solution has the following advantages or beneficial effects:

[0049] 1) By collecting underwater images of the mud-water separation tank, full monitoring coverage is achieved, and qualitative judgment of the sedimentation effect is made based on image processing technology, so that the sedimentation effect monitoring is visualized, accurate, and scientific, effectively saving labor costs while greatly improving the timeliness of sedimentation effect monitoring;

[0050] 2) It can provide corresponding feedback measures based on the monitoring results of sedimentation effects, provide refined guidance measures for water plant managers, and realize more intelligent water plant management and operation;

[0051] 3) The image acquisition device is easy to install, has strong adaptability, and is easy to promote. It can be widely used in new construction or renovation projects of mud-water separation tanks to improve the intelligence of water plant operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 This is a structural diagram of a sedimentation effect monitoring system for a mud-water separation tank in a preferred embodiment of the present invention;

[0053] Figure 2 A schematic diagram of the location of the image acquisition device in a preferred embodiment of the present invention;

[0054] Figure 3 A schematic diagram of the shooting control principle of the image acquisition device in a preferred embodiment of the present invention;

[0055] Figure 4 Schematic diagram of the clear-voided dividing line in a preferred embodiment of the present invention;

[0056] Figure 5 1 is a flow chart of a method for monitoring the sedimentation effect of a mud-water separation tank in a preferred embodiment of the present invention;

[0057] Figure 6 Schematic diagram of the process of step S1 in a preferred embodiment of the present invention;

[0058] Figure 7 FIG. 1 is a flow chart of step S21 in a preferred embodiment of the present invention;

[0059] Figure 8 FIG. 1 is a flow chart of step S22 in a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0060] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments. The present invention is not limited to this embodiment, and other embodiments may also fall within the scope of the present invention as long as they conform to the gist of the present invention.

[0061] In a preferred embodiment of the present invention, based on the above problems existing in the prior art, a sedimentation effect monitoring system for a mud-water separation tank is provided. Figure 1 Shown, including:

[0062] Multiple groups of image acquisition devices 1 are respectively arranged in a mud-water separation tank and distributed along the water flow direction of the mud-water separation tank, and are used to respectively acquire and output underwater images in corresponding acquisition areas in real time;

[0063] The image processing module 2 is connected to each image acquisition device 1 and is used to obtain the clear and turbid dividing line along the water flow direction of the mud-water separation tank according to each underwater image in real time processing, so as to monitor the sedimentation effect of the mud-water separation tank in real time according to the clear and turbid dividing line.

[0064] Specifically, in this embodiment, the image acquisition device 1 may be an underwater camera, and the water flow direction may be from the water inlet to the water outlet of the mud-water separation tank, typically along the length of the mud-water separation tank. Such mud-water separation tanks include, but are not limited to, sedimentation tanks, concentration tanks, and clear water tanks. The following uses a sedimentation tank as an example to illustrate this technical solution. Conventional sedimentation tanks typically have a depth of 3 to 5 meters and a width of 5 to 8 meters (for example, a horizontal flow sedimentation tank, the maximum width typically does not exceed 15 meters). Therefore, the number of image acquisition devices 1 deployed in the depth and width directions can be minimized. However, along the length of the sedimentation tank, which is often on the order of hundreds of meters, the number of image acquisition devices 1 in this direction will be relatively large. When deploying image acquisition devices, it is preferred to achieve full coverage of the water flow. In other words, the total capture range of all image acquisition devices 1 preferably fully covers the longitudinal cross-section of the sedimentation tank along the water flow direction, i.e., fully covers the length and depth of the sedimentation tank. Furthermore, to facilitate operation, installation, and possible maintenance and replacement of the image acquisition devices 1, the image acquisition devices 1 may be installed on both sides of the sedimentation tank.

[0065] Taking sedimentation tank as an example, Figure 2 As shown, the sedimentation tank includes multiple grids 100, with each image acquisition device 1 disposed in at least one grid 100 along the direction of water flow. The image processing module 2 can be configured on a local host computer or a remote server, both of which have corresponding storage and computing capabilities. Considering the storage and computing capabilities of the image processing module 2, when arranging the image acquisition devices 1, it is preferred to only place multiple groups of image acquisition devices 1 along the length of a single grid 100. In this case, the total capture range of all image acquisition devices 1 preferably fully covers the length and depth of the corresponding grid.

[0066] As a preference, Figure 2As shown, this technical solution also provides a shooting control module 4 that establishes a communication connection with each image acquisition device 1 and is used to remotely control each image acquisition device 1 to capture underwater images. This shooting control module 4 can pre-store attribute information for each image acquisition device 1, including but not limited to the shooting parameters of each image acquisition device 1, the pre-configured unique number of the corresponding image acquisition device 1, and the location of the device. Water plant managers can use this shooting control module 4 to remotely control the capture time of each image acquisition device 1. This remote control includes but is not limited to individual control, group control, and centralized control to meet the needs of different scenarios. This individual control, group control, and centralized control can be implemented based on the configuration of the attribute information of each image acquisition device 2.

[0067] More preferably, Figure 3 As shown, during underwater image capture, the capture control module 4 can control all image capture devices 1 in a grid 100 to capture images simultaneously. Alternatively, each group of image capture devices 1 can be controlled to capture images sequentially according to the direction of water flow. That is, the same group of image capture devices 1 capture images simultaneously, while the image capture times of two adjacent groups of image capture devices 1 are separated by a time interval. This time interval can be set based on factors such as the current inflow rate and water flow rate of the sedimentation tank. Because the clear-turbidity dividing line is used to represent the overall sedimentation situation in the direction of water flow, regardless of whether the image capture of each image capture device 1 is synchronized, the image processing module 2 needs to process all captured underwater images to determine the clear-turbidity dividing line during image processing. This allows for real-time monitoring of the sedimentation effect in the mud-water separation tank based on the clear-turbidity dividing line.

[0068] Furthermore, taking the example of each image acquisition device 1 synchronously acquiring underwater images, a collection cycle can be pre-configured, that is, an image is captured every time the collection cycle lasts. Based on the underwater images obtained by the image capture, the corresponding clear-turbidity boundary is processed to achieve real-time acquisition of the clear-turbidity boundary. The collection cycle can be set according to needs. For example, if the current water flow rate is large or the water flow rate is high, a relatively short collection cycle can be set. Conversely, a relatively long collection cycle can be set to achieve real-time monitoring of the sedimentation effect while saving energy consumption.

[0069] As a preferred embodiment, the above-mentioned image acquisition devices 1 can also be set in multiple grids 100, such as Figure 2As shown, based on this, in the first acquisition cycle, each image acquisition device 1 in the first grid 100 can be controlled to capture underwater images, and then the corresponding clear-turbidity dividing line can be obtained by processing. In the second acquisition cycle, each image acquisition device 1 in the second grid can be controlled to capture underwater images, and then the corresponding clear-turbidity dividing line can be obtained by processing. And so on, the underwater image acquisition is cyclically performed, so that under the current storage capacity and computing power, the monitoring method of the sedimentation effect is more flexible, and at the same time, it is ensured that the processed clear-turbidity dividing line more comprehensively reflects the sedimentation effect of the entire mud-water separation tank. It should be noted that the cyclic shooting order of each image acquisition device 1 corresponding to each grid 100 is not limited and can be adjusted according to needs. For example, in the first acquisition cycle, each image acquisition device 1 in the first grid 100 can be controlled to perform underwater image shooting, and in the second acquisition cycle, each image acquisition device 1 in the fourth grid can be controlled to perform underwater image shooting. Alternatively, the water inlet of the mud-water separation tank can be used as the starting position, and in the first acquisition cycle, the first group of image acquisition devices 1 closest to the water inlet in the first grid 100 can be controlled to perform underwater image shooting, and in the second acquisition cycle, the second group of image acquisition devices 1 in the second grid 100 can be controlled to perform underwater image shooting. And so on, the underwater image capture is performed in a skipping manner. The order of skipping is also not limited, as long as all the underwater images finally obtained can fully cover the water flow direction.

[0070] In order to more intuitively monitor the sedimentation effect of the mud-water separation tank, after obtaining the clear-turbidity dividing line based on each underwater image processing, the clear-turbidity dividing line is further processed to obtain the sedimentation efficiency. Based on this, in a preferred embodiment of the present invention, the image processing module 2 includes:

[0071] The first processing unit 21 is used to process each underwater image to obtain a clear-turbidity boundary line;

[0072] The second processing unit 22 is connected to the first processing unit 21 and is used to obtain a sedimentation efficiency according to the clear-turbidity dividing line, so as to monitor the sedimentation effect according to the sedimentation efficiency.

[0073] In a preferred embodiment of the present invention, the first processing unit 21 includes:

[0074] The first processing sub-unit 211 is configured to process the underwater image according to a pre-trained machine learning model for each group of image acquisition devices to obtain a corresponding sub-dividing line;

[0075] The second processing sub-unit 212 is connected to the first processing sub-unit 211 and is used to sequentially splice the sub-dividing lines to obtain the clear-to-turbidity dividing line according to the setting order of each group of image acquisition devices.

[0076] Specifically, in this embodiment, each set of image acquisition devices may include only one image acquisition device, which then captures an underwater image. In this case, the image can be directly processed according to the machine learning model to obtain the corresponding sub-dividing line. When each set of image acquisition devices includes multiple image acquisition devices, based on the limitations of the shooting range of a single image acquisition device 1, the underwater image captured is a segmented image of the mud-water separation tank. Preferably, the underwater images captured separately are first stitched together according to the location of each image acquisition device. The stitched image corresponding to each set of image acquisition devices is an underwater image of a section of the mud-water separation tank. This stitched image is then used as input to the machine learning model to process the corresponding sub-dividing line. Since the underwater image captured by each set of image acquisition devices is a segmented image of the water flow direction, the corresponding sub-dividing line is the clear-turbidity dividing line of the corresponding image acquisition area. Therefore, it is necessary to stitch each sub-dividing line in sequence to obtain a complete clear-turbidity dividing line. Preferably, the sub-dividing line and the clear-turbidity dividing line have the water flow direction as the horizontal axis and the depth direction of the mud-water separation tank as the vertical axis, respectively. It is understandable that Figure 3 In the figure, the direction from left to right along the horizontal axis is the water flow direction. The left side is the direction close to the water inlet of the mud-water separation tank, and the right side is the direction close to the water outlet of the mud-water separation tank. There are more sediments at the water inlet and less sediments at the water outlet.

[0077] Further preferably, the above-mentioned image stitching is performed based on the setting positions of each image acquisition device 1. For example, each group of image acquisition devices 1 includes three image acquisition devices 1 sequentially arranged along the depth direction of the mud-water separation tank. Then, when performing image stitching, the image acquisition devices 1 are stitched in sequence according to their setting depths to form a stitched image. In addition, each group of acquisition devices 1 may also include two image acquisition devices 1 arranged along the width direction of the mud-water separation tank in the same depth direction. In addition, in addition to splicing based on the setting depth, splicing also includes splicing based on the setting position of the width. The specific splicing method is not limited here.

[0078] As a preferred embodiment, the clear and turbid dividing line can also be obtained by the following method, specifically:

[0079] For a scene in which each group of image acquisition devices includes only one image acquisition device, which corresponds to capturing an underwater image, a sliding window and a threshold value can be pre-configured in the first processing unit 21. After acquiring the underwater image, the first processing unit 21 divides the underwater image into multiple sub-images along the depth direction of the mud-water separation tank through the sliding window, and then uses image recognition technology to process each of the sub-images separately to obtain the clarity and turbidity corresponding to each sub-image, and compares each clarity and turbidity with the above-mentioned threshold value. When the clarity and turbidity are less than the threshold value, the underwater area corresponding to the sub-image is defined as a clear water area. When the clarity and turbidity are not less than the threshold value, the underwater area corresponding to the sub-image is defined as a turbid water area, and then the boundary line between the clear water area and the turbid water area is used as the clarity and turbidity boundary line.

[0080] For each group of image acquisition devices including multiple image acquisition devices, which correspond to the scene of capturing multiple underwater images, the first processing unit 21 can arrange the underwater images along the depth direction of the mud-water separation tank, and use each underwater image as the above-mentioned sub-image to perform clarity and turbidity identification, and then divide the clear water area and the turbid water area based on the threshold, thereby obtaining the clear and turbid water dividing line.

[0081] For a scene in which each group of image acquisition devices includes multiple image acquisition devices and captures multiple underwater images, the first processing unit 21 can also first stitch the underwater images together, and then use the above-mentioned sliding window and threshold to process the stitched images to obtain the clear-turbidity boundary line.

[0082] It should be noted that the above-mentioned processing method of the clear-to-voided boundary line is only an embodiment of the present technical solution and does not limit the present technical solution.

[0083] In a preferred embodiment of the present invention, the second processing unit 22 includes:

[0084] The third processing sub-unit 221 is used to sequentially extract multiple coordinate points from the clear-voiced boundary line and process them to obtain the tangent slope corresponding to each coordinate point;

[0085] The fourth processing subunit 222 is connected to the third processing subunit 221 and is used to obtain a key slope according to the tangent slopes, and obtain a clear-turbidity separation sedimentation velocity according to the key slopes;

[0086] The fifth processing subunit 223 is connected to the fourth processing subunit 222 and is used to process and obtain the ratio between the clear and turbid separation sedimentation velocity and a pre-acquired theoretical interception sedimentation velocity as the sedimentation efficiency.

[0087] Specifically, in this embodiment, based on the clear-turbidity dividing line obtained, the tangent slope of the clear-turbidity dividing line can be used to characterize the movement speed of particles in the mud-water separation tank. In order to further intuitively obtain the sedimentation effect of the mud-water separation tank, it is necessary to process multiple tangent slopes to obtain the slope with the widest distribution coverage and the most representative as the corresponding key slope. The key slope can represent the movement speed of most particles. The processing method of the key slope can be obtained based on existing statistical analysis methods, including but not limited to first processing each tangent slope to obtain the slope interval with the largest distribution density of each tangent slope, and then processing the slope interval to obtain the key slope, including but not limited to obtaining the average value of each tangent slope in the slope interval, or selecting the median of each tangent slope in the slope interval, etc.

[0088] like Figure 4 As shown in the figure, after obtaining the key slope, the key slope can be decomposed into horizontal and vertical vectors. The vertical component velocity obtained by vertical decomposition can be approximately considered as the movement velocity of the particles, that is, the clear and turbid separation sedimentation velocity. The vertical component velocity of each coordinate point is respectively Figure 3 In this example, the clear-turbid separation settling velocity is compared with the theoretical interception settling velocity, and the ratio between the two is used as the settling efficiency, reflecting the real-time settling performance of the mud-water separation tank. This settling efficiency measurement can better reflect the sedimentation effect, allowing water plant managers ample time to take feedback measures.

[0089] The calculation formula of sedimentation efficiency is as follows:

[0090]

[0091] Where E represents the settling efficiency, u represents the clear-turbidity separation settling velocity, u0 represents the theoretical interception settling velocity, Q represents the real-time influent flow rate into the sludge-water separation tank, L represents the tank length, and B represents the tank width. The tank length and width are fixed values. The real-time influent flow rate can be measured by installing a flow meter at the inlet of the sludge-water separation tank.

[0092] In a preferred embodiment of the present invention, a monitoring feedback module 3 is further included, connected to the image processing module 2, and the monitoring feedback module 3 includes:

[0093] The storage unit 31 is configured to store at least one pre-configured efficiency interval and feedback measures associated with the efficiency interval;

[0094] The feedback unit 32 is connected to the storage unit 31 and is used to output feedback measures when it is determined that the sedimentation efficiency is within the efficiency range, so as to prompt the water plant management personnel to respond in time.

[0095] In a preferred embodiment of the present invention, the efficiency range includes:

[0096] The first efficiency interval, the feedback measure associated with the first efficiency interval is to reduce the dosage or increase the water intake; and / or

[0097] A second efficiency interval, wherein the feedback measure associated with the second efficiency interval is to maintain the current sedimentation measure; and / or

[0098] The third efficiency interval, the feedback measure associated with the third efficiency interval is to increase the dosage or reduce the water intake; and / or

[0099] The fourth efficiency range: the feedback measure associated with the fourth efficiency range is suspending water intake.

[0100] Preferably, the first efficiency range may be no less than 99%, and the sedimentation efficiency in this first efficiency range indicates that the current sedimentation performance of the mud-water separation tank is good. It can be judged that the current flocculation dosage is too much or the water intake is too little. Although the sedimentation performance is good, it is an uneconomical range from an economic point of view. Feedback measures such as reducing the dosage or increasing the water intake can be given; the second efficiency range may be between 85% and 95%, and the sedimentation efficiency in this second efficiency range indicates that the sedimentation performance of the mud-water separation tank is within a reasonable range. The second efficiency range can be used as a benchmark range, that is, within this range, the water treatment operation is technically stable and cost-effective, and the sedimentation measures may not be adjusted temporarily; the third efficiency range may be between 70% and 80%, and the third efficiency range can be used as a reference range. The sedimentation efficiency in the third efficiency interval indicates that the water quality has deteriorated. It can be judged that the current dosage is too little or the water intake is too large, and feedback measures such as increasing the dosage or reducing the water intake can be given; the above-mentioned fourth efficiency interval can be less than 70%, and the fourth efficiency interval can be defined as an alarm interval. The sedimentation efficiency in the fourth efficiency interval indicates that the water quality of the influent has deteriorated considerably at this time, and feedback measures such as suspending water intake can be given so that water plant managers can take corresponding safety emergency measures in time to improve the problem of deterioration of the influent quality. It should be noted that the number of divisions of the above-mentioned efficiency intervals and the feedback measures corresponding to each efficiency interval are only one embodiment of the present technical solution, and are not used to limit the present technical solution.

[0101] In a preferred embodiment of the present invention, the mud-water separation tank includes a plurality of compartments, and at least one set of image acquisition devices 1 is provided in each compartment.

[0102] In a preferred embodiment of the present invention, each group of image acquisition devices 1 includes at least one image acquisition device 1 and is distributed perpendicular to the direction of water flow.

[0103] Specifically, in this embodiment, due to the limited capture range of a single image acquisition device 1, when a single grid is relatively wide and the mud-water separation tank is relatively deep, multiple image acquisition devices 1 can be installed in a cross section perpendicular to the water flow direction, which in this embodiment is the cross section along the width of the grid. Adjacent groups of image acquisition devices 1 are spaced a first distance apart along the water flow direction, and within each group of image acquisition devices 1, adjacent groups of image acquisition devices 1 are spaced a second distance apart along the depth direction of the mud-water separation tank. These first and second spacings can be adjusted based on the water inflow, width, length, and water depth of the mud-water separation tank, as well as the capture range of the image acquisition devices 1, so that the underwater images captured by each image acquisition device 1 fully cover the longitudinal cross section of the mud-water separation tank along the water flow direction.

[0104] This application also provides a sedimentation effect monitoring method for a mud-water separation tank, which is applied to the above-mentioned sedimentation effect monitoring system, such as Figure 5 As shown, the sedimentation effect monitoring method includes:

[0105] Step S1: The sedimentation effect monitoring system collects multiple underwater images of the mud-water separation tank along the water flow direction in real time;

[0106] In step S2, the sedimentation effect monitoring system obtains the clear-turbidity dividing line of the mud-water separation tank along the water flow direction according to each underwater image processing, so as to monitor the sedimentation effect of the mud-water separation tank in real time according to the clear-turbidity dividing line.

[0107] In a preferred embodiment of the present invention, Figure 6 As shown, step S2 includes:

[0108] Step S21: The sedimentation effect monitoring system processes each underwater image to obtain a clear-turbidity boundary line;

[0109] In step S22, the sedimentation effect monitoring system obtains a sedimentation efficiency according to the clear and turbid water boundary line, and monitors the sedimentation effect according to the sedimentation efficiency.

[0110] In a preferred embodiment of the present invention, Figure 7 As shown, step S21 includes:

[0111] Step S211: The sedimentation effect monitoring system processes the underwater image according to a pre-trained machine learning model for each group of image acquisition devices to obtain a corresponding sub-dividing line;

[0112] In step S212 , the sedimentation effect monitoring system sequentially stitches together the sub-dividing lines according to the setting order of each group of image acquisition devices to obtain a clear-turbidity dividing line.

[0113] In a preferred embodiment of the present invention, Figure 8 As shown, step S22 includes:

[0114] In step S221, the sedimentation effect monitoring system sequentially extracts multiple coordinate points on the clear-turbidity boundary line and processes them to obtain the tangent slope corresponding to each coordinate point;

[0115] Step S222: The sedimentation effect monitoring system processes each tangent slope to obtain a key slope, and then processes the key slope to obtain a clear-turbid separation sedimentation velocity;

[0116] In step S223, the sedimentation effect monitoring system processes and obtains the ratio between the clear and turbid separation sedimentation velocity and a pre-acquired theoretical interception sedimentation velocity as the sedimentation efficiency.

[0117] In a preferred embodiment of the present invention, the precipitation effect monitoring system is pre-configured with at least one efficiency interval and feedback measures associated with the efficiency interval; after executing step S2, the system further includes:

[0118] The sedimentation effect monitoring system outputs feedback measures when it determines that the sedimentation efficiency is within the efficiency range to prompt water plant managers to respond in time.

[0119] In a preferred embodiment of the present invention, the efficiency range includes:

[0120] The first efficiency interval, the feedback measure associated with the first efficiency interval is to reduce the dosage or increase the water intake; and / or

[0121] A second efficiency interval, wherein the feedback measure associated with the second efficiency interval is to maintain the current sedimentation measure; and / or

[0122] The third efficiency interval, the feedback measure associated with the third efficiency interval is to increase the dosage or reduce the water intake; and / or

[0123] The fourth efficiency range: the feedback measure associated with the fourth efficiency range is suspending water intake.

[0124] The above description is only a preferred embodiment of the present invention and does not limit the implementation mode and protection scope of the present invention. For those skilled in the art, it should be aware that all solutions obtained by equivalent substitutions and obvious changes made using the content of this specification and illustrations should be included in the protection scope of the present invention.

Claims

1. A sedimentation effect monitoring system for a mud-water separation tank, characterized in that: include: Multiple groups of image acquisition devices are respectively arranged in a mud-water separation tank and distributed along the water flow direction of the mud-water separation tank, and are used to respectively acquire and output underwater images in corresponding acquisition areas in real time; an image processing module, connected to each of the image acquisition devices, for obtaining a clear-turbidity dividing line of the mud-water separation tank along the water flow direction according to each of the underwater images in real time, so as to monitor the sedimentation effect of the mud-water separation tank in real time according to the clear-turbidity dividing line; The mud-water separation tank comprises a plurality of compartments, and each compartment is provided with at least one set of the image acquisition devices; The total shooting range of all the image acquisition devices fully covers the longitudinal section of the mud-water separation tank along the water flow direction; In each acquisition cycle, each image acquisition device in a corresponding grid is controlled to capture underwater images, and then processed to obtain a corresponding clear-turbidity dividing line. The underwater image acquisition is repeated for multiple acquisition cycles, and finally all underwater images are obtained to fully cover the clear-turbidity dividing line along the water flow direction of the mud-water separation tank; The image processing module includes: a first processing unit, configured to obtain the clear-to-turbidity boundary line according to the underwater images; a second processing unit, connected to the first processing unit, for obtaining a sedimentation efficiency according to the clear-turbidity dividing line, and monitoring the sedimentation effect according to the sedimentation efficiency; The first processing unit includes: A first processing sub-unit is configured to process the underwater image according to a pre-trained machine learning model for each group of image acquisition devices to obtain a corresponding sub-dividing line; a second processing sub-unit, connected to the first processing sub-unit, configured to sequentially splice the sub-dividing lines according to the arrangement order of the image acquisition devices of each group to obtain the clear-to-turbid dividing line; Each group of image acquisition devices includes only one image acquisition device, which correspondingly captures an underwater image; A sliding window and a threshold are pre-configured in the first processing unit. After acquiring the underwater image, the first processing unit divides the underwater image into a plurality of sub-images along the depth direction of the mud-water separation tank through the sliding window, and then uses image recognition technology to process each of the sub-images separately to obtain the clarity and turbidity corresponding to each sub-image, and compares the clarity and turbidity with the threshold. When the clarity and turbidity are less than the threshold, the underwater area corresponding to the sub-image is defined as a clear water area; when the clarity and turbidity are not less than the threshold, the underwater area corresponding to the sub-image is defined as a turbid water area, thereby obtaining the corresponding sub-dividing line.

2. The precipitation effect monitoring system according to claim 1, characterized in that: The second processing unit includes: a third processing sub-unit, configured to sequentially extract a plurality of coordinate points from the clear-voided dividing line and process the extracted coordinate points to obtain a tangent slope corresponding to each of the coordinate points; a fourth processing subunit, connected to the third processing subunit, configured to obtain a key slope based on each of the tangent slopes, and obtain a clear-turbidity separation sedimentation velocity based on the key slope; The fifth processing subunit is connected to the fourth processing subunit and is used to process and obtain the ratio between the clear and turbid separation sedimentation velocity and a pre-acquired theoretical interception sedimentation velocity as the sedimentation efficiency.

3. The precipitation effect monitoring system according to claim 1, characterized in that: The system further includes a monitoring and feedback module connected to the image processing module, wherein the monitoring and feedback module includes: a storage unit, configured to store at least one pre-configured efficiency interval and a feedback measure associated with the efficiency interval; A feedback unit is connected to the storage unit and is used to output the feedback measure when it is determined that the sedimentation efficiency is within the efficiency range, so as to prompt the water plant management personnel to respond in time.

4. The precipitation effect monitoring system according to claim 3, characterized in that: The efficiency range includes: A first efficiency interval, wherein the feedback measure associated with the first efficiency interval is to reduce the dosage or increase the water intake; and / or A second efficiency interval, wherein the feedback measure associated with the second efficiency interval is a measure to maintain current precipitation; and / or A third efficiency interval, wherein the feedback measure associated with the third efficiency interval is to increase the dosage or reduce the water intake; and / or A fourth efficiency interval, wherein the feedback measure associated with the fourth efficiency interval is suspending water intake.

5. The precipitation effect monitoring system according to claim 1, characterized in that: Each group of the image acquisition devices includes at least one image acquisition device and is distributed perpendicular to the water flow direction.

6. A method for monitoring the sedimentation effect of a mud-water separation tank, characterized in that: Applied to the sedimentation effect monitoring system according to any one of claims 1 to 5, the sedimentation effect monitoring method comprises: Step S1, the sedimentation effect monitoring system collects multiple underwater images of the mud-water separation tank along the water flow direction in real time; In step S2, the sedimentation effect monitoring system obtains a clear-turbidity dividing line of the mud-water separation tank along the water flow direction according to each of the underwater images, so as to monitor the sedimentation effect of the mud-water separation tank in real time according to the clear-turbidity dividing line.

7. The precipitation effect monitoring method according to claim 6, characterized in that: The step S2 comprises: Step S21, the sedimentation effect monitoring system processes each of the underwater images to obtain the clear-turbidity dividing line; In step S22, the sedimentation effect monitoring system obtains a sedimentation efficiency according to the clear-turbidity dividing line, and monitors the sedimentation effect according to the sedimentation efficiency.

8. The precipitation effect monitoring method according to claim 7, characterized in that: The step S21 includes: Step S211: the sedimentation effect monitoring system processes the underwater image according to a pre-trained machine learning model for each group of image acquisition devices to obtain a corresponding sub-dividing line; In step S212, the sedimentation effect monitoring system sequentially splices the sub-dividing lines according to the setting order of each group of the image acquisition devices to obtain the clear-turbidity dividing line.

9. The precipitation effect monitoring method according to claim 7, characterized in that: The step S22 includes: Step S221, the sedimentation effect monitoring system sequentially extracts a plurality of coordinate points on the clear-turbidity dividing line and processes them to obtain the tangent slope corresponding to each of the coordinate points; Step S222, the sedimentation effect monitoring system obtains a key slope based on each of the tangent slopes, and obtains a clear-turbid separation sedimentation velocity based on the key slope; In step S223, the sedimentation effect monitoring system processes and obtains a ratio between the clear and turbid separation sedimentation velocity and a pre-acquired theoretical interception sedimentation velocity as the sedimentation efficiency.

10. The precipitation effect monitoring method according to claim 7, characterized in that: The precipitation effect monitoring system is pre-configured with at least one efficiency interval and feedback measures associated with the efficiency interval; after executing step S2, the system further includes: The sedimentation effect monitoring system outputs the feedback measure when determining that the sedimentation efficiency is within the efficiency range, so as to prompt the water plant management personnel to respond in time.

11. The precipitation effect monitoring method according to claim 10, characterized in that: The efficiency range includes: A first efficiency interval, wherein the feedback measure associated with the first efficiency interval is to reduce the dosage or increase the water intake; and / or A second efficiency interval, wherein the feedback measure associated with the second efficiency interval is a measure to maintain current precipitation; and / or A third efficiency interval, wherein the feedback measure associated with the third efficiency interval is to increase the dosage or reduce the water intake; and / or A fourth efficiency interval, wherein the feedback measure associated with the fourth efficiency interval is suspending water intake.

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