A self-adaptive removal method, system and related equipment for sludge accumulation in a sedimentation tank inclined pipe

Abstract

The application provides a self-adaptive removal method and system for sludge accumulation in a slanted pipe of a sedimentation tank and related equipment, and relates to the technical field of sludge removal in a slanted pipe of a sedimentation tank. The method comprises the following steps: after the water level of the sedimentation tank drops to expose the water surface at the top opening area of the slanted pipe, a self-adaptive execution mechanism is controlled to move for inspection, and a laser ranging sensor is used to scan the slanted pipe in sections to obtain sludge accumulation thickness distribution data; an intelligent decision model is used to perform correlation analysis on the sludge accumulation thickness distribution data and first real-time water quality state data to obtain a self-adaptive cleaning strategy containing a cleaning movement path and cleaning operation parameters; the self-adaptive execution mechanism is driven to move according to the cleaning movement path, and a self-adaptive lifting mechanism is controlled to adjust a flushing component to a target operation height, and the flushing component is started to perform flushing operation on the slanted pipe; and in the cleaning process, a pressure flow self-adaptive adjustment module is controlled according to second real-time water quality state data to dynamically adjust the cleaning operation parameters.

Description

Technical Field

[0001] This application relates to the field of sludge removal technology using inclined tubes in sedimentation tanks, and more particularly to an adaptive method, system, and related equipment for removing sludge accumulated in inclined tubes of sedimentation tanks. Background Technology

[0002] With the increasing demands for effluent quality in the water treatment industry, inclined tube sedimentation tanks, designed based on shallow pool theory, are widely used due to their high sedimentation efficiency and small footprint. However, during long-term operation of inclined tube sedimentation tanks, suspended solids in the wastewater flowing through the inclined tubes tend to gradually deposit on the inner wall and bottom of the tubes, forming sludge. As the sludge thickens, the effective flow area of ​​the inclined tubes gradually decreases, thereby reducing sedimentation efficiency. Therefore, it is usually necessary to clean the inclined tubes regularly to maintain the normal operation of the sedimentation tank.

[0003] In related technologies, two cleaning methods are commonly used. One is manual high-pressure water gun rinsing, where after the sedimentation tank is shut down, operators enter the sedimentation tank or stand at the edge of the tank and use a high-pressure water gun to rinse the surface of the inclined tube at close range. The other is external cleaning by hoisting, where the entire inclined tube module is hoisted out of the sedimentation tank, transported to an open area for large-scale rinsing or soaking, and then hoisted back into the sedimentation tank after drying.

[0004] However, for manual high-pressure water jet cleaning, the setting of cleaning parameters mainly relies on the operator's subjective experience. When faced with tightly adhered sludge, the operator may increase the water jet pressure or bring the nozzle close to the surface of the inclined tube based on experience, resulting in scratches or damage to the tube wall. Conversely, if the water pressure is reduced or the cleaning distance is increased to prevent damage to the inclined tube, it is difficult to effectively remove stubborn sludge located deep inside the inclined tube. For external cleaning methods using hoisting, since the inclined tubes are mostly made of plastic materials such as polypropylene (PP) and polyvinyl chloride (PVC), the toughness of the material often decreases after long-term immersion, making them prone to breakage or deformation due to mechanical impact or compression. This makes it difficult to achieve effective removal of sludge inside the inclined tube while avoiding mechanical damage to the tube in related technologies. Summary of the Invention

[0005] This application provides an adaptive method, system, and related equipment for removing sludge from inclined tubes in sedimentation tanks, which can effectively remove sludge from inside the inclined tubes while avoiding mechanical damage to the tubes.

[0006] Firstly, this application provides an adaptive removal method for sludge accumulated in inclined tubes of a sedimentation tank, applied to the aforementioned integrated control system. The integrated control system is communicatively connected to a cleaning execution subsystem and a smart sensing and decision-making subsystem. The cleaning execution subsystem includes an adaptive actuator, which comprises a flushing component, a pressure and flow adaptive adjustment module, and an adaptive lifting mechanism. The smart sensing and decision-making subsystem includes an intelligent decision-making model, a laser ranging sensor mounted on the adaptive actuator, and water quality monitoring units distributed throughout the sedimentation tank. The method includes: after the water level in the sedimentation tank drops below a preset scanning operation water level, and the top opening area of ​​the inclined tube is exposed above the water surface, controlling the adaptive actuator to perform a patrol movement, and using the laser ranging sensor to scan the inclined tubes of the sedimentation tank in sections to obtain sludge thickness distribution data; using... The intelligent decision-making model performs correlation analysis on the sludge thickness distribution data and the first real-time water quality status data obtained from the water quality monitoring unit to obtain an adaptive cleaning strategy. The adaptive cleaning strategy includes a cleaning movement path and cleaning operation parameters. Cleaning control commands are issued to the adaptive actuator to drive it to move according to the cleaning movement path. Based on the sludge thickness distribution data, the adaptive lifting mechanism is controlled to adjust the flushing component to the target operating height and start the flushing component to perform flushing operation on the inclined tube according to the cleaning operation parameters. During the cleaning movement of the adaptive actuator, the pressure and flow adaptive adjustment module is controlled to dynamically adjust the cleaning operation parameters based on the second real-time water quality status data obtained from the water quality monitoring unit, so that the flushing component performs flushing operation on the inclined tube according to the adjusted cleaning operation parameters.

[0007] By adopting the above technical solution, the laser ranging sensor obtains the mud thickness distribution data by scanning the inclined tube in sections during the inspection movement of the adaptive actuator. This data provides accurate data support for the intelligent decision-making model to generate an adaptive cleaning strategy. The cleaning movement path and cleaning operation parameters generated by the intelligent decision-making model based on the mud thickness distribution data and the first real-time water quality status data can guide the adaptive actuator to perform targeted flushing operations on the inclined tube areas with different mud conditions according to differentiated cleaning strategies. At the same time, the adaptive lifting mechanism adjusts the flushing component to the target operating height based on the mud thickness distribution data, ensuring that the flushing component maintains the optimal operating distance between the flushing component and the top of the inclined tube. This ensures the cleaning effect while avoiding excessive impact due to excessive distance. In addition, the pressure and flow adaptive adjustment module dynamically adjusts the cleaning operation parameters based on the second real-time water quality status data, enabling real-time feedback control of the cleaning process. This keeps the flushing intensity within a reasonable range that effectively removes the mud without damaging the inclined tube. This solves the technical problem of effectively removing sludge from the inside of the inclined tube while avoiding mechanical damage to the tube, thus achieving the technical effect of effectively removing sludge from the inside of the inclined tube while avoiding mechanical damage to the tube.

[0008] Optionally, the sludge thickness distribution data and the first real-time water quality status data are input into the intelligent decision-making model, so that the intelligent decision-making model performs the following operations: The intelligent decision-making model maps the sludge thickness distribution data to the three-dimensional coordinate system of the sedimentation tank, constructs a sludge thickness distribution cloud map, and the sludge thickness distribution cloud map represents the correspondence between the inclined tube position coordinates and the sludge thickness value; The intelligent decision-making model extracts the target inclined tube position coordinates from the sludge thickness distribution cloud map, and performs spatial clustering processing on the target inclined tube position coordinates to obtain multiple areas to be cleaned, and each area to be cleaned corresponds to a region sludge thickness value; The intelligent decision-making model determines the cleaning urgency index of each area to be cleaned according to the first real-time water quality status data and the region sludge thickness value, and sorts the multiple areas to be cleaned according to the cleaning urgency index to obtain a cleaning operation sequence; The intelligent decision-making model generates a cleaning movement path connecting each area to be cleaned according to the cleaning operation sequence; The intelligent decision-making model matches the cleaning operation parameters of each area to be cleaned from the preset cleaning parameter database according to the region sludge thickness value, the preset inclined tube structure configuration parameters, and the preset inclined tube material safety threshold.

[0009] By adopting the above technical solutions, the intelligent decision-making model maps the sludge thickness distribution data to a sludge thickness distribution cloud map constructed in a three-dimensional coordinate system. This allows for a visual representation of the spatial distribution of sludge in the inclined tubes, providing a visual data foundation for subsequent area division and cleaning priority ranking. Spatial clustering merges the target inclined tube location coordinates into multiple areas to be cleaned, integrating discrete sludge points into continuous cleaning operation units. This facilitates continuous and efficient cleaning operations by the adaptive actuator. Simultaneously, the cleaning urgency index is calculated based on the first real-time water quality status data and the area sludge thickness value, and a cleaning operation sequence is generated accordingly. This ensures that areas with a significant impact on effluent quality or severe sludge accumulation are cleaned first. Furthermore, by matching cleaning operation parameters from a preset cleaning parameter database based on the area sludge thickness value, preset inclined tube structure configuration parameters, and preset inclined tube material safety thresholds, the cleaning intensity and the inclined tube's bearing capacity can be precisely matched, ensuring that the flushing water pressure is always controlled within the safe threshold range of the inclined tube material.

[0010] Optionally, the intelligent decision-making model extracts the target inclined tube's position coordinates from the mud thickness distribution cloud map and performs spatial clustering on these coordinates to obtain multiple areas to be cleaned. Specifically, the intelligent decision-making model obtains the projected coverage area of ​​the inclined tube in the horizontal direction and, using the single effective cleaning width of the adaptive actuator as the grid width, divides the projected coverage area into a virtual grid matrix composed of multiple rectangular grid units along the track travel direction of the adaptive actuator. The intelligent decision-making model maps the target inclined tube's position coordinates into the virtual grid matrix and determines the grid point density value based on the number of coordinates in each rectangular grid unit. The intelligent decision-making model compares the grid point density value with a preset trigger threshold to mark rectangular grid units with grid point density values ​​greater than the preset trigger threshold as abnormal grid units. The intelligent decision-making model performs spatial clustering on all abnormal grid units to merge spatially adjacent abnormal grid units with the same mud thickness level into the same connected domain, and determines each connected domain as an area to be cleaned, thus obtaining multiple areas to be cleaned.

[0011] By adopting the above technical solution, the virtual grid matrix is ​​divided with the single effective cleaning width of the adaptive actuator as the grid width. This ensures that the grid division matches the actual cleaning capacity of the adaptive actuator, guaranteeing that each rectangular grid cell corresponds to the effective cleaning range of the adaptive actuator in one operation. By calculating the grid point density value and comparing it with a preset trigger threshold to mark abnormal grid cells, areas with concentrated mud accumulation problems that require focused cleaning can be identified. Furthermore, by performing spatial clustering on abnormal grid cells, spatially adjacent abnormal grid cells with the same mud accumulation thickness level are merged into the same connected domain. This integrates adjacent areas with similar mud accumulation characteristics into a unified area to be cleaned, thereby reducing the repeated movement of the adaptive actuator between adjacent areas and improving the overall efficiency of the cleaning operation.

[0012] Optionally, before using the intelligent decision-making model to perform correlation analysis on the sludge thickness distribution data and the first real-time water quality status data to obtain the adaptive cleaning strategy, the method further includes: obtaining the end time of the previous round of cleaning of the sedimentation tank from the historical cleaning records, and performing time interval analysis between the current time and the end time of the previous round of cleaning to obtain the sludge deposition duration; using the intelligent decision-making model to perform multi-dimensional comprehensive analysis on the maximum sludge thickness value in the sludge thickness distribution data, the effluent turbidity value in the first real-time water quality status data, and the sludge deposition duration to obtain the cleaning necessity index at the current time; determining whether the cleaning necessity index is greater than the preset cleaning start threshold; if the cleaning necessity index is greater than the preset cleaning start threshold, then the current time is determined as the start time of the next round of cleaning of the sedimentation tank; if the cleaning necessity index is less than or equal to the preset cleaning start threshold, then the adaptive actuator is controlled to maintain the inspection scanning state or enter the standby state.

[0013] By adopting the above technical solution, a cleaning necessity index is obtained through multi-dimensional comprehensive analysis by calculating the sediment deposition time and combining the maximum sediment thickness and effluent turbidity value. This allows for a comprehensive assessment of the cleaning timing, taking into account multiple dimensions such as the sediment accumulation cycle, the severity of sediment accumulation, and the actual impact on effluent quality. By comparing the cleaning necessity index with a preset cleaning start threshold, a decision is made on whether to initiate the cleaning operation. This enables a shift from the traditional periodic cleaning mode to an on-demand cleaning mode, avoiding both the wear and waste of resources caused by excessively frequent cleaning and the risk of sediment accumulation leading to deformation or collapse of the inclined tube structure due to untimely cleaning.

[0014] Optionally, during the cleaning movement of the adaptive actuator, the pressure and flow adaptive adjustment module is dynamically adjusted to control the cleaning operation parameters based on the second real-time water quality status data obtained from the water quality monitoring unit, so that the flushing component performs flushing operation on the inclined tube according to the adjusted cleaning operation parameters. Specifically, this includes: determining the effluent turbidity value in the first real-time water quality status data as the cleaning reference turbidity value; extracting the current effluent turbidity value from the second real-time water quality status data, and performing turbidity deviation analysis between the current effluent turbidity value and the cleaning reference turbidity value to obtain the turbidity change; and comparing the turbidity change with the preset turbidity value. The turbidity change range is compared to determine the current flushing effect of the inclined tube. If the turbidity change is less than the lower limit threshold of the preset turbidity change range, the current flushing effect is considered insufficient. If the turbidity change is greater than the upper limit threshold of the preset turbidity change range, the current flushing effect is considered excessive. If the turbidity change is within the preset turbidity change range, the current flushing effect is considered moderate. Based on the current flushing effect, the pressure and flow adaptive adjustment module dynamically adjusts the cleaning operation parameters to obtain the adjusted cleaning operation parameters.

[0015] By adopting the above technical solution, the effluent turbidity value in the first real-time water quality status data is determined as the cleaning reference turbidity value, which can provide a stable reference benchmark for subsequent flushing effect evaluation. By performing turbidity deviation analysis between the current effluent turbidity value and the cleaning reference turbidity value to obtain the turbidity change, the real-time impact of the flushing operation on the effluent water quality can be quantitatively characterized. At the same time, by comparing the turbidity change with the preset turbidity change range to determine the current flushing effect status, the automatic identification of three states—insufficient flushing, excessive flushing, and moderate flushing—can be achieved, thereby providing a clear decision basis for the pressure and flow adaptive adjustment module to dynamically adjust the cleaning operation parameters.

[0016] Optionally, the pressure and flow adaptive adjustment module dynamically adjusts the cleaning operation parameters based on the current rinsing effect status to obtain the adjusted cleaning operation parameters. Specifically, this includes: when the current rinsing effect status is insufficient, the pressure and flow adaptive adjustment module incrementally increases the cleaning water pressure in the cleaning operation parameters according to a preset pressure increase step, and / or incrementally increases the cleaning flow rate in the cleaning operation parameters according to a preset flow rate increase step, until the turbidity change is within a preset turbidity change range; when the current rinsing effect status is excessive, the pressure and flow adaptive adjustment module incrementally decreases the cleaning water pressure in the cleaning operation parameters according to a preset pressure decrease step, and / or incrementally increases the rinsing angle in the cleaning operation parameters according to a preset angle adjustment step, until the turbidity change is within a preset turbidity change range; when the current rinsing effect status is moderate, the pressure and flow adaptive adjustment module maintains the current cleaning operation parameters unchanged.

[0017] By adopting the above technical solution, in the case of insufficient flushing, the flushing intensity can be gradually increased by adjusting the preset pressure increase step and preset flow rate increment step until an effective cleaning effect is achieved, avoiding sudden changes in impact force caused by excessive parameter adjustment. In the case of excessive flushing, the flushing intensity can be gradually decreased by adjusting the preset pressure decrease step and combined with the flushing angle adjustment. This can reduce the flushing intensity to protect the inclined tube while maintaining a certain cleaning effect by changing the water flow impact direction. At the same time, in the case of moderate flushing, the current cleaning operation parameters can be kept unchanged, maintaining the stable and continuous optimal cleaning state, thereby achieving a dynamic balance between cleaning effect and inclined tube protection.

[0018] Optionally, a laser rangefinder sensor is used to scan the inclined tubes of the sedimentation tank in sections to obtain data on the sludge thickness distribution. Specifically, this includes: dividing the sedimentation tank into multiple scanning sections based on its dimensions and the arrangement parameters of the inclined tubes, and configuring a corresponding scanning path for each section; controlling an adaptive actuator to sequentially enter each scanning section according to the scanning path for inspection; after the adaptive actuator enters the current scanning section, determining multiple scanning points in the opening area at the top of the inclined tube within the current scanning section based on a preset scanning angle; using the laser rangefinder sensor to sequentially emit laser beams to the multiple scanning points and receiving multiple laser echo signals reflected from the multiple scanning points; and analyzing the direction of each laser echo signal... The time-return method determines the real-time distance between the laser rangefinder and the corresponding scanning point, obtaining multiple real-time distance values ​​corresponding one-to-one with multiple scanning points. The distance difference analysis is performed between each of the multiple real-time distance values ​​and the preset standard depth of the inclined tube to obtain multiple opening mud thickness values ​​corresponding one-to-one with multiple scanning points. Based on the distribution characteristics of the multiple real-time distance values, the effective water passage ratio of the current scanning zone is determined. The spatial coordinates of multiple scanning points are associated with the corresponding opening mud thickness values ​​and the effective water passage ratio values, generating the mud accumulation data for the current scanning zone. After the adaptive actuator completes the inspection and movement of all scanning zones, the mud accumulation data of all zones is integrated and processed to obtain mud thickness distribution data.

[0019] By adopting the above technical solution, the sedimentation tank is divided into multiple scanning zones and a scanning path is configured according to the tank size and inclined tube arrangement parameters. This enables orderly zone scanning of a large-area sedimentation tank, avoiding omissions or duplicate scans. By emitting laser beams to the scanning points using a laser rangefinder and calculating the real-time distance value based on the round-trip time of the laser echo signal, non-contact and accurate measurement of the distance to the opening area at the top of the inclined tube can be achieved. Simultaneously, by analyzing the distance difference between the real-time distance value and the preset standard depth of the inclined tube, the thickness of the sludge accumulation at the opening can be obtained, indirectly acquiring information on the sludge thickness inside the inclined tube. Furthermore, by determining the effective water flow cross-sectional ratio based on the distribution characteristics of multiple real-time distance values, the degree of flow capacity attenuation caused by sludge accumulation in the inclined tube can be assessed. Finally, by integrating and processing the sludge accumulation data of all zones, sludge thickness distribution data covering the entire sedimentation tank is generated, providing complete data support for the intelligent decision-making model to generate adaptive cleaning strategies.

[0020] In a second aspect, embodiments of this application provide an integrated control system, which includes: one or more processors and a memory; the memory is coupled to one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors invoke the computer instructions to cause the integrated control system to perform the methods described in the first aspect and any possible implementation thereof.

[0021] Thirdly, embodiments of this application provide a computer program product containing instructions that, when the computer program product is run on an integrated control system, cause the integrated control system to perform the method described in the first aspect and any possible implementation thereof.

[0022] Fourthly, embodiments of this application provide a computer-readable storage medium including instructions that, when executed on an integrated control system, cause the integrated control system to perform the method described in the first aspect and any possible implementation thereof. Attached Figure Description

[0023] Figure 1 This is a flowchart illustrating an adaptive sludge removal method for inclined tube sedimentation tanks in an embodiment of this application. Figure 2 This is a structural block diagram of an adaptive sludge removal system for inclined tubes in a sedimentation tank, as described in this application. Figure 3 This is a schematic diagram of the physical device structure of an integrated control system in the embodiments of this application. Detailed Implementation

[0024] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification and appended claims of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to any or all possible combinations including one or more of the listed items.

[0025] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0026] This application provides an adaptive sludge removal method for inclined tube sedimentation tanks, applied to an integrated control system. The integrated control system is communicatively connected to a cleaning execution subsystem and a smart sensing and decision-making subsystem. The cleaning execution subsystem includes an adaptive actuator, which comprises a flushing component, a pressure and flow adaptive adjustment module, and an adaptive lifting mechanism. The smart sensing and decision-making subsystem includes an intelligent decision-making model, a laser ranging sensor mounted on the adaptive actuator, and water quality monitoring units distributed throughout the sedimentation tank. (See reference...) Figure 1 , Figure 1 This is a schematic flowchart of an adaptive sludge removal method for inclined tube sedimentation tanks in this application embodiment, including the following steps: Step S101: After the water level in the sedimentation tank drops below the preset scanning operation water level and the top opening area of ​​the inclined tube is exposed above the water surface, the adaptive actuator is controlled to perform inspection movement, and the inclined tube of the sedimentation tank is scanned in sections using a laser range sensor to obtain the sludge thickness distribution data. Step S102: Use the intelligent decision model to perform correlation analysis on the mud thickness distribution data and the first real-time water quality status data obtained from the water quality monitoring unit to obtain an adaptive cleaning strategy. The adaptive cleaning strategy includes the cleaning movement path and cleaning operation parameters. Step S103: Send a cleaning control command to the adaptive actuator to drive the adaptive actuator to move along the cleaning movement path, and control the adaptive lifting mechanism to adjust the flushing component to the target working height according to the mud thickness distribution data, and start the flushing component to perform flushing operation on the inclined tube according to the cleaning operation parameters. In step S104, during the cleaning movement of the adaptive actuator, the pressure and flow adaptive adjustment module is controlled to dynamically adjust the cleaning operation parameters according to the second real-time water quality status data obtained from the water quality monitoring unit, so that the flushing component performs the flushing operation on the inclined tube according to the adjusted cleaning operation parameters.

[0027] The integrated control system refers to the central control unit used to receive sensing signals, execute decision-making algorithms, and drive the cleaning execution subsystem. The integrated control system can be an industrial control computer, a programmable logic controller (PLC), or a distributed control system (DCS), possessing data processing and control output capabilities. The cleaning execution subsystem refers to the mechanical execution unit used to perform the inclined tube flushing operation, and includes an adaptive actuator. The adaptive actuator is a mobile gantry structure that can automatically move along a track set above the sedimentation tank. The adaptive actuator includes a flushing assembly, a pressure-flow adaptive adjustment module, and an adaptive lifting mechanism. The flushing assembly is an array of nozzles installed on the adaptive actuator for spraying cleaning water into the inclined tube. The flushing assembly can include multiple conical nozzles arranged laterally along the adaptive actuator. The pressure-flow adaptive adjustment module is a regulating device used to dynamically adjust the flushing water pressure and flow rate according to control commands. The pressure-flow adaptive adjustment module can include components such as a variable frequency pump, an electric regulating valve, and a pressure sensor. The adaptive lifting mechanism is a lifting device used to adjust the vertical distance between the flushing assembly and the top of the inclined tube. The adaptive lifting mechanism can include an electric push rod, a screw jack, or a hydraulic lifting cylinder. The intelligent sensing and decision-making subsystem refers to the sensing and decision-making unit used to collect data on sludge condition and water quality, and to perform intelligent analysis and decision-making. The intelligent decision-making model refers to the algorithmic model deployed in the integrated control system used to generate cleaning strategies based on sludge thickness distribution data and water quality condition data. The intelligent decision-making model can be a rule-based expert system model or a machine learning-based decision-making model. The laser rangefinder is a non-contact measuring device installed on the adaptive actuator to measure the distance between the adaptive actuator and the opening area at the top of the inclined tube. The laser rangefinder calculates the distance value by emitting a laser beam and receiving the reflected echo signal.

[0028] It should be noted that the laser rangefinder's emission direction is set vertically downwards. When the laser beam illuminates the opening area at the top of the inclined tube, due to the 60-degree inclination angle and the honeycomb array arrangement of the tubes, the laser beam can enter the interior of the inclined tube through the opening. Specifically, the single-tube aperture of the inclined tube is typically 50 mm to 80 mm, and the projection of the opening at the top of the inclined tube onto the horizontal plane is elliptical. The projection width along its major axis is the single-tube aperture divided by sin60° (approximately 1.15 times the single-tube aperture), which is sufficient to accommodate the vertically downward-emitted laser beam entering the interior of the inclined tube. When there is no or little mud accumulation inside the inclined tube, the laser beam propagates along the axial direction of the inclined tube through the opening, and diffuse reflection occurs after reaching the bottom of the inclined tube or the surface of the mud accumulation. Part of the reflected light returns along the original path and is received by the laser rangefinder. When there is mud accumulation and overflow at the opening of the inclined tube, the laser beam is directly reflected after illuminating the surface of the mud accumulation. Since the sludge mainly deposits in the lower half of the inclined tube under gravity, the difference between the real-time distance measured by the laser rangefinder and the preset standard depth of the inclined tube is the projected thickness of the sludge in the direction of laser beam propagation. Considering that the laser beam is emitted vertically downwards and the inclined tube is tilted at 60 degrees, the angle between the laser beam and the axis of the inclined tube is 30 degrees. The integrated control system performs geometric calculations based on this projected thickness and the tilt angle of the inclined tube to obtain the actual thickness of the sludge along the axial direction of the inclined tube. The conversion formula is: Actual sludge thickness = Projected thickness / cos30° ≈ Projected thickness × 1.155. It should be further noted that when the sludge inside the inclined tube is thick enough that the laser beam cannot reach the bottom of the inclined tube, the laser rangefinder measures the distance between the laser rangefinder and the surface of the sludge. In this case, the measured thickness of the sludge at the opening is the depth difference from the top opening of the inclined tube to the surface of the sludge. This depth difference can directly reflect the degree of influence of the sludge on the effective flow section of the inclined tube.

[0029] The water quality monitoring unit refers to online monitoring instruments distributed at the inlet, outlet, or inside the sedimentation tank for real-time monitoring of water quality parameters. These units may include turbidity meters, concentration meters, and flow meters. An inclined tube refers to an inclined pipe structure installed inside the sedimentation tank. Inclined tubes are typically made of plastic materials such as polypropylene (PP) or polyvinyl chloride (PVC), and their inclination angle is usually 60 degrees. Sludge thickness distribution data refers to the spatial distribution data characterizing the sludge thickness at various locations within the sedimentation tank's inclined tubes. First real-time water quality status data refers to the water quality parameter data obtained from the water quality monitoring unit during the adaptive actuator's inspection scan. This data may include effluent turbidity, influent concentration, and treatment flow rate. The adaptive cleaning strategy refers to the cleaning operation plan generated by the intelligent decision-making model based on the sludge thickness distribution data and water quality status data. The adaptive cleaning strategy includes a cleaning movement path and cleaning operation parameters. The cleaning movement path refers to the movement trajectory of the adaptive actuator during the cleaning operation. Cleaning operation parameters refer to the working parameters of the flushing assembly when performing the flushing operation. These parameters may include cleaning water pressure, cleaning flow rate, and flushing angle. The target operating height refers to the optimal operating distance between the flushing assembly and the opening area at the top of the inclined tube. The target operating height is determined based on the sludge thickness distribution data and the effective flushing range of the flushing assembly. The second real-time water quality status data refers to the water quality parameter data acquired in real-time from the water quality monitoring unit during the cleaning operation performed by the adaptive actuator. The preset scanning operating water level refers to the highest water level at which the laser rangefinder sensor can effectively operate. When the water level in the sedimentation tank drops below the preset scanning operating water level, the opening area at the top of the inclined tube is exposed above the water surface, allowing the laser rangefinder sensor to effectively scan the inclined tube.

[0030] In the above embodiments, see Figure 2 , Figure 2 This is a structural block diagram of an adaptive sludge removal system for inclined tube sedimentation tanks, as described in this application. The adaptive removal system comprises three main components: an integrated control system, a cleaning execution subsystem, and an intelligent sensing and decision-making subsystem. Figure 2 As shown, the integrated control system is located at the core of the entire system. It connects to both the cleaning execution subsystem and the intelligent sensing and decision-making subsystem via communication lines. The integrated control system receives sensing data collected by the intelligent sensing and decision-making subsystem and sends control commands to the cleaning execution subsystem. The integrated control system is housed in a control cabinet next to the sedimentation tank, which contains a processor, memory, and communication interface. The integrated control system also includes a human-machine interface (HMI), located on the control cabinet panel. The HMI includes a display screen and operation buttons. The display screen shows the real-time operating status of the sedimentation tank, a cloud map of sludge thickness distribution, cleaning progress, and historical cleaning records. The operation buttons allow operators to manually set cleaning parameters, start and stop the cleaning operation, and switch working modes.

[0031] In the above embodiments, the integrated control system supports three operating modes: fully automatic, semi-automatic, and manual. Operators can switch between these modes via a human-machine interface. In fully automatic mode, the integrated control system automatically determines the cleaning timing based on an intelligent decision-making model and executes the cleaning operation automatically, without manual intervention. In semi-automatic mode, the integrated control system automatically generates an adaptive cleaning strategy and prompts the operator for confirmation via the human-machine interface before executing the cleaning operation. In manual mode, the operator directly controls the movement direction and speed of the adaptive actuator, the start and stop of the flushing components, and manually sets cleaning parameters such as cleaning water pressure, cleaning flow rate, and flushing angle through the human-machine interface. The communication interface of the integrated control system supports data communication with the upper-level automation system of the water treatment plant. The upper-level automation system can be a programmable logic controller (PLC), a distributed control system (DCS), or a supervisory control and data acquisition system (SCADA). The integrated control system uploads information such as sludge thickness distribution data, water quality monitoring data, cleaning operation records, and equipment operating status to the upper-level automation system via the communication interface, facilitating remote monitoring and data analysis by maintenance personnel. The supervisory control system can also send remote start and stop commands to the integrated control system via the communication interface to realize remote control of the adaptive sludge removal system of the inclined tube in the sedimentation tank.

[0032] In the above embodiments, such as Figure 2 As shown, the cleaning execution subsystem includes an adaptive actuator, which is a gantry structure. Wheels are mounted on the bottom of the two side columns of the adaptive actuator, allowing it to reciprocate along parallel tracks located on both sides of the top of the sedimentation tank. These tracks extend along the length of the sedimentation tank. The adaptive actuator includes a crossbeam horizontally positioned between the two side columns. A flushing assembly is installed below the crossbeam of the adaptive actuator. The flushing assembly includes multiple conical nozzles evenly distributed along the crossbeam, with the nozzles spraying towards the opening area at the top of the inclined tube below. An adaptive lifting mechanism is installed between the crossbeam of the adaptive actuator and the flushing assembly. The adaptive lifting mechanism includes multiple electric push rods. The fixed end of each electric push rod is connected to the crossbeam, and the telescopic end is connected to the mounting bracket of the flushing assembly. The adaptive lifting mechanism is used to adjust the vertical distance between the flushing assembly and the top of the inclined tube. A pressure and flow adaptive adjustment module is connected to the flushing assembly. This module includes a variable frequency water pump and an electric regulating valve. The variable frequency water pump provides the flushing water source and adjusts the flushing water pressure, while the electric regulating valve adjusts the flushing flow rate.

[0033] In the above embodiment, multiple sets of ultrasonic array generators are also installed on the crossbeam of the adaptive actuator. The ultrasonic array generators are evenly distributed along the crossbeam, with their transmitting ends facing the opening area at the top of the inclined tube below. The ultrasonic array generators work in conjunction with the flushing assembly. The ultrasonic array generators emit ultrasonic waves onto the surface of the inclined tube before or during the flushing operation. The ultrasonic waves generate cavitation and micro-jet effects in the sludge layer, loosening and peeling off the stubborn sludge tightly attached to the inner wall of the inclined tube. This reduces the flushing water pressure required by the flushing assembly, improving sludge removal efficiency while reducing mechanical impact on the inclined tube. The integrated control system is connected to the ultrasonic array generator via a communication cable and is used to control the start / stop of the ultrasonic array generator and the ultrasonic power adjustment. The cleaning execution subsystem also includes a water supply pipeline connected between the pressure-flow adaptive adjustment module and the flushing assembly. The water supply pipeline is arranged along the crossbeam of the adaptive actuator and extends to each conical nozzle. The integrated control system is connected to the drive motor on the adaptive actuator via a communication cable. The drive motor is used to drive the adaptive actuator to move along the track. The integrated control system is also connected to the electric push rod in the adaptive lifting mechanism via a communication cable to control the extension and retraction of the electric push rod. The integrated control system is also connected to the variable frequency water pump and electric regulating valve in the pressure and flow adaptive adjustment module via a communication cable to control the adjustment of flushing water pressure and flushing flow.

[0034] In the above embodiments, such as Figure 2 As shown, the intelligent sensing and decision-making subsystem includes an intelligent decision-making model, laser ranging sensors, and a water quality monitoring unit. The intelligent decision-making model is deployed within the integrated control system and connects to the processor of the integrated control system via a data interface. Laser ranging sensors are mounted on the crossbeam of the adaptive actuator. The number of laser ranging sensors corresponds to the number of conical nozzles in the flushing assembly. The emitting end of the laser ranging sensor faces the opening area at the top of the inclined tube below. The laser ranging sensor is connected to the integrated control system via a signal line. The water quality monitoring unit includes an effluent turbidity meter at the sedimentation tank outlet, an influent concentration meter at the sedimentation tank inlet, a flow meter on the sedimentation tank outlet pipeline, and an effluent turbidity meter at the sedimentation tank sludge discharge outlet. The effluent turbidity meter, influent concentration meter, flow meter, and effluent turbidity meter are all connected to the integrated control system via signal lines. The effluent turbidity meter monitors the turbidity value of the sludge discharged during the flushing operation. The integrated control system evaluates the sludge flushing effect based on the effluent turbidity value.

[0035] In the above embodiments, such as Figure 2As shown, the sedimentation tank has a rectangular structure, including side walls and a bottom. An inlet is located at one end, and an outlet at the other. Inclined tubes are installed inside the sedimentation tank. These tubes are composed of multiple honeycomb-shaped polypropylene pipes arranged at an angle of 60 degrees, with the top openings facing upwards. The inclined tube area is located between the inlet and outlet of the sedimentation tank. A sludge collection area is located below the inclined tube area to collect the sludge washed down from the inclined tubes. Figure 2 The diagram also illustrates the data and control flows. Distance data collected by the laser rangefinder is transmitted to the integrated control system via signal lines. Water quality data collected by the water quality monitoring unit is also transmitted to the integrated control system via signal lines. The integrated control system inputs the received data into the intelligent decision-making model for analysis and processing. The adaptive cleaning strategy generated by the intelligent decision-making model is then distributed to the cleaning execution subsystem for execution via the integrated control system. The integrated control system sends movement control commands to the adaptive actuator, lifting control commands to the adaptive lifting mechanism, pressure and flow adjustment commands to the pressure and flow adaptive adjustment module, and start / stop control commands to the flushing components.

[0036] In the above embodiment, an inclined tube sedimentation tank of a municipal wastewater treatment plant is used as an example. The sedimentation tank has dimensions of 30 meters long, 10 meters wide, and 5 meters deep. An inclined tube made of polypropylene with an inclination angle of 60 degrees is installed inside the sedimentation tank. The single-tube diameter of the inclined tube is 50 mm, and the standard depth of the inclined tube is 1 meter. The adaptive actuator's travel speed is set to 0.5 meters per minute, the nozzle spacing of the flushing assembly is 0.5 meters, and a total of 20 conical nozzles are installed. The laser rangefinder has a measurement accuracy of ±1 mm and a measurement frequency of 10 times per second. First, by closing the sedimentation tank inlet valve or opening the drain valve, the water level in the sedimentation tank is lowered to a preset scanning operation water level approximately 0.2 meters below the top of the inclined tube. At this point, the opening area at the top of the inclined tube is exposed above the water surface. Subsequently, the integrated control system sends an inspection command to the adaptive actuator. The adaptive actuator moves along the track from one end of the sedimentation tank to the other. During the movement, the laser rangefinder continuously emits a laser beam towards the opening area at the top of the inclined tube and receives the reflected signal. By calculating the round-trip time of the laser signal, the laser rangefinder measures the distance between itself and the mud surface at the opening of the inclined tube. For example, if the real-time distance at a certain scanning point is 0.7 meters and the preset standard depth of the inclined tube is 1 meter, the mud thickness at that point can be calculated to be 0.3 meters.

[0037] In the above embodiment, after the adaptive actuator completes a full-pool scan, the integrated control system integrates all the acquired scan data to generate sludge thickness distribution data. Simultaneously, the integrated control system obtains first real-time water quality status data from the water quality monitoring unit, such as an effluent turbidity of 15 NTU, an influent concentration of 200 mg / L, and a treatment flow rate of 1000 cubic meters per hour. The intelligent decision model generates an adaptive cleaning strategy based on the sludge thickness distribution data and the first real-time water quality status data. For example, the intelligent decision model determines the cleaning movement path to proceed sequentially from the area with the most severe sludge accumulation to areas with lighter sludge accumulation. The initial cleaning water pressure is set to 0.3 MPa, the initial cleaning flow rate to 5 cubic meters per hour, and the initial flushing angle to 75 degrees in the cleaning operation parameters. During the cleaning operation, the integrated control system issues a cleaning control command to the adaptive actuator. The adaptive actuator moves to above the first area to be cleaned according to the cleaning movement path, and the adaptive lifting mechanism lowers the flushing component to the target operating height of 0.3 meters from the opening area at the top of the inclined tube, based on the sludge thickness value of that area. Subsequently, the flushing component is activated, spraying cleaning water into the inclined tube according to the cleaning operation parameters. During the cleaning process, the integrated control system continuously acquires real-time water quality status data from the water quality monitoring unit. For example, when the current effluent turbidity value is detected to rise from 15 NTU before cleaning to 25 NTU, the integrated control system calculates the turbidity change as 10 NTU. This turbidity change is within the preset turbidity change range (5 NTU to 30 NTU), indicating that the current flushing effect is moderate, and the pressure and flow adaptive adjustment module maintains the current cleaning operation parameters unchanged. If the turbidity change is less than 5 NTU, it indicates insufficient flushing, and the pressure and flow adaptive adjustment module gradually increases the cleaning water pressure according to the preset pressure increase step of 0.05 MPa until the turbidity change reaches the preset range; if the turbidity change is greater than 30 NTU, it indicates over-flushing, and the pressure and flow adaptive adjustment module gradually decreases the cleaning water pressure according to the preset pressure decrease step of 0.05 MPa to protect the inclined tube.

[0038] Through the above steps, the laser ranging sensor obtains the mud thickness distribution data by scanning the inclined tube in sections during the inspection movement of the adaptive actuator. This data provides accurate data support for the intelligent decision-making model to generate an adaptive cleaning strategy. The cleaning movement path and cleaning operation parameters generated by the intelligent decision-making model based on the mud thickness distribution data and the first real-time water quality status data guide the adaptive actuator to perform targeted flushing operations on the inclined tube areas with different mud conditions according to differentiated cleaning strategies. At the same time, the adaptive lifting mechanism adjusts the flushing component to the target operating height based on the mud thickness distribution data, ensuring that the flushing component maintains the optimal operating distance between the flushing component and the top of the inclined tube. This ensures the cleaning effect while avoiding excessive impact due to excessive distance. In addition, the pressure and flow adaptive adjustment module dynamically adjusts the cleaning operation parameters based on the second real-time water quality status data, enabling real-time feedback control of the cleaning process and keeping the flushing intensity within a reasonable range that effectively removes mud without damaging the inclined tube. This solves the technical problem of effectively removing sludge from the inside of the inclined tube while avoiding mechanical damage to the tube, thus achieving the technical effect of effectively removing sludge from the inside of the inclined tube while avoiding mechanical damage to the tube.

[0039] The entity performing the above steps may be a system or device, or a controller or processor in the device or system, or a separate controller or processor, or other processing devices or processing units with similar processing functions, but is not limited to these.

[0040] In an optional embodiment, the sludge thickness distribution data and the first real-time water quality status data are input into the intelligent decision-making model, so that the intelligent decision-making model performs the following operations: The intelligent decision-making model maps the sludge thickness distribution data to the three-dimensional coordinate system of the sedimentation tank, constructing a sludge thickness distribution cloud map, which represents the correspondence between the inclined tube position coordinates and the sludge thickness value; The intelligent decision-making model extracts the target inclined tube position coordinates from the sludge thickness distribution cloud map and performs spatial clustering processing on the target inclined tube position coordinates to obtain multiple areas to be cleaned, and each area to be cleaned corresponds to a regional sludge thickness value; The intelligent decision-making model determines the cleaning urgency index of each area to be cleaned based on the first real-time water quality status data and the regional sludge thickness value, and sorts the multiple areas to be cleaned according to the cleaning urgency index to obtain a cleaning operation sequence; The intelligent decision-making model generates a cleaning movement path connecting each area to be cleaned according to the cleaning operation sequence; The intelligent decision-making model matches the cleaning operation parameters of each area to be cleaned from the preset cleaning parameter database based on the regional sludge thickness value, the preset inclined tube structure configuration parameters, and the preset inclined tube material safety threshold.

[0041] The three-dimensional coordinate system refers to a spatial rectangular coordinate system established with a corner point of the sedimentation tank as the origin, the length direction of the sedimentation tank as the X-axis, the width direction of the sedimentation tank as the Y-axis, and the direction perpendicular to the water surface as the Z-axis. The sludge thickness distribution cloud map is a visual image in the three-dimensional coordinate system that uses different colors or grayscale values ​​to represent different sludge thickness values, intuitively presenting the sludge distribution of the inclined tubes at various locations within the sedimentation tank. The target inclined tube position coordinates refer to the position coordinates of inclined tubes whose sludge thickness exceeds a preset sludge thickness threshold in the three-dimensional coordinate system. Spatial clustering processing refers to a data processing method that groups the position coordinates of spatially adjacent target inclined tubes with similar sludge characteristics into the same category. The area to be cleaned refers to the continuous area that needs cleaning operations after spatial clustering processing. The area sludge thickness value refers to the average or maximum value of the sludge thickness values ​​corresponding to the position coordinates of all target inclined tubes within the area to be cleaned. The cleaning urgency index is a quantitative indicator used to characterize the cleaning priority of the area to be cleaned; the higher the cleaning urgency index value, the higher the priority of cleaning the area. The cleaning operation sequence refers to a list of areas to be cleaned, arranged from highest to lowest cleaning urgency index. Preset inclined tube structure configuration parameters refer to the inclined tube structure parameters pre-stored in the integrated control system, which may include the single tube diameter, inclination angle, and arrangement density of the inclined tubes. Preset inclined tube material safety thresholds refer to the inclined tube material bearing capacity parameters pre-stored in the integrated control system, which may include the maximum allowable flushing water pressure and maximum allowable flushing temperature. The preset cleaning parameter database refers to a pre-established database storing the mapping relationship between different sludge conditions and corresponding cleaning parameters.

[0042] In the above embodiment, the inclined tube sedimentation tank of a municipal wastewater treatment plant is used as an example. Assuming the sludge thickness distribution data includes 1000 scanning points, the intelligent decision-making model maps the sludge thickness values ​​of these 1000 scanning points to the three-dimensional coordinate system of the sedimentation tank, constructing a sludge thickness distribution cloud map. In the sludge thickness distribution cloud map, areas with sludge thickness values ​​between 0 and 0.1 meters are represented in green, areas between 0.1 and 0.2 meters in yellow, areas between 0.2 and 0.3 meters in orange, and areas with sludge thickness values ​​greater than 0.3 meters in red. The intelligent decision-making model extracts scanning points with sludge thickness values ​​greater than a preset sludge thickness threshold of 0.15 meters from the sludge thickness distribution cloud map as the target inclined tube location coordinates; assuming a total of 200 target inclined tube location coordinates are extracted. The intelligent decision-making model performs spatial clustering on the coordinates of these 200 target inclined tubes, grouping spatially adjacent target inclined tubes with the same mud thickness into the same cleaning area. Assume that after spatial clustering, five cleaning areas are obtained: A, B, C, D, and E, with mud thicknesses of 0.35 m, 0.28 m, 0.22 m, 0.18 m, and 0.16 m, respectively.

[0043] In the above embodiment, the intelligent decision-making model calculates the cleaning urgency index based on the effluent turbidity value (15 NTU) and the regional sludge thickness value of each area to be cleaned from the first real-time water quality status data. The formula for calculating the cleaning urgency index is: Cleaning Urgency Index = Regional Sludge Thickness Value × Weighting Coefficient 1 + Effluent Turbidity Value × Weighting Coefficient 2, where Weighting Coefficient 1 and Weighting Coefficient 2 are determined based on historical cleaning data and sedimentation tank operation experience. Specifically, Weighting Coefficient 1 is used to normalize the sludge thickness value (unit: meters) to a scoring range related to cleaning urgency, and Weighting Coefficient 2 is used to normalize the effluent turbidity value (unit: NTU) to the same scoring range. In this embodiment, based on the analysis of historical operating data of the sedimentation tank, the sedimentation efficiency significantly decreases when the sludge thickness reaches 0.4 meters, and the effluent quality fails to meet standards when the effluent turbidity exceeds 20 NTU. Therefore, a weighting coefficient 1 is set to 10 (corresponding to 4 points for a 0.4-meter sludge thickness) and a weighting coefficient 2 is set to 0.5 (corresponding to 10 points for 20 NTU turbidity). It should be noted that weighting coefficients 1 and 2 can be adjusted according to the design parameters and operating conditions of different sedimentation tanks. The cleaning urgency index for area A is 0.35×10+15×0.5=11, for area B it is 0.28×10+15×0.5=10.3, for area C it is 0.22×10+15×0.5=9.7, for area D it is 0.18×10+15×0.5=9.3, and for area E it is 0.16×10+15×0.5=9.1. The intelligent decision-making model sorts the five areas based on their cleaning urgency indices, resulting in the following cleaning sequence: Area A → Area B → Area C → Area D → Area E.

[0044] In the above embodiments, the intelligent decision-making model generates cleaning movement paths connecting each area to be cleaned based on the cleaning operation sequence. The generation of the cleaning movement paths employs a shortest path algorithm to ensure that the adaptive actuator travels the shortest distance between each area to be cleaned. The intelligent decision-making model matches the cleaning operation parameters for each area to be cleaned from a preset cleaning parameter database based on the area's accumulated mud thickness, preset inclined tube structure configuration parameters (single tube diameter 50 mm, inclination angle 60 degrees), and preset inclined tube material safety thresholds (maximum allowable flushing water pressure for polypropylene material is 0.5 MPa). For example, area A has an accumulated mud thickness of 0.35 meters, classifying it as heavily accumulated mud, and the matched cleaning operation parameters are: cleaning water pressure 0.4 MPa, cleaning flow rate 8 cubic meters / hour, and flushing angle 60 degrees; area E has an accumulated mud thickness of 0.16 meters, classifying it as lightly accumulated mud, and the matched cleaning operation parameters are: cleaning water pressure 0.25 MPa, cleaning flow rate 4 cubic meters / hour, and flushing angle 80 degrees.

[0045] In an optional embodiment, the intelligent decision-making model extracts the target inclined tube's position coordinates from the mud thickness distribution cloud map and performs spatial clustering processing on the target inclined tube's position coordinates to obtain multiple areas to be cleaned. Specifically, the intelligent decision-making model obtains the projected coverage area of ​​the inclined tube in the horizontal direction, and uses the single effective cleaning width of the adaptive actuator as the grid width. Along the track travel direction of the adaptive actuator, the projected coverage area is divided into a virtual grid matrix composed of multiple rectangular grid units. The intelligent decision-making model maps the target inclined tube's position coordinates to the virtual grid matrix and determines the grid point density value based on the number of coordinates in each rectangular grid unit. The intelligent decision-making model compares the grid point density value with a preset trigger threshold to mark rectangular grid units with grid point density values ​​greater than the preset trigger threshold as abnormal grid units. The intelligent decision-making model performs spatial clustering processing on all abnormal grid units to merge spatially adjacent abnormal grid units with the same mud thickness level into the same connected domain, and determines each connected domain as an area to be cleaned, thus obtaining multiple areas to be cleaned.

[0046] The projected coverage area refers to the area covered by the orthographic projection of the inclined tube on the horizontal plane, and its shape is usually rectangular. The single effective cleaning width refers to the lateral width that the flushing assembly can effectively cover in a single cleaning operation; the single effective cleaning width is determined by the nozzle spacing and spray diffusion angle in the flushing assembly. The track travel direction refers to the direction in which the adaptive actuator moves along the track, and the track travel direction is usually parallel to the length of the sedimentation tank. The virtual grid matrix refers to a grid array composed of multiple rectangular grid cells formed by dividing the projected coverage area according to a preset grid width. The grid point density value refers to the number of target inclined tube position coordinates contained within a single rectangular grid cell. The preset trigger threshold is the grid point density value threshold used to determine whether a rectangular grid cell needs cleaning. An abnormal grid cell is a rectangular grid cell with a grid point density value greater than the preset trigger threshold; an abnormal grid cell indicates that there are many inclined tubes in the area that need cleaning. A connected region refers to a continuous region composed of multiple spatially adjacent abnormal grid cells.

[0047] In the above embodiment, the inclined tube sedimentation tank of a municipal wastewater treatment plant is used as an example for further explanation. The projected area of ​​the inclined tube in the horizontal direction is a rectangular area with a length of 20 meters and a width of 10 meters. The effective cleaning width of the adaptive actuator in a single operation is 0.5 meters × 20 nozzles = 10 meters, which is equal to the width of the sedimentation tank. The intelligent decision-making model divides the projected area into four rectangular grid units with a grid length of 5 meters along the trajectory of the adaptive actuator (the length direction of the sedimentation tank), forming a 1×4 virtual grid matrix. The four rectangular grid units are labeled as grid unit G1, grid unit G2, grid unit G3, and grid unit G4, respectively. The intelligent decision-making model maps the previously extracted coordinates of the 200 target inclined tube positions to the virtual grid matrix and counts the number of coordinates within each rectangular grid unit. Assume that grid cell G1 contains 70 target inclined tube location coordinates with a grid point density of 70; grid cell G2 contains 80 target inclined tube location coordinates with a grid point density of 80; grid cell G3 contains 35 target inclined tube location coordinates with a grid point density of 35; and grid cell G4 contains 15 target inclined tube location coordinates with a grid point density of 15. A preset trigger threshold is set to 50. The intelligent decision model compares the grid point density values ​​with the preset trigger threshold. Grid cell G1, with a grid point density of 70, is marked as an abnormal grid cell because it exceeds the preset trigger threshold of 50. Grid cell G2, with a grid point density of 80, is also marked as an abnormal grid cell because it exceeds the preset trigger threshold of 50. Grid cell G3, with a grid point density of 35, is marked as a normal grid cell because it is less than the preset trigger threshold of 50. Grid cell G4, with a grid point density of 15, is also marked as a normal grid cell because it is less than the preset trigger threshold of 50.

[0048] In the above embodiment, the intelligent decision-making model performs spatial clustering processing on abnormal grid units G1 and G2. Since abnormal grid units G1 and G2 are spatially adjacent, the intelligent decision-making model further determines whether their mud accumulation thickness levels are the same. Assuming the average mud accumulation thickness corresponding to the target inclined tube position coordinates within abnormal grid unit G1 is 0.25 meters, belonging to the moderate mud accumulation level; and the average mud accumulation thickness corresponding to the target inclined tube position coordinates within abnormal grid unit G2 is 0.32 meters, belonging to the heavy mud accumulation level. Because their mud accumulation thickness levels are different, the intelligent decision-making model determines abnormal grid units G1 and G2 as two independent connected domains, i.e., two independent areas to be cleaned. It should be noted that in this embodiment, the division of the virtual grid matrix is ​​used for spatial clustering preprocessing. The final number of areas to be cleaned depends on the mud accumulation thickness level distribution of the abnormal grid units. When multiple adjacent abnormal grid units have the same mud accumulation thickness level, they will be merged into the same area to be cleaned; when the mud accumulation thickness levels are different, they will be determined as independent areas to be cleaned.

[0049] In an optional embodiment, before using an intelligent decision-making model to perform correlation analysis on the sludge thickness distribution data and the first real-time water quality status data to obtain an adaptive cleaning strategy, the method further includes: obtaining the end time of the previous round of cleaning of the sedimentation tank from historical cleaning records, and performing time interval analysis between the current time and the end time of the previous round of cleaning to obtain the sludge deposition duration; using the intelligent decision-making model to perform multi-dimensional comprehensive analysis on the maximum sludge thickness value in the sludge thickness distribution data, the effluent turbidity value in the first real-time water quality status data, and the sludge deposition duration to obtain the cleaning necessity index at the current time; determining whether the cleaning necessity index is greater than a preset cleaning start threshold; if the cleaning necessity index is greater than the preset cleaning start threshold, then the current time is determined as the start time of the next round of cleaning of the sedimentation tank; if the cleaning necessity index is less than or equal to the preset cleaning start threshold, then the adaptive actuator is controlled to maintain the inspection scanning state or enter the standby state.

[0050] The historical cleaning record refers to the time and parameter records of each cleaning operation of the sedimentation tank stored in the integrated control system. The previous cleaning cycle end time refers to the time when the last cleaning operation of the sedimentation tank was completed. The sludge deposition time refers to the elapsed time between the end time of the previous cleaning cycle and the current time; the sludge deposition time reflects the accumulation cycle of sludge in the inclined tube. The maximum sludge thickness value refers to the maximum value among all scanning points in the sludge thickness distribution data. The effluent turbidity value refers to the turbidity measurement value of the sedimentation tank effluent in the first real-time water quality status data. The cleaning necessity index is a quantitative indicator used to comprehensively assess whether a cleaning operation needs to be initiated at the current time; a higher cleaning necessity index value indicates a greater necessity to initiate the cleaning operation. The preset cleaning initiation threshold is a pre-set threshold value for the cleaning necessity index used to determine whether to initiate a cleaning operation. The inspection scanning state refers to the working state where the adaptive actuator moves along the track and continuously performs laser scanning when not performing cleaning operations. The standby state refers to the low-power working state where the adaptive actuator stops moving and stops laser scanning.

[0051] In the above embodiment, the inclined tube sedimentation tank of a municipal wastewater treatment plant is used as an example for further explanation. The integrated control system obtains the end time of the previous cleaning cycle of the sedimentation tank from historical cleaning records as 10:00:00 on January 1st of a certain year, and the current time as 10:00:00 on January 15th of the same year. Through time interval analysis, the sludge deposition time is calculated to be 14 days (336 hours). The integrated control system extracts the maximum sludge thickness value of 0.35 meters from the sludge thickness distribution data and the effluent turbidity value of 15 NTU from the first real-time water quality status data. The intelligent decision-making model performs multi-dimensional comprehensive analysis on the maximum sludge thickness value, effluent turbidity value, and sludge deposition time to calculate the cleaning necessity index at the current time. The formula for calculating the cleaning necessity index is: Cleaning necessity index = Maximum sludge thickness value × Weighting coefficient A + Effluent turbidity value × Weighting coefficient B + Sludge deposition time × Weighting coefficient C, where weighting coefficients A, B, and C are determined based on historical operating data and the design parameters of the inclined tube sedimentation tank. Specifically, weighting coefficient A reflects the influence of sludge thickness on the necessity of cleaning, weighting coefficient B reflects the influence of water turbidity on the necessity of cleaning, and weighting coefficient C reflects the influence of sludge deposition time on the necessity of cleaning. In this embodiment, based on statistical analysis of historical operating data, when the sludge thickness exceeds 0.35 meters, the flow capacity of the inclined tube decreases by more than 30%; when the effluent turbidity exceeds 15 NTU, the effluent quality approaches the discharge standard limit; and when sludge deposition exceeds 14 days, the degree of sludge consolidation increases, leading to increased cleaning difficulty. Therefore, weighting coefficient A is set to 100 (corresponding to 35 points for 0.35 meters of sludge), weighting coefficient B is set to 2 (corresponding to 30 points for 15 NTU turbidity), and weighting coefficient C is set to 0.1 (corresponding to 33.6 points for 336 hours of sludge deposition time), with the three weights roughly balanced. Assuming weight coefficient A is 100, weight coefficient B is 2, and weight coefficient C is 0.1, then the cleaning necessity index = 0.35 × 100 + 15 × 2 + 336 × 0.1 = 35 + 30 + 33.6 = 98.6. It should be noted that weight coefficients A, B, and C can be adjusted according to the operating characteristics and management requirements of different sedimentation tanks, or they can be automatically optimized by fitting historical data using machine learning algorithms.

[0052] In the above embodiment, the preset cleaning start threshold is set to 80. The integrated control system compares the cleaning necessity index of 98.6 with the preset cleaning start threshold of 80. Since the cleaning necessity index of 98.6 is greater than the preset cleaning start threshold of 80, the integrated control system determines the current time of 10:00:00 on January 15th of the same year as the start time of the next round of cleaning of the sedimentation tank, and then the intelligent decision model begins to generate an adaptive cleaning strategy. In another scenario, the maximum sludge thickness is 0.12 meters, the effluent turbidity is 8 NTU, and the sludge deposition time is 5 days (120 hours), then the cleaning necessity index = 0.12 × 100 + 8 × 2 + 120 × 0.1 = 12 + 16 + 12 = 40. Since the cleaning necessity index of 40 is less than the preset cleaning start threshold of 80, the integrated control system controls the adaptive actuator to maintain the inspection and scanning state, continue to monitor the sludge condition of the inclined tube, and start the cleaning operation when the cleaning necessity index rises to exceed the preset cleaning start threshold.

[0053] In an optional embodiment, during the cleaning movement of the adaptive actuator, the pressure and flow adaptive adjustment module dynamically adjusts the cleaning operation parameters based on the second real-time water quality status data obtained from the water quality monitoring unit, so that the flushing component performs flushing operation on the inclined tube according to the adjusted cleaning operation parameters. Specifically, this includes: determining the effluent turbidity value in the first real-time water quality status data as the cleaning reference turbidity value; extracting the current effluent turbidity value from the second real-time water quality status data, and performing turbidity deviation analysis between the current effluent turbidity value and the cleaning reference turbidity value to obtain the turbidity change; and comparing the turbidity change with... The turbidity variation range is preset to compare turbidity and determine the current flushing effect of the inclined tube. If the turbidity variation is less than the lower threshold of the preset turbidity variation range, the current flushing effect is considered insufficient. If the turbidity variation is greater than the upper threshold of the preset turbidity variation range, the current flushing effect is considered excessive. If the turbidity variation is within the preset turbidity variation range, the current flushing effect is considered moderate. Based on the current flushing effect, the pressure and flow adaptive adjustment module dynamically adjusts the cleaning operation parameters to obtain the adjusted cleaning operation parameters.

[0054] The cleaning baseline turbidity value refers to the turbidity value of the sedimentation tank effluent before the start of the cleaning operation, serving as a reference for evaluating the effectiveness of the flushing operation. The current effluent turbidity value refers to the real-time turbidity value of the sedimentation tank effluent obtained from the water quality monitoring unit during the cleaning operation. Turbidity deviation analysis refers to the analytical process of calculating the difference between the current effluent turbidity value and the cleaning baseline turbidity value. Turbidity change refers to the difference between the current effluent turbidity value and the cleaning baseline turbidity value; a positive turbidity change indicates an increase in effluent turbidity, while a negative turbidity change indicates a decrease in effluent turbidity. The preset turbidity change range refers to a pre-set reasonable range of turbidity change, within which the flushing effect is considered moderate. The lower limit threshold of turbidity change refers to the lower boundary value of the preset turbidity change range; a turbidity change less than the lower limit threshold indicates that the amount of sludge released during the flushing operation is too small, resulting in insufficient flushing intensity. The upper limit threshold for turbidity change refers to the upper boundary value of the preset turbidity change range. A turbidity change greater than the upper limit threshold indicates that the amount of sludge released during the flushing operation is too large, and the flushing intensity is too high. The current flushing effect status refers to the flushing operation effect status determined based on the turbidity change. The current flushing effect status includes three types: insufficient flushing, excessive flushing, and moderate flushing.

[0055] In the above embodiment, the inclined tube sedimentation tank of a municipal wastewater treatment plant is used as an example for further explanation. Before the cleaning operation begins, the integrated control system obtains the effluent turbidity value of 15 NTU from the first real-time water quality status data and determines this value as the cleaning baseline turbidity value. The preset turbidity variation range is set to 5 NTU to 30 NTU, that is, the lower limit threshold for turbidity variation is 5 NTU, and the upper limit threshold for turbidity variation is 30 NTU. The determination of the preset turbidity variation range is based on the following: the lower limit threshold of 5 NTU means that the effluent turbidity should increase by at least 5 NTU after the start of the flushing operation to indicate that the accumulated sludge has been effectively flushed. If the turbidity change is less than 5 NTU, it means that the flushing intensity is insufficient to remove the accumulated sludge. The upper limit threshold of 30 NTU means that the increase in effluent turbidity caused by the flushing operation should not exceed 30 NTU. If the turbidity change exceeds 30 NTU, it means that the flushing intensity is too high, causing a large amount of accumulated sludge to fall off, which may cause excessive impact on the inclined tube. This preset turbidity variation range can be adjusted according to the effluent water quality control requirements of the sedimentation tank and the material bearing capacity of the inclined tube.

[0056] In the above embodiments, during the flushing operation, the integrated control system continuously acquires second real-time water quality status data from the water quality monitoring unit. Assuming that at a certain moment, the current effluent turbidity value is 18 NTU, the integrated control system performs turbidity deviation analysis and calculates the turbidity change as: current effluent turbidity value - cleaning baseline turbidity value = 18 NTU - 15 NTU = 3 NTU. Since the turbidity change of 3 NTU is less than the lower limit threshold of 5 NTU, the integrated control system determines that the current flushing effect is insufficient, indicating that the amount of sludge released during the flushing operation is too small, and the flushing intensity needs to be increased. Assuming that at another moment, the current effluent turbidity value is 38 NTU, the turbidity change is: 38 NTU - 15 NTU = 23 NTU. Since the turbidity change of 23 NTU is within the preset turbidity change range of 5 NTU to 30 NTU, the integrated control system determines that the current flushing effect is moderate, indicating that the flushing operation is effective. Suppose at another moment, the current effluent turbidity is 50 NTU, and the turbidity change is 50 NTU - 15 NTU = 35 NTU. Since the turbidity change of 35 NTU exceeds the upper limit threshold of 30 NTU, the integrated control system determines that the current flushing effect is in an over-flushing state, indicating that the flushing operation has released too much sludge and the flushing intensity is too high, potentially causing excessive impact on the inclined pipe. Therefore, the flushing intensity needs to be reduced. Based on the current flushing effect, the integrated control system controls the pressure and flow adaptive adjustment module to dynamically adjust the cleaning operation parameters accordingly.

[0057] In an optional embodiment, the pressure and flow adaptive adjustment module dynamically adjusts the cleaning operation parameters according to the current rinsing effect status to obtain the adjusted cleaning operation parameters. Specifically, this includes: when the current rinsing effect status is insufficient, the pressure and flow adaptive adjustment module adjusts the cleaning water pressure in the cleaning operation parameters in a step-by-step manner according to a preset pressure increase step, and / or adjusts the cleaning flow rate in the cleaning operation parameters in a step-by-step manner according to a preset flow rate increase step, until the turbidity change is within a preset turbidity change range; when the current rinsing effect status is excessive, the pressure and flow adaptive adjustment module adjusts the cleaning water pressure in the cleaning operation parameters in a step-by-step manner according to a preset pressure decrease step, and / or adjusts the rinsing angle in the cleaning operation parameters in a step-by-step manner according to a preset angle adjustment step, until the turbidity change is within a preset turbidity change range; when the current rinsing effect status is moderate, the pressure and flow adaptive adjustment module maintains the cleaning operation parameters unchanged at the current moment.

[0058] Among them, the preset pressure increment step refers to the incremental value of the cleaning water pressure adjustment each time when the flushing is insufficient. The preset flow rate increment step refers to the incremental value of the cleaning flow rate adjustment each time when the flushing is insufficient. Step-by-step incremental adjustment refers to the adjustment method of increasing the parameter value step by step according to the preset step. The preset pressure reduction step refers to the incremental value of the cleaning water pressure adjustment each time when the flushing is excessive. The preset angle adjustment step refers to the incremental value of the flushing angle adjustment each time. Step-by-step decremental adjustment refers to the adjustment method of decreasing the parameter value step by step according to the preset step. Cleaning water pressure refers to the pressure value of the cleaning water flow at the nozzle outlet of the flushing component. Cleaning flow rate refers to the volume of cleaning water sprayed by the flushing component per unit time. The flushing angle refers to the angle between the direction of the cleaning water flow and the normal direction of the inclined tube surface. The larger the flushing angle, the more inclined the cleaning water flow is, and the smaller the positive impact force on the inclined tube surface.

[0059] In the above embodiment, the inclined tube sedimentation tank of a municipal sewage treatment plant is used as an example for further explanation. The preset pressure increase step is set to 0.05 MPa, the preset flow rate increase step is set to 1 cubic meter / hour, the preset pressure reduction step is set to 0.05 MPa, and the preset angle adjustment step is set to 5 degrees. The inclined tube material is polypropylene, and the maximum allowable water pressure in the preset inclined tube material safety threshold is 0.5 MPa. Assume the initial cleaning operation parameters are: cleaning water pressure 0.3 MPa, cleaning flow rate 5 cubic meters / hour, and flushing angle 75 degrees. Scenario 1 (Insufficient flushing state): The current flushing effect is judged to be insufficient flushing. The integrated control system controls the pressure and flow adaptive adjustment module to perform the first step-by-step incremental adjustment, increasing the cleaning water pressure from 0.3 MPa to 0.35 MPa. After adjustment, the turbidity change is re-detected. If the turbidity change is still less than the lower limit threshold of 5 NTU, the second step-by-step incremental adjustment is performed, increasing the cleaning water pressure from 0.35 MPa to 0.4 MPa. If the turbidity change reaches 8 NTU, within the preset turbidity change range of 5 NTU to 30 NTU, the incremental adjustment stops, and the cleaning water pressure is maintained at 0.4 MPa. During the step-by-step incremental adjustment, the integrated control system continuously monitors whether the current cleaning water pressure reaches the maximum allowable flushing water pressure of 0.5 MPa, which is within the preset safety threshold for the inclined tube material. If the turbidity change is still less than the lower limit threshold of 5 NTU after the cleaning water pressure is increased to 0.5 MPa, the integrated control system stops the incremental adjustment of the cleaning water pressure and instead adjusts the cleaning flow rate in a step-by-step manner according to a preset flow rate increment of 1 cubic meter per hour. For example, the cleaning flow rate is increased from 5 cubic meters per hour to 6 cubic meters per hour until the turbidity change is within the preset turbidity change range. Through this safety threshold constraint mechanism, it is ensured that the cleaning water pressure never exceeds the bearing capacity of the inclined tube material, thereby improving the cleaning effect while avoiding mechanical damage to the inclined tube caused by excessive water pressure.

[0060] In the above embodiments, Scenario 2 (Over-rinsing state): The current rinsing effect is judged to be over-rinsing. The integrated control system controls the pressure and flow adaptive adjustment module to perform the first step-decreasing adjustment, reducing the cleaning water pressure from 0.3MPa to 0.25MPa. After adjustment, the turbidity change is re-detected. If the turbidity change is still greater than the upper limit threshold of 30NTU, the second step-decreasing adjustment is performed, reducing the cleaning water pressure from 0.25MPa to 0.2MPa, while increasing the rinsing angle from 75 degrees to 80 degrees. After the rinsing angle is increased, the positive impact force of the cleaning water flow on the inclined tube surface is reduced, which helps to protect the inclined tube while maintaining a certain cleaning effect. If the turbidity change drops to 25NTU at this time, which is within the preset turbidity change range, the adjustment is stopped. Scenario 3 (Moderate rinsing state): The current rinsing effect is judged to be moderate rinsing. The integrated control system uses a pressure and flow adaptive adjustment module to maintain the current cleaning parameters constant: cleaning water pressure at 0.3 MPa, cleaning flow rate at 5 cubic meters per hour, and rinsing angle at 75 degrees. Throughout the dynamic adjustment process, the integrated control system continuously monitors whether the cleaning water pressure exceeds the maximum permissible pressure of 0.5 MPa, which is within the preset safety threshold for the inclined tube material. If the adjusted cleaning water pressure is about to exceed 0.5 MPa, the integrated control system will stop increasing the cleaning water pressure and instead improve the cleaning effect by increasing the cleaning flow rate or extending the rinsing time, ensuring that the inclined tube is not mechanically damaged by excessively high rinsing water pressure.

[0061] In an optional embodiment, a laser rangefinder sensor is used to scan the inclined tubes of the sedimentation tank in sections to obtain data on the sludge thickness distribution of the inclined tubes. Specifically, this includes: dividing the sedimentation tank into multiple scanning sections based on the tank dimensions and the arrangement parameters of the inclined tubes, and configuring a corresponding scanning path for each scanning section; controlling an adaptive actuator to sequentially enter each scanning section according to the scanning path for inspection; after the adaptive actuator enters the current scanning section, determining multiple scanning points in the opening area at the top of the inclined tube within the current scanning section based on a preset scanning angle; using the laser rangefinder sensor to sequentially emit laser beams to the multiple scanning points and receiving multiple laser echo signals reflected from the multiple scanning points; and analyzing each laser echo signal... The round-trip time of the signal is used to determine the real-time distance between the laser rangefinder and the corresponding scanning point, resulting in multiple real-time distance values ​​corresponding to multiple scanning points. Distance difference analysis is performed on each of these real-time distance values ​​and the preset standard depth of the inclined tube to obtain multiple opening mud thickness values ​​corresponding to multiple scanning points. Based on the distribution characteristics of the multiple real-time distance values, the effective water passage ratio of the current scanning zone is determined. The spatial coordinates of multiple scanning points are associated with the corresponding opening mud thickness values ​​and the effective water passage ratio values ​​to generate the mud accumulation data for the current scanning zone. After the adaptive actuator completes the inspection and movement of all scanning zones, the mud accumulation data of all zones is integrated to obtain mud thickness distribution data.

[0062] Here, "tank dimensions" refers to the length, width, and depth of the sedimentation tank. "Layout parameters" refer to the installation position and arrangement of the inclined tubes within the sedimentation tank, including the starting position of the inclined tube area, its coverage area, and the spacing between the inclined tubes. "Scanning zones" refer to the sub-regions obtained after dividing the sedimentation tank; each scanning zone corresponds to an independent scanning operation unit. "Zoned scanning path" refers to the movement trajectory of the adaptive actuator during inspection within a single scanning zone. "Preset scanning angle" refers to the deflection angle of the laser beam emitted by the laser rangefinder relative to the vertical direction. "Scanning point" refers to the position where the laser beam emitted by the laser rangefinder illuminates the opening area at the top of the inclined tube. "Laser beam" refers to the laser beam emitted by the laser rangefinder for distance measurement. "Laser echo signal" refers to the light signal reflected back to the laser rangefinder after the laser beam illuminates the scanning point. "Round trip time" refers to the time it takes for the laser beam to travel from the laser rangefinder to the scanning point and back to the laser rangefinder.

[0063] The real-time distance value refers to the distance between the laser rangefinder and the scanning point, calculated based on the round-trip time of the laser echo signal. The preset standard depth of the inclined tube refers to the normal depth of the inclined tube when no sludge accumulation occurs. The distance difference is the difference between the real-time distance value and the preset standard depth of the inclined tube. The sludge thickness at the opening refers to the sludge thickness in the opening area at the top of the inclined tube, determined by the distance difference. Distribution characteristics refer to the spatial distribution patterns and statistical properties of multiple real-time distance values. The effective flow cross-sectional area ratio refers to the ratio of the actual flow cross-sectional area of ​​the inclined tube to the standard cross-sectional area, reflecting the degree of flow capacity attenuation caused by sludge accumulation. Spatial coordinates refer to the position coordinates of the scanning point in the three-dimensional coordinate system of the sedimentation tank. Zoned sludge accumulation data refers to the associated data set of spatial coordinates, opening sludge thickness values, and effective flow cross-sectional area ratios of all scanning points within a single scanning zone. Data integration processing refers to the process of merging the sludge accumulation data of all scanning zones into a complete dataset covering the entire sedimentation tank area.

[0064] In the above embodiment, the inclined tube sedimentation tank of a municipal wastewater treatment plant is used as an example for further explanation. The sedimentation tank has dimensions of 30 meters long, 10 meters wide, and 5 meters deep. The inclined tube layout parameters are as follows: the inclined tube area starts 5 meters from the inlet of the sedimentation tank, covering a length of 20 meters and a width of 10 meters. The single tube diameter is 50 millimeters, and the preset standard depth of the inclined tube is 1 meter. The integrated control system divides the inclined tube area of ​​the sedimentation tank into four scanning zones according to the tank dimensions and layout parameters. The coverage area of ​​the inclined tube area is 20 meters long and 10 meters wide, and each scanning zone is 5 meters long and 10 meters wide. The four scanning zones are arranged sequentially along the length of the sedimentation tank and are labeled as scanning zone S1, scanning zone S2, scanning zone S3, and scanning zone S4, respectively. The integrated control system configures a corresponding scanning path for each scanning zone. The scanning path adopts a bow-shaped reciprocating movement method to ensure that the laser rangefinder can cover all the inclined tubes within the scanning zone. The integrated control system controls the adaptive actuator to sequentially enter and move through four scanning zones according to the zone scanning path. Taking scanning zone S1 as an example, the scanning process is as follows: After entering scanning zone S1, the adaptive actuator determines multiple scanning points in the opening area at the top of the inclined tube within the current scanning zone based on a preset scanning angle of 0 degrees (i.e., the laser beam is emitted vertically downwards). Assume that a total of 250 scanning points are determined within scanning zone S1, and the scanning points are distributed in a matrix of 10 rows × 25 columns, with a spacing of 0.2 meters between adjacent scanning points.

[0065] In the above embodiment, the laser rangefinder sequentially emits laser beams to 250 scanning points and receives laser echo signals. Taking the scanning point P1 in the first row and first column as an example: the laser rangefinder emits a laser beam to the scanning point P1. The laser beam travels at the speed of light to the mud surface at the scanning point P1 and is reflected back to the laser rangefinder. The round-trip time of the laser echo signal is t1. The formula for calculating the real-time distance d1 between the laser rangefinder and the scanning point P1 is: d1 = c × t1 / 2, where c is the speed of light (approximately 3 × 10^8 m / s). Assuming the round-trip time t1 is 5 nanoseconds, the real-time distance d1 = 3 × 10^8 × 5 × 10^-9 / 2 = 0.75 meters. The integrated control system performs a distance difference analysis between the real-time distance value of 0.75 meters and the preset standard depth of the inclined tube (1 meter). The distance difference = preset standard depth of the inclined tube - real-time distance value = 1 meter - 0.75 meters = 0.25 meters. Since the laser rangefinder is positioned above the opening at the top of the inclined tube, the real-time distance value reflects the distance from the laser rangefinder to the mud surface. The distance difference is the mud thickness at the opening. Therefore, the mud thickness at the scanning point P1 is 0.25 meters.

[0066] In the above embodiment, the integrated control system sequentially calculates the opening mud thickness values ​​for 250 scanning points in the same manner. It is assumed that the real-time distance distribution characteristics of the 250 scanning points are as follows: 70% of the scanning points have real-time distance values ​​within the range of 0.85 meters to 1 meter (opening mud thickness value 0 to 0.15 meters), 20% have real-time distance values ​​within the range of 0.7 meters to 0.85 meters (opening mud thickness value 0.15 meters to 0.3 meters), and 10% have real-time distance values ​​within the range of 0.6 meters to 0.7 meters (opening mud thickness value 0.3 meters to 0.4 meters). The integrated control system calculates the effective water passage ratio of scanning zone S1 based on this distribution characteristic. Specifically, for a single inclined tube, the standard water passage cross-section of the inclined tube is circular or hexagonal. When mud accumulates at the bottom of the inclined tube, the effective water passage cross-sectional area decreases. The integrated control system calculates the effective cross-sectional area of ​​the inclined tube corresponding to each scanning point based on the thickness of the sludge buildup at the opening, the inclination angle of the inclined tube, and the diameter of the single tube. Then, it sums the effective cross-sectional areas of all scanning points and divides this sum by the sum of the standard cross-sectional areas of the corresponding inclined tubes at all scanning points to obtain the effective cross-sectional area ratio. The calculation formula is: Effective cross-sectional area ratio = Σ(Standard cross-sectional area of ​​a single tube - Area occupied by sludge buildup in a single tube) / ΣStandard cross-sectional area of ​​a single tube. Assuming the effective cross-sectional area ratio calculated based on the sludge buildup thickness distribution is 75%, it indicates that the flow capacity of the inclined tubes in scanning zone S1 has decreased by 25% due to the sludge buildup. The integrated control system associates and records the spatial coordinates of 250 scanning points with the corresponding thickness of the sludge buildup at the opening and the effective cross-sectional area ratio of 75%, generating the sludge buildup data for scanning zone S1. After the adaptive actuator completes the inspection and scanning of scanning partitions S2, S3 and S4 in sequence, the integrated control system integrates and processes the sludge accumulation data of the four scanning partitions to generate sludge thickness distribution data covering the entire sedimentation tank.

[0067] In an optional embodiment, the adaptive actuator is further provided with an ultrasonic array generator. Before the flushing assembly is started to perform the flushing operation on the inclined tube according to the cleaning operation parameters, the integrated control system controls the ultrasonic array generator to emit ultrasonic waves to the top opening area of ​​the inclined tube, so that the accumulated mud attached to the inner wall of the inclined tube is loosened under the cavitation effect of the ultrasonic waves. After the ultrasonic array generator continuously emits ultrasonic waves for a preset time, the flushing assembly is started to perform the flushing operation on the inclined tube that has been pretreated by ultrasonic waves.

[0068] The ultrasonic array generator refers to multiple sets of ultrasonic transmitting devices evenly distributed along the crossbeam direction of the adaptive actuator, with the transmitting end of the ultrasonic array generator facing the opening area at the top of the inclined tube. Cavitation effect refers to the phenomenon where tiny bubbles generated by ultrasonic waves at the liquid or solid-liquid interface rapidly expand and collapse under the action of ultrasonic pressure changes. The micro-jet and shock wave generated when the bubbles collapse can act on the surface of the accumulated mud adhering to the inner wall of the inclined tube, weakening the bonding force between the mud and the inner wall of the inclined tube, thereby loosening the mud. The preset duration refers to the length of time the ultrasonic array generator continuously emits ultrasonic waves before the start of the flushing operation. The preset duration is determined based on the degree of consolidation of the accumulated mud and the material of the inclined tube.

[0069] In the above embodiment, the inclined tube sedimentation tank of a municipal wastewater treatment plant is used as an example for further explanation. The ultrasonic array generator is located below the crossbeam of the adaptive actuator, adjacent to the flushing assembly. Ten sets of ultrasonic array generators are evenly distributed along the crossbeam, with a spacing of 1 meter between adjacent generators. The operating frequency of the ultrasonic array generator is set to the range of 25kHz to 40kHz, and the ultrasonic power density is set to the range of 0.5W / cm² to 2W / cm². When the adaptive actuator moves above the area to be cleaned, the integrated control system first controls the ultrasonic array generator to start and emit ultrasonic waves to the opening area at the top of the inclined tube. The ultrasonic waves pass through the opening at the top of the inclined tube and enter the interior of the tube, generating a cavitation effect on the surface of the sludge and at the interface between the sludge and the inner wall of the inclined tube.

[0070] In the above embodiment, the integrated control system determines the preset duration based on the thickness of the accumulated mud in the area to be cleaned. Specifically, when the thickness of the accumulated mud is less than 0.2 meters, the mud is considered lightly accumulated with a low degree of consolidation, and the preset duration is set to 30 seconds; when the thickness of the accumulated mud is between 0.2 and 0.3 meters, the mud is considered moderately accumulated with a moderate degree of consolidation, and the preset duration is set to 60 seconds; when the thickness of the accumulated mud is greater than 0.3 meters, the mud is considered heavily accumulated with a high degree of consolidation, and the preset duration is set to 90 seconds. After the ultrasonic array generator continuously emits ultrasonic waves for the preset duration, the integrated control system activates the rinsing assembly to perform a rinsing operation on the inclined tube after ultrasonic pretreatment. Since the accumulated mud has been loosened under the action of ultrasonic waves, the rinsing assembly only needs to use a lower cleaning water pressure to effectively peel off the accumulated mud, thereby reducing the mechanical impact of the rinsing operation on the inclined tube.

[0071] In the above embodiments, the integrated control system can also control the ultrasonic array generator and the flushing assembly to work together. That is, while the flushing assembly performs the flushing operation, the ultrasonic array generator continuously emits ultrasonic waves. The ultrasonic waves and the cleaning water flow act together on the surface of the accumulated mud. The cavitation effect of the ultrasonic waves continuously disrupts the structural integrity of the accumulated mud, and the cleaning water flow washes the loosened mud away from the inner wall of the inclined tube and carries it out of the inclined tube. This collaborative working mode is suitable for cleaning stubborn accumulated mud, and can improve the mud removal efficiency without significantly increasing the cleaning water pressure. The integrated control system determines whether to activate the collaborative working mode based on the mud consolidation characteristics in the mud thickness distribution data. When the mud deposition time exceeds a preset consolidation time threshold (e.g., 30 days), the collaborative working mode of the ultrasonic array generator and the flushing assembly is automatically activated. Through the above ultrasonic-assisted cleaning method, the ultrasonic waves emitted by the ultrasonic array generator can pre-treat the stubborn accumulated mud attached to the inner wall of the inclined tube, causing the mud to loosen under the action of cavitation effect, thereby reducing the cleaning water pressure required for subsequent flushing operations. This improves the mud removal efficiency while further reducing the risk of mechanical damage to the inclined tube.

[0072] Through the embodiments of this application, the pressure and flow adaptive adjustment module dynamically adjusts the cleaning operation parameters step by step according to real-time water quality data, ensuring that the flushing intensity is always maintained within a reasonable range that can effectively remove accumulated mud without damaging the inclined tube. This fundamentally solves the problem of the contradiction between cleaning effect and inclined tube protection, and achieves a dynamic balance between cleaning effect and inclined tube protection.

[0073] The integrated control system in the embodiments of this invention is described below from the perspective of hardware processing. (See attached document.) Figure 3 , Figure 3 This is a schematic diagram of the physical device structure of an integrated control system in the embodiments of this application.

[0074] It should be noted that, Figure 3 The structure of the integrated control system shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0075] like Figure 3 As shown, the integrated control system includes a Central Processing Unit (CPU) 301, which can perform various appropriate actions and processes according to a program stored in Read-Only Memory (ROM) 302 or a program loaded from storage portion 308 into Random Access Memory (RAM) 303, such as performing the methods described in the above embodiments. The RAM 303 also stores... The system contains various programs and data required for operation. CPU 301, ROM 302, and RAM 303 are interconnected via bus 304. Input / output (I / O) interface 305 is also connected to bus 304. The following components are connected to I / O interface 305: input section 306, including audio input devices, push-button switches, etc.; output section 307, including a liquid crystal display (LCD), audio output devices, indicator lights, etc.; storage section 308, including a hard disk, etc.; and communication section 309, including a network interface card such as a LAN (Local Area Network) card, modem, etc. Communication section 309 performs communication processing via a network such as the Internet. Drive 310 is also connected to I / O interface 305 as needed. Removable media 311, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 310 as needed so that computer programs read from them can be installed into storage section 308 as needed.

[0076] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing computer programs for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 309, and / or installed from removable medium 311. When the computer program is executed by central processing unit (CPU) 301, it performs the various functions defined in the present invention.

[0077] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0078] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. Each block in a flowchart or block diagram may represent a module, program segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those shown in the drawings.

[0079] Specifically, the integrated control system of this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, it implements the adaptive removal method for sludge accumulated in the inclined tube of the sedimentation tank provided in the above embodiment.

[0080] In another aspect, the present invention also provides a computer-readable storage medium, which may be included in the integrated control system described in the above embodiments; or it may exist independently and not assembled into the integrated control system. The storage medium carries one or more computer programs that, when executed by a processor of the integrated control system, cause the integrated control system to implement the adaptive sludge removal method for inclined tubes in sedimentation tanks provided in the above embodiments.

[0081] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit it. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0082] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. An adaptive method for removing sludge accumulated in inclined tubes of a sedimentation tank, characterized in that, The method is applied to an integrated control system, which is communicatively connected to a cleaning execution subsystem and a smart sensing and decision-making subsystem. The cleaning execution subsystem includes an adaptive actuator, which comprises a flushing component, a pressure and flow adaptive adjustment module, and an adaptive lifting mechanism. The smart sensing and decision-making subsystem includes an intelligent decision-making model, a laser ranging sensor mounted on the adaptive actuator, and water quality monitoring units distributed in the sedimentation tank. After the water level in the sedimentation tank drops below the preset scanning operation water level and the top opening area of ​​the inclined tube is exposed above the water surface, the adaptive actuator is controlled to perform inspection movement, and the laser range sensor is used to scan the inclined tube of the sedimentation tank in sections to obtain the sludge thickness distribution data. The intelligent decision-making model is used to perform correlation analysis on the sludge thickness distribution data and the first real-time water quality status data obtained from the water quality monitoring unit to obtain an adaptive cleaning strategy, which includes a cleaning movement path and cleaning operation parameters. A cleaning control command is issued to the adaptive actuator to drive the adaptive actuator to move according to the cleaning movement path, and the adaptive lifting mechanism is controlled to adjust the flushing component to the target working height according to the mud thickness distribution data, and the flushing component is started to perform flushing operation on the inclined pipe according to the cleaning operation parameters; During the cleaning movement of the adaptive actuator, the pressure and flow adaptive adjustment module is controlled to dynamically adjust the cleaning operation parameters according to the second real-time water quality status data obtained from the water quality monitoring unit, so that the flushing component performs the flushing operation on the inclined tube according to the adjusted cleaning operation parameters.

2. The method according to claim 1, characterized in that, The step of using the intelligent decision-making model to perform correlation analysis on the sludge thickness distribution data and the first real-time water quality status data to obtain an adaptive cleaning strategy specifically includes: The sediment thickness distribution data and the first real-time water quality status data are input into the intelligent decision-making model, so that the intelligent decision-making model performs the following operations: The intelligent decision-making model maps the sludge thickness distribution data to the three-dimensional coordinate system of the sedimentation tank to construct a sludge thickness distribution cloud map, which represents the correspondence between the inclined tube position coordinates and the sludge thickness value. The intelligent decision-making model extracts the target inclined tube position coordinates from the mud thickness distribution cloud map, and performs spatial clustering processing on the target inclined tube position coordinates to obtain multiple areas to be cleaned. Each area to be cleaned corresponds to a mud thickness value in a region. The intelligent decision-making model determines the cleaning urgency index of each area to be cleaned based on the first real-time water quality status data and the regional sludge thickness value, and sorts the multiple areas to be cleaned according to the cleaning urgency index to obtain a cleaning operation sequence. The intelligent decision-making model generates a cleaning movement path connecting each area to be cleaned based on the cleaning operation sequence. The intelligent decision-making model matches the cleaning operation parameters for each area to be cleaned from the preset cleaning parameter database based on the thickness of the mud accumulation in the area, the preset inclined tube structure configuration parameters, and the preset inclined tube material safety threshold.

3. The method according to claim 2, characterized in that, The intelligent decision-making model extracts the target inclined pipe location coordinates from the mud thickness distribution cloud map, and performs spatial clustering processing on the target inclined pipe location coordinates to obtain multiple areas to be cleaned, specifically including: The intelligent decision-making model obtains the projected coverage area of ​​the inclined tube in the horizontal direction, and uses the single effective cleaning width of the adaptive actuator as the grid width. Along the track travel direction of the adaptive actuator, the projected coverage area is divided into a virtual grid matrix composed of multiple rectangular grid units. The intelligent decision-making model maps the target inclined tube position coordinates to the virtual grid matrix and determines the grid point density value based on the number of coordinates in each rectangular grid cell. The intelligent decision-making model compares the grid point density value with a preset trigger threshold to mark rectangular grid cells with grid point density values ​​greater than the preset trigger threshold as abnormal grid cells. The intelligent decision-making model performs spatial clustering processing on all the abnormal grid cells to merge spatially adjacent abnormal grid cells with the same mud thickness level into the same connected domain, and determines each connected domain as a region to be cleaned, thus obtaining the multiple regions to be cleaned.

4. The method according to claim 1, characterized in that, Before using the intelligent decision-making model to perform correlation analysis on the sludge thickness distribution data and the first real-time water quality status data to obtain an adaptive cleaning strategy, the method further includes: The previous cleaning cycle end time of the sedimentation tank is obtained from the historical cleaning records, and the time interval between the current time and the previous cleaning cycle end time is analyzed to obtain the sludge deposition time. The intelligent decision-making model is used to conduct a multi-dimensional comprehensive analysis of the maximum sludge thickness value in the sludge thickness distribution data, the effluent turbidity value in the first real-time water quality status data, and the sludge deposition time to obtain the cleaning necessity index at the current moment. If the cleaning necessity index is greater than the preset cleaning start threshold, the current time is determined as the start time of the next round of cleaning of the sedimentation tank. If the cleaning necessity index is less than or equal to the preset cleaning start threshold, the adaptive actuator is controlled to maintain the inspection scanning state or enter the standby state.

5. The method according to claim 1, characterized in that, During the cleaning movement of the adaptive actuator, the pressure-flow adaptive adjustment module is controlled to dynamically adjust the cleaning operation parameters based on the second real-time water quality status data obtained from the water quality monitoring unit, so that the flushing component performs the flushing operation on the inclined tube according to the adjusted cleaning operation parameters. Specifically, this includes: The turbidity value of the effluent in the first real-time water quality status data is determined as the cleaning reference turbidity value; Extract the current effluent turbidity value from the second real-time water quality status data, and perform turbidity deviation analysis between the current effluent turbidity value and the cleaning reference turbidity value to obtain the turbidity change. The turbidity change is compared with a preset turbidity change range to determine the current flushing effect status of the inclined tube. If the turbidity change is less than the lower limit threshold of the preset turbidity change range, the current flushing effect status is insufficient. If the turbidity change is greater than the upper limit threshold of the preset turbidity change range, the current flushing effect status is excessive. If the turbidity change is within the preset turbidity change range, the current flushing effect status is moderate. Based on the current rinsing effect status, the pressure and flow adaptive adjustment module is controlled to dynamically adjust the cleaning operation parameters to obtain the adjusted cleaning operation parameters.

6. The method according to claim 5, characterized in that, The step of controlling the pressure and flow adaptive adjustment module to dynamically adjust the cleaning operation parameters based on the current flushing effect status, to obtain the adjusted cleaning operation parameters, specifically includes: When the current rinsing effect is insufficient, the pressure and flow adaptive adjustment module is controlled to incrementally adjust the rinsing water pressure in the rinsing operation parameters according to a preset pressure increment step, and / or incrementally adjust the rinsing flow rate in the rinsing operation parameters according to a preset flow increment step, until the turbidity change is within the preset turbidity change range. When the current rinsing effect is in the state of excessive rinsing, the pressure and flow adaptive adjustment module is controlled to adjust the cleaning water pressure in the cleaning operation parameters in a step-by-step manner according to a preset pressure reduction step, and / or to adjust the rinsing angle in the cleaning operation parameters in a step-by-step manner according to a preset angle adjustment step, until the turbidity change is within the preset turbidity change range. When the current rinsing effect is at the moderate rinsing state, the pressure and flow adaptive adjustment module is controlled to maintain the cleaning operation parameters at the current moment unchanged.

7. The method according to claim 1, characterized in that, The step of using the laser ranging sensor to scan the inclined tubes of the sedimentation tank in sections to obtain data on the distribution of sludge thickness in the inclined tubes specifically includes: Based on the dimensions of the sedimentation tank and the arrangement parameters of the inclined tubes, the sedimentation tank is divided into multiple scanning zones, and a corresponding scanning path is configured for each scanning zone. The adaptive actuator is controlled to sequentially enter each of the scanning partitions according to the partition scanning path for inspection and movement. After the adaptive actuator enters the current scanning partition, multiple scanning points in the opening area at the top of the inclined tube within the current scanning partition are determined according to a preset scanning angle. The laser ranging sensor sequentially emits laser beams to the plurality of scanning points and receives the plurality of laser echo signals reflected from the plurality of scanning points. Based on the round-trip time of each laser echo signal in the plurality of laser echo signals, the real-time distance value between the laser ranging sensor and the corresponding scanning point is determined, thereby obtaining a plurality of real-time distance values ​​that correspond one-to-one with the plurality of scanning points; The distance difference analysis is performed between each of the multiple real-time distance values ​​and the preset standard depth of the inclined tube to obtain multiple opening mud thickness values ​​that correspond one-to-one with the multiple scanning points, and the effective water passage section ratio of the current scanning zone is determined according to the distribution characteristics of the multiple real-time distance values. The spatial coordinates of the multiple scanning points are associated with the corresponding opening mud thickness value and the effective water passage ratio value to generate the mud accumulation data of the current scanning zone. After the adaptive actuator completes the inspection movement of all the scanning partitions, the mud accumulation data of all the partitions are integrated to obtain the mud accumulation thickness distribution data.

8. An integrated control system, characterized in that, The integrated control system includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code including computer instructions, and the one or more processors call the computer instructions to cause the integrated control system to perform the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that, When the instructions are executed on the integrated control system, the integrated control system performs the method as described in any one of claims 1-7.

10. A computer program product, characterized in that, When the computer program product is run on the integrated control system, the integrated control system performs the method as described in any one of claims 1-7.

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