A smart flushing self-cleaning filter system for industrial water treatment

Abstract

This invention relates to the field of industrial water treatment equipment technology, and discloses an intelligent flushing self-cleaning filter system for industrial water treatment. The system's suspended solids dynamic monitoring module collects real-time three-dimensional distribution data of deposits on the filter media surface, and identifies the mixed deposition characteristics of suspended solids and oil through multispectral imaging technology. A differential pressure trigger control module establishes a mapping relationship between deposit thickness and fluid resistance based on the three-dimensional distribution data, generating a cleaning trigger command when the real-time differential pressure data reaches a dynamic threshold range. A spiral trajectory planning module analyzes the cleaning trigger command and generates a composite motion trajectory including rotational angular velocity and axial displacement rate by combining the current position of the suction nozzle and the filter screen topology. A multimodal flushing execution module synchronously adjusts the torque output of the hydraulic motor and the linear displacement of the piston cylinder based on the composite motion trajectory, ensuring that the suction nozzle covers the entire effective filtration area of ​​the filter screen along the spiral path.

Description

Technical Field

[0001] This invention relates to the field of industrial water treatment equipment technology, specifically to an intelligent flushing self-cleaning filter system for industrial water treatment. Background Technology

[0002] In industrial production, water treatment systems are a crucial component ensuring production continuity and stability. As the core equipment of these systems, filters directly impact water quality compliance and the smoothness of the production process. Currently, commonly used filter types in industrial water treatment include bag filters, basket filters, and self-cleaning filters. Among these, self-cleaning filters are widely used in circulating water systems, cooling water treatment, and wastewater treatment due to their ability to operate continuously without frequent disassembly and replacement of filter media.

[0003] Existing self-cleaning filters still have many technical limitations in actual operation. Most self-cleaning filters rely on fixed time intervals or preset differential pressure thresholds to trigger cleaning, lacking precise sensing of the state of deposits on the filter media surface. For example, some devices only start cleaning by monitoring whether the inlet and outlet differential pressure reaches a fixed value. If the deposits contain a large amount of viscous substances such as oil, even if the differential pressure does not reach the threshold, it may still lead to filter clogging and reduced filtration efficiency. Conversely, if the deposits are loose inorganic suspended solids, a fixed differential pressure threshold may cause unnecessary frequent cleaning, resulting in waste of water and energy resources.

[0004] Existing equipment uses relatively simple cleaning trajectory planning and execution methods, making it difficult to achieve comprehensive coverage and efficient cleaning of the filter surface. Traditional self-cleaning filters often use a single rotational or axial movement for their nozzles, which can easily create cleaning blind spots for irregularly distributed deposits on the filter surface, especially in special areas such as filter edges and folds, where deposit residue is a significant problem. Furthermore, the hydraulic motor and piston cylinder are often controlled independently during the cleaning process, failing to synchronously optimize torque output and linear displacement based on the deposit distribution characteristics. This results in over-cleaning in some areas, leading to accelerated filter wear, while under-cleaning in others causes repeated clogging.

[0005] Existing self-cleaning filters lack the ability to differentiate the composition of sediments, failing to distinguish between the mixed sediment characteristics of suspended solids and oil. When both inorganic suspended solids and organic oil are present on the filter media surface, a single hydraulic flushing method is insufficient to effectively remove highly viscous oil. Long-term accumulation leads to a decrease in the hydrophilicity of the filter screen and a continuous increase in filtration resistance, affecting not only filtration efficiency but also shortening the filter screen's lifespan and increasing equipment maintenance costs and downtime. These technical deficiencies make existing self-cleaning filters unable to meet the demands of industrial water treatment for high efficiency, precision, and energy saving. There is an urgent need for an intelligent flushing self-cleaning filter system capable of dynamic sediment monitoring, intelligent triggering of cleaning, precise trajectory planning, and efficient execution. Summary of the Invention

[0006] The purpose of this invention is to provide an intelligent flushing self-cleaning filter system for industrial water treatment, in order to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides an intelligent flushing self-cleaning filter system for industrial water treatment, the system comprising:

[0008] The suspended solids dynamic monitoring module collects three-dimensional distribution data of deposits on the surface of the filter media in real time, and identifies the mixed deposition characteristics of solid suspended solids and oil through multispectral imaging technology;

[0009] The differential pressure trigger control module establishes a mapping relationship between sediment thickness and fluid resistance based on the three-dimensional distribution data, and generates a cleaning trigger command when the real-time differential pressure data reaches the dynamic threshold range.

[0010] The spiral trajectory planning module parses the cleaning trigger command and generates a composite motion trajectory that includes rotational angular velocity and axial displacement rate by combining the current position of the suction nozzle with the filter topology.

[0011] The multimodal flushing execution module synchronously adjusts the torque output of the hydraulic motor and the linear displacement of the piston cylinder based on the composite motion trajectory, so that the suction nozzle covers the entire effective filtration area of ​​the filter screen along the spiral path.

[0012] Preferably, the suspended matter dynamic monitoring module includes:

[0013] The high-frequency ultrasonic thickness measurement unit periodically scans the deposition height on the inner surface of the filter screen to form a thickness distribution heat map;

[0014] The oil pollution feature identification unit distinguishes the spectral reflectance differences between organic pollutants and inorganic suspended matter through near-infrared spectral analysis and outputs an oil pollution coverage density matrix.

[0015] The dynamic threshold range is dynamically adjusted based on the weighted calculation results of the thickness distribution heatmap and the oil stain coverage density matrix.

[0016] Preferably, the differential pressure trigger control module executes:

[0017] A sediment thickness-pressure difference conversion model was established to convert the thickness values ​​of each zone in the thickness distribution thermogram into predicted local pressure difference values;

[0018] When more than 60% of the predicted differential pressure values ​​in the zones reach the lower limit of the dynamic threshold range, the primary cleaning mode is activated.

[0019] When the predicted differential pressure values ​​of any three consecutive zones exceed the upper limit of the dynamic threshold range, the emergency cleaning mode is forcibly activated.

[0020] Preferably, the spiral trajectory planning module includes:

[0021] The filter topology reconstruction unit constructs a three-dimensional point cloud model of the filter surface using laser ranging data;

[0022] The kinematics calculation unit calculates the shortest motion path of the nozzle from its current pose to the target cleaning point based on the three-dimensional point cloud model.

[0023] The rotational angular velocity of the composite motion trajectory is inversely proportional to the radius of curvature of the filter screen surface, and the axial displacement rate is directly proportional to the sediment thickness gradient.

[0024] Preferably, the multimodal flushing execution module includes:

[0025] The dual-channel pressure regulating unit independently controls the inlet pressure of the hydraulic motor and the back pressure of the piston cylinder;

[0026] The motion coupling verification unit compares the deviation between the actual motion trajectory of the nozzle and the composite motion trajectory in real time.

[0027] When the deviation exceeds the tolerance range, the composite motion trajectory is recalculated and the torque distribution ratio of the hydraulic motor is updated.

[0028] Preferably, the system further includes:

[0029] The cleaning efficiency assessment module collects filter transmittance data after each rinsing cycle.

[0030] The suspended matter dynamic monitoring module corrects the calibration parameters of the thickness distribution heatmap based on the transmittance data;

[0031] The differential pressure trigger control module updates the weight coefficients of the sediment thickness-differential pressure conversion model based on the corrected calibration parameters.

[0032] Preferably, the multimodal flushing execution module is further provided with:

[0033] The vortex suppression unit adjusts the injection angle of the auxiliary nozzle during the axial movement of the suction nozzle;

[0034] The spray angle of the auxiliary nozzle is dynamically adjusted according to the flow velocity distribution data on the filter screen surface to ensure that the angle between the rinsing water flow and the filter screen normal is kept within the set range.

[0035] Preferably, the spiral trajectory planning module further includes:

[0036] The wear compensation unit records the mechanical wear data of the historical cleaning path;

[0037] When generating new composite motion trajectories, filter areas with wear values ​​below the threshold are preferentially selected as cleaning starting points.

[0038] The kinematics calculation unit dynamically optimizes the axial motion acceleration curve of the nozzle based on wear data.

[0039] Preferably, the system further includes:

[0040] When the oil stain dissolution auxiliary module identifies an area covered by high concentrations of oil stains, it controls the opening duration of the slow-release chemical agent tank.

[0041] The release rate of the chemical reagent tank is exponentially related to the characteristic value of the corresponding area in the oil spill coverage density matrix;

[0042] The spiral trajectory planning module pauses the rotation of the nozzle during chemical release, maintaining only axial vibration.

[0043] Preferably, the differential pressure trigger control module integrates:

[0044] The pressure fluctuation suppression unit monitors the pressure change rate of the main pipeline during the cleaning process;

[0045] When the rate of pressure change exceeds the stable threshold, a buffer cycle is automatically inserted to temporarily slow down the displacement of the piston cylinder.

[0046] The duration of the buffer period is proportional to the integral value of the pressure change rate.

[0047] Compared with the prior art, the beneficial effects of the present invention are:

[0048] In the sediment monitoring stage, the suspended matter dynamic monitoring module employs multispectral imaging technology to acquire real-time three-dimensional distribution data of sediments on the filter media surface and accurately identify the mixed sedimentation characteristics of suspended solids and oil. Compared to existing equipment that relies solely on pressure difference or time triggering, this module provides a more comprehensive understanding of the distribution and compositional differences of sediments on the filter surface, avoiding incomplete or over-cleaning issues caused by misjudgment of sediment composition. The acquisition of three-dimensional distribution data also visually reflects the thickness differences of sediments in different areas, providing detailed information for precise planning of subsequent cleaning actions. This ensures that cleaning resources are concentrated on areas with severe sediment accumulation, improving the targeting of cleaning efforts.

[0049] The differential pressure trigger control module establishes a mapping relationship between sediment thickness and fluid resistance based on three-dimensional distribution data, overcoming the limitations of existing fixed threshold triggering. It can dynamically adjust the threshold range according to the actual sediment accumulation, achieving intelligent triggering of cleaning actions. When the sediment thickness is small, the dynamic threshold range is correspondingly increased to avoid unnecessary cleaning actions and reduce water and energy consumption. When the sediment thickness increases, causing fluid resistance to rise to the threshold range, a cleaning trigger command is generated promptly to prevent filter clogging leading to decreased filtration efficiency and abnormal system pressure, ensuring the continuous and stable operation of the filtration system. Simultaneously, it extends the filter's lifespan in non-essential cleaning conditions, reducing filter wear rate.

[0050] The spiral trajectory planning module analyzes the cleaning trigger command and generates a composite motion trajectory that includes rotational angular velocity and axial displacement rate, based on the nozzle's current position and the filter topology. Compared to traditional single motion methods, this allows the nozzle to fully cover the filter surface along the spiral path, effectively eliminating cleaning blind spots. The coordinated design of rotational angular velocity and axial displacement rate in the composite motion trajectory optimizes motion parameters according to the filter topology and sediment distribution characteristics. For example, in areas with thick sediment accumulation, the axial displacement rate is reduced and the rotational angular velocity is increased to ensure the nozzle has sufficient time to complete cleaning of that area; in areas with thinner sediment accumulation, the axial displacement rate is appropriately increased to improve overall cleaning efficiency, achieving a balance between cleaning effect and efficiency.

[0051] The multimodal flushing execution module synchronously adjusts the torque output of the hydraulic motor and the linear displacement of the piston cylinder based on a composite motion trajectory. This ensures the suction nozzle operates strictly according to the planned trajectory, avoiding trajectory deviation caused by asynchronous torque and displacement adjustments. Dynamic adjustment of the hydraulic motor's torque output optimizes power output based on resistance changes in different areas of the filter screen, preventing excessive torque from damaging the filter or insufficient torque from causing nozzle jamming. Precise control of the piston cylinder's linear displacement ensures the smoothness of the nozzle's axial movement, further improving trajectory execution accuracy. Through the collaborative work of multiple modules, this system not only achieves precise monitoring of sediments, intelligent triggering of cleaning actions, and comprehensive coverage of the cleaning trajectory, but also improves cleaning effectiveness while reducing energy and water consumption, decreasing equipment maintenance costs, and extending filter lifespan, providing strong support for the efficient, stable, and energy-saving operation of industrial water treatment systems. Attached Figure Description

[0052] Figure 1 This is a timing diagram of the intelligent flushing self-cleaning filter system for industrial water treatment described in this invention.

[0053] Figure 2 This is a schematic diagram illustrating the working principle of the suspended matter dynamic monitoring module.

[0054] Figure 3 This is a schematic diagram of the working principle of the differential pressure trigger control module. Detailed Implementation

[0055] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0056] Please see Figure 1 This invention provides an intelligent flushing self-cleaning filter system for industrial water treatment. The system integrates multiple modules that work together to achieve efficient cleaning of the filter media. The core of the system includes a suspended solids dynamic monitoring module, a differential pressure trigger control module, a spiral trajectory planning module, and a multi-modal flushing execution module.

[0057] The suspended solids dynamic monitoring module is responsible for real-time acquisition of three-dimensional distribution data of deposits on the filter media surface. This module uses multispectral imaging technology to capture the mixed deposition characteristics of suspended solids and oil, generating data including deposit thickness, spatial distribution, and contaminant type information. The differential pressure trigger control module receives the three-dimensional distribution data and establishes a mapping relationship between deposit thickness and fluid resistance. This relationship is based on a fluid dynamics model to calculate local differential pressure changes. When the real-time monitored differential pressure data enters the dynamic threshold range, the module generates a cleaning trigger command. The spiral trajectory planning module parses the cleaning trigger command and, combined with the current position of the suction nozzle and the filter screen topology, generates a composite motion trajectory including rotational angular velocity and axial displacement rate. The trajectory planning ensures that the suction nozzle's movement path covers the entire effective filtration area of ​​the filter screen. The multimodal flushing execution module synchronously adjusts the torque output of the hydraulic motor and the linear displacement of the piston cylinder based on the composite motion trajectory. The hydraulic motor drives the suction nozzle to rotate, and the piston cylinder controls axial movement, causing the suction nozzle to perform the cleaning action along a spiral path. During system operation, the modules exchange information through the data bus. The suspended matter dynamic monitoring module updates the three-dimensional distribution data periodically, the differential pressure trigger control module dynamically adjusts the threshold range to adapt to changes in operating conditions, the spiral trajectory planning module optimizes motion efficiency, and the multimodal flushing execution module provides feedback on the actual motion status to achieve closed-loop control.

[0058] Example 1: See Figure 2 The suspended solids dynamic monitoring module acquires and analyzes the three-dimensional distribution information of sediments on the filter media surface in real time. The high-frequency ultrasonic thickness measurement unit, as the basic sensing layer of this network, consists of multiple piezoelectric ceramic transducers embedded in a regular grid array within the non-working area of ​​the filter screen. Each transducer, triggered periodically by a microcontroller, periodically emits high-frequency ultrasonic pulses in the 1MHz to 5MHz range. The sound waves penetrate the water flow medium, impact the sediment surface, and generate echo signals. The flight time and amplitude characteristics of the echoes are captured by a high-speed data acquisition card. The thickness calculation engine, based on a sound wave propagation velocity model in sediments, converts the time difference into a precise thickness value. This model is pre-calibrated using calibration samples to compensate for sound velocity drift caused by changes in water temperature and density. The scanning cycle can be dynamically configured according to the system load, automatically shortening to once per second when the influent suspended solids concentration is high, and extending to once every ten seconds when the concentration is low to reduce the computational load. Measurement data from all transducers are synchronously transmitted to the signal conditioning circuit via fieldbus. After amplification, filtering, and analog-to-digital conversion, the digital signal processor executes a spatial interpolation algorithm to reconstruct a continuous two-dimensional thickness distribution heat map from the discrete thickness matrix. This heat map is displayed on the monitoring interface in pseudo-color encoded form, with red areas indicating severe sediment accumulation and blue areas indicating cleanliness.

[0059] The oil contamination feature identification unit, operating in parallel with the ultrasonic thickness measurement unit, employs optical sensing technology to distinguish contaminant types. At the core of this unit is a near-infrared spectral analysis system, containing a broadband near-infrared light-emitting diode array as the light source, with an emission spectrum covering 700 nm to 2500 nm. Light is perpendicularly incident on the filter surface through a sapphire window, and an InGaAs spectral sensor receives the reflected beam at a 45-degree angle. The reflected light signal is dispersed by a spectrometer and then received by a photodiode array, generating a complete reflectance spectrum curve for each measurement point. The data processing firmware incorporates a standard spectral library containing characteristic absorption spectra of various common contaminants such as mineral particles, algae, lubricating oil, and hydraulic oil. Using a partial least squares regression algorithm, the real-time acquired spectra are matched with the standard library to calculate the relative concentration ratio of organic oil contaminants to inorganic suspended matter. The analysis process pays particular attention to the characteristic absorption peaks of carbon-hydrogen bonds near 1200 nm, 1400 nm, and 1700 nm, quantifying the oil contamination density by analyzing the depth and width of these absorption peaks. Ultimately, the oil concentration at each measurement point is quantized into a characteristic value between 0 and 1, and an oil coverage density matrix with the same resolution as the thickness heatmap is generated through spatial interpolation.

[0060] The generation of the dynamic threshold interval is a multivariate fusion decision-making process. Its computational engine continuously receives thickness distribution heatmaps and oil contamination density matrices from the two aforementioned units. The algorithm first partitions the heatmap into grids, dividing the entire filter surface into hundreds of virtual computational units. For each unit, the algorithm extracts its average thickness and average oil contamination density. The formula for calculating the lower limit of the dynamic threshold interval introduces a weighting factor, where the weighting coefficient for oil contamination density is typically set to 1.5 to 2 times the thickness weight. This is because oily contaminants usually have higher adhesion and lower permeability, contributing far more to pressure differential growth than inorganic particulate matter of the same thickness. When the oil contamination density matrix value of a certain area exceeds a preset alarm threshold, the algorithm dynamically increases the weighting percentage of that area in the overall weighted calculation, making the system more sensitive to pressure differential changes in that area. The upper limit of the threshold interval is positively correlated with the average deposition rate of the sediment, which is obtained by comparing the thickness change trend over multiple consecutive scanning cycles. The weighted calculation results of all partitions are fused using a Kalman filter to output a dynamic threshold range that changes smoothly over time. This range is updated every second and broadcast to the differential pressure trigger control module via the control network.

[0061] The data synchronization and spatial registration between the high-frequency ultrasonic thickness measurement unit and the oil stain feature recognition unit are crucial. This system employs a hardware synchronization signal to ensure that the measurement points of the two units are perfectly aligned in time and space. At the start of each measurement cycle, the main controller simultaneously sends trigger pulses to both the ultrasonic transmission circuit and the spectral acquisition circuit, and all sensor data are timestamped with the same timestamp. Spatial registration is achieved by setting optical reference points on the filter frame. During system initialization, a calibration program runs to establish the coordinate transformation relationship between the ultrasonic sensor array and the optical measurement points, ensuring that thickness data and spectral data acquired from the same physical location accurately correspond. The data fusion processor uses a data confidence evaluation algorithm based on DS evidence theory. When there is a discrepancy between the ultrasonic thickness data and the optical oil stain recognition data at a certain measurement point, the algorithm reduces the confidence weight of that point's data and triggers a local remeasurement to eliminate measurement errors caused by temporary obstruction from air bubbles or foreign objects.

[0062] This module also features online self-diagnosis and calibration capabilities. The embedded processor periodically executes self-test routines. For example, the ultrasonic thickness measurement unit verifies the accuracy of the sound velocity model by measuring a standard test block of known thickness. If the deviation exceeds the tolerance, the calibration parameters are automatically updated. The oil stain feature recognition unit has a built-in standard reflector that periodically measures its reflection spectrum to compensate for light intensity attenuation caused by light source aging or optical window contamination. All diagnostic logs and calibration history are stored in non-volatile memory, providing a data foundation for predictive maintenance of the system. Communication between the module and the upper-level control system uses the industrial Ethernet protocol. In addition to transmitting real-time thickness distribution heatmaps and oil stain coverage density matrices, it also periodically reports key information such as module health status and sensor lifespan warnings, forming a complete and reliable dynamic monitoring solution for suspended matter.

[0063] Example 2: See Figure 3 The differential pressure trigger control module continuously receives thickness distribution thermal maps and oil contamination density matrices from the suspended matter dynamic monitoring module. Its internal sediment thickness-differential pressure conversion model is an empirical model trained on extensive historical data. This model is not a simple linear mapping but rather a multivariate nonlinear function considering sediment porosity, particle size distribution, fluid viscosity, and the inherent resistance of the filter screen. The model divides the entire filter screen surface into hundreds of virtual computational units. For each unit, the model reads its latest average sediment thickness and oil contamination density values. Combining this with real-time acquired fluid temperature and flow data, and using a simplified computational fluid dynamics algorithm running on an embedded processor, it calculates in real-time the additional differential pressure prediction value generated by sediment accumulation in that local area. All these local differential pressure prediction values ​​together constitute a total differential pressure prediction distribution field for the entire filter screen. This distribution field is then compared with the global differential pressure values ​​read by the actual differential pressure transmitters installed at the filter screen inlet and outlet for background verification.

[0064] The cleaning trigger logic employs a tiered early warning mechanism. When the system calculates and finds that the predicted local differential pressure in more than 60% of the filter zones has reached the lower limit of the dynamic threshold range, the module generates a trigger command for the primary cleaning mode. This mode is designed for preventative, low-intensity cleaning, intervening before deposits harden and when fluid resistance is slowly increasing. In this mode, the nozzle's rotational angular velocity and axial displacement rate are set to lower levels to reduce energy consumption and impact on the system's flow rate. In contrast, the emergency cleaning mode has more stringent trigger conditions. When the system detects that the predicted differential pressure in any three consecutive zones (this continuity is determined based on the filter topology, typically referring to physically adjacent critical areas downstream of the water flow) exceeds the upper limit of the dynamic threshold range, the module immediately and forcibly activates the emergency cleaning mode. This situation usually indicates severe local clogging of the filter or a sudden change in the nature of contaminants, posing a risk of a sharp increase in system differential pressure. In this mode, the cleaning trajectory prioritizes covering this high-risk area, and the nozzle's motion parameters and suction strength are set to maximum to quickly eliminate the clogging threat. The upper and lower limits of the dynamic threshold range are not fixed values. The lower limit is determined by the baseline pressure difference statistically obtained from long-term system operation and the current influent water quality, while the upper limit is related to the safe working pressure difference of the filter material and the maximum pressure drop allowed by the process. The range width is dynamically adjusted according to the rate of sediment accumulation. When the accumulation rate is fast, the range is narrowed to increase sensitivity, and when the rate is slow, it is appropriately widened to reduce false triggering.

[0065] The integrated pressure fluctuation suppression unit focuses on maintaining the stability of the main pipeline pressure during the cleaning process. This unit monitors the pressure change rate of the main pipeline in real time through a high-frequency pressure sensor, with a sampling frequency far exceeding that required by conventional process control. The core algorithm of this unit continuously calculates the first derivative of the instantaneous pressure with respect to time, i.e., the pressure change rate, and compares it with a stability threshold statistically derived from historical stable operating data. When the cleaning action (especially the sudden start or reversal of the piston cylinder) causes the pressure change rate to exceed this stability threshold, the pressure fluctuation suppression unit does not immediately stop the cleaning, but instead sends a command to the actuator to insert a buffer period. During this buffer period, the linear displacement of the piston cylinder is paused, its control valve maintains its current opening, and the hydraulic motor reduces its speed or maintains a low speed rotation according to the command. This "dynamic rotation, stopped displacement" mode provides a time window for pressure recovery and stabilization of the pipeline system without excessively affecting the cleaning progress. The duration of the buffer cycle is not a fixed value, but is dynamically determined by an integrator that calculates the area (i.e., the integral value) where the rate of pressure change exceeds a threshold. Slight, short-lived pressure fluctuations may only require a buffer of a few hundred milliseconds, while severe and continuous fluctuations may trigger a buffer cycle of several seconds to ensure that the system pressure returns to a stable level before continuing the axial feed action.

[0066] The sediment thickness-differential pressure conversion model features online self-learning capabilities, with its model weight coefficients fine-tuned based on the actual effects after each cleaning cycle. Specifically, after each cleaning cycle triggered and completed by the model, the module compares the predicted differential pressure drop before cleaning with the actual differential pressure drop recorded by the differential pressure transmitter. If a significant deviation exists, a feedback adjustment algorithm is activated. This algorithm adjusts the weight coefficients related to parameters such as thickness and oil density in the model in reverse, based on the direction and magnitude of the deviation, so that the model's predicted values ​​better approximate the response of the real system. This continuous self-optimization allows the model to adapt to the performance degradation of the filter due to long-term use or the slow changes in the characteristics of the influent water. The logic of the pressure fluctuation suppression unit is also linked to the cleaning mode. In the primary cleaning mode, the stability threshold is set relatively loosely, with a higher system tolerance, prioritizing the continuity of cleaning. In the emergency cleaning mode, the stability threshold is tightened because the system is already at a high differential pressure level, and any additional pressure fluctuations may trigger the safety threshold, thus requiring more agile suppression measures to ensure pipeline safety. The entire module's operating status, triggered cleaning modes, pressure fluctuation events, and model correction records are all recorded in detail to form an operating log, which is used for subsequent performance analysis and maintenance decisions.

[0067] Example 3: The filter topology reconstruction unit serves as the starting point of the entire process. Its operation relies on a sophisticated laser scanning system. A set of imaging laser rangefinders is mounted on a slide that can move along the filter axis. Before system initialization or the start of each major cleaning cycle, the slide moves the sensors at a constant speed, traversing the effective length of the filter. Simultaneously, the laser emits a ranging beam at an extremely high frequency, and the receiver records the three-dimensional coordinate data of millions of points. This massive amount of raw point cloud data is first preprocessed using algorithms to remove outliers caused by water flow fluctuations, suspended debris obstruction, or electronic noise. Subsequently, a surface reconstruction algorithm based on Delaunay triangulation is used to generate a three-dimensional mesh model describing the true surface morphology of the filter. This model not only accurately records the spatial position of each vertex but also derives key topological properties such as the local radius of curvature, describing the degree of surface curvature, by calculating the normal vector of the vertex and the rate of change of the angle between adjacent triangular faces.

[0068] The 3D mesh model is constructed based on the discrete vertex coordinates of the filter surface obtained by laser ranging, and the local curvature radius of the filter is also considered. The formula is calculated based on the spatial relationship between three adjacent vertices, as follows:

[0069]

[0070] in, The radius of curvature of a filter screen, measured in meters (m), reflects the degree of curvature at a point on the filter screen surface. , In a 3D mesh model, the vector formed by the target point and its two adjacent vertices. , ; The three-dimensional coordinates of the target point (unit: m) are directly acquired by the laser ranging unit; , : The three-dimensional coordinates (unit: m) of the two adjacent vertices of the target point, which are the basic data of the three-dimensional mesh model; The modulus of the cross product result (unit: m²); The result is the dot product (unit: m²). , :vector , The magnitude (unit: m) reflects the length of the vector.

[0071] Rotational angular velocity With respect to the local radius of curvature of the filter Inversely proportional, the linear velocity of the suction nozzle is kept constant by adjusting the proportional coefficient, as shown in the following formula:

[0072]

[0073] in, : Angular velocity of the suction nozzle, in radians per second (rad / s); Angular velocity adjustment coefficient, in radians·meters / second (rad·m / s), is preset according to the filter material (such as stainless steel, polymer) and cleaning pressure, with a value range of 0.05-0.2 rad·m / s (to ensure that the nozzle linear velocity is maintained in a constant range of 0.05-0.2 m / s).

[0074] Axial displacement rate With sediment thickness gradient Proportional to the desired cleanup requirements of different types of sediments, using a proportionality coefficient, the formula is as follows:

[0075]

[0076] in, : Axial displacement rate of the suction nozzle, in meters per second (m / s); Displacement rate adjustment coefficient, in seconds. -1 (s) -1 The time should be set according to the sediment type: 0.8-1.2s for inorganic suspended matter (loose and easy to wash). -1 For oily (highly viscous) stains, use 0.3-0.6 seconds. -1 ; Sediment thickness gradient, calculated by the suspended matter dynamic monitoring module (i.e., the ratio of the difference in sediment thickness between two adjacent monitoring points to the distance between the two points, reflecting the degree of drastic change in thickness).

[0077] Rotational angular velocity The determination of the local radius of curvature of the filter corresponding to the current position of the suction nozzle. Closely related to the radius of curvature of the filter screen, such as bends, connecting pipes, or local protrusions. Smaller area This will significantly improve, thus ensuring that the linear velocity of the suction nozzle remains within a constant range, guaranteeing uniform cleaning intensity; while in the straight cylindrical section of the filter, i.e., the radius of curvature... Larger areas It will be appropriately reduced to save energy.

[0078] Axial displacement rate The regulation primarily responds to the accumulation of pollutants, especially in situations with drastic changes in thickness and gradients. Larger areas This will accelerate the response to pollution risks; while in areas where sediments are evenly distributed ( (smaller) It can then maintain a constant speed or reduce speed.

[0079] The kinematics calculation unit performs real-time path planning and motion parameter calculation on this 3D mesh model. Its calculation process needs to simultaneously satisfy multiple objectives, including coverage integrity, motion smoothness, and optimal efficiency. The core output of the calculation is the composite motion trajectory of the suction nozzle, which is determined by the rotational angular velocity. and axial displacement rate It consists of two time-varying parameter sequences. Rotational angular velocity. The determination of the local radius of curvature of the filter corresponding to the current position of the suction nozzle. Closely related to the radius of curvature of the filter screen, such as bends, connecting pipes, or local protrusions. For smaller areas, a significant increase in angular velocity is required. This ensures that the linear velocity of the suction nozzle remains within a constant range, thereby ensuring the uniformity of cleaning intensity; and in the straight cylindrical section of the filter screen, i.e., the radius of curvature... For larger areas, the angular velocity can be appropriately reduced to save energy. Axial displacement rate The regulation primarily responds to the accumulation of pollutants, based on sediment thickness gradient information from the suspended matter dynamic monitoring module. In areas with drastic thickness changes and gradients In larger areas, faster axial feed is needed to quickly address contamination risks; while in areas with uniform sediment distribution, a constant or reduced speed can be maintained. The kinematics calculation unit uses an optimization algorithm to dynamically balance these two parameters, generating an efficient trajectory that can fully cover the filter surface and adaptively adjust according to the severity and urgency of contamination.

[0080] The wear compensation unit is a key design feature for extending the mechanical life of the system. It uses a continuously running database to record and analyze the cumulative wear effects on mechanical components caused by historical cleaning paths. During each cleaning cycle, various operating parameters of the suction nozzle drive mechanism, such as the hydraulic motor's working pressure, piston cylinder thrust, reversing frequency, and running time, are monitored and recorded in real time. Combined with known fatigue characteristics of component materials, an algorithm model based on cumulative damage theory calculates the equivalent wear of different areas on the filter surface (corresponding to different motion postures and loads) and the transmission mechanism itself (such as bearings and lead screws). This creates a "wear distribution map" that updates over time. When generating new composite motion trajectories, the path planning algorithm prioritizes paths with historically accumulated wear. Below the preset safety threshold The filter area serves as the starting point or main path for cleaning, thus preventing mechanical parts from failing prematurely due to prolonged operation under specific high-load conditions. Simultaneously, it addresses wear... In areas where the load is already high, the algorithm will consciously schedule the path through non-critical cleaning phases with a lower load.

[0081] When planning the path, the kinematics calculation unit deeply integrates the information provided by the wear compensation unit. It not only seeks the path with the shortest time, but also dynamically optimizes the axial motion acceleration curve of the nozzle. For high-risk wear areas (typically areas with high curvature, requiring frequent starts, stops, or turns), the algorithm uses a smoother S-shaped acceleration curve instead of a trapezoidal or triangular acceleration curve to significantly reduce the impact load on the transmission mechanism during start-up and braking. This optimization is performed in real-time during the trajectory generation stage. The calculation unit selects the most suitable one from multiple preset acceleration curve templates based on the expected wear contribution of the next path segment, or calculates an optimal curve in real time. Its mathematical expression can focus on the jerk (the derivative of acceleration). To impose constraints, for example, make Not greater than a certain limit This ensures the smoothness of the motion, and the formula can be expressed as:

[0082]

[0083] in: Represents jerk, reflecting the degree of drastic change in acceleration. It is a function of acceleration as a function of time. It is the absolute value of the maximum allowable jerk of the system, which is a design constant related to the stiffness of the mechanical system and the inertia of the components.

[0084] The filter topology reconstruction unit and the kinematics solution unit maintain a close closed-loop data interaction. The 3D model provided by the reconstruction unit is the basis for the solution, while the expected path planned by the solution unit is fed back to the reconstruction unit for verification of key areas in the next scan. The data update of the wear compensation unit is not a simple linear accumulation; it introduces a time decay factor. (0< <1) This design gradually reduces the weight of wear data from the past on the current assessment, allowing the system to better adapt to changes in the filter's condition after physical cleaning or component replacement, ensuring that wear assessments always reflect recent operational status. The entire module uses a real-time operating system for multi-task scheduling and interrupt management, ensuring stable and controllable latency from receiving the cleaning command to generating the final motion trajectory, meeting the stringent requirements of industrial real-time control. All generated motion trajectories, corresponding wear data update records, and model calibration logs are stored in non-volatile memory, providing a complete data chain for long-term performance analysis, fault diagnosis, and predictive maintenance.

[0085] Example 4: The multimodal flushing execution module achieves its design functions through the ingenious coordination and real-time feedback control of its internal units. Its core lies in converting planned motion trajectory commands into precise, stable, and adaptive mechanical actions of the on-site actuators. A dual-channel pressure regulation unit forms the foundation of the module's power control. This unit contains two independent electro-hydraulic proportional pressure control systems, which precisely regulate the inlet pressure driving the hydraulic motor's rotation and the back pressure controlling the linear displacement of the piston cylinder, respectively. The inlet pressure setpoint is directly derived from the real-time torque requirement calculated by the helical trajectory planning module. This requirement is related to the frictional resistance the suction nozzle needs to overcome and the maintenance of the target rotational angular velocity. The back pressure setting is related to the required axial thrust and displacement rate, and its value must ensure smooth feeding of the piston cylinder rather than impact-like movement. Both systems use high-response-frequency proportional pressure valves as core components. The valve opening is controlled by a pulse-width modulation signal output from the embedded controller, forming an independent pressure closed loop. This ensures that the deviation between the actual pressure and the set pressure is controlled within a minimal range, thereby providing a stable and independently adjustable power source for the hydraulic motor and piston cylinder.

[0086] The motion coupling verification unit acts as a supervisor, continuously comparing the actual trajectory of the nozzle in three-dimensional space with the expected trajectory issued by the helical trajectory planning module. The acquisition of the actual trajectory relies on high-precision feedback elements: an absolute rotary encoder mounted on the output shaft of the hydraulic motor measures the actual rotation angle of the nozzle in real time; a linear encoder is mounted parallel to the piston rod of the piston cylinder to accurately detect the actual axial position of the nozzle. The controller's motion control card synchronously reads these feedback values ​​at millisecond intervals and compares them with the theoretical expected values ​​at the same time. Trajectory deviation is typically evaluated using the magnitude of the comprehensive error vector. When the system detects that the deviation exceeds the preset tolerance range (the tolerance range can be dynamically adjusted according to the cleaning mode; for example, it can be appropriately widened in emergency cleaning mode to prioritize speed), the verification unit does not simply command the actuator to rigidly correct back to the original planned trajectory. Instead, it sends a trajectory replanning request to the helical trajectory planning module, attaching the current deviation data and actual pose. Upon receiving the request, the trajectory planning module uses the nozzle's current actual position and pose as a new starting point and quickly recalculates an optimized trajectory leading to the original target point. This newly generated trajectory takes into account the current mechanical state and load conditions, and updates the torque distribution ratio command output to the hydraulic motor accordingly. For example, it temporarily increases torque output during phases requiring acceleration to catch up with the trajectory. The eddy current suppression unit focuses on improving the hydrodynamic efficiency of the rinsing process and reducing side effects. This unit achieves its function by adjusting the spray angle of one or more auxiliary nozzles mounted on the side of the suction nozzle. The main function of the auxiliary nozzles is not to directly suck up contaminants, but to suppress eddies that may be generated behind the suction nozzle during its movement by spraying a controlled guiding water stream. These eddies can disturb suspended contaminants and even cause them to re-adhere to the cleaned area.

[0087] The eddy current suppression unit receives flow velocity distribution data from multiple ultrasonic flow meters or fluid velocity sensors installed in different areas of the filter screen. Based on this data, an embedded algorithm dynamically calculates and adjusts the spray angle of the auxiliary nozzle. The goal of the adjustment is to ensure that the flushing water flow ejected from the auxiliary nozzle forms a small and stable angle with the normal direction of the local surface of the filter screen. This angle is typically set between 5 and 15 degrees. Maintaining this angle helps to generate a sweeping flow that adheres to the filter screen surface and points towards the suction nozzle. This sweeping flow can loosen deposits in advance, smoothly guide the precipitated contaminants to the main drain port of the suction nozzle, and effectively fill the space left after the suction nozzle moves, suppressing the formation of large-scale eddies (see Table 1).

[0088] Table 1: Key data records of the multimodal flushing execution module during a trajectory deviation event.

[0089]

[0090] Consider a specific operational example: The system is performing a routine cleaning cycle. Initially, everything is normal, and the trajectory deviation monitored by the motion coupling verification unit remains below the tolerance limit of 0.3. At a certain moment, the suction nozzle encounters a highly viscous clump of oil during axial movement, causing a sudden increase in the axial thrust resistance of the piston cylinder. Although the dual-channel pressure regulation unit attempts to maintain the thrust speed by increasing the back pressure, the axial displacement still lags behind for a short period, and the actual position begins to lag significantly behind the commanded position (see the data at time T0+20 ms in the table above). Due to the axial jamming indirectly affecting the rotational resistance, the actual speed of the hydraulic motor also decreases. The comprehensive trajectory deviation calculated in real time by the motion coupling verification unit reaches 0.45 at time T0+20 ms, exceeding the threshold of 0.4.

[0091] The system responded immediately, with the motion coupling verification unit sending a trajectory replanning request to the helical trajectory planning module. Within the next 10 milliseconds (from time T0+20 to T0+30), the helical trajectory planning module, starting from the nozzle's current actual pose (actual rotational angular velocity 0.88 radians / second, actual axial position 150.0 millimeters), quickly recalculated a smooth trajectory connecting to the next critical path point. This new trajectory no longer attempted to force the actuator back to the lagging original trajectory point; instead, it set a slightly lower initial angular velocity command (0.98 radians / second) and a more achievable axial position command (150.2 millimeters) based on the actual situation, and adjusted the subsequent path point sequence accordingly. Simultaneously, upon receiving the new trajectory command, the multimodal flushing execution module moderately increased the inlet pressure of the hydraulic motor through the dual-channel pressure regulation unit to provide greater torque to help overcome rotational resistance, and fine-tuned the piston cylinder's back pressure setting. Starting from time T0+40 milliseconds, the actuator begins to follow the new trajectory, and the deviation between the actual pose and the commanded pose decreases rapidly (see data at time T0+50 milliseconds). The eddy current suppression unit is always working. It fine-tunes the angle of the auxiliary nozzle based on real-time flow rate data to ensure that the rinsing flow field remains stable when the suction nozzle speed fluctuates, avoiding interference with the cleaning effect caused by new eddies generated by speed changes.

[0092] Example 5: The cleaning efficiency evaluation module is activated after each preset rinsing cycle is completed. Its primary function is to collect the filter's transmittance data to quantify the cleaning effect. A set of high-precision optical sensors are symmetrically installed on both sides of the filter. One side has an LED light source array emitting visible light of a specific wavelength, and the other side has a photodetector array of the corresponding wavelength. The light beam emitted by the light source penetrates the filter cylinder wall and the fluid medium inside. The detectors measure the intensity of the transmitted light and calculate the transmittance percentage. These point measurement data are scanned and combined into a transmittance distribution map covering most of the filter area. This distribution map is compared differentially with the image recorded before cleaning to determine the degree of improvement in optical properties achieved by this cleaning. The suspended matter dynamic monitoring module then joins this process. It receives the transmittance distribution data after cleaning and performs correlation analysis with the thickness distribution heat map generated by itself before cleaning. The analysis aims to correct the calibration parameters of the thickness measurement model. For example, if the transmittance of a certain area increases significantly after cleaning, but the initial thickness value calculated based on the ultrasonic echo is not high, the system may determine that the ultrasonic thickness measurement unit has a systematic calibration deviation in that area (possibly due to the shadow of the filter structure or the refraction of sound waves), and automatically fine-tune the sensor gain or sound velocity compensation coefficient corresponding to that area, so that the inversion result of the thickness model is closer to the actual cleanliness observed by the optical system.

[0093] The differential pressure trigger control module uses a thickness thermal map verified with transmittance data to update the weight coefficients of its core sediment thickness-differential pressure conversion model. This update process is an optimization iteration based on the gradient descent principle. The model compares the actual measured changes in system differential pressure before and after cleaning with the model's predicted changes in differential pressure to calculate the prediction error. The error signal is backpropagated to adjust the contribution weights of various input variables (such as base thickness, oil density, local flow velocity, etc.) to the final differential pressure prediction result. Through continuous learning over multiple cleaning cycles, the model can gradually adapt to the true relationship between sediment characteristics and fluid resistance under specific water quality conditions, thereby improving the predictability and accuracy of the next cleaning trigger command. The oil dissolution auxiliary module is specifically designed to handle viscous oil pollution that is difficult to handle with conventional hydraulic flushing. This module is activated when the oil coverage density matrix output by the suspended solids dynamic monitoring module shows that the characteristic value of a certain area exceeds the high concentration threshold. It controls a slow-release chemical agent tank integrated near the suction nozzle. The outlet of the agent tank is managed by a small precision solenoid valve, and the valve's opening duration and degree are determined by the module's algorithm. The release rate of the chemical agent is not linearly adjusted, but rather exponentially related to the characteristic value of the corresponding grid in the oil stain coverage density matrix that triggers the operation. This means that for areas with light oil stains, only a short, small dose of agent is needed; while for areas with heavily oiled stains, the system calculates a longer opening time and a larger opening, releasing emulsifiers or decomposers at an exponentially increasing rate to ensure that sufficient chemically active substances penetrate and decompose the oil stains.

[0094] During this critical period of chemical spraying, the spiral trajectory planning module receives a coordination command instructing it to pause the nozzle's rotational cleaning motion. At this time, the nozzle's main sludge removal function is temporarily silenced, maintaining only a low-frequency axial reciprocating vibration. This vibration mechanically assists in the diffusion and penetration of the chemical agent. The minute reciprocating motion disturbs the boundary layer water flow on the filter surface, promoting the mixing of the chemical agent and oil, preventing the agent from becoming locally saturated and ineffective in a static state. Simultaneously, the vibration helps break down the adhesive structure of the oil, preparing for the subsequent main cleaning stage. Pausing rotation also reduces premature rinsing of the agent by the water flow, allowing sufficient contact time for the chemical reaction.

[0095] Assuming a routine cleaning cycle is completed, the cleaning efficiency assessment module, through transmittance scanning, discovers that the optical improvement in a certain fan-shaped area in the lower part of the filter is significantly lower than that in other areas. The suspended matter dynamic monitoring module is then instructed to perform a focused re-scan of this area. The corrected thickness thermal map shows that residual sediment signals remain in this area, while the oil stain feature recognition unit simultaneously confirms a high oil stain coverage density in this area. Before the next cleaning cycle begins, the differential pressure trigger control module has updated its model weights based on the efficiency assessment data from the previous cycle, making it more sensitive to similar oil stain mixed sediments. When the system runs again, if the suspended matter dynamic monitoring module detects a rapid increase in the contaminant characteristic value in this area and it reaches a threshold, the differential pressure trigger control module will generate a specific cleaning instruction for this area. The spiral trajectory planning module plans a trajectory that prioritizes reaching this area. Once the nozzle is positioned in the target area, the oil stain dissolution auxiliary module is activated, opening the chemical agent valve and spraying the agent at a high release rate. The planning module instructs the nozzle to stop rotating, only performing small axial vibrations at a frequency of a few times per second. After several seconds of chemical pretreatment, the chemical valve closes, and the spiral trajectory planning module immediately resumes the normal rotation and axial feed motion of the suction nozzle, initiating a high-intensity water flush to thoroughly remove the oil stains that have been emulsified and loosened by the chemical agents.

[0096] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0097] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A smart flushing self-cleaning filter system for industrial water treatment, characterized in that, include: The suspended solids dynamic monitoring module collects three-dimensional distribution data of deposits on the surface of the filter media in real time, and identifies the mixed deposition characteristics of solid suspended solids and oil through multispectral imaging technology; The differential pressure trigger control module establishes a mapping relationship between sediment thickness and fluid resistance based on the three-dimensional distribution data, and generates a cleaning trigger command when the real-time differential pressure data reaches the dynamic threshold range. The spiral trajectory planning module parses the cleaning trigger command and generates a composite motion trajectory that includes rotational angular velocity and axial displacement rate by combining the current position of the suction nozzle with the filter topology. The multimodal flushing execution module synchronously adjusts the torque output of the hydraulic motor and the linear displacement of the piston cylinder based on the composite motion trajectory, so that the suction nozzle covers the entire effective filtration area of ​​the filter screen along the spiral path; The suspended matter dynamic monitoring module includes: The high-frequency ultrasonic thickness measurement unit periodically scans the deposition height on the inner surface of the filter screen to form a thickness distribution heat map; The oil pollution feature identification unit distinguishes the spectral reflectance differences between organic pollutants and inorganic suspended matter through near-infrared spectral analysis and outputs an oil pollution coverage density matrix. The dynamic threshold range is dynamically adjusted based on the weighted calculation results of the thickness distribution heat map and the oil stain coverage density matrix; The differential pressure trigger control module executes: A sediment thickness-pressure difference conversion model was established to convert the thickness values ​​of each zone in the thickness distribution thermogram into predicted local pressure difference values; When more than 60% of the predicted differential pressure values ​​in the zones reach the lower limit of the dynamic threshold range, the primary cleaning mode is activated. When the predicted differential pressure values ​​of any three consecutive zones exceed the upper limit of the dynamic threshold range, the emergency cleaning mode is forcibly activated. The spiral trajectory planning module includes: The filter topology reconstruction unit constructs a three-dimensional point cloud model of the filter surface using laser ranging data; The kinematics calculation unit calculates the shortest motion path of the nozzle from its current position to the target cleaning point based on the three-dimensional point cloud model. The rotational angular velocity of the composite motion trajectory is inversely proportional to the radius of curvature of the filter screen surface, and the axial displacement rate is directly proportional to the sediment thickness gradient. The multimodal flushing execution module includes: The dual-channel pressure regulating unit independently controls the inlet pressure of the hydraulic motor and the back pressure of the piston cylinder; The motion coupling verification unit compares the deviation between the actual motion trajectory of the nozzle and the composite motion trajectory in real time. When the deviation exceeds the tolerance range, the composite motion trajectory is recalculated and the torque distribution ratio of the hydraulic motor is updated. Also includes: The cleaning efficiency assessment module collects filter transmittance data after each rinsing cycle. The suspended matter dynamic monitoring module corrects the calibration parameters of the thickness distribution heatmap based on the transmittance data; The differential pressure trigger control module updates the weight coefficients of the sediment thickness-differential pressure conversion model based on the corrected calibration parameters; The multimodal flushing execution module is further enhanced with: The vortex suppression unit adjusts the injection angle of the auxiliary nozzle during the axial movement of the suction nozzle; The spray angle of the auxiliary nozzle is dynamically adjusted according to the flow velocity distribution data on the filter screen surface to ensure that the angle between the rinsing water flow and the filter screen normal is kept within the set range.

2. The intelligent flushing self-cleaning filter system according to claim 1, characterized in that, The spiral trajectory planning module further includes: The wear compensation unit records the mechanical wear data of the historical cleaning path; When generating new composite motion trajectories, filter areas with wear values ​​below the threshold are preferentially selected as cleaning starting points. The kinematics calculation unit dynamically optimizes the axial motion acceleration curve of the nozzle based on wear data.

3. The intelligent flushing self-cleaning filter system according to claim 2, characterized in that, Also includes: When the oil stain dissolution auxiliary module identifies an area covered by high concentrations of oil stains, it controls the opening duration of the slow-release chemical agent tank. The release rate of the chemical reagent tank is exponentially related to the characteristic value of the corresponding area in the oil spill coverage density matrix; The spiral trajectory planning module pauses the rotation of the nozzle during chemical release, maintaining only axial vibration.

4. The intelligent flushing self-cleaning filter system according to claim 3, characterized in that, The differential pressure trigger control module integrates: The pressure fluctuation suppression unit monitors the pressure change rate of the main pipeline during the cleaning process; When the rate of pressure change exceeds the stable threshold, a buffer cycle is automatically inserted to temporarily slow down the displacement of the piston cylinder. The duration of the buffer period is proportional to the integral value of the pressure change rate.

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