Intelligent flushing self-cleaning filter system for industrial water treatment

Abstract

The invention relates to the technical field of industrial water treatment equipment, and discloses an intelligent flushing self-cleaning filter system for industrial water treatment. A dynamic suspended matter monitoring module of the system collects three-dimensional distribution data of deposits on the surface of a filtering medium in real time, and mixed deposition characteristics of suspended solids and oil stains are recognized through a multispectral imaging technology; the differential pressure trigger control module establishes a mapping relation between sediment thickness and fluid resistance according to the three-dimensional distribution data, and generates a cleaning trigger instruction when the real-time differential pressure data reaches a dynamic threshold interval; the spiral trajectory planning module analyzes the cleaning trigger instruction, and generates a composite motion trajectory containing a rotation angular velocity and an axial displacement rate in combination with the current position of a suction nozzle and a filter screen topological structure; the multi-mode flushing execution module synchronously adjusts torque output of a hydraulic motor and linear displacement of a piston cylinder based on the composite motion trail, so that a suction nozzle covers the whole effective filtering area of a filter screen along a spiral path.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of industrial water treatment equipment, in particular to an intelligent flushing self-cleaning filter system for industrial water treatment. BACKGROUND

[0002] In the industrial production process, the water treatment system is an important part to ensure the continuity and stability of production, and the filter as the core equipment of the water treatment system directly affects the water quality and the smoothness of the production process. At present, the commonly used filter types in the field of industrial water treatment include bag filter, basket filter, self-cleaning filter, etc., among which the self-cleaning filter is widely used in circulating water system, cooling water treatment, sewage treatment and other scenes due to its characteristics of no need to frequently disassemble and replace filter material and continuous operation.

[0003] The existing self-cleaning filter still has many technical limitations in actual operation. Most self-cleaning filters rely on fixed time intervals or preset pressure difference thresholds to trigger cleaning actions, and lack precise perception of the state of deposits on the surface of the filter medium. For example, some devices only start cleaning by monitoring whether the inlet and outlet pressure difference reaches a fixed value, if the deposits contain a large amount of oil and other viscous substances, even if the pressure difference does not reach the threshold, it may also cause filter screen blockage and reduce filtration efficiency, on the contrary, if the deposits are loose inorganic suspended solids, the fixed pressure difference threshold may cause unnecessary frequent cleaning, resulting in waste of water resources and energy.

[0004] The cleaning trajectory planning and execution mode of the existing equipment is relatively simple, and it is difficult to achieve comprehensive coverage and efficient cleaning of the filter screen surface. The suction nozzle of the traditional self-cleaning filter mostly adopts a single rotation or axial movement mode, for the irregularly distributed deposits on the filter screen surface, blind areas are easy to appear during cleaning, especially in special areas such as filter screen edges and wrinkles, the problem of deposit residue is prominent. At the same time, the control of the water motor and the piston cylinder during the cleaning process is mostly independent adjustment, which cannot optimize the torque output and linear displacement according to the distribution characteristics of the deposits, resulting in over-cleaning in some areas, aggravating filter screen wear, and insufficient cleaning in some areas, repeatedly appearing the problem of blockage.

[0005] The existing self-cleaning filter lacks the ability to differentiate the composition of the deposits and cannot distinguish the mixed deposition characteristics of solid suspended matter and oil stains. When inorganic suspended matter and organic oil stains exist on the surface of the filter medium at the same time, a single hydraulic flushing method cannot effectively remove the oil stains with strong viscosity, and long-term accumulation will cause the hydrophilicity of the filter screen to decrease and the filtration resistance to continuously increase, which not only affects the filtration effect, but also shortens the service life of the filter screen and increases the equipment maintenance cost and downtime. These technical defects make it difficult for the existing self-cleaning filter to meet the needs of industrial water treatment for high efficiency, precision and energy saving, and an intelligent flushing self-cleaning filter system capable of realizing dynamic monitoring of deposits, intelligent triggering of cleaning, precise trajectory planning and efficient execution is urgently needed. SUMMARY

[0006] The purpose of the present application is to provide an intelligent flushing self-cleaning filter system for industrial water treatment to solve the problems raised in the background art.

[0007] To achieve the above-mentioned purpose, the present application provides an intelligent flushing self-cleaning filter system for industrial water treatment, which comprises:

[0008] The suspended matter dynamic monitoring module collects three-dimensional distribution data of the deposits accumulated on the surface of the filter medium in real time, and identifies the mixed deposition characteristics of solid suspended matter and oil stains through multi-spectral imaging technology;

[0009] The differential pressure trigger control module establishes a mapping relationship between the deposit thickness and the fluid resistance according to the three-dimensional distribution data, and generates a cleaning trigger instruction when the real-time differential pressure data reaches the dynamic threshold interval;

[0010] The spiral trajectory planning module analyzes the cleaning trigger instruction, and generates a composite motion trajectory containing rotational angular velocity and axial displacement rate in combination with the current position of the suction nozzle and the topology of the filter screen;

[0011] The multi-modal 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 all the effective filter area of the filter screen along the spiral path.

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

[0013] The high-frequency ultrasonic thickness measurement unit periodically scans the deposit accumulation height of the inner surface of the filter screen to form a thickness distribution thermogram;

[0014] The oil stain feature recognition unit distinguishes the spectral reflectance difference between organic pollutants and inorganic suspended matter through near-infrared spectral analysis and outputs an oil stain coverage density matrix;

[0015] The dynamic threshold interval is dynamically adjusted according to the weighted calculation results of the thickness distribution thermogram and the oil stain coverage density matrix.

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

[0017] establishing a deposit thickness-differential pressure conversion model to convert the thickness values of each sub-area in the thickness distribution thermal map into local differential pressure prediction values;

[0018] activating the primary cleaning mode when more than 60% of the sub-area differential pressure prediction values reach the lower limit of the dynamic threshold interval;

[0019] forcibly activating the emergency cleaning mode when the differential pressure prediction values of any three consecutive sub-areas exceed the upper limit of the dynamic threshold interval.

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

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

[0022] The kinematics calculation unit calculates the shortest motion path of the suction nozzle from the current pose to the target cleaning point according to the three-dimensional point cloud model;

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

[0024] Preferably, the multi-modal flushing execution module comprises:

[0025] The dual-channel pressure regulation 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 value of the actual motion trajectory of the suction nozzle and the composite motion trajectory in real time;

[0027] When the deviation value 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 comprises:

[0029] The cleaning efficiency evaluation module collects the filter screen light transmittance data after each flushing cycle;

[0030] The suspended solids dynamic monitoring module corrects the calibration parameters of the thickness distribution thermal map according to the light transmittance data;

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

[0032] Preferably, the multi-modal flushing execution module is additionally provided with:

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

[0034] The spray angle of the auxiliary nozzle is dynamically adjusted according to the filter screen surface flow rate distribution data, ensuring that the included angle between the flushing water flow and the normal direction of the filter screen is maintained within a set range.

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

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

[0037] When generating a new composite motion trajectory, the filter screen area with a wear value below a threshold value is preferentially selected as the cleaning starting point;

[0038] The kinematics calculation unit dynamically optimizes the axial motion acceleration curve of the suction nozzle according to the wear data.

[0039] Preferably, the system further comprises:

[0040] The oil stain dissolving auxiliary module controls the opening duration of the slow-release chemical agent tank when a high-concentration oil stain covered area is identified;

[0041] The release rate of the chemical agent tank is in an exponential relationship with the eigenvalue of the corresponding area in the oil stain coverage density matrix;

[0042] The spiral trajectory planning module suspends the rotational motion of the suction nozzle during the release of the chemical agent, and only maintains 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 pressure change rate exceeds a stable threshold value, a buffer period is automatically inserted to suspend the piston cylinder movement action;

[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 application are:

[0048] In the sediment monitoring link, the suspended solids dynamic monitoring module adopts multi-spectral imaging technology, which can collect three-dimensional distribution data of the accumulated sediment on the surface of the filter medium in real time, and accurately identify the mixed sediment characteristics of solid suspended solids and oil stains. Compared with existing devices that only rely on pressure difference or time triggering, the distribution state and composition difference of the sediment on the filter surface can be more comprehensively mastered, and the problem of incomplete cleaning or excessive cleaning caused by misjudgment of sediment composition can be avoided. Through the collection of three-dimensional distribution data, the thickness difference of sediment in different areas can also be intuitively reflected, providing detailed basis for the accurate planning of subsequent cleaning actions, and ensuring that cleaning resources are concentrated in areas with serious sediment accumulation, improving the cleaning pertinence.

[0049] The differential pressure trigger control module establishes a mapping relationship between sediment thickness and fluid resistance based on three-dimensional distribution data, breaking through the limitations of existing fixed threshold triggering, and can dynamically adjust the threshold interval according to the actual accumulation of sediment, realizing intelligent triggering of cleaning actions. When the sediment thickness is small, the dynamic threshold interval is correspondingly increased to avoid unnecessary cleaning actions and reduce water resource and energy consumption; when the sediment thickness increases and the fluid resistance rises to the threshold interval, a cleaning trigger instruction is generated in time to prevent filter efficiency decline and system pressure abnormalities caused by filter screen blockage, ensuring the continuous and stable operation of the filtration system, while prolonging the use time of the filter screen in the unnecessary cleaning state and reducing the wear rate of the filter screen.

[0050] The spiral trajectory planning module generates a complex motion trajectory containing rotational angular velocity and axial displacement rate by analyzing the cleaning trigger instruction, combining the current position of the suction nozzle and the filter screen topology. Compared with traditional single motion mode, it can realize the comprehensive coverage of the filter screen surface by the suction nozzle along the spiral path, effectively eliminating the cleaning blind area. The cooperative design of rotational angular velocity and axial displacement rate in the complex motion trajectory can optimize the motion parameters according to the filter screen topology and sediment distribution characteristics, such as reducing the axial displacement rate and increasing the rotational angular velocity in areas with thick sediment accumulation, ensuring that the suction nozzle has enough time to complete cleaning in that area; appropriately increasing the axial displacement rate in areas with thin sediment to improve overall cleaning efficiency, achieving a balance between cleaning effect and efficiency.

[0051] The multi-modal 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, ensures that the suction nozzle strictly operates according to the planned trajectory, and avoids trajectory deviation caused by asynchronous adjustment of torque and displacement. Dynamic adjustment of the torque output of the hydraulic motor can optimize power output according to the resistance changes of different areas of the filter screen, prevent damage to the filter screen caused by excessive torque, or cause the suction nozzle to be stuck due to insufficient torque; precise control of the linear displacement of the piston cylinder ensures the stability of the axial movement of the suction nozzle, further improving the trajectory execution precision. Through the collaborative work of multiple modules, the system can not only realize accurate monitoring of sediments, intelligent triggering of cleaning actions and comprehensive coverage of cleaning trajectories, but also reduce energy and water consumption while improving cleaning effect, reduce equipment maintenance cost, prolong the service life of the filter screen, and provide strong support for efficient, stable and energy-saving operation of industrial water treatment systems. BRIEF DESCRIPTION OF DRAWINGS

[0052] Figure 1 The timing diagram of the intelligent flushing self-cleaning filter system for industrial water treatment according to the present application;

[0053] Figure 2 The working principle diagram of the suspended solids dynamic monitoring module;

[0054] Figure 3 The working principle diagram of the differential pressure trigger control module. DETAILED DESCRIPTION

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

[0056] Please refer to Figure 1 The present application provides an intelligent flushing self-cleaning filter system for industrial water treatment, which includes integrated multi-module collaborative work to achieve efficient cleaning of filter media. The system core 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 matter dynamic monitoring module is responsible for real-time acquisition of three-dimensional distribution data of the accumulated matter on the surface of the filter medium. The module uses multi-spectral imaging technology to capture the mixed deposition characteristics of solid suspended matter and oil stains, and the generated data includes deposition thickness, spatial distribution, and pollutant type information. After receiving the three-dimensional distribution data, the differential pressure trigger control module establishes a mapping relationship between the deposition thickness and the fluid resistance. This relationship is calculated based on a fluid mechanics model to calculate the local differential pressure changes. When the real-time monitored differential pressure data enters the dynamic threshold interval, the module generates a cleaning trigger instruction. The spiral trajectory planning module analyzes the cleaning trigger instruction, combines the current position of the suction nozzle and the filter screen topology, and generates a composite motion trajectory including the rotational angular velocity and the axial displacement rate. The trajectory planning ensures that the motion path of the suction nozzle covers all the effective filtering area of the filter screen. The multi-modal 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 the axial movement, so that the suction nozzle performs the cleaning action along the spiral path. When the system is running, each module interacts information through the data bus. The suspended matter dynamic monitoring module regularly updates the three-dimensional distribution data, the differential pressure trigger control module dynamically adjusts the threshold interval to adapt to the working condition changes, the spiral trajectory planning module optimizes the motion efficiency, and the multi-modal flushing execution module feedbacks the actual motion state to realize closed-loop control.

[0058] Embodiment 1: refer to Figure 2 The suspended matter dynamic monitoring module real-time acquires and analyzes the three-dimensional distribution information of the deposition on the surface of the filter medium. The high-frequency ultrasonic thickness measurement unit serves as the basic perception layer of the network. A plurality of piezoelectric ceramic transducers are embedded in the non-working area on the inner side of the filter screen in the form of a regular grid array. Each transducer periodically emits high-frequency ultrasonic wave pulses with a frequency in the range of 1 MHz to 5 MHz under the timing trigger of the microcontroller. The sound waves pass through the water flow medium to hit the surface layer of the deposition and generate a return signal. The time of flight and amplitude characteristics of the return signal are captured by a high-speed data acquisition card. The thickness calculation engine converts the time difference into an accurate thickness value based on a sound wave propagation speed model in the deposition. The model is calibrated in advance through a calibration sample to compensate for the sound speed drift caused by changes in water temperature and density. The scanning period can be dynamically configured according to the system load. When the concentration of suspended matter in the incoming water is high, the scanning period is automatically shortened to once every one second. When the concentration is low, the scanning period is lengthened to once every ten seconds to reduce the calculation load. The measurement data of all transducers are synchronously transmitted to the signal conditioning circuit through the field bus. After amplification, filtering, and analog-to-digital conversion, the digital signal processor executes a spatial interpolation algorithm to reconstruct the discrete thickness point array into a continuous two-dimensional thickness distribution heat map. The heat map is displayed in the form of pseudo-color coding on the monitoring interface. The red area represents severe deposition accumulation, and the blue area represents cleanliness.

[0059] The oil contamination identification unit, which works in parallel with the ultrasonic thickness measurement unit, adopts optical sensing technology to distinguish the types of contaminants. The core of this unit is a near-infrared spectroscopy system, which contains a wide-spectrum near-infrared light-emitting diode array as the light source, with an emission spectrum range covering 700 nanometers to 2500 nanometers. The light is vertically irradiated onto the filter surface through a sapphire window, and an InGaAs spectral sensor receives the reflected light beam at a 45-degree angle. After being dispersed by a spectrometer, the reflected light signal is received by a photodiode array, generating a complete reflectance spectrum curve for each measurement point. The data processing firmware has a built-in standard spectrum library containing characteristic absorption spectra of various common contaminants such as mineral particles, algae, lubricating oil, hydraulic oil, etc. Through the partial least squares regression algorithm, the real-time collected spectrum is matched with the standard library, and the relative concentration ratio of organic oil contamination and inorganic suspended matter is calculated. The analysis process pays special attention to the characteristic absorption peaks of carbon-hydrogen bonds near 1200 nanometers, 1400 nanometers, and 1700 nanometers, and quantifies the oil contamination coverage density by analyzing the depth and width of the absorption peaks. Finally, the oil contamination concentration of each measurement point is quantified as a characteristic value between 0 and 1, and an oil contamination coverage density matrix with the same resolution as the thickness heat map is generated through spatial interpolation.

[0060] The generation of the dynamic threshold interval is a multivariate fusion decision-making process, and its calculation engine continuously receives the thickness distribution heat map and the oil contamination coverage density matrix from the above two units. The calculation algorithm first performs grid partitioning on the heat map, dividing the entire filter surface into hundreds of virtual calculation units. For each unit, the algorithm extracts its average thickness value and average oil contamination coverage density value. The lower limit value of the dynamic threshold interval is calculated by introducing a weighting factor, where the weight coefficient of the oil contamination coverage density is usually set to 1.5 to 2 times the thickness weight. This is because oily contaminants usually have higher adhesion and lower water permeability, and their contribution to the pressure difference growth is much greater than that of inorganic particulate matter with the same thickness. When the oil contamination density matrix value of a certain area exceeds the preset alarm threshold, the algorithm dynamically increases the weight proportion of that area in the overall weighted calculation, making the system more sensitive to the pressure difference change in that area. The upper limit value of the threshold interval is positively related to the average deposition rate of the sediment, which is obtained by comparing the thickness change trends of multiple consecutive scanning periods. The weighted calculation results of all partitions are fused by a Kalman filter, outputting a dynamically changing threshold interval that is updated every second and broadcast to the pressure difference trigger control module through 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. The system uses a hardware synchronization signal to ensure that the measurement points of the two units are completely aligned in time and space. At the beginning of each measurement cycle, the main controller sends a trigger pulse to the ultrasonic transmission circuit and the spectrum acquisition circuit at the same time, and all sensor data are stamped with the same timestamp. Spatial registration is achieved by setting optical reference points on the screen frame. During the system initialization phase, a calibration program runs to establish the coordinate transformation relationship between the ultrasonic sensor array and the optical measurement points, ensuring that the thickness data and spectrum data collected from the same physical location can be accurately matched. The data fusion processor uses a data confidence evaluation algorithm based on D-S evidence theory. When the ultrasonic thickness data and the optical oil stain recognition data of a certain measurement point conflict, the algorithm reduces the confidence weight of the data of that point and triggers a local re-measurement to exclude measurement errors caused by temporary obstruction of bubbles or foreign matter.

[0062] The module also has online self-diagnosis and calibration functions. The embedded processor periodically executes self-checking routines. For example, the ultrasonic thickness measurement unit checks the accuracy of the sound velocity model by measuring standard test blocks of known thickness. If the deviation exceeds the tolerance, the calibration parameters are automatically updated. The oil stain feature recognition unit has a standard reflector built-in, which measures its reflection spectrum regularly to compensate for the 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 basis for predictive maintenance of the system. The communication between the module and the upper control system uses the industrial Ethernet protocol. In addition to transmitting real-time thickness distribution heat maps and oil stain coverage density matrices, the module also periodically reports key information such as module health status and sensor life warning, forming a complete and reliable suspended matter dynamic monitoring solution.

[0063] Example 2: refer to Figure 3 The differential pressure trigger control module continuously receives thickness distribution heat maps and oil stain coverage density matrices from the suspended matter dynamic monitoring module. The sediment thickness-differential pressure conversion model established in it is an empirical model trained by a large amount of historical data. This model is not a simple linear mapping, but a multivariate nonlinear function considering sediment porosity, particle size distribution, fluid viscosity, and inherent resistance of the screen. The model divides the entire screen surface into hundreds of virtual calculation units. For each unit, the model reads its latest average sediment thickness value and oil stain coverage density value, combines the real-time collected fluid temperature and flow data, and uses an embedded processor running a simplified computational fluid dynamics algorithm to calculate the additional pressure difference prediction value of the local area caused by sediment accumulation. All these local pressure difference prediction values together form a pressure difference prediction distribution field for the entire screen. This distribution field is background-verified with the global pressure difference value read by the actual differential pressure transmitters installed at the inlet and outlet of the screen.

[0064] The cleaning trigger logic adopts a hierarchical warning mechanism. When the system calculation finds that more than 60% of the filter partition local pressure difference prediction value reaches the lower limit of the dynamic threshold interval, the module will generate a trigger instruction for the primary cleaning mode. The original design of this mode is to perform preventive and low-intensity cleaning, aiming to intervene when the sediment has not yet hardened and the fluid resistance is in a slow rising period. At this time, the rotation angular velocity and axial displacement rate of the suction nozzle are set to a lower gear to reduce energy consumption and impact on the system processing flow. In contrast, the trigger condition of the emergency cleaning mode is more stringent. When the system detects that the pressure difference prediction value of any three consecutive partitions (the continuity is based on the filter topology judgment, usually referring to the key areas adjacent in physical position and in the downstream direction of the water flow) breaks through the upper limit of the dynamic threshold interval, the module will immediately activate the emergency cleaning mode. This situation usually indicates that the filter has a serious local blockage or the nature of the pollutants has changed, which has the risk of causing the system pressure difference to rise sharply. In this mode, the cleaning track will preferentially cover the high-risk area, and the motion parameters and suction strength of the suction nozzle are set to the maximum value to remove the blockage threat as quickly as possible. The upper and lower limits of the dynamic threshold interval are not fixed values. The lower limit is determined by the baseline pressure difference statistically calculated by the system during long-term operation and the current water quality. The upper limit is associated with the safe working pressure difference of the filter material and the maximum pressure drop allowed by the process. The interval width will be dynamically adjusted according to the speed of sediment accumulation. When the accumulation rate is fast, the interval is narrowed to increase sensitivity. When the rate is slow, it is appropriately relaxed to reduce false triggering.

[0065] The pressure fluctuation suppression unit integrated inside the module focuses on maintaining the stability of the system main pipeline pressure during the cleaning process. The unit monitors the rate of change of the main pipeline pressure in real time through a high-frequency pressure sensor, with a sampling frequency much higher than that required for conventional process control. The core algorithm of the unit continuously calculates the first derivative of the instantaneous pressure with respect to time, i.e., the rate of change of pressure, and compares it with a stable threshold value obtained from historical stable operation data. When the cleaning action (especially the sudden start or reversal of the piston cylinder) causes the rate of change of pressure to exceed the stable threshold value, the pressure fluctuation suppression unit does not immediately stop cleaning, but sends an instruction to the actuator to insert a buffer period. During this buffer period, the linear motion of the piston cylinder is suspended, its control valve maintains the current opening, and the hydraulic motor reduces its speed or maintains a low speed according to the instruction. This "moving rotation, stopping displacement" mode can provide a time window for the pipeline system to recover and stabilize the pressure without significantly affecting the cleaning progress. The duration of the buffer period 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 change of pressure exceeds the threshold value. A slight and short pressure fluctuation may only require a few hundred milliseconds of buffering, while a severe and sustained fluctuation may trigger a few seconds of buffering to ensure that the system pressure fully returns to stability before continuing the axial feeding action.

[0066] The deposit thickness-pressure difference conversion model has an online self-learning function, and its model weight coefficients are adjusted according to the actual effect after each cleaning action. Specifically, after each cleaning cycle triggered by the model and completed, the module compares the predicted pressure difference drop before cleaning with the actual pressure difference drop recorded by the differential pressure transmitter. If there is a significant deviation, a feedback adjustment algorithm is activated, which adjusts the weight coefficients related to thickness, oil density, etc. in the model in the opposite direction and size, so that the model's predicted value can better approximate the real system's response. This continuous self-optimization enables the model to adapt to the performance degradation of the filter screen due to long-term use, or the slow changes in water quality characteristics. The logic of the pressure fluctuation suppression unit is also linked to the cleaning mode. In the primary cleaning mode, the stable threshold is set relatively loose, and the system tolerance is higher to prioritize the continuity of cleaning. In the emergency cleaning mode, the stable threshold is tightened because the system is already at a high pressure difference, and any additional pressure fluctuation may reach the safety red line, so more agile suppression measures are needed to ensure pipeline safety. The running state of the entire module, the triggered cleaning mode, the pressure fluctuation events, and the model correction records are all recorded in detail to form a running log for subsequent performance analysis and maintenance decisions.

[0067] Example 3: The filter screen topology reconstruction unit serves as the starting point of the entire process, and its work relies on a precise laser scanning system. A set of imaging laser ranging sensors is installed on a sliding table that can move along the filter screen axis. Before system initialization or the start of each major cleaning cycle, the sliding table drives the sensors to traverse the effective length of the filter screen at a constant speed, while the laser emits ranging beams at an extremely high frequency, and the receiver records the three-dimensional coordinate data of millions of points. These massive raw point cloud data are first pre-processed to remove abnormal points caused by water flow fluctuations, suspended matter shielding, or electronic noise, and then a curved surface reconstruction algorithm based on Delaunay triangulation is used to generate a three-dimensional mesh model that describes the true surface morphology of the filter screen. This model not only accurately records the spatial position of each vertex, but also calculates the normal vector of the vertex and the angle change rate of adjacent triangular faces to obtain key topological properties such as local curvature radius that describe the degree of curvature of the curved surface.

[0068] The three-dimensional mesh model is constructed based on the discrete vertex coordinates of the filter screen surface obtained by laser ranging. The local curvature radius of the filter screen is calculated through the spatial positional relationship of the adjacent three vertices, as follows:

[0069]

[0070] wherein, : the local curvature radius of the filter screen, with a unit of meters (m), reflecting the degree of curvature of a point on the filter screen surface; , : the vector formed by the target point and the adjacent two vertices in the three-dimensional mesh model, , ; and : the three-dimensional coordinates of the target point (unit: m), directly collected by the laser ranging unit; , : the three-dimensional coordinates of the adjacent two vertices of the target point (unit: m), which are the basic data of the three-dimensional mesh model; is the modulus of the cross product result (unit: m²); is the dot product result (unit: m²); , : the modulus of the vector , (unit: m), reflecting the length of the vector.

[0071] The rotational angular velocity is inversely proportional to the local curvature radius of the filter screen, and is adjusted by a proportional coefficient to ensure that the nozzle linear velocity is constant, as follows:

[0072]

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

[0074] Axial displacement rate Proportional to the sediment thickness gradient, with a proportional coefficient adapted to the cleaning requirements of different types of sediments, as follows:

[0075]

[0076] Wherein, : Axial displacement rate of the suction nozzle, in m / s; : Displacement rate adjustment coefficient, in s -1 , set according to the type of sediment: 0.8-1.2 s -1 for inorganic suspended solids (loose and easy to clean), and 0.3-0.6 s -1 for oil stains (strongly viscous). -1 ; : Sediment thickness gradient, calculated by the suspended solids 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 thickness change).

[0077] Rotational angular velocity is closely related to the local radius of curvature of the filter screen corresponding to the current position of the suction nozzle, and in areas where the radius of curvature of the filter screen, such as elbows, connections, or local protrusions, is small, the rotational angular velocity will be significantly increased, thereby ensuring that the linear velocity of the suction nozzle is maintained within a constant range and ensuring the uniformity of cleaning intensity; while in areas where the filter screen is flat and cylindrical, i.e., where the radius of curvature is large, the rotational angular velocity will be appropriately reduced to save energy.

[0078] Axial displacement rate is mainly adjusted in response to the accumulation of pollutants, and in areas where the thickness changes dramatically and the gradient is large, the axial displacement rate will be increased to quickly respond to pollution risks; while in areas where the sediment is evenly distributed (with a small gradient),

[0079] The kinematics solver unit then performs real-time path planning and motion parameter calculation on this three-dimensional grid model, and its solving process needs to meet multiple goals such as complete coverage, motion smoothness, and efficiency optimization. The core output of the solution is the complex motion trajectory of the suction nozzle, which is composed of two time-varying parameter sequences: the rotational angular velocity and the axial displacement rate . The determination of the rotational angular velocity is closely related to the local radius of curvature of the filter screen corresponding to the current position of the suction nozzle . In areas with small radius of curvature such as filter screen bends, connections, or local protrusions, the angular velocity needs to be significantly increased to ensure that the linear velocity of the suction nozzle remains within a constant range, thereby ensuring the uniformity of cleaning intensity; in areas with large radius of curvature such as straight sections of the filter screen, the angular velocity can be appropriately reduced to save energy. The adjustment of the axial displacement rate is mainly in response to the accumulation of pollutants, and its basis is the sediment thickness gradient information from the suspended solids dynamic monitoring module. In areas with sharp thickness changes and large gradients , the axial feed needs to be accelerated to quickly respond to pollution risks; in areas with uniform sediment distribution, uniform or reduced speed operation can be maintained. The kinematics solver unit dynamically balances these two parameters through an optimization algorithm to generate a high-efficiency trajectory that can fully cover the surface of the filter screen and adaptively adjust according to the severity of pollution.

[0080] The role of the wear compensation unit is a key design to extend the mechanical life of the system. It records and analyzes the cumulative wear effects of historical cleaning paths on mechanical components through a continuously running database. During each cleaning action, various operating parameters of the suction nozzle drive mechanism, such as the working pressure of the hydraulic motor, the thrust of the piston cylinder, the frequency of reversal, the running time, etc., are monitored and recorded in real time. Combined with the known fatigue characteristics of component materials, an algorithm model based on cumulative damage theory is used to calculate the equivalent wear of different areas of the filter screen surface (corresponding to different motion postures and loads) and the transmission mechanism itself (such as bearings, lead screws) , and form a "wear distribution map" that is updated over time. When generating a new complex motion trajectory, the path planning algorithm will preferentially select filter screen areas with a historical cumulative wear below a pre-set safety threshold as the starting point or main path area for cleaning, thereby avoiding premature failure of mechanical components under specific high-load postures. At the same time, for areas with high wear , the algorithm will consciously arrange them to pass through at a lower load during non-critical cleaning stages.

[0081] The kinematics solver unit deeply integrates the information provided by the wear compensation unit when planning the path, it not only seeks the shortest path in time, but also dynamically optimizes the acceleration profile of the nozzle's axial movement . For high-risk areas of wear (usually high-curvature, frequent start-stop or turning areas), the algorithm will use a smoother S-shaped acceleration curve instead of a trapezoidal or triangular acceleration curve to significantly reduce the impact load on the transmission mechanism when starting and braking. This optimization is done in real time during the trajectory generation phase, the solver selects the most suitable one from the pre-set multiple acceleration curve templates, or calculates an optimal curve in real time, which can focus on the jerk (derivative of acceleration ) constraint, such as making not greater than a certain limit , so as to ensure the smoothness of the movement, the formula can be expressed as:

[0082]

[0083] Where: represents the jerk, reflecting the severity of acceleration change, is a function of acceleration with time, is the maximum absolute value of jerk allowed by the system, which is a design constant related to the stiffness of the mechanical system and the inertia of the components.

[0084] The filter screen topology reconstruction unit and the kinematics solver unit maintain close closed-loop data interaction, the three-dimensional model provided by the reconstruction unit is the basis for the solution, and the expected path planned by the solver is fed back to the reconstruction unit for review of key areas during the next scan. The data update of the wear compensation unit is not simply linear accumulation, it introduces a time decay factor (0< <1), so that the impact weight of long-ago wear data on the current evaluation gradually decreases, which makes the system better adapt to the state refresh after the filter screen is physically cleaned or the components are replaced, ensuring that the wear evaluation always reflects the recent actual operating conditions. The entire module is multi-task scheduled and interrupt managed by a real-time operating system, ensuring that the whole process from receiving the cleaning instruction to generating the final movement trajectory has stable and controllable delay, meeting the harsh requirements of industrial real-time control. All generated movement 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 of the system.

[0085] Example 4: The multi-modal flushing execution module realizes the designed function through the sophisticated cooperation of its internal units and real-time feedback control. The core of the module is to convert the planned motion trajectory instructions into accurate, stable and adaptive mechanical actions of the on-site execution mechanism. The dual-channel pressure regulation unit constitutes the power control basis of the module. The unit contains two independent electro-hydraulic proportional pressure control systems, which accurately regulate the inlet pressure driving the hydraulic motor rotation and the back pressure controlling the linear displacement of the piston cylinder, respectively. The set value of the inlet pressure is directly derived from the real-time torque demand calculated by the spiral trajectory planning module, which is related to the frictional resistance that the suction nozzle needs to overcome and the target rotational angular velocity to be maintained. The setting of the back pressure is related to the required axial thrust and displacement rate, and its value needs to ensure the smooth feeding of the piston cylinder rather than impact motion. Both systems use a proportional pressure valve with high response frequency as the core component. The opening of the valve is controlled by the pulse width modulation signal output by the embedded controller, forming an independent pressure closed loop, ensuring that the deviation between the actual pressure and the set pressure is controlled within a very small range, thereby providing a stable and independently adjustable power source for the hydraulic motor and the piston cylinder.

[0086] The motion coupling verification unit plays the role of a supervisor, continuously comparing the actual motion trajectory of the suction nozzle in three-dimensional space with the expected trajectory issued by the spiral trajectory planning module. The actual trajectory is obtained by relying on high-precision feedback elements: an absolute rotary encoder is installed on the output shaft of the hydraulic motor to measure the actual rotation angle of the suction nozzle in real time; a grating ruler is installed parallel to the piston rod of the piston cylinder to accurately detect the actual axial position of the suction nozzle. The motion control card of the controller synchronously reads these feedback values at a millisecond level and compares them with the theoretically expected values at the same time. The trajectory deviation is usually evaluated by using the modulus of the comprehensive error vector. When the system detects that the deviation value exceeds the preset tolerance range (the tolerance range can be dynamically adjusted according to the cleaning mode, such as being appropriately relaxed in the emergency cleaning mode to prioritize speed), the verification unit does not simply command the actuator to hard-correct back to the original planned trajectory, but sends a trajectory re-planning request to the spiral trajectory planning module, along with the current deviation data and actual pose. Upon receiving the request, the trajectory planning module quickly recalculates an optimized trajectory from the current actual position and attitude of the suction nozzle as the new starting point 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, temporarily increasing the torque output during the phase of needing to accelerate to catch up with the trajectory. The vortex suppression unit focuses on improving the hydrodynamic efficiency of the flushing process and reducing side effects. This unit achieves its function by adjusting the spray angle of one or more auxiliary nozzles installed on the side of the suction nozzle. The main role of the auxiliary nozzle is not to directly suck dirt, but to suppress the vortex that may be generated behind the suction nozzle during movement by spraying a controllable guiding water flow, which can disturb the suspended pollutants and even cause them to re-attach to the cleaned area.

[0087] The vortex suppression unit receives flow rate distribution data on the filter surface from multiple ultrasonic flow meters or fluid velocity sensors installed at different areas of the filter. Based on these data, an embedded algorithm dynamically calculates and adjusts the spray angle of the auxiliary nozzles. The goal of the adjustment is to ensure that the flushing water flow from the auxiliary nozzles forms a small and stable angle with the normal direction of the local surface of the filter, which is usually set between 5 degrees and 15 degrees. Maintaining this angle helps to generate a sweeping flow that adheres to the surface of the filter and points in the direction of the suction nozzle, which can loosen the deposits in advance and smoothly guide the separated pollutants to the main dirt outlet of the suction nozzle, while effectively filling the space left by the movement of the suction nozzle and suppressing the formation of large-scale vortices, as shown in Table 1.

[0088] Table 1: Key data records of the multi-modal flushing execution module in a trajectory deviation event

[0089]

[0090] Consider a specific operational instance: the system is performing a regular cleaning cycle, everything is going well in the initial stage, the trajectory deviation monitored by the motion coupling verification unit is always below the tolerance limit of 0.3. At a certain moment, the suction nozzle encounters a very sticky oil blob during axial movement, causing the axial thrust resistance of the piston cylinder to suddenly increase, although the dual-channel pressure regulation unit tries to maintain the thrust speed by increasing the back pressure, the axial displacement still lags in a short time, and the actual position starts to lag behind the command position (see the data at time T0+20 milliseconds in the table above). Due to the indirect influence of axial jamming on rotational resistance, the actual rotational speed of the hydraulic motor also decreases. The integrated trajectory deviation calculated by the motion coupling verification unit in real time reaches 0.45 at time T0+20 milliseconds, exceeding the threshold of 0.4.

[0091] The system responds immediately, and the motion coupling verification unit sends a trajectory re-planning request to the spiral trajectory planning module. In the next 10 milliseconds (from time T0+20 milliseconds to time T0+30 milliseconds), the spiral trajectory planning module quickly recalculates a smooth trajectory connecting to the next key path point, starting from the current actual pose of the suction nozzle (actual rotational angular velocity 0.88 rad / s, actual axial position 150.0 mm). This new trajectory no longer tries to force the actuator to return to the original trajectory point that has lagged behind, but sets a slightly lower initial angular velocity command (0.98 rad / s) and a more easily reached axial position command (150.2 mm) based on the actual situation, and adjusts the subsequent path point sequence accordingly. At the same time, the multi-modal flushing execution module, after receiving the new trajectory command, adjusts the inlet pressure of the hydraulic motor through the dual-channel pressure regulation unit to provide more torque to help overcome the rotational resistance, and fine-tunes the back pressure setting of the piston cylinder. From time T0+40 milliseconds, the actuator starts to follow the new trajectory motion, and the deviation between the actual pose and the command pose decreases rapidly (see the data at time T0+50 milliseconds). The vortex suppression unit is always working, and it adjusts the angle of the auxiliary nozzle according to the real-time flow rate data to ensure that the flushing flow field remains stable when the speed of the suction nozzle fluctuates, avoiding the generation of new vortexes due to speed changes that interfere with the cleaning effect.

[0092] Example 5: The cleaning performance evaluation module is activated at the end of each preset cleaning cycle. Its first action is to collect the light transmittance data of the filter screen to quantify the cleaning effect. A set of high-precision optical sensors are symmetrically installed on both sides of the filter screen. One side is an LED light source array that emits specific wavelength visible light, and the other side is a corresponding photodetector array. The light beam emitted by the light source passes through the filter screen wall and the fluid medium inside, and the detector measures the light intensity of the transmitted light and calculates the light transmittance percentage. These point measurement data are scanned and combined into a light transmittance distribution map covering most of the filter screen area. This distribution map is compared with the recorded image before cleaning to determine the degree of improvement in optical properties after this cleaning. The suspended matter dynamic monitoring module is then connected to this process. It receives the light transmittance distribution data after cleaning and correlates it with the thickness distribution thermal map generated by the scanning before cleaning. The analysis aims to correct the calibration parameters of the thickness measurement model; for example, if the light transmittance of a certain area improves significantly after cleaning, but the initial thickness value calculated from the ultrasonic echo is not high, the system may judge that there is a systematic calibration deviation in that area (possibly due to filter screen structure shadow or sound wave refraction), and automatically fine-tune the sensor gain or sound speed compensation coefficient in that area, so that the inversion result of the thickness model is closer to the actual cleaning state observed by optical observation.

[0093] The differential pressure trigger control module then uses the thickness thermal map that has been verified by the transmittance data to update the weight coefficients of the sediment thickness-differential pressure conversion model in its core. This updating process is an optimization iteration based on the gradient descent principle. The model compares the actual measured system differential pressure change before and after cleaning with the model predicted differential pressure change, and calculates the prediction error. The error signal is back propagated to adjust the contribution weight of each input variable (such as the base thickness value, oil density value, local flow rate, etc.) to the final differential pressure prediction result. After continuous learning for several cleaning cycles, the model can gradually adapt to the real relationship between sediment characteristics and fluid resistance under specific water quality conditions, thereby improving the predictability and accuracy of the next trigger cleaning instruction. The oil stain dissolution auxiliary module is specifically designed to deal with the problem of viscous oil stains that cannot be handled by conventional hydraulic flushing. When the oil stain coverage density matrix output by the suspended solids dynamic monitoring module shows that the eigenvalue of a certain area exceeds the high concentration threshold, the module is activated. It controls a slow-release chemical agent tank integrated near the suction nozzle, and the outlet of the agent tank is managed by a small precision electromagnetic valve. The opening time and opening degree of the valve are determined by the module algorithm. The release rate of the chemical agent is not linearly adjusted, but is exponentially related to the eigenvalue of the corresponding grid in the oil stain coverage density matrix that triggers this operation. This means that for areas with slight oil stains, only a short and small dose of agent injection is required; while for identified areas with severe oil stain attachment, the system will calculate a longer opening time and larger opening degree to release emulsifiers or decomposers at an exponentially increasing rate, ensuring that there is enough chemical active substance to penetrate and decompose the oil stains.

[0094] During this critical period of chemical agent injection, the spiral trajectory planning module receives a cooperative work instruction that requires it to suspend the rotational cleaning motion of the suction nozzle. At this time, the main decontamination function of the suction nozzle is temporarily silenced, and only a low-frequency axial reciprocating vibration is maintained. The purpose of this vibration is to mechanically assist the diffusion and penetration of the chemical agent. By disturbing the boundary layer flow on the filter screen surface through small reciprocating movements, the mixing of chemical agents and oil stains is promoted, and the local saturation of the agent in a static state is avoided. At the same time, vibration helps to break the adhesion structure of the oil stains, preparing for the subsequent main cleaning stage. Pausing rotation also reduces the premature flushing of water flow on the agent, allowing the chemical reaction to have sufficient contact time.

[0095] Assuming that after a regular cleaning cycle, the cleaning performance evaluation module discovers through the transmittance scan that the degree of optical improvement in a certain sector in the lower part of the filter is significantly lower than in other sectors. The suspended solids dynamic monitoring module is then instructed to perform a focused review scan of this sector, and the corrected thickness thermogram shows that there is still a signal of residual deposits in this area, while the oil stain feature recognition unit simultaneously confirms that there is a high oil stain coverage density in this area. Before the start of the next cleaning cycle, the differential pressure trigger control module has updated the model weights based on the performance evaluation data from the previous cycle, making it more sensitive to similar forms of mixed oil stain deposits. When the system runs again, the suspended solids dynamic monitoring module identifies that the pollutant characteristic value in this area quickly rises and reaches the threshold value, and the differential pressure trigger control module generates a special cleaning instruction for this area. The spiral trajectory planning module plans a trajectory that preferentially reaches this area, and when the suction nozzle is positioned at the target area, the oil stain dissolving auxiliary module is started, and the chemical agent valve is opened to spray the agent at a higher release rate. The planning module instructs the suction nozzle to stop rotating and only perform small amplitude axial vibration at a frequency of several times per second. After several seconds of chemical pretreatment, the agent valve is closed, and the spiral trajectory planning module immediately resumes the normal rotation and axial feed motion of the suction nozzle, and starts high-intensity hydraulic flushing to completely remove the oil stains that have been emulsified and loosened by the chemical agent.

[0096] It should be noted that the relational terms herein such as first and second and the like are used solely to distinguish one entity or action from another, without necessarily requiring or implying any such actual relationship or order between such entities or actions. Moreover, the terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0097] Although embodiments of the present application have been shown and described, it is to be understood that various modifications, substitutions, replacements and changes can be made to these embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. An intelligent backflush self-cleaning filter system for industrial water treatment, characterized by, Comprise: The suspended solids dynamic monitoring module collects the three-dimensional distribution data of the accumulated material on the surface of the filter medium in real time, and identifies the mixed deposition characteristics of solid suspended solids and oil stains through multi-spectral imaging technology; The differential pressure trigger control module establishes a mapping relationship between the thickness of the deposit and the fluid resistance according to the three-dimensional distribution data, and generates a cleaning trigger instruction when the real-time differential pressure data reaches the dynamic threshold interval; The spiral trajectory planning module analyzes the cleaning trigger instruction, combines the current position of the suction nozzle and the filter screen topology to generate a composite motion trajectory including the rotational angular velocity and the axial displacement rate; The multi-modal 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 filter area of the filter screen along the spiral path.

2. The intelligent flush self-cleaning filter system of claim 1, wherein, The suspended solids dynamic monitoring module comprises: The high-frequency ultrasonic thickness measurement unit periodically scans the deposit accumulation height of the inside surface of the filter screen to form a thickness distribution heat map; The oil stain feature recognition unit distinguishes the spectral reflectance difference between organic pollutants and inorganic suspended solids through near-infrared spectral analysis, and outputs an oil stain coverage density matrix; The dynamic threshold interval is dynamically adjusted according to the weighted calculation results of the thickness distribution heat map and the oil stain coverage density matrix.

3. The intelligent flush self-cleaning filter system of claim 2, wherein, The differential pressure trigger control module performs: Establish a deposit thickness-differential pressure conversion model to convert the thickness values of each partition in the thickness distribution heat map into local differential pressure prediction values; When more than 60% of the partition differential pressure prediction values reach the lower limit of the dynamic threshold interval, the primary cleaning mode is activated; When the differential pressure prediction values of any three consecutive partitions exceed the upper limit of the dynamic threshold interval, the emergency cleaning mode is forcibly activated.

4. The intelligent flush self-cleaning filter system of claim 3, wherein, The spiral trajectory planning module comprises: The filter screen topology reconstruction unit constructs a three-dimensional point cloud model of the filter screen surface through laser ranging data; The kinematics solving unit calculates the shortest motion path of the suction nozzle from the current pose to the target cleaning point according to 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 proportional to the deposit thickness gradient.

5. The intelligent flush self-cleaning filter system of claim 4, wherein, The multi-modal flushing execution module includes: The dual-channel pressure regulation 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 value of the actual motion trajectory of the suction nozzle with the composite motion trajectory in real time; When the deviation value exceeds the tolerance range, the composite motion trajectory is recalculated and the torque distribution ratio of the hydraulic motor is updated.

6. The intelligent flush self-cleaning filter system of claim 5, wherein, Further comprising: The cleaning efficiency evaluation module collects the filter screen light transmittance data after each flushing cycle; The suspended solids dynamic monitoring module corrects the calibration parameters of the thickness distribution heat map according to the light transmittance data; The differential pressure trigger control module updates the weight coefficients of the deposit thickness-differential pressure conversion model based on the corrected calibration parameters.

7. The intelligent flush self-cleaning filter system of claim 6, wherein, The multi-modal flushing execution module adds: The vortex suppression unit adjusts the spray angle of the auxiliary nozzle during the axial motion of the suction nozzle; The spray angle of the auxiliary nozzle is dynamically adjusted according to the filter screen surface flow rate distribution data to ensure that the included angle between the flushing water flow and the filter screen normal remains within the set range.

8. The intelligent flush self-cleaning filter system of claim 7, wherein, The spiral trajectory planning module further comprises: The wear compensation unit records the mechanical wear data of the historical cleaning path; In generating a new composite motion trajectory, the filter screen area with a wear value below a threshold value is preferentially selected as a cleaning starting point; The kinematics calculation unit dynamically optimizes the axial motion acceleration curve of the suction nozzle according to the wear data.

9. The intelligent flush self-cleaning filter system of claim 8, wherein, Further comprising: The oil stain dissolving auxiliary module controls the opening duration of the slow-release chemical tank when a high-concentration oil stain covered area is identified; The release rate of the chemical tank is in an exponential relationship with the eigenvalue of the corresponding area in the oil stain coverage density matrix; The spiral trajectory planning module suspends the rotational motion of the suction nozzle during the release of the chemical, and only maintains axial vibration.

10. The intelligent flush self-cleaning filter system of claim 9, wherein, 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 pressure change rate exceeds a stable threshold, a buffer period is automatically inserted to suspend the piston cylinder motion; The duration of the buffer period is proportional to the integral value of the pressure change rate.

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