Intelligent track planning method and system for universal cleaning head of hollow fiber membrane

Abstract

The invention relates to the technical field of water treatment equipment cleaning, and discloses a hollow fiber membrane universal cleaning head track intelligent planning method and system. The method comprises the following steps: acquiring membrane bundle installation parameter information to generate three-dimensional position data of an area to be cleaned; calculating an effective cleaning range of a cleaning nozzle to determine a lap joint degree and a track interval; dividing the surface of the membrane cluster into a plurality of sub-regions and sorting the sub-regions according to the distribution density of pollutants; cleaning path coordinate points are calculated based on the effective cleaning range, the lap joint degree and the track interval parameters, and a cleaning track is determined; and converting into a motion control instruction. The membrane surface is accurately and uniformly cleaned, and the cleaning efficiency and quality are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of water treatment equipment cleaning, in particular to a hollow fiber membrane universal cleaning head trajectory intelligent planning method and system. BACKGROUND

[0002] Hollow fiber membranes are a kind of filtration material commonly used in water treatment engineering. Due to their large specific surface area and high flux, they are widely used in drinking water purification, wastewater treatment, and seawater desalination. During long-term operation, pollutants inevitably accumulate on the surface of hollow fiber membranes, leading to a decrease in membrane flux and filtration efficiency, so regular cleaning and maintenance are required.

[0003] Currently, there are two main methods for cleaning hollow fiber membranes: chemical cleaning and physical cleaning. Although chemical cleaning is effective, it can shorten the service life of the membrane and cause secondary pollution. Physical cleaning is more environmentally friendly, and the universal cleaning head technology using high-pressure water jets is a relatively effective physical cleaning method.

[0004] However, the existing hollow fiber membrane universal cleaning head technology has the following defects and shortcomings: the existing cleaning technology lacks the ability to accurately model the three-dimensional structure of the hollow fiber membrane bundle, and cannot adaptively adjust the cleaning strategy according to the installation parameters of different membrane assemblies, leading to problems such as cleaning dead angles and uneven cleaning, affecting the cleaning effect. The existing cleaning technology usually uses a fixed trajectory mode for cleaning, without considering the effective cleaning range of the cleaning nozzle and the reasonable overlap degree between trajectories, resulting in insufficient or repeated cleaning in some areas, causing low cleaning efficiency and energy waste. The existing cleaning scheme lacks the ability to perceive and respond to uneven distribution of membrane surface pollutants, and cannot prioritize cleaning order based on the pollution level of different areas, leading to unreasonable allocation of cleaning time and difficulty in achieving efficient and accurate targeted cleaning, reducing the overall cleaning effect and equipment service life. SUMMARY

[0005] The hollow fiber membrane universal cleaning head trajectory intelligent planning method and system provided by the embodiments of the present application can at least solve some of the problems existing in the prior art.

[0006] In a first aspect of the embodiments of the present application, a hollow fiber membrane universal cleaning head trajectory intelligent planning method is provided, comprising: Obtaining installation parameter information of a hollow fiber membrane bundle, generating three-dimensional to-be-cleaned region position data of the hollow fiber membrane bundle based on the installation parameter information; Sampling real-time working parameters of the hollow fiber membrane universal cleaning head, calculating the effective cleaning range of the cleaning nozzle according to the real-time working parameters, determining the overlap degree between adjacent cleaning trajectories based on the effective cleaning range, and generating trajectory spacing parameters for uniform cleaning of the surface of the hollow fiber membrane bundle according to the overlap degree; According to the three-dimensional to-be-cleaned region position data, a hollow fiber membrane bundle surface is divided into a plurality of cleaning sub-regions, each cleaning sub-region is prioritized according to the pollution distribution density of the cleaning sub-region, and a cleaning sequence is determined; According to the effective cleaning range, the overlap degree, and the trajectory spacing parameter, cleaning path coordinate points of the plurality of cleaning sub-regions are calculated in the cleaning sequence, and a cleaning trajectory curve is determined based on the cleaning path coordinate points. The cleaning trajectory curve is converted into a hollow fiber membrane universal cleaning head motion control instruction, and the motion control instruction is issued to a cleaning execution device.

[0007] Obtain installation parameter information of a hollow fiber membrane bundle, and generate three-dimensional to-be-cleaned region position data of the hollow fiber membrane bundle based on the installation parameter information, including: A strain sensing array is constructed on the surface of the hollow fiber membrane bundle support, the strain sensing array acquires support deformation data of the hollow fiber membrane bundle during installation based on the installation parameter information, and installation stress distribution of the hollow fiber membrane bundle is calculated according to the support deformation data; A capacitive sensing array is constructed on the surface of the hollow fiber membrane bundle based on the installation stress distribution of the hollow fiber membrane bundle, dielectric constant change data between the hollow fiber membrane wire and the capacitive sensing array is acquired through the capacitive sensing array, and three-dimensional space coordinates of the hollow fiber membrane wire are determined according to the dielectric constant change data; Surface charge distribution data of the hollow fiber membrane wire is acquired, installation stress deformation regions of the hollow fiber membrane wire are determined based on the charge distribution data and the installation stress distribution of the hollow fiber membrane bundle, stress size and stress direction of the installation stress deformation regions are determined, three-dimensional topographic data of the surface of the hollow fiber membrane bundle is generated based on the stress size and the stress direction; The three-dimensional space coordinates of the hollow fiber membrane wire and the three-dimensional topographic data of the surface of the hollow fiber membrane bundle are spatially registered to generate three-dimensional to-be-cleaned region position data of the hollow fiber membrane bundle.

[0008] Real-time working parameters of a hollow fiber membrane universal cleaning head are acquired, a cleaning jet effective cleaning range is calculated according to the real-time working parameters, an adjacent cleaning trajectory overlap degree is determined based on the effective cleaning range, and a trajectory spacing parameter for uniform cleaning of the surface of the hollow fiber membrane bundle is generated according to the overlap degree, including: A jet flow pressure sensing array is arranged circumferentially around the cleaning jet, and a sensitivity calibration coefficient of the jet flow pressure sensing array is generated based on the real-time working parameters; Using the sensitivity calibration coefficient to enable the jet pressure sensor array to collect pressure field distribution data of the cleaning jet, calculating the jet pressure field gradient threshold according to the pressure field distribution data, and determining the effective cleaning range of the cleaning nozzle based on the jet pressure field gradient threshold; Move the cleaning nozzle along a preset trajectory, calibrate the jet pressure field data of adjacent trajectories collected by the jet pressure sensor array based on the sensitivity calibration coefficient, and calculate the spatial change rate of the jet pressure field data of adjacent trajectories; Determine a pressure superposition intensity matrix based on the spatial variation rate and the jet pressure field gradient threshold, spatially map the pressure superposition intensity matrix to the jet pressure isosurface boundary, identify the distribution positions of cleaning dead zones and over-cleaning areas, and adjust the overlap of adjacent cleaning trajectories based on the distribution positions; The overlapping degree of adjacent cleaning tracks and the effective cleaning range are correlated and calculated to generate track spacing parameters for uniform cleaning of the hollow fiber membrane bundle surface.

[0009] Determining a pressure stacking intensity matrix based on the spatial variation rate and the jet pressure field gradient threshold, spatially mapping the pressure stacking intensity matrix to the jet pressure isosurface boundary, identifying the distribution positions of cleaning dead zones and over-cleaning areas, and adjusting the overlap of adjacent cleaning trajectories based on the distribution positions, including: generating a three-dimensional tensor matrix according to the spatial change rate, and projecting the three-dimensional tensor matrix onto a two-dimensional plane to form a spatial change rate distribution matrix; Comparing the spatial change rate distribution matrix with the jet pressure field gradient threshold, determining the coordinates of the boundary points where the spatial change rate exceeds the jet pressure field gradient threshold, and generating a pressure superposition intensity matrix based on the boundary point coordinates; Performing surface fitting on the jet pressure isosurface boundary to generate a continuous and smooth jet pressure distribution surface, performing spatial coordinate registration on the pressure superposition intensity matrix and the jet pressure distribution surface to generate a three-dimensional distribution map of the cleaning pressure field; Calculating the spatial position coordinates of the pressure field zero value area and the pressure field peak area based on the three-dimensional distribution map of the cleaning pressure field, and mapping the spatial position coordinates to the surface of the hollow fiber membrane to form a distribution map of the cleaning dead zone and the over-cleaning area; The ratio of the area of ​​the cleaning dead zone to the area of ​​the over-cleaning area is calculated according to the distribution diagram of the cleaning dead zone and the over-cleaning area, and the overlap degree of adjacent cleaning tracks is adjusted based on the ratio.

[0010] Dividing the surface of the hollow fiber membrane bundle into a plurality of cleaning sub-areas according to the three-dimensional position data of the area to be cleaned, prioritizing the cleaning sub-areas according to the distribution density of pollutants in each of the cleaning sub-areas, and determining a cleaning order, including: extracting surface feature point coordinates of the hollow fiber membrane bundle in the three-dimensional to-be-cleaned region position data, and dividing the surface of the hollow fiber membrane bundle into a plurality of cleaning sub-regions based on the surface feature point coordinates; calculating a boundary point set of each of the cleaning sub-regions, and determining an area and a perimeter of each of the cleaning sub-regions according to the boundary point set; capturing a pollution distribution image of the surface of the hollow fiber membrane bundle, extracting a gray value of the pollution distribution image, mapping the gray value to a boundary point set corresponding to the cleaning sub-region, calculating a pollution distribution density of each of the cleaning sub-regions based on the boundary point set, and generating a pollution load index of each cleaning sub-region according to the pollution distribution density and the area; identifying a common boundary coordinate of adjacent cleaning sub-regions based on the boundary point set, calculating a connected length of the common boundary coordinate, and determining a position correlation strength as a ratio of the connected length to the perimeter; calculating a pollution distribution density gradient between adjacent cleaning sub-regions according to the position correlation strength, prioritizing the cleaning sub-regions based on the pollution distribution density gradient and the pollution load index, and determining a cleaning sequence.

[0011] According to the effective cleaning range, the overlap degree and the trajectory spacing parameter, the cleaning path coordinate points of the plurality of cleaning sub-regions are calculated in the cleaning sequence, and a cleaning trajectory curve is determined based on the cleaning path coordinate points, including: According to the effective cleaning range data, a spatial curvature distribution of the surface of the hollow fiber membrane bundle is extracted, the effective cleaning range data is projected onto the spatial curvature distribution, a projection deformation coefficient is calculated, the effective cleaning range data is corrected according to the projection deformation coefficient, and an actual coverage area parameter is generated; According to the actual coverage area parameter, the overlap degree and the trajectory spacing parameter, a local trajectory offset is calculated, and cleaning path coordinate points are generated based on the local trajectory offset and the projection deformation coefficient; Based on the cleaning path coordinate points, an included angle between a jet flow direction of a cleaning nozzle and a membrane surface normal vector is calculated, a local jet effective action distance is calculated based on the included angle, and a region range that needs to be optimized is determined according to the local jet effective action distance and the projection deformation coefficient; In the region range, a coordinate point optimization criterion is established, the cleaning path coordinate points are adjusted in density and optimized in position according to the coordinate point optimization criterion, and a cleaning trajectory curve that meets the requirements of local jet action and surface curvature constraints is generated.

[0012] Establish a coordinate point optimization criterion in the region range, and perform density adjustment and position optimization on the cleaning path coordinate points according to the coordinate point optimization criterion, to generate a cleaning trajectory curve meeting the requirements of local jet action and surface curvature constraints, comprising: Calculate the topological connection features between the cleaning path coordinate points in the region range, compare the topological connection features with the minimum trajectory turning radius, and determine a to-be-optimized cleaning sub-region that needs to be optimized; Extract local surface geometric features in the to-be-optimized cleaning sub-region, calculate the principal curvature and Gaussian curvature of the local surface, determine a coordinate point density weight coefficient according to the numerical distribution of the principal curvature and Gaussian curvature, and combine the density weight coefficient with the cleaning path coordinate points to generate a coordinate point optimization criterion; Generate position information of new coordinate points and to-be-deleted coordinate points in the to-be-optimized cleaning sub-region based on the coordinate point optimization criterion, input the position information into a coordinate point self-organizing optimization equation based on fluid diffusion principle, and calculate the spatial position correction amount of each cleaning path coordinate point in the to-be-optimized cleaning sub-region; Reconstruct the cleaning path coordinate points according to the spatial position correction amount, extract the direction features of the reconstructed coordinate point sequence, and generate a cleaning trajectory curve meeting the requirements of local jet action and surface curvature constraints based on the direction features.

[0013] In a second aspect of the embodiment of the application, a hollow fiber membrane universal cleaning head trajectory intelligent planning system is provided, comprising: A first unit is configured to acquire installation parameter information of a hollow fiber membrane bundle, and generate three-dimensional to-be-cleaned region position data of the hollow fiber membrane bundle based on the installation parameter information; A second unit is configured to acquire real-time working parameters of a hollow fiber membrane universal cleaning head, calculate an effective cleaning range of the cleaning nozzle based on the real-time working parameters, determine an adjacent cleaning trajectory lap degree based on the effective cleaning range, and generate a trajectory spacing parameter for uniform cleaning of the surface of the hollow fiber membrane bundle based on the lap degree; A third unit is configured to divide the surface of the hollow fiber membrane bundle into a plurality of cleaning sub-regions based on the three-dimensional to-be-cleaned region position data, perform priority sorting on the cleaning sub-regions according to the pollutant distribution density of each cleaning sub-region, and determine a cleaning sequence; A fourth unit is configured to calculate cleaning path coordinate points of the plurality of cleaning sub-regions according to the effective cleaning range, the lap degree, and the trajectory spacing parameter, and determine a cleaning trajectory curve based on the cleaning path coordinate points; A fifth unit is configured to convert the cleaning trajectory curve into a hollow fiber membrane universal cleaning head motion control instruction, and issue the motion control instruction to a cleaning execution device.

[0014] According to a third aspect of an embodiment of the present invention, an electronic device is provided, including: processor; a memory for storing processor-executable instructions; The processor is configured to call the instructions stored in the memory to execute the aforementioned method.

[0015] According to a fourth aspect of an embodiment of the present invention, a computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method described above is implemented.

[0016] The present invention obtains the installation parameter information of the hollow fiber membrane bundle and generates three-dimensional position data of the area to be cleaned, collects real-time working parameters to determine the overlap and spacing parameters of the cleaning trajectory, thereby achieving precise positioning and uniform cleaning of the surface of the hollow fiber membrane bundle, and improving the cleaning efficiency and cleaning quality.

[0017] The present invention divides the surface of the hollow fiber membrane bundle into multiple cleaning sub-areas, and prioritizes them according to the distribution density of pollutants to determine the order of cleaning. It can adopt differentiated cleaning strategies for areas with different degrees of pollution, effectively improving the targeted cleaning and resource utilization.

[0018] The present invention determines the cleaning trajectory curve based on the cleaning path coordinate points, and converts it into motion control instructions and sends them to the cleaning execution device, thereby realizing intelligent and automated control of the cleaning process, reducing manual intervention, lowering operation difficulty and labor costs, and improving the safety and reliability of the cleaning operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 Schematic diagram of the process of intelligent planning method of hollow fiber membrane universal cleaning head trajectory according to an embodiment of the present invention.

[0020] Figure 2 Schematic diagram of a flow chart for determining track spacing parameters according to an embodiment of the present invention. DETAILED DESCRIPTION

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0022] The technical solutions of the present application will be described in detail below with specific examples. The following specific examples can be combined with each other, and the same or similar concepts or processes may not be described in detail in some examples.

[0023] Figure 1 The flowchart of the intelligent trajectory planning method for the hollow fiber membrane universal cleaning head of the embodiment of the present application is shown in FIG. 1. Figure 1 As shown in the figure, the method comprises: Obtaining installation parameter information of the hollow fiber membrane bundle, generating three-dimensional to-be-cleaned region position data of the hollow fiber membrane bundle based on the installation parameter information; Obtaining real-time working parameters of the hollow fiber membrane universal cleaning head, calculating the effective cleaning range of the cleaning nozzle according to the real-time working parameters, determining the adjacent cleaning trajectory overlap degree based on the effective cleaning range, and generating the trajectory spacing parameter for uniform cleaning of the surface of the hollow fiber membrane bundle according to the overlap degree; Dividing the surface of the hollow fiber membrane bundle into a plurality of cleaning sub-regions according to the three-dimensional to-be-cleaned region position data, prioritizing each cleaning sub-region according to the pollution distribution density of the cleaning sub-region, and determining the cleaning sequence; According to the effective cleaning range, the overlap degree, and the trajectory spacing parameter, calculating the cleaning path coordinate points of the plurality of cleaning sub-regions according to the cleaning sequence, and determining the cleaning trajectory curve based on the cleaning path coordinate points; Converting the cleaning trajectory curve into a motion control instruction of the hollow fiber membrane universal cleaning head, and issuing the motion control instruction to a cleaning execution device.

[0024] In an optional embodiment, obtaining installation parameter information of the hollow fiber membrane bundle, generating three-dimensional to-be-cleaned region position data of the hollow fiber membrane bundle based on the installation parameter information, comprises: Constructing a strain sensing array on the surface of the hollow fiber membrane bundle support, causing the strain sensing array to collect support deformation data of the hollow fiber membrane bundle during installation based on the installation parameter information, and calculating the installation stress distribution of the hollow fiber membrane bundle according to the support deformation data; Based on the installation stress distribution of the hollow fiber membrane bundle, constructing a capacitive sensing array on the surface of the hollow fiber membrane bundle, collecting dielectric constant change data between the hollow fiber membrane wire and the capacitive sensing array through the capacitive sensing array, and determining the three-dimensional space coordinates of the hollow fiber membrane wire according to the dielectric constant change data; The surface charge distribution data of the hollow fiber membrane filaments in the set is collected, based on the charge distribution data and the stress distribution of the hollow fiber membrane set installation, a stress deformation area of the hollow fiber membrane filaments is determined, the stress size and the stress direction of the stress deformation area are determined, and based on the stress size and the stress direction, surface three-dimensional topography data of the hollow fiber membrane set is generated; The three-dimensional spatial coordinates of the hollow fiber membrane filaments are spatially registered with the surface three-dimensional topography data of the hollow fiber membrane set, and three-dimensional to-be-cleaned region position data of the hollow fiber membrane set is generated.

[0025] In the specific embodiment of the method of the application, the installation parameter information of the hollow fiber membrane set is obtained, including the model of the membrane set, the installation position, the installation angle, the fixed point position and other related data. These parameter information provides basic data support for subsequent processing.

[0026] When constructing the strain sensor array on the surface of the hollow fiber membrane set support, a flexible strain sensor such as a graphene-based flexible strain sensor is selected and attached to the key stress point positions on the surface of the support in a 10x10 matrix arrangement. The size of each sensor is 5mmx5mm, the thickness is 0.2mm, and the sensitivity is 0.02% / N. The strain sensor array is connected to the data acquisition module through wires, and the sampling frequency is set to 100Hz to monitor the deformation of the support in real time during installation. When the hollow fiber membrane set is installed, the strain sensor array collects deformation data of each point of the support, for example, during installation, the maximum strain value measured at the fixed point of a support is 2.5%, indicating that the stress at this point is relatively large.

[0027] When calculating the stress distribution of the hollow fiber membrane set installation based on the collected deformation data of the support, the deformation data is processed using the principles of elasticity. By establishing a virtual model of the support structure and combining material property parameters such as a stainless steel support with an elastic modulus of 210GPa, the collected deformation data is converted into stress distribution data. The calculation results show that the stress concentration value at the upper connection of the support reaches 75MPa, and the stress distribution at the bottom fixed region is relatively uniform, about 30MPa.

[0028] When constructing the capacitive sensor array based on the stress distribution of the hollow fiber membrane set installation, according to the aforementioned stress distribution data, the density of sensors in the stress concentration area is increased. Using flexible printed circuit technology, an 8x12 capacitive sensor array is constructed on the surface of the hollow fiber membrane set, each capacitive unit has an area of 10mmx10mm, and the dielectric layer has a thickness of 0.5mm. The capacitive sensor array works under an alternating current signal with a frequency of 1MHz, and the sensitivity can reach a change of 0.01pF.

[0029] The capacitance value of each sensing unit is continuously monitored by recording the permittivity change data between the hollow fiber membrane and the capacitive sensing array. When the distance or medium properties between the hollow fiber membrane and the capacitive sensor change, the corresponding capacitance value will also change. In actual measurement, the capacitance value of the normal region is 5.2 pF, and when the membrane is displaced or deformed, the capacitance value can change to the range of 4.8-5.5 pF. The capacitance distribution of the entire array is recorded to generate a spatial capacitance distribution matrix.

[0030] When determining the three-dimensional spatial coordinates of the hollow fiber membrane based on the permittivity change data, the spatial position of the membrane is calculated by triangulation method using the relationship between capacitance and distance. For an 8x12 sensing array, 96 spatial sampling points are obtained, and after data interpolation processing, a high-precision spatial position mapping is formed. Through the inversion algorithm, the capacitance value change is converted into distance parameters, and then the three-dimensional coordinates of each membrane are determined. For example, in a certain test area, the spatial distribution range of the membrane is x axis ± 50 mm, y axis ± 75 mm, and z axis 0-120 mm, and the spatial positioning accuracy reaches ± 0.5 mm.

[0031] When collecting the surface charge distribution data of the hollow fiber membrane, a high-sensitivity surface potential meter array is used, with a measurement range of ± 500 V and a resolution of 0.1 V. 25 measurement points are set on the surface of the membrane to form a surface charge distribution map. The measurement results show that the surface potential of the normal region is -5 V to +5 V, and the stress deformation region can have abnormal potential values of -15 V to +20 V.

[0032] Based on the charge distribution data and the stress distribution of the membrane bundle installation, the installation stress deformation region is determined by spatial registration and correlation analysis of the two sets of data. The pattern recognition algorithm is applied to identify the regions with abnormal charge distribution and stress concentration, which are usually manifested as twisted, bent or over-stretched regions of the membrane. By setting threshold values (stress threshold value is 60 MPa, charge abnormal threshold value is ± 15 V), the stress deformation region is marked.

[0033] When determining the stress size and stress direction of the installation stress deformation region, the principal stress value and its direction are calculated by tensor analysis method. For a typical stress deformation region, the principal stress value reaches 65 MPa, and the stress direction is 35° angle with the membrane axis, indicating that there is obvious shear stress in this region, which is easy to cause accumulation of surface pollutants or structural damage of the membrane.

[0034] When generating the three-dimensional topography data of the hollow fiber membrane bundle surface based on the stress size and the stress direction, the stress information is integrated to construct the stress topography map of the membrane bundle surface. By using the stress-deformation relationship, the deformation state of the membrane filaments under the stress is predicted to generate the surface micro-topography data. The resolution of the topography data reaches 10 μm, and the surface wrinkles and deformation of the membrane filaments can be clearly displayed.

[0035] When spatially registering the three-dimensional spatial coordinates of the hollow fiber membrane filaments and the three-dimensional topography data of the hollow fiber membrane bundle surface, the feature point matching and spatial transformation technology are used to establish the corresponding relationship between the two sets of data. The registration accuracy is controlled within ±0.2 mm to ensure that the topography information and the spatial position are accurately corresponding. The generated three-dimensional position data of the cleaning area of the hollow fiber membrane bundle contains accurate spatial position information and surface topography characteristics, which provides a basis for subsequent accurate cleaning.

[0036] In an alternative embodiment, the real-time working parameters of the universal cleaning head of the hollow fiber membrane are collected, the effective cleaning range of the cleaning nozzle is calculated according to the real-time working parameters, the adjacent cleaning track overlap degree is determined based on the effective cleaning range, and the track spacing parameters for uniform cleaning of the hollow fiber membrane bundle surface are generated according to the overlap degree, including: A jet pressure sensing array is arranged circumferentially around the cleaning nozzle, and the sensitivity calibration coefficient of the jet pressure sensing array is generated based on the real-time working parameters; The sensitivity calibration coefficient is used to make the jet pressure sensing array collect the pressure field distribution data of the cleaning jet, and the jet pressure field gradient threshold value is calculated according to the pressure field distribution data, and the effective cleaning range of the cleaning nozzle is determined based on the jet pressure field gradient threshold value; The cleaning nozzle is moved along the preset track, the adjacent track jet pressure field data collected by the jet pressure sensing array is calibrated based on the sensitivity calibration coefficient, and the spatial variation rate of the adjacent track jet pressure field data is calculated; The pressure superposition intensity matrix is determined according to the spatial variation rate and the jet pressure field gradient threshold value, the pressure superposition intensity matrix is spatially mapped with the jet pressure isosurface boundary, the distribution positions of the cleaning dead zone and the over-cleaning area are identified, and the adjacent cleaning track overlap degree is adjusted according to the distribution positions; The adjacent cleaning track overlap degree and the effective cleaning range are associated and calculated to generate the track spacing parameters for uniform cleaning of the hollow fiber membrane bundle surface.

[0037] The specific embodiment collects the real-time working parameters by the jet pressure sensing array and calculates the effective cleaning range, and then determines the adjacent cleaning track overlap degree to generate the track spacing parameters for uniform cleaning.

[0038] Figure 2A flowchart for determining trajectory spacing parameters of an embodiment of the present application is shown. As shown in Figure 2 In the process of determining trajectory spacing parameters, first, a jet pressure sensor array is uniformly arranged circumferentially around the cleaning nozzle, which consists of 8 micro pressure sensors, one installed every 45 degrees along the circumference of the outer wall of the cleaning nozzle, for collecting pressure field distribution data of the cleaning jet. For the cleaning nozzle, real-time working parameters are collected, including water supply pressure, flow rate, nozzle rotation speed and movement speed. The cleaning nozzle is placed on a standard test platform, and under different water supply pressures of 0.5 MPa to 2.5 MPa, the output signals of the jet pressure sensor array are collected and compared with the readings of a standard pressure gauge, and a sensitivity calibration coefficient matrix K of the sensor array is calculated. For example, when the water supply pressure is 1.5 MPa, the calibration coefficient K value obtained is [1.03, 0.98, 1.05, 0.97, 1.02, 0.99, 1.01, 0.96], corresponding to the calibration coefficients of the 8 sensors respectively.

[0039] Using the calibrated jet pressure sensor array, the jet pressure field distribution within a 360-degree range around the cleaning nozzle is measured in a stationary state. During measurement, the pressure value is measured every 5 mm in the radial region from 10 mm to 500 mm from the nozzle, forming a jet pressure field distribution data P(r, θ). By calculating the pressure difference between adjacent measurement points divided by the distance, the jet pressure field gradient value G(r, θ) is obtained. The gradient threshold Gth is set to 0.05 MPa / cm, and when the pressure gradient G(r, θ) is less than the threshold Gth, it is considered that the jet pressure in this region changes smoothly and belongs to the effective cleaning range. By this method, the effective cleaning range of the cleaning nozzle is determined to be an irregular area, which can reach 380 mm in the direction of the main jet and 120 mm perpendicular to the direction of the main jet.

[0040] The cleaning nozzle is moved along a preset horizontal straight trajectory, and the movement speed is set to 10 cm / s. During movement, the calibrated jet pressure sensor array is used to continuously collect jet pressure field data. When the cleaning nozzle moves along a second trajectory parallel to the first trajectory, jet pressure field data is collected again. The initial spacing of the two trajectories is set to 90% of the effective cleaning range, i.e. 108 mm. For the collected pressure field data P1(x, y) and P2(x, y) of the two trajectories, the pressure field spatial variation rate V(x, y) at the spatial coordinates (x, y) is calculated, which is represented as the ratio of the pressure difference between adjacent sampling points to the distance.

[0041] The spatial variation rate V(x, y) is compared with the jet pressure field gradient threshold Gth to construct a pressure superposition intensity matrix S(x, y). When V(x, y) is less than 0.8Gth, it is determined to be a cleaning dead zone, and S(x, y) is assigned a value of 0; when V(x, y) is greater than 1.2Gth, it is determined to be an over-cleaning region, and S(x, y) is assigned a value of 2; the remaining regions are appropriate cleaning regions, and S(x, y) is assigned a value of 1. The distribution positions of the cleaning dead zone and the over-cleaning region are identified by this method, and the results show that the initial setting of a 108mm track spacing results in a cleaning dead zone of about 15mm wide in the middle region.

[0042] Based on the analysis results of the pressure superposition intensity matrix S(x, y), the overlap degree of adjacent cleaning tracks is adjusted. The track spacing is reduced from 108mm to 95mm, and the pressure superposition intensity matrix is analyzed again after testing. It is found that the cleaning dead zone is significantly reduced, but a small amount of over-cleaning region appears. The track spacing is further adjusted to 100mm, and it is found that there is neither a significant cleaning dead zone nor an over-cleaning region after testing again. The area ratio of S(x, y)=1 in the pressure superposition intensity matrix is more than 95%, indicating that the cleaning uniformity is best at this time.

[0043] The optimal overlap degree is associated with the effective cleaning range to calculate the track spacing parameter D=100mm. In addition, considering the influence of different cleaning head moving speeds on the cleaning effect, the corresponding relationship between speed and track spacing is established: when the moving speed is 5cm / s, the best track spacing is 90mm; when the moving speed is 15cm / s, the best track spacing is 110mm. These parameters form the track spacing parameter set for uniform cleaning of the surface of a hollow fiber membrane bundle, and appropriate parameter combinations can be selected according to actual cleaning needs.

[0044] The track spacing parameters obtained by the above method are applied to actual hollow fiber membrane bundle cleaning operations, and the test results show that the cleaning uniformity is improved by 37%, the cleaning efficiency is improved by 25%, and the risk of membrane surface wear is significantly reduced, prolonging the service life of the membrane module.

[0045] In an alternative embodiment, a pressure superposition intensity matrix is determined based on the spatial variation rate and the jet pressure field gradient threshold, the pressure superposition intensity matrix is spatially mapped with a jet pressure isosurface boundary to identify the distribution positions of a cleaning dead zone and an over-cleaning region, and the overlap degree of adjacent cleaning tracks is adjusted according to the distribution positions, comprising: A three-dimensional tensor matrix is generated based on the spatial variation rate, and the three-dimensional tensor matrix is projected onto a two-dimensional plane to form a spatial variation rate distribution matrix; The spatial variation rate distribution matrix is compared with the jet flow pressure field gradient threshold value to determine the boundary point coordinates of the spatial variation rate exceeding the jet flow pressure field gradient threshold value, and a pressure superposition intensity matrix is generated based on the boundary point coordinates; The jet flow pressure isosurface boundary is subjected to surface fitting to generate a continuous and smooth jet flow pressure distribution surface, the pressure superposition intensity matrix is subjected to spatial coordinate registration with the jet flow pressure distribution surface to generate a cleaning pressure field three-dimensional distribution map; Based on the cleaning pressure field three-dimensional distribution map, the spatial position coordinates of the pressure field zero value region and the pressure field peak value region are calculated, and the spatial position coordinates are mapped to the hollow fiber membrane surface to form a distribution map of the cleaning dead zone and the over-cleaning region; According to the distribution map of the cleaning dead zone and the over-cleaning region, the ratio of the cleaning dead zone area to the over-cleaning region area is calculated, and the adjacent cleaning track lap degree is adjusted based on the ratio.

[0046] In this embodiment, first, the spatial variation rate data of the jet flow pressure field and the preset jet flow pressure field gradient threshold value are obtained. The spatial variation rate of the jet flow pressure field represents the degree of change of the pressure in the three-dimensional space, which is usually characterized by the first derivative or gradient value of the pressure field. For example, under a certain test working condition, a high-precision pressure sensor array can be used to collect pressure data at grid points on the surface of the hollow fiber membrane at intervals of 5 mm, forming a spatial pressure distribution matrix.

[0047] Based on the collected pressure data, a three-dimensional tensor matrix is generated. Each element value of the three-dimensional tensor matrix represents the pressure field variation rate at the corresponding spatial position. For example, in the test, the generated three-dimensional tensor matrix has a size of 100x100x50, covering a spatial area of 500mmx500mmx250mm. The three-dimensional tensor matrix is projected onto a two-dimensional plane by the maximum value projection method to form a spatial variation rate distribution matrix with a size of 100x100.

[0048] The spatial variation rate distribution matrix is compared with the preset jet flow pressure field gradient threshold value. In actual application, the threshold value can be set to 0.5 kPa / mm, which means that when the pressure change per millimeter exceeds 0.5 kPa, it is considered that the gradient is too large and may cause uneven cleaning. Through comparison, the boundary point coordinates of the spatial variation rate exceeding the threshold value are determined. For example, 285 boundary points are identified in the test data, and the coordinates of these points are recorded as Based on these boundary point coordinates, a pressure superposition intensity matrix is generated, which reflects the cumulative pressure intensity of each region in the cleaning process.

[0049] The jet pressure isosurface boundary is fitted with a curved surface to generate a continuous and smooth jet pressure distribution surface. In this embodiment, a radial basis function is used for surface fitting, the spline smoothing parameter is set to 0.85, and the number of control points is 500. A continuous pressure distribution surface is fitted. The surface can accurately express the continuous distribution characteristics of the jet pressure in space, and the fitting accuracy reaches 96.8% of the original discrete data.

[0050] The pressure superimposed strength matrix is spatially coordinated with the jet pressure distribution surface. In the registration process, the iterative closest point algorithm is used, the maximum number of iterations is set to 200, the convergence threshold is 0.001 mm, and the registration error is controlled within 0.05 mm. Through registration, the pressure superimposed strength information is associated with the actual jet pressure distribution to generate a three-dimensional distribution map of the cleaning pressure field.

[0051] Based on the three-dimensional distribution map of the cleaning pressure field, the spatial position coordinates of the pressure field zero value area and the pressure field peak value area are calculated. The zero value area is defined as the area with a pressure lower than 0.2 kPa, representing an area with insufficient cleaning force; the peak value area is defined as the area with a pressure higher than 2.5 kPa, representing an area with excessive cleaning force. In the test example, 32 zero value area clusters are identified, with a total area of 125 cm²; 18 peak value area clusters are identified, with a total area of 78 cm².

[0052] These spatial position coordinates are mapped to the surface of the hollow fiber membrane to form a distribution map of the cleaning dead zone and the over-cleaning area. The nearest distance projection method is used to project the pressure field feature points in three-dimensional space onto the surface model of the hollow fiber membrane. In this embodiment, the surface model of the hollow fiber membrane is represented by a mesh composed of 80000 triangular facets, and the projection accuracy is controlled within 0.02 mm.

[0053] According to the distribution map of the cleaning dead zone and the over-cleaning area, the ratio of the cleaning dead zone area to the over-cleaning area is calculated. In the test example, the cleaning dead zone area is 125 cm², the over-cleaning area is 78 cm², and the ratio is about 1.6. Based on the ratio, the overlap degree of adjacent cleaning tracks is adjusted. When the ratio is greater than 1.5, it indicates that there are more cleaning dead zones, and the track overlap degree needs to be increased; when the ratio is less than 0.5, it indicates that there are more over-cleaning areas, and the track overlap degree needs to be reduced.

[0054] When the initial overlap degree is 30%, the ratio obtained by the above method is 1.6, so the overlap degree is adjusted to 42%. After adjustment, the cleaning effect is evaluated again, the cleaning dead zone area is reduced to 58 cm², the over-cleaning area is slightly increased to 85 cm², the ratio is reduced to 0.68, and it is within the ideal interval (0.5~1.5), indicating that the cleaning uniformity has been significantly improved.

[0055] Through the implementation of the above technical means, the dead zones and over-cleaning areas in the hollow fiber membrane cleaning process can be accurately identified, and the cleaning uniformity can be improved by optimizing and adjusting the lap degree of adjacent cleaning tracks. In actual application tests, the optimized cleaning track parameters are used for hollow fiber membrane cleaning, the membrane flux recovery rate is increased by 18%, the cleaning time is shortened by 23%, and the cleaning medium consumption is reduced by 15%, which significantly improves the cleaning efficiency and service life of the hollow fiber membrane.

[0056] In an optional embodiment, the surface of the hollow fiber membrane bundle is divided into a plurality of cleaning sub-regions according to the three-dimensional to-be-cleaned region position data, the cleaning sub-regions are prioritized according to the pollution distribution density of each cleaning sub-region, and the cleaning sequence is determined, including: The surface feature point coordinates of the hollow fiber membrane bundle are extracted from the three-dimensional to-be-cleaned region position data, and the surface of the hollow fiber membrane bundle is divided into a plurality of cleaning sub-regions based on the surface feature point coordinates; The boundary point set of each cleaning sub-region is calculated, and the area and perimeter of each cleaning sub-region are determined according to the boundary point set; A pollution distribution image of the surface of the hollow fiber membrane bundle is taken, the gray value of the pollution distribution image is extracted, the gray value is mapped to the boundary point set corresponding to the cleaning sub-region, the pollution distribution density of each cleaning sub-region is calculated based on the boundary point set, and the pollution load index of each cleaning sub-region is generated according to the pollution distribution density and the area; The common boundary coordinates of adjacent cleaning sub-regions are identified based on the boundary point set, the connected length of the common boundary coordinates is calculated, and the ratio of the connected length to the perimeter is determined as the position correlation strength; The pollution distribution density gradient between adjacent cleaning sub-regions is calculated according to the position correlation strength, and the cleaning sub-regions are prioritized based on the pollution distribution density gradient and the pollution load index to determine the cleaning sequence.

[0057] According to the specific embodiment of dividing the surface of the hollow fiber membrane bundle into a plurality of cleaning sub-regions according to the three-dimensional to-be-cleaned region position data, a series of technical steps are used to effectively clean the pollution on the surface of the hollow fiber membrane bundle.

[0058] First, the three-dimensional position data of the cleaning region of the hollow fiber membrane bundle is acquired. These data can be collected by a three-dimensional laser scanner, a depth camera, or other three-dimensional imaging devices. Surface feature point coordinates are extracted from these three-dimensional data. During the extraction process, a surface feature detection algorithm is used, which identifies significant features by calculating the normal vector and curvature variation of each point in the three-dimensional point cloud. For example, in one practical application, 25,000 surface feature points were extracted from the three-dimensional point cloud, which accurately described the surface geometry of the hollow fiber membrane bundle.

[0059] Based on the extracted surface feature point coordinates, a region growing division algorithm is used to divide the surface of the hollow fiber membrane bundle into multiple cleaning sub-regions. This algorithm takes the feature points as seed points and expands the regions according to the Euclidean distance and normal vector angle similarity between points. In actual operation, when the Euclidean distance between adjacent points is less than 5 mm and the normal vector angle difference is less than 15 degrees, these points are divided into the same sub-region. Through this method, a hollow fiber membrane bundle with a diameter of 60 cm and a height of 120 cm is divided into 48 cleaning sub-regions.

[0060] For each divided cleaning sub-region, its boundary point set is calculated. The identification of the boundary point set uses a contour tracking technique to determine the boundary by detecting the point density change between adjacent regions. The boundary points of each sub-region are sorted to form a closed boundary curve. Taking a typical sub-region as an example, its boundary point set contains about 350 three-dimensional coordinate points, which accurately define the boundary contour of the sub-region.

[0061] Based on the determined boundary point set, the area and perimeter of each cleaning sub-region are calculated. The area calculation is achieved by projecting the three-dimensional surface onto its principal plane and then applying the polygon area calculation method. The perimeter is obtained by accumulating the Euclidean distance between adjacent points in the boundary point set. In the example, the cleaning sub-region numbered 12 has an area of 245 square centimeters and a perimeter of 68 centimeters.

[0062] A high-resolution industrial camera is used to capture the pollutant distribution image of the hollow fiber membrane bundle surface. During the acquisition process, to ensure image quality, a ring-shaped LED light source is used to provide uniform illumination, and the camera resolution is set to 4096x3072 pixels. The captured image is preprocessed, including noise removal and contrast enhancement, to highlight the pollutant features.

[0063] For the preprocessed pollutant distribution image, its gray value is extracted. During the extraction process, an image grayscale conversion algorithm is used to convert the color image to a 256-level grayscale image. The gray value ranges from 0 to 255, where lower gray values indicate more severely polluted areas, and higher gray values indicate cleaner areas.

[0064] The extracted gray values are mapped to the corresponding boundary point sets of each cleaning sub-region. The mapping process uses a three-dimensional to two-dimensional projection transformation to establish a correspondence between the boundary point sets in three-dimensional space and the two-dimensional image plane. The mapping accuracy is ensured by calibration with a calibration plate, with a positioning error controlled within 2 millimeters.

[0065] Based on the mapped boundary point sets and corresponding gray values, the contaminant distribution density of each cleaning sub-region is calculated. The calculation method is to take the average of the gray values of all points in the sub-region and convert it according to the pre-set gray value-contaminant density reference table. For example, in one test, the average gray value of the cleaning sub-region numbered 8 is 78, and the corresponding contaminant distribution density is 6.5 grams per square meter.

[0066] According to the contaminant distribution density and area, the pollution load index of each cleaning sub-region is generated. This index is calculated by multiplying the contaminant distribution density and the area of the sub-region, reflecting the total contaminant content of the sub-region. Taking the cleaning sub-region numbered 15 as an example, its contaminant distribution density is 4.2 grams per square meter, and the area is 320 square centimeters, and the calculated pollution load index is 13.44 grams.

[0067] Based on the boundary point sets, the common boundary coordinates of adjacent cleaning sub-regions are identified. The identification process compares the coordinate points in the boundary point sets of different sub-regions, and when the distance between two points is less than a pre-set threshold (such as 1 millimeter), it is considered that these points are located on the common boundary. For example, the two adjacent sub-regions numbered 23 and 24 have 42 common boundary points, forming a common boundary about 15 centimeters long.

[0068] The connected length of the common boundary coordinates is calculated, and the ratio of this length to the perimeter of the sub-region is determined as the position correlation strength. In actual application, the perimeter of the sub-region numbered 17 is 52 centimeters, and the common boundary length with the adjacent sub-region is 18 centimeters, so its position correlation strength is 0.346.

[0069] According to the position correlation strength, the contaminant distribution density gradient between adjacent cleaning sub-regions is calculated. The calculation method is to divide the difference of the contaminant distribution densities of adjacent regions by the connected length of the common boundary. For example, the contaminant distribution densities of the two adjacent sub-regions numbered 31 and 32 are 7.8 grams per square meter and 5.3 grams per square meter respectively, and the connected length of the common boundary is 12 centimeters, so the calculated contaminant distribution density gradient is 0.21 grams per square meter per centimeter.

[0070] Finally, the cleaning sub-regions are prioritized based on the pollution distribution density gradient and the pollution load index to determine the cleaning sequence. The sorting strategy is that the sub-region with high pollution load index has high priority, and when the pollution load index is similar, the sub-region with large pollution distribution density gradient between the high pollution regions has higher priority. In the actual application case, the priority order from 1 to 48 is determined for the 48 cleaning sub-regions, and the cleaning robot cleans according to the order, effectively improving the cleaning efficiency and quality.

[0071] In an optional embodiment, according to the effective cleaning range, the overlap degree and the trajectory spacing parameter, the cleaning path coordinate points of the plurality of cleaning sub-regions are calculated in the cleaning sequence, and the cleaning trajectory curve is determined based on the cleaning path coordinate points, comprising: According to the effective cleaning range data, the spatial curvature distribution of the hollow fiber membrane bundle surface is extracted, the effective cleaning range data is projected onto the spatial curvature distribution, the projection deformation coefficient is calculated, the effective cleaning range data is corrected according to the projection deformation coefficient, and the actual coverage area parameter is generated; According to the actual coverage area parameter, the overlap degree and the trajectory spacing parameter, the local trajectory offset is calculated, and the cleaning path coordinate points are generated based on the local trajectory offset and the projection deformation coefficient; Based on the cleaning path coordinate points, the angle between the jet flow direction of the cleaning nozzle and the normal vector of the membrane surface is calculated, the local jet effective action distance is calculated based on the angle, and the region range that needs to be optimized is determined according to the local jet effective action distance and the projection deformation coefficient; In the region range, the coordinate point optimization criterion is established, the cleaning path coordinate points are adjusted in density and optimized in position according to the coordinate point optimization criterion, and the cleaning trajectory curve that meets the local jet action requirement and the surface curvature constraint is generated.

[0072] In order to accurately calculate the cleaning path coordinate points and determine the cleaning trajectory curve, first, the spatial curvature distribution of the hollow fiber membrane bundle surface is extracted according to the effective cleaning range data. This process obtains point cloud data by three-dimensional scanning of the hollow fiber membrane bundle surface, extracts a surface grid model from the point cloud data, and calculates the principal curvature and Gaussian curvature of each point of the grid model. For example, for a cylindrical hollow fiber membrane bundle with a diameter of 200 mm and a height of 1000 mm, the curvature in the top region is larger, and the middle region is approximately a cylindrical surface, which can identify a hemispherical structure with a curvature radius of about 100 mm in the top region and a cylindrical structure in the middle region.

[0073] When projecting the effective cleaning range data onto the spatial curvature distribution, a ray tracing algorithm is used to calculate the intersection points of the cleaning jet from the nozzle at different positions with the surface of the membrane bundle. For a nozzle with an effective range of 250 mm and a jet radius of 30 mm, a circular coverage area with a diameter of about 60 mm is formed when the nozzle is aligned vertically to the membrane surface in a planar area. When projected onto a curved surface, the coverage shape is deformed, and the projection deformation coefficient is calculated by comparing the areas before and after projection. For example, in an area with a curvature radius of 100 mm, an elliptical coverage area with a major axis of about 70 mm and a minor axis of about 50 mm can be formed under the same jet conditions, and the projection deformation coefficient is about 1.17.

[0074] The actual coverage area parameters are calculated by correcting the effective cleaning range data according to the projection deformation coefficient. For example, for a cleaning jet with a nominal coverage diameter of 60 mm, the actual coverage area on a surface area with a curvature radius of 100 mm is about 2750 square millimeters, which is different from the 2827 square millimeters of the non-planar projection. This difference is particularly evident in high-curvature areas, such as the top edge area of the membrane bundle, where the actual coverage area can be 20% smaller than the theoretical calculation value.

[0075] Based on the actual coverage area parameters, the preset overlap degree and the track spacing parameters, the local track offset is calculated. Assuming that the overlap degree is set to 30%, the standard track spacing in a planar area is 42 mm. However, in high-curvature areas, the track spacing needs to be adjusted according to the projection deformation coefficient, for example, in an area with a curvature radius of 100 mm, the track spacing may need to be reduced to 36 mm to ensure an effective overlap degree of 30%. Based on these local track offsets and the projection deformation coefficient, a series of cleaning path coordinate points are generated to form a preliminary cleaning path plan.

[0076] When determining the angle between the jet direction of the cleaning nozzle and the normal vector of the membrane filament surface, the normal vector of the membrane bundle surface at each coordinate point is calculated, and the angle with the jet direction vector is calculated. For an ideal case, the jet direction of the nozzle should be parallel to the surface normal vector, with an angle of 0 degrees, which is the best cleaning effect. In actual operation, due to the movement restrictions of the mechanical arm, the angle often cannot be kept at 0 degrees, and an acceptable maximum angle threshold of 30 degrees is set, and areas exceeding this threshold need special treatment.

[0077] When calculating the local jet effective action distance based on the angle, a jet attenuation model is applied. When the angle is 0 degrees, the effective action distance can reach 250 mm. As the angle increases, the effective action distance decreases, such as when the angle is 15 degrees, the effective action distance is about 230 mm, and when the angle is 30 degrees, the effective action distance is about 200 mm. Combined with these local jet effective action distance data and the projection deformation coefficient, the range of areas that need to be optimized is determined, especially those areas with an angle greater than 20 degrees or a projection deformation coefficient greater than 1.5.

[0078] After determining the region range that needs to be optimized, coordinate point optimization criteria are established. These criteria include: ensuring that the minimum overlap degree is not less than 25%; the distance between adjacent track points is not more than 5 mm; the angle between the jet direction of the nozzle and the surface normal vector is not more than 30 degrees; and the cleaning coverage rate is more than 95%. Taking the top region of the membrane bundle as an example, the original planning may have uneven coverage. By increasing the track point density (from the original one point per 10 mm to one point per 3 mm) and adjusting the nozzle angle (so that the jet direction is closer to the surface normal vector), the optimized coverage rate is increased from the original 85% to 98%.

[0079] Finally, the density of the cleaning path coordinate points is adjusted and the positions are optimized. The coordinate point density is increased in high-curvature regions, and the point density is appropriately reduced in low-curvature regions. For example, in the cylindrical region in the middle of the membrane bundle, a larger point spacing (about 8 mm) can be used; while in the high-curvature regions such as the top and the connection, the point spacing may need to be reduced to 2 mm to ensure accurate coverage. Through such adaptive density adjustment, the final cleaning trajectory curve that meets the requirements of local jet action and surface curvature constraints is generated, so that the entire hollow fiber membrane bundle surface can be effectively cleaned, the cleaning uniformity is improved by about 40%, and the cleaning time is reduced by about 25%.

[0080] In an alternative embodiment, coordinate point optimization criteria are established within the region range, the density of the cleaning path coordinate points is adjusted and the positions are optimized according to the coordinate point optimization criteria, and a cleaning trajectory curve that meets the requirements of local jet action and surface curvature constraints is generated, including: The topological connection features between the cleaning path coordinate points within the region range are calculated, the topological connection features are compared with the minimum turning radius of the track, and a to-be-optimized cleaning sub-region that needs to be optimized is determined; The local surface geometric features in the to-be-optimized cleaning sub-region are extracted, the principal curvatures and Gaussian curvatures of the local surface are calculated, the density weight coefficient is determined according to the numerical distribution of the principal curvatures and Gaussian curvatures, the coordinate point optimization criteria are generated by combining the density weight coefficient with the cleaning path coordinate points; Based on the coordinate point optimization criteria, the position information of the newly added coordinate points and the to-be-deleted coordinate points in the to-be-optimized cleaning sub-region is generated, the position information is input into the coordinate point self-organizing optimization equation based on the fluid diffusion principle, and the spatial position correction amount of each cleaning path coordinate point in the to-be-optimized cleaning sub-region is calculated; According to the spatial position correction amount, the cleaning path coordinate points are reconstructed, the direction features of the reconstructed coordinate point sequence are extracted, and a cleaning trajectory curve that meets the requirements of local jet action and surface curvature constraints is generated based on the direction features.

[0081] In one embodiment of the present application, for a given area range, the topological connection features between the coordinate points of the cleaning path need to be calculated first. This step is achieved by calculating the spatial distance and the included angle between adjacent coordinate points. Specifically, for each coordinate point Pi in the area, the included angle θi formed by Pi and its adjacent points Pi-1 and Pi+1 is calculated, as well as the lengths of the connecting lines li,i-1 and li,i+1. When the included angle θi is less than a preset threshold (e.g. 120 degrees) or any of the connecting line lengths exceeds a set value (e.g. 5 mm), the point is recorded as a potential optimization point. By performing the above calculation on all coordinate points, a set of points whose topological connection features do not meet the requirements is determined.

[0082] Next, the topological connection features calculated above are compared with the minimum turning radius of the trajectory. Assuming that the minimum turning radius of the cleaning device is R = 10 mm, for each potential optimization point Pi, the radius of curvature ri = 1 / κi is calculated, where κi is the curvature of the point. If ri < R, the point needs to be optimized. By cluster analysis, these points that need to be optimized are summarized into several cleaning sub-areas to be optimized, for example, the optimization points with a spatial distance less than 15 mm are grouped into the same sub-area.

[0083] For each determined cleaning sub-area to be optimized, the present application extracts the local surface geometric features. The specific implementation is as follows: dense sampling points are selected in the sub-area, the local surface equation is obtained by surface fitting method, the principal curvatures κ1 and κ2 at each sampling point are calculated, and the Gaussian curvature K = κ1 x κ2. For example, in a certain cleaning sub-area to be optimized, the principal curvatures at the sampling point p1 are κ1 = 0.05 mm-1 and κ2 = 0.02 mm-1, and the Gaussian curvature K = 0.001 mm-2.

[0084] According to the numerical distribution of the principal curvatures and the Gaussian curvature, the present application determines the coordinate point density weight coefficient. Specifically, a weight function w = f(|κ1|, |κ2|, |K|) is designed, so that the weight value of the area with larger curvature is higher. For example, the calculation method w = 0.3 x (|κ1| + |κ2|) + 0.4 x |K| can be used. At the above sampling point p1, the weight coefficient is w = 0.3 x (0.05 + 0.02) + 0.4 x 0.001 = 0.0214. The weight coefficients of all points in the entire sub-area are normalized to obtain the final density weight coefficient distribution map.

[0085] The coordinate point optimization criterion is generated by combining the density weight coefficient described above with the cleaning path coordinate points. In areas with higher weight coefficients, the coordinate points should be denser; in areas with lower weight coefficients, the coordinate points can be appropriately sparse. For example, set the reference density d0=1 point / mm2, then in the area with weight w, the local density is set to d=d0×(1+5w). In this way, in high-curvature areas such as edges or corners, the density of coordinate points will increase significantly, while in flat areas, the density will remain low.

[0086] Based on the above coordinate point optimization criterion, the application generates the position information of newly added coordinate points and coordinate points to be deleted in the cleaning sub-area to be optimized. The specific method is: for areas with a density lower than the required local density, new points are inserted between two adjacent points to make the local density meet the requirements; for areas with a density higher than the required density, redundant points are marked as points to be deleted. For example, in a sub-area with a larger curvature, the original point density is 0.8 points / mm2, and the required density is 1.2 points / mm2, so 50% more points are needed; while in a flat area, the original density is 1.5 points / mm2, and the required density is 1.0 points / mm2, so about 33% of the points need to be deleted.

[0087] The above position information is input into the coordinate point self-organizing optimization equation based on the principle of fluid diffusion to calculate the spatial position correction amount of each cleaning path coordinate point. In this step, the coordinate points are regarded as particles in a fluid, and there is repulsive force and attractive force between the particles. Through iterative calculation, the system reaches a balanced state. Specifically, for each point Pi, the total force Fi acting on it is calculated, and the displacement vector Δxi is determined according to Fi. After multiple iterations (e.g. 50 times), when the displacement of all points is less than a threshold value (e.g. 0.01 mm), it is considered to have reached equilibrium, and the cumulative displacement of each point at this time is recorded as the spatial position correction amount.

[0088] According to the calculated spatial position correction amount, the cleaning path coordinate points are reconstructed. For each original coordinate point Pi, its new position is Pi'=Pi+Δxi; at the same time, new points are inserted in areas where the density needs to be increased, and marked points are deleted in redundant areas. After reconstruction, the direction features of the coordinate point sequence are extracted, including the tangent direction and normal direction at each point.

[0089] Finally, based on the above direction features, the cleaning trajectory curve that meets the local jet action requirements and surface curvature constraints is generated. Specifically, the cubic spline interpolation method is used to connect the optimized coordinate point sequence to ensure that the tangent direction of the generated trajectory curve at each point is consistent with the calculated direction features, the curvature meets the minimum turning radius constraint, and the jet angle with the surface is within the effective range (e.g. 30°-90°). For example, at a corner of a curved surface, the jet angle before optimization may be 15°, and after optimization, it is adjusted to 45°, significantly improving the cleaning effect.

[0090] The cleaning trajectory curve generated by the above method can effectively adapt to the geometric characteristics of complex surfaces, ensure the smoothness of the trajectory of the cleaning equipment during operation, and at the same time meet the angle requirements of the local jet action, thereby improving the cleaning efficiency and quality.

[0091] The hollow fiber membrane universal cleaning head trajectory intelligent planning system of the embodiment of the present invention includes: The first unit is configured to obtain installation parameter information of the hollow fiber membrane bundle and generate three-dimensional position data of a to-be-cleaned area of ​​the hollow fiber membrane bundle based on the installation parameter information; The second unit is used to collect real-time working parameters of the hollow fiber membrane universal cleaning head, calculate the effective cleaning range of the cleaning nozzle according to the real-time working parameters, determine the overlap degree of adjacent cleaning tracks based on the effective cleaning range, and generate a track spacing parameter for uniform cleaning of the hollow fiber membrane bundle surface according to the overlap degree; The third unit is used to divide the surface of the hollow fiber membrane bundle into a plurality of cleaning sub-areas according to the three-dimensional position data of the area to be cleaned, and prioritize the cleaning sub-areas according to the distribution density of pollutants in each of the cleaning sub-areas to determine the cleaning order; a fourth unit, configured to calculate cleaning path coordinate points of the plurality of cleaning sub-areas according to the effective cleaning range, the overlap degree, and the track spacing parameter in the cleaning sequence, and determine a cleaning track curve based on the cleaning path coordinate points; The fifth unit is configured to convert the cleaning trajectory curve into a motion control instruction for the hollow fiber membrane universal cleaning head, and send the motion control instruction to the cleaning execution device.

[0092] According to a third aspect of an embodiment of the present invention, an electronic device is provided, including: processor; a memory for storing processor-executable instructions; The processor is configured to call the instructions stored in the memory to execute the aforementioned method.

[0093] According to a fourth aspect of an embodiment of the present invention, a computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the method described above is implemented.

[0094] The present invention may be a method, an apparatus, a system and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for executing various aspects of the present invention.

[0095] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit the present application; although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions recorded in the above embodiments can be modified, or some or all of the technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. Intelligent trajectory planning method for hollow fiber membrane universal cleaning head, characterized in that: include: Acquiring installation parameter information of the hollow fiber membrane bundle, and generating three-dimensional position data of the area to be cleaned of the hollow fiber membrane bundle based on the installation parameter information; Collecting real-time operating parameters of the hollow fiber membrane universal cleaning head, calculating the effective cleaning range of the cleaning nozzle based on the real-time operating parameters, determining the overlap degree of adjacent cleaning tracks based on the effective cleaning range, and generating track spacing parameters for uniform cleaning of the hollow fiber membrane bundle surface based on the overlap degree; Dividing the surface of the hollow fiber membrane bundle into a plurality of cleaning sub-areas according to the three-dimensional position data of the area to be cleaned, and prioritizing the cleaning sub-areas according to the distribution density of pollutants in each of the cleaning sub-areas to determine the cleaning order; calculating, according to the effective cleaning range, the overlap degree, and the track spacing parameter, cleaning path coordinate points of the plurality of cleaning sub-areas in the cleaning sequence, and determining a cleaning track curve based on the cleaning path coordinate points; The cleaning trajectory curve is converted into a motion control instruction for the hollow fiber membrane universal cleaning head, and the motion control instruction is sent to the cleaning execution device.

2. The method according to claim 1, characterized in that Acquiring installation parameter information of the hollow fiber membrane bundle, and generating three-dimensional position data of the area to be cleaned of the hollow fiber membrane bundle based on the installation parameter information, including: constructing a strain sensing array on the surface of the hollow fiber membrane bundle support, so that the strain sensing array collects the support deformation data of the hollow fiber membrane bundle during the installation process based on the installation parameter information, and calculates the installation stress distribution of the hollow fiber membrane bundle according to the support deformation data; A capacitive sensing array is constructed on the surface of the hollow fiber membrane bundle based on the stress distribution of the hollow fiber membrane bundle installation, and dielectric constant change data between the hollow fiber membrane filaments and the capacitive sensing array is collected through the capacitive sensing array, and the three-dimensional spatial coordinates of the hollow fiber membrane filaments are determined according to the dielectric constant change data; Collecting charge distribution data on the surface of the hollow fiber membrane filaments, determining the installation stress deformation area of ​​the hollow fiber membrane filaments based on the charge distribution data and the installation stress distribution of the hollow fiber membrane bundle, determining the stress magnitude and stress direction of the installation stress deformation area, and generating three-dimensional morphology data of the surface of the hollow fiber membrane bundle based on the stress magnitude and the stress direction; The three-dimensional spatial coordinates of the hollow fiber membrane yarn are spatially aligned with the three-dimensional morphology data of the surface of the hollow fiber membrane bundle to generate three-dimensional position data of the area to be cleaned of the hollow fiber membrane bundle.

3. The method according to claim 1, characterized in that Collecting real-time working parameters of the hollow fiber membrane universal cleaning head, calculating the effective cleaning range of the cleaning nozzle according to the real-time working parameters, determining the overlap degree of adjacent cleaning tracks based on the effective cleaning range, and generating track spacing parameters for uniform cleaning of the hollow fiber membrane bundle surface according to the overlap degree, including: Arrange a jet pressure sensor array around the cleaning nozzle, and generate a sensitivity calibration coefficient of the jet pressure sensor array based on the real-time working parameters; Using the sensitivity calibration coefficient to enable the jet pressure sensor array to collect pressure field distribution data of the cleaning jet, calculating the jet pressure field gradient threshold according to the pressure field distribution data, and determining the effective cleaning range of the cleaning nozzle based on the jet pressure field gradient threshold; Move the cleaning nozzle along a preset trajectory, calibrate the jet pressure field data of adjacent trajectories collected by the jet pressure sensor array based on the sensitivity calibration coefficient, and calculate the spatial change rate of the jet pressure field data of adjacent trajectories; Determine a pressure superposition intensity matrix based on the spatial variation rate and the jet pressure field gradient threshold, spatially map the pressure superposition intensity matrix to the jet pressure isosurface boundary, identify the distribution positions of cleaning dead zones and over-cleaning areas, and adjust the overlap of adjacent cleaning trajectories based on the distribution positions; The overlapping degree of adjacent cleaning tracks and the effective cleaning range are correlated and calculated to generate track spacing parameters for uniform cleaning of the hollow fiber membrane bundle surface.

4. The method according to claim 3, characterized in that Determining a pressure stacking intensity matrix based on the spatial variation rate and the jet pressure field gradient threshold, spatially mapping the pressure stacking intensity matrix to the jet pressure isosurface boundary, identifying the distribution positions of cleaning dead zones and over-cleaning areas, and adjusting the overlap of adjacent cleaning trajectories based on the distribution positions, including: generating a three-dimensional tensor matrix according to the spatial change rate, and projecting the three-dimensional tensor matrix onto a two-dimensional plane to form a spatial change rate distribution matrix; Comparing the spatial change rate distribution matrix with the jet pressure field gradient threshold, determining the coordinates of the boundary points where the spatial change rate exceeds the jet pressure field gradient threshold, and generating a pressure superposition intensity matrix based on the boundary point coordinates; Performing surface fitting on the jet pressure isosurface boundary to generate a continuous and smooth jet pressure distribution surface, performing spatial coordinate registration on the pressure superposition intensity matrix and the jet pressure distribution surface to generate a three-dimensional distribution map of the cleaning pressure field; Calculating the spatial position coordinates of the pressure field zero value area and the pressure field peak area based on the three-dimensional distribution map of the cleaning pressure field, and mapping the spatial position coordinates to the surface of the hollow fiber membrane to form a distribution map of the cleaning dead zone and the over-cleaning area; The ratio of the area of ​​the cleaning dead zone to the area of ​​the over-cleaning area is calculated according to the distribution diagram of the cleaning dead zone and the over-cleaning area, and the overlap degree of adjacent cleaning tracks is adjusted based on the ratio.

5. The method according to claim 1, wherein Dividing the surface of the hollow fiber membrane bundle into a plurality of cleaning sub-areas according to the three-dimensional position data of the area to be cleaned, prioritizing the cleaning sub-areas according to the distribution density of pollutants in each of the cleaning sub-areas, and determining a cleaning order, including: Extracting the coordinates of surface feature points of the hollow fiber membrane bundle from the three-dimensional position data of the area to be cleaned, and dividing the surface of the hollow fiber membrane bundle into a plurality of cleaning sub-areas based on the coordinates of the surface feature points; Calculating a boundary point set of each cleaning sub-region, and determining the area and perimeter of each cleaning sub-region according to the boundary point set; Collecting a pollutant distribution image on the surface of the hollow fiber membrane bundle, extracting the grayscale value of the pollutant distribution image, mapping the grayscale value to a boundary point set corresponding to the cleaning sub-area, calculating the pollutant distribution density of each cleaning sub-area based on the boundary point set, and generating a pollution load index for each cleaning sub-area based on the pollutant distribution density and the area; identifying common boundary coordinates of adjacent cleaning sub-areas based on the boundary point set, calculating a connection length of the common boundary coordinates, and determining a ratio of the connection length to the perimeter as a position association strength; The pollutant distribution density gradient between adjacent cleaning sub-areas is calculated according to the position correlation strength, and the cleaning sub-areas are prioritized based on the pollutant distribution density gradient and the pollution load index to determine a cleaning order.

6. The method according to claim 1, characterized in that The method further comprises calculating the cleaning path coordinate points of the plurality of cleaning sub-areas according to the effective cleaning range, the overlap degree, and the track spacing parameter in accordance with the cleaning sequence, and determining a cleaning track curve based on the cleaning path coordinate points, including: Extracting the spatial curvature distribution of the hollow fiber membrane bundle surface based on the effective cleaning range data, projecting the effective cleaning range data onto the spatial curvature distribution, calculating the projection deformation coefficient, and correcting the effective cleaning range data based on the projection deformation coefficient to generate an actual coverage area parameter; Calculating a local track offset according to the actual coverage area parameter, the overlap degree, and the track spacing parameter, and generating a cleaning path coordinate point based on the local track offset and the projection deformation coefficient; Calculating the angle between the jet direction of the cleaning nozzle and the normal vector of the membrane surface based on the cleaning path coordinate points, calculating the effective action distance of the local jet based on the angle, and determining the area to be optimized based on the effective action distance of the local jet and the projection deformation coefficient; A coordinate point optimization criterion is established within the region, and density adjustment and position optimization are performed on the cleaning path coordinate points according to the coordinate point optimization criterion to generate a cleaning trajectory curve that meets the local jet action requirements and surface curvature constraints.

7. The method according to claim 6, characterized in that Establishing a coordinate point optimization criterion within the region, adjusting the density and optimizing the positions of the cleaning path coordinate points according to the coordinate point optimization criterion, and generating a cleaning trajectory curve that meets the local jet action requirements and surface curvature constraints, including: Calculating the topological connection features between the coordinate points of the cleaning path within the area, comparing the topological connection features with the minimum turning radius of the trajectory, and determining the cleaning sub-area to be optimized where the coordinate points need to be optimized; Extracting the local surface geometric features within the cleaning sub-region to be optimized, calculating the principal curvature and Gaussian curvature of the local surface, determining the coordinate point density weight coefficient based on the numerical distribution of the principal curvature and Gaussian curvature, and combining the density weight coefficient with the cleaning path coordinate points to generate a coordinate point optimization criterion; Based on the coordinate point optimization criterion, position information of newly added coordinate points and coordinate points to be deleted is generated in the cleaning sub-region to be optimized, the position information is input into a coordinate point self-organization optimization equation based on the fluid diffusion principle, and a spatial position correction amount of each cleaning path coordinate point in the cleaning sub-region to be optimized is calculated; The cleaning path coordinate points are reconstructed according to the spatial position correction amount, the directional features of the reconstructed coordinate point sequence are extracted, and a cleaning trajectory curve that meets the local jet action requirements and surface curvature constraints is generated based on the directional features.

8. A hollow fiber membrane universal cleaning head trajectory intelligent planning system, used to implement the method according to any one of claims 1 to 7, characterized in that: include: The first unit is configured to obtain installation parameter information of the hollow fiber membrane bundle and generate three-dimensional position data of a to-be-cleaned area of ​​the hollow fiber membrane bundle based on the installation parameter information; The second unit is used to collect real-time working parameters of the hollow fiber membrane universal cleaning head, calculate the effective cleaning range of the cleaning nozzle according to the real-time working parameters, determine the overlap degree of adjacent cleaning tracks based on the effective cleaning range, and generate a track spacing parameter for uniform cleaning of the hollow fiber membrane bundle surface according to the overlap degree; The third unit is used to divide the surface of the hollow fiber membrane bundle into a plurality of cleaning sub-areas according to the three-dimensional position data of the area to be cleaned, and prioritize the cleaning sub-areas according to the distribution density of pollutants in each of the cleaning sub-areas to determine the cleaning order; a fourth unit, configured to calculate cleaning path coordinate points of the plurality of cleaning sub-areas according to the effective cleaning range, the overlap degree, and the track spacing parameter in the cleaning sequence, and determine a cleaning track curve based on the cleaning path coordinate points; The fifth unit is configured to convert the cleaning trajectory curve into a motion control instruction for the hollow fiber membrane universal cleaning head, and send the motion control instruction to the cleaning execution device.

9. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor is configured to call the instructions stored in the memory to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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