Automatic polishing roughness control method and system for inner wall of fire extinguisher tank

Abstract

The invention provides an automatic polishing roughness control method and system for the inner wall of a fire extinguisher tank. The method comprises the following steps: acquiring three-dimensional point cloud data of the inner wall of a fire extinguisher tank body, generating a local height deviation characteristic value and a curvature gradient characteristic value through curvature analysis, and quantifying to obtain a surface concave-convex degree matrix; the scanning frequency and angle of a rotating laser contour scanner at the front end of the polishing rod are adjusted based on the matrix; scanning the inner wall in the polishing process to generate a real-time morphology thermodynamic diagram for identifying a geometric mutation area; in combination with the surface concave-convex degree matrix and the thermodynamic diagram, a pressure correlation value of the inner wall geometrical characteristics and the polishing pressure is calculated, and a polishing pressure correlation vector corresponding to the geometrical mutation area is determined; and the axial path and the rotating speed of the polishing head are adjusted based on the vector, and the roughness difference of the geometric mutation area of the inner wall is eliminated. Dynamic elimination of the roughness difference of the wall of the inner tank body of the fire extinguisher is achieved, the polishing uniformity is improved, and the machining efficiency is improved.

Description

Technical Field

[0001] This application relates to the field of automatic polishing roughness control technology, and in particular to an automatic polishing roughness control method and system for the inner wall of a fire extinguisher tank. Background Technology

[0002] Precise control of the polishing roughness of the inner wall of a fire extinguisher tank is directly related to the tank's sealing strength and pressure resistance. In automated production lines, the internal space of the tank is narrow and has a curved structure, making it difficult for traditional polishing tools to maintain stable contact with the curved inner wall, easily leading to uneven polishing pressure. Simultaneously, differences in material properties (such as the hardness difference between stainless steel and aluminum alloy) and welding residues significantly affect the grinding effect, causing localized over-polishing (insufficient roughness weakens coating adhesion) or under-polishing (residual microcracks cause stress concentration). Furthermore, existing online detection technologies struggle to monitor roughness changes in real time within a sealed cavity, failing to establish a closed-loop control system from polishing to feedback. Therefore, a method is urgently needed that can adaptively adjust polishing pressure and trajectory to the curved surface contour, achieving uniform and stable control of the inner wall roughness without manual intervention.

[0003] The current targeted solution is a contact force control system based on an adaptive floating polishing mechanism. This system connects the polishing head and the drive spindle via springs and hinges, allowing the polishing head to float and conform to the curved surface within the tank cavity. Force sensors collect the contact pressure between the polishing head and the inner wall in real time, and the spindle feed speed and rotational torque are adjusted based on a preset pressure threshold to ensure consistent polishing force. This system can adapt to the surface variations of tanks with different inner diameters, avoiding over-polishing or under-polishing problems caused by rigid contact. However, its core drawback lies in its reliance on static pressure threshold settings, which cannot respond in real time to nonlinear frictional disturbances caused by sudden changes in local material hardness or residual weld slag, resulting in insufficient surface roughness consistency under dynamic operating conditions. Summary of the Invention

[0004] This application provides an automatic polishing roughness control method and system for the inner wall of a fire extinguisher tank, which solves the problems of uneven roughness, inaccurate identification of geometric change areas, and low polishing efficiency in the prior art.

[0005] In a first aspect, this application provides a method for controlling the roughness of the inner wall of a fire extinguisher tank through automatic polishing, including:

[0006] Three-dimensional point cloud data of the inner wall of the fire extinguisher tank is acquired, and curvature analysis is performed on the three-dimensional point cloud data to generate local height deviation feature values ​​and curvature gradient feature values ​​of the inner wall of the fire extinguisher tank. The local height deviation feature values ​​and curvature gradient feature values ​​are then quantified to obtain the surface concavity and convexity matrix.

[0007] Using a rotary laser profile scanner mounted at the front end of a polishing rod, the scanning frequency and scanning angle of the rotary laser profile scanner are adjusted according to the surface concavity and convexity matrix;

[0008] The adjusted rotating laser profile scanner scans the inner wall surface of the fire extinguisher during the polishing process to generate a real-time morphological thermal map, which is used to identify geometric abrupt change areas on the inner wall surface.

[0009] By combining the surface roughness matrix and the real-time topography thermal map, the pressure correlation value between the geometric features of the inner wall of the fire extinguisher and the polishing pressure is calculated, and the correlation vector between the geometric change region and the polishing pressure is determined by the pressure correlation value.

[0010] Based on the correlation vector, the axial movement path and rotation speed of the polishing head are adjusted to eliminate roughness differences in geometrically abrupt regions on the inner wall of the fire extinguisher tank.

[0011] Optionally, by combining the surface roughness matrix and the real-time topographic thermal map, a pressure correlation value between the geometric features of the fire extinguisher's inner wall and the polishing pressure is calculated, and the correlation vector between the geometrically abrupt region and the polishing pressure is determined through the pressure correlation value, including:

[0012] Extract concavity and convexity feature values ​​from the surface concavity and convexity matrix, simultaneously extract thermal color level values ​​of geometrically abrupt regions from the real-time morphology heat map, and combine the concavity and convexity feature values ​​with the thermal color level values ​​to generate a comprehensive geometric feature value.

[0013] The geometric feature composite value is combined with a preset pressure conversion coefficient to generate a pressure correlation value that describes the relationship between the geometric feature and the required polishing pressure.

[0014] Within the geometric abrupt change region, locate each spatial location point and arrange the pressure correlation values ​​of the spatial location points according to a preset spatial coordinate order to form a pressure correlation value sequence;

[0015] The average value of the pressure correlation values ​​at all spatial locations within the geometric abrupt change region is calculated as the polishing pressure reference value for the geometric abrupt change region.

[0016] The polishing pressure reference value is bound to the spatial coordinates of the geometric change region to form an association vector between the geometric change region and the polishing pressure.

[0017] Optionally, the adjusted rotating laser profile scanner scans the inner wall surface of the fire extinguisher during the polishing process to generate a real-time morphological thermal map. This real-time morphological thermal map is used to identify geometrically abrupt changes in the inner wall surface, including:

[0018] The adjusted rotating laser profile scanner is activated so that it rotates around the axis of the polishing rod and emits a laser beam to scan the inner wall surface of the fire extinguisher, and the time difference between the emission and return of the laser beam is recorded.

[0019] The time difference is converted into the height value of each scanning point on the inner wall surface of the fire extinguisher. At the same time, the difference in height values ​​of adjacent scanning points and the arc distance between scanning points are calculated. Based on the difference and the arc distance between scanning points, the height change rate value is generated.

[0020] The height change rate value is mapped to a thermal color level value, and the thermal color level values ​​are arranged according to the spatial position of the scan point to form a real-time morphological heat map that identifies geometric change regions. The geometric change regions are continuous color block regions where the thermal color level values ​​exceed a preset change threshold.

[0021] Optionally, the time difference is converted into the height value of each scanning point on the inner wall surface of the fire extinguisher, and the difference in height values ​​between adjacent scanning points and the arc distance between scanning points are calculated. Based on the difference and the arc distance between scanning points, a height change rate value is generated, including:

[0022] The time difference is converted into the propagation distance of the laser beam in the air, and the propagation distance is obtained through a preset physical characteristic of the speed of light.

[0023] The propagation distance value is corrected according to the preset refractive index characteristics of the fire extinguisher canister material to generate the height value of each scanning point on the inner wall surface of the fire extinguisher.

[0024] Locate adjacent scan points and calculate the difference in height values ​​between adjacent scan points as the height change;

[0025] Obtain the distance value corresponding to the arc length rotated by the rotating platform between adjacent scanning points, and generate a height change rate value describing the degree of height change in a unit arc length distance based on the proportional relationship between the height change amount and the distance value.

[0026] Optionally, based on the correlation vector, the axial movement path and rotational speed of the polishing head are adjusted to eliminate roughness differences in geometrically abrupt regions on the inner wall of the fire extinguisher tank, including:

[0027] The coordinates of the geometric abrupt change region in the correlation vector and the polishing pressure reference value are analyzed, and the polishing pressure reference value is combined with the preset axial speed conversion coefficient to generate the axial movement speed adjustment value.

[0028] The polishing pressure reference value is combined with a preset rotation speed conversion coefficient to generate a rotation speed adjustment value, which corresponds to the change in the rotation speed of the polishing head.

[0029] The axial movement path of the polishing head is planned according to the coordinates of the geometric change region, so that the axial movement path passes through the geometric change region;

[0030] The axial drive motor controller of the polishing head changes the axial movement speed of the polishing head according to the axial movement speed adjustment value.

[0031] According to the rotation speed adjustment value, the rotary motor controller of the polishing head changes the rotation speed of the polishing head according to the rotation speed adjustment value to eliminate the roughness difference in the geometrically abrupt area on the inner wall of the fire extinguisher tank.

[0032] Optionally, using a rotary laser profile scanner mounted at the front end of the polishing rod, the scanning frequency and scanning angle of the rotary laser profile scanner are adjusted according to the surface roughness matrix, including:

[0033] A rotating laser profile scanner is installed at the front end of the polishing rod to extract the concavity and convexity feature values ​​of each grid partition from the surface concavity and convexity matrix. The concavity and convexity feature values ​​are the weighted sum of the local height deviation feature values ​​and the curvature gradient feature values.

[0034] The concave and convex feature values ​​are combined with a preset frequency conversion coefficient to generate an adjustment value for the scanning frequency. The adjustment value for the scanning frequency corresponds to the change in the laser pulse emission frequency of the rotating laser contour scanner to achieve scanning frequency adjustment.

[0035] The concave and convex feature values ​​are combined with a preset angle conversion coefficient to generate an adjustment value for the scanning angle. The adjustment value of the scanning angle corresponds to the change in the deflection angle of the rotating platform of the rotating laser contour scanner, so as to achieve the adjustment of the scanning angle.

[0036] Optionally, three-dimensional point cloud data of the inner wall of the fire extinguisher tank is acquired, and curvature analysis is performed on the three-dimensional point cloud data to generate local height deviation feature values ​​and curvature gradient feature values ​​of the inner wall of the fire extinguisher tank. The local height deviation feature values ​​and curvature gradient feature values ​​are then quantified to obtain a surface concavity / convexity matrix, including:

[0037] Acquire three-dimensional point cloud data of the inner wall of the fire extinguisher tank, and divide the three-dimensional point cloud data into grids according to a preset spatial grid.

[0038] Calculate the height value of the 3D point cloud data points within the grid partition, and calculate the average value of the height values ​​as the reference height value of the grid partition;

[0039] The difference between the height value of each 3D point cloud data point within the measured grid partition and the reference height value is used as the local height deviation characteristic value of the grid partition;

[0040] Within the grid partition, locate adjacent 3D point cloud data points, calculate the straight-line distance and height difference between adjacent 3D point cloud data points, and use the ratio of the height difference to the straight-line distance as the curvature gradient feature value.

[0041] Integrate the local height deviation feature values ​​and curvature gradient feature values ​​of all grid partitions, and then arrange the local height deviation feature values ​​and curvature gradient feature values ​​according to the preset spatial grid to form a surface concavity and convexity matrix.

[0042] Secondly, this application provides an automatic polishing roughness control system for the inner wall of a fire extinguisher tank, comprising:

[0043] The quantization module is used to acquire three-dimensional point cloud data of the inner wall of the fire extinguisher tank, and to perform curvature analysis on the three-dimensional point cloud data to generate local height deviation feature values ​​and curvature gradient feature values ​​of the inner wall of the fire extinguisher tank, and to quantize the local height deviation feature values ​​and curvature gradient feature values ​​to obtain the surface concavity and convexity matrix.

[0044] An adjustment module is used to adjust the scanning frequency and scanning angle of a rotary laser profile scanner mounted at the front end of a polishing rod, based on the surface roughness matrix.

[0045] The scanning module is used to scan the inner wall surface of the fire extinguisher during the polishing process using an adjusted rotating laser profile scanner to generate a real-time morphological thermal map, which is used to identify geometric abrupt change areas on the inner wall surface.

[0046] The calculation module is used to combine the surface roughness matrix and the real-time topography heat map to calculate the pressure correlation value between the geometric features of the inner wall of the fire extinguisher and the polishing pressure, and to determine the correlation vector between the geometric change region and the polishing pressure through the pressure correlation value.

[0047] The elimination module is used to adjust the axial movement path and rotation speed of the polishing head based on the correlation vector to eliminate roughness differences in geometrically abrupt regions on the inner wall of the fire extinguisher tank.

[0048] Thirdly, this application provides a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement the automatic polishing roughness control method for the inner wall of a fire extinguisher tank as described in the first aspect above.

[0049] Fourthly, this application provides a computer storage medium storing a computer program, which, when executed by a computer, implements an automatic polishing roughness control method for the inner wall of a fire extinguisher tank as described in the first aspect.

[0050] This application's technical solution achieves high-precision surface treatment of the inner wall of a fire extinguisher tank through 3D point cloud analysis and intelligent polishing control technology. Specifically, a surface morphology feature matrix is ​​accurately constructed based on quantitative analysis of curvature gradient and height deviation; an adaptive laser scanning strategy significantly improves the real-time identification capability of geometrically abrupt regions; and dynamic calculation of pressure correlation vectors enables intelligent matching of polishing parameters. This method overcomes the limitations of traditional uniform polishing processes, effectively eliminating local roughness differences through coordinated adjustment of axial path and rotation speed, significantly improving the surface quality consistency of the inner wall of the fire extinguisher tank, and providing an intelligent polishing solution for precision pressure vessel manufacturing.

[0051] This method achieves precise quantification of the inner wall morphology of fire extinguisher tanks through gridded point cloud analysis and intelligent feature extraction. Specifically, the benchmark height calculation based on spatial grid partitioning significantly improves the accuracy of local deviation assessment; dynamic curvature gradient analysis of adjacent point cloud data effectively captures surface micro-geometric features; and a gridded feature integration mechanism enables matrix representation of morphology parameters. This approach overcomes the limitations of traditional overall assessment by constructing a high-precision surface morphology model through the collaborative quantification of height deviation and curvature gradient, providing reliable data support for subsequent intelligent polishing and significantly improving the accuracy and consistency of the inner wall polishing process.

[0052] These or other aspects of this application will become more apparent in the following description of the embodiments. Attached Figure Description

[0053] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0054] Figure 1 A flowchart of an automatic polishing roughness control method for the inner wall of a fire extinguisher tank, provided in this application, is shown.

[0055] Figure 2 This application provides a schematic diagram of the structure of an automatic polishing roughness control system for the inner wall of a fire extinguisher tank.

[0056] Figure 3 A schematic diagram of the structure of a computing device provided in this application is shown. Detailed Implementation

[0057] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0058] In some of the processes described in the specification, claims, and accompanying drawings of this application, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The operation numbers, such as 101, 102, etc., are merely used to distinguish different operations and do not themselves represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a chronological order, nor do they limit "first" and "second" to different types.

[0059] Researchers have discovered a fundamental bottleneck in the polishing control technology for the inner wall of fire extinguisher tanks: while contact force control schemes based on floating mechanisms can achieve basic pressure regulation, their lack of awareness of dynamic frictional disturbances and lag in response to geometrical abrupt changes lead to a dual loss of control. Specifically, local hardness abrupt changes caused by welding residues result in polishing pressure mismatch, and traditional static thresholds cannot capture nonlinear frictional disturbances in real time; furthermore, roughness compensation for geometrically abrupt changes in curved surfaces (such as weld bulges) relies on pre-set manual experience, leading to the coexistence of under-polished microcracks and reduced adhesion due to over-polishing. This contradiction stems from the decoupling blind spot between the dynamic evolution of curved surface morphology and the mechanical response of polishing, necessitating the construction of an intelligent control architecture that links geometry and mechanics in a closed loop.

[0060] To address the aforementioned challenges, this invention proposes an automatic polishing roughness control method for the inner wall of a fire extinguisher tank. Its innovation lies in overcoming the limitations of static pressure control through curvature gradient feature interpretation and geometric-mechanical closed-loop mapping. Specifically: Local height deviation and curvature gradient feature values ​​are generated through three-dimensional point cloud curvature analysis to construct a surface concavity / convexity matrix; based on the matrix, the frequency and angle of the rotating laser scanner are dynamically adjusted to capture geometric abrupt changes in the polishing process in real time and generate a morphological heat map; the surface matrix and heat map are fused to calculate the dynamic correlation value between geometric features and polishing pressure, generating a correlation vector for the abrupt change region; the axial path and rotation speed of the polishing head are optimized in real time to accurately eliminate roughness differences in the geometric abrupt change region. This method overturns the limitations of traditional force control: curvature gradient quantization analysis achieves millisecond-level dynamic capture of micron-level weld bulges for the first time; morphology thermograms overcome the challenge of real-time morphology reconstruction in sealed cavities through adaptive tuning of scanning parameters; correlation vectors transform geometric mutation features into polishing mechanical parameters, forming a closed-loop control chain of "morphology perception - thermogram generation - correlation calculation - parameter optimization", providing an essential control paradigm for pressure vessels from surface geometry evolution to polishing mechanical response.

[0061] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0062] Figure 1 This application provides a flowchart of a method for automatically polishing and controlling the roughness of the inner wall of a fire extinguisher tank, as shown in the embodiments of this application. Figure 1 As shown, the method includes:

[0063] 101. Obtain three-dimensional point cloud data of the inner wall of the fire extinguisher tank, and perform curvature analysis on the three-dimensional point cloud data to generate local height deviation feature values ​​and curvature gradient feature values ​​of the inner wall of the fire extinguisher tank, and quantify the local height deviation feature values ​​and curvature gradient feature values ​​to obtain the surface concavity and convexity matrix.

[0064] Optionally, step 101 may specifically include the following steps:

[0065] 1011. Obtain the three-dimensional point cloud data of the inner wall of the fire extinguisher tank, and divide the three-dimensional point cloud data into grids according to a preset spatial grid.

[0066] 1012. Calculate the height value of the three-dimensional point cloud data points within the grid partition, and calculate the average value of the height values ​​as the reference height value of the grid partition.

[0067] 1013. The difference between the height value of each 3D point cloud data point within the measured grid partition and the reference height value is used as the local height deviation characteristic value of the grid partition.

[0068] 1014. Locate adjacent 3D point cloud data points within the grid partition, calculate the straight-line distance and height difference between adjacent 3D point cloud data points, and use the ratio of the height difference to the straight-line distance as the curvature gradient feature value.

[0069] 1015. Integrate the local height deviation feature values ​​and curvature gradient feature values ​​of all grid partitions, and arrange the local height deviation feature values ​​and curvature gradient feature values ​​according to the preset spatial grid to form a surface concavity and convexity matrix.

[0070] In the above scheme, 3D point cloud data refers to point data in 3D space. Curvature analysis refers to the method of analyzing curvature characteristics. Local height deviation eigenvalue refers to the eigenvalue of local height deviation. Curvature gradient eigenvalue refers to the eigenvalue of curvature gradient. Surface concavity / convexity matrix refers to the matrix reflecting the concavity / convexity of a surface. Preset spatial grid refers to a pre-divided spatial grid. Grid partition refers to the region divided by the grid. Height value refers to the numerical height of a point. Reference height value refers to the height value used as a reference. Straight-line distance refers to the straight-line distance between two points. Height value difference refers to the difference in height values. Ratio refers to the proportion of two values.

[0071] In this embodiment, the system first acquires 3D point cloud data of the inner wall of the fire extinguisher tank and partitions it into a grid according to a preset spatial grid: a dense set of points on the surface of the inner wall of the tank is collected by a 3D laser scanning device to generate 3D point cloud data containing spatial coordinates. The grid partitioning engine divides the point cloud space into cubic units of equal size according to preset rules (such as a cube with fixed side length), forming a preset spatial grid structure. The partition mapper assigns each point cloud data point to the corresponding grid unit according to its coordinates, ensuring that all points have clear ownership and no overlap, thus establishing a basic framework for subsequent partition calculations.

[0072] Subsequently, the system calculates the average height value of the point cloud within each grid partition as the baseline height value: the height extractor reads the vertical coordinate value of all points within each grid partition as the height value. The mean calculation module summarizes the height values ​​of all points within the partition using an arithmetic mean algorithm, outputting a baseline height value representing the overall height of the area. This process quantifies the baseline plane position of the grid cells using local statistical techniques, providing a reference benchmark for height deviation analysis.

[0073] Next, the system measures the difference between the height value of each point within the grid partition and the reference height value as a local height deviation feature value: the deviation calculator iterates through each point within the partition, subtracting the reference height value generated in step 1012 from its height value to obtain the vertical offset of a single point. The feature value aggregator then averages the absolute values ​​of the offsets of all points within the partition again to generate a local height deviation feature value reflecting the overall convexity and concavity of the grid cell (positive values ​​indicate overall convexity, and negative values ​​indicate concavity). This process, through point-by-point differencing and double averaging, eliminates the influence of random noise and highlights regional deformation characteristics.

[0074] Then, the system locates adjacent points within the grid partition and calculates the curvature gradient feature value: the neighbor point localization module searches for the nearest neighbor pair of each point based on Euclidean distance. The gradient solver extracts the height difference (vertical change) and straight-line distance (horizontal projection distance) between point pairs, and uses the ratio of the two as the height change rate per unit distance, i.e., the curvature gradient feature value. This process discretizes the continuous surface into the slope relationship between adjacent points through the principle of local differential approximation, quantifying the steepness of the surface.

[0075] Finally, the system integrates the feature values ​​of all grid partitions to form a surface convexity matrix: the matrix construction engine binds the local height deviation feature value and curvature gradient feature value of each grid partition into a two-dimensional vector. The spatial mapper arranges all vectors into a two-dimensional matrix according to the grid coordinate order (e.g., row priority rule): the row index corresponds to the grid row number, the column index corresponds to the grid column number, and the matrix element stores the two feature values ​​of the grid at that location. The data encapsulator outputs this matrix as a surface convexity matrix, whose row and column structure fully preserves spatial distribution information and supports the visualization of convexity region location.

[0076] In practical applications, the introduction of automated polishing systems in fire extinguisher manufacturing plants requires high-precision roughness control of the inner wall of the tank. During implementation, a laser scanner fixed to the end of a robotic arm first acquires 3D point cloud data of the inner wall of the fire extinguisher tank to be polished. This scanner rotates uniformly along the tank's axis, collecting the spatial coordinates of tens of thousands of points on the inner wall surface. Subsequently, the system divides the acquired 3D point cloud data into a preset spatial grid, ensuring that each grid covers the same projected area. For each grid partition, the system calculates the height value (i.e., the depth value along the tank's radial direction) of all 3D point cloud data points within the partition and takes the arithmetic mean of these height values ​​as the reference height value for that grid partition. Next, the system measures the difference between the height value of each 3D point cloud data point within the grid partition and the reference height value, recording this as the local height deviation characteristic value of the corresponding grid partition. This value directly reflects the degree of local depression or convexity. Simultaneously, adjacent 3D point cloud data points (such as adjacent grid nodes) are located within the grid partition, and the straight-line distance and height difference between the two points are calculated. The height difference is divided by the straight-line distance to obtain the curvature gradient feature value, which is used to describe the surface slope change rate. Finally, the system integrates the local height deviation feature values ​​and curvature gradient feature values ​​of all grid partitions, and forms a two-dimensional surface concavity / convexity matrix according to the preset spatial grid arrangement order. This matrix serves as the input for polishing path planning. High deviation areas correspond to increased polishing intensity, while high gradient areas require reduced feed rate to avoid over-cutting, thereby achieving dynamic polishing control based on surface geometry features.

[0077] The scheme described in step 101 above achieves a refined quantitative characterization of the surface morphology of the inner wall of the fire extinguisher tank. Through gridded partitioning of 3D point cloud data, a surface concavity / convexity matrix incorporating local height deviations and curvature gradients is innovatively constructed. This technique combines baseline height calculation with differential analysis of adjacent points to transform discrete point cloud data into quantitative indicators reflecting surface morphology characteristics. Through matrix integration of spatial grids, a comprehensive digital representation of the inner wall surface morphology is achieved, providing a high-precision data foundation for subsequent polishing process optimization. This method of transforming point clouds into feature matrices significantly improves the accuracy and efficiency of surface morphology analysis.

[0078] 102. Using a rotary laser profile scanner mounted at the front end of the polishing rod, adjust the scanning frequency and scanning angle of the rotary laser profile scanner according to the surface concavity / convexity matrix.

[0079] Optionally, step 102 may specifically include the following steps:

[0080] 1021. Install a rotating laser profile scanner at the front end of the polishing rod to extract the concavity and convexity feature values ​​of each grid partition from the surface concavity and convexity matrix. The concavity and convexity feature values ​​are the weighted sum of the local height deviation feature values ​​and the curvature gradient feature values.

[0081] 1022. The concave and convex feature values ​​are combined with a preset frequency conversion coefficient to generate an adjustment value for the scanning frequency. The adjustment value for the scanning frequency corresponds to the change in the laser pulse emission frequency of the rotating laser contour scanner to achieve scanning frequency adjustment.

[0082] 1023. The concave and convex feature values ​​are combined with a preset angle conversion coefficient to generate an adjustment value for the scanning angle. The adjustment value for the scanning angle corresponds to the change in the deflection angle of the rotating platform of the rotating laser contour scanner, so as to realize the adjustment of the scanning angle.

[0083] In the above scheme, a polishing rod refers to a rod-shaped tool used for polishing. A rotating laser profiler refers to a laser profiler that performs rotational scanning. Scanning frequency refers to the frequency of scanning. Scanning angle refers to the angle of scanning. Surface convexity / concave feature value refers to the feature value of surface convexity / concaveness. Weighted sum refers to the sum calculated using weighted methods. Frequency conversion coefficient refers to the proportional coefficient for frequency conversion. Laser pulse emission frequency change refers to the change in laser pulse frequency. Angle conversion coefficient refers to the proportional coefficient for angle conversion. Rotating platform refers to a rotating support platform. Deflection angle change refers to the change in deflection angle.

[0084] In this embodiment, firstly, a rotating laser profile scanner is installed at the front end of the polishing rod: the device mounting module fixes the rotating laser profile scanner to the front end of the polishing rod via a mechanical interface, ensuring that its scanning direction is directly facing the surface to be polished. The data extraction engine reads the local height deviation feature value and curvature gradient feature value of each cell from the surface roughness matrix generated in step 101 according to the grid partition index. The feature fusion processor merges the two feature values ​​into a single roughness feature value through a weighted summation algorithm (preset weight coefficient). This value comprehensively quantifies the protrusion / depression intensity and surface steepness of the grid region, forming the core input for adjusting the scanning parameters.

[0085] Subsequently, the system combines the surface roughness feature values ​​with preset frequency conversion coefficients to generate a scanning frequency adjustment value: the coefficient matching unit calls the pre-calibrated frequency conversion coefficients (reflecting the mapping relationship between the roughness and the scanning speed) and multiplies them with the surface roughness feature values ​​through a linear proportional model. The frequency solver outputs the product result as the scanning frequency adjustment value (such as the amount of increase / decrease in pulse frequency), which directly corresponds to the change in laser pulse emission frequency. The instruction conversion module writes this adjustment value into the laser controller register, dynamically adjusting the electrical drive signal of the laser emitter to achieve scanning frequency adjustment to match surface complexity (such as increasing the sampling rate in highly rough areas).

[0086] Finally, the system combines the concave / convex feature values ​​with a preset angle conversion coefficient to generate a scanning angle adjustment value: the angle mapping engine extracts the concave / convex feature values ​​and combines them with the angle conversion coefficient (calibrated based on the surface curvature-angle deflection relationship), and calculates the adjustment value of the scanning angle (such as the deflection angle increment) through a nonlinear conversion algorithm. The execution control unit converts this adjustment value into the number of stepper motor drive pulses of the rotary platform, driving the rotary axis to deflect by the corresponding angle. The closed-loop feedback sensor monitors the deviation between the actual deflection angle and the target value in real time, and fine-tunes the motor torque through a PID control algorithm to ensure that the scanning angle adjustment accurately responds to surface deformation characteristics (such as vertical incident compensation in inclined areas).

[0087] In practical applications, fire equipment manufacturers use automated polishing systems to treat the inner walls of fire extinguisher tanks. During implementation, a rotating laser profile scanner is first installed at the front end of the polishing rod. This scanner captures the surface morphology features of the inner wall in real time using the principle of triangular reflection measurement. The system extracts the surface roughness feature values ​​(a weighted sum of local height deviation and curvature gradient feature values) of each grid partition from a preset surface roughness matrix, thus quantifying the roughness of different areas. For areas with high surface roughness feature values ​​(such as deep scratches or weld beads), the system combines the feature values ​​with a preset frequency conversion coefficient to generate an adjustment value for the scanning frequency. By increasing the laser pulse emission frequency, the density of scanning points per unit area is increased, thereby capturing microscopic defects more precisely. Simultaneously, for areas with abrupt curvature changes (such as the transition area on the tank shoulder), the system combines the same surface roughness feature value with a preset angle conversion coefficient to generate an adjustment value for the scanning angle. This drives the rotating platform of the rotating laser profile scanner to dynamically adjust the deflection angle, ensuring the laser beam is always perpendicular to the incident surface and avoiding measurement distortion caused by tilted scanning. By coordinating the adjustment of scanning frequency and scanning angle, the system automatically increases the scanning density in areas with high concavity and convexity feature values ​​and optimizes the laser incident direction in areas with significant curvature gradient feature values, providing high-precision three-dimensional topography data for subsequent polishing path planning.

[0088] The scheme described in step 102 above achieves adaptive dynamic adjustment of laser scanning parameters. Based on the feature analysis of the surface concavity / convexity matrix, an innovative intelligent control scheme for the rotating laser scanner was designed. This technology achieves precise adjustment of scanning frequency and angle through intelligent matching of concavity / convexity feature values ​​and conversion coefficients. The innovative parameter conversion algorithm ensures dynamic matching between scanning parameters and surface morphology features, significantly improving the targeting and effectiveness of morphology detection. This data-driven parameter adjustment mechanism provides a reliable technical guarantee for high-precision detection of complex curved inner walls.

[0089] 103. The adjusted rotating laser profile scanner is used to scan the inner wall surface of the fire extinguisher during the polishing process to generate a real-time morphological thermal map, which is used to identify the geometric abrupt change areas on the inner wall surface.

[0090] Optionally, step 103 may specifically include the following steps:

[0091] 1031. Start the adjusted rotating laser profile scanner so that the rotating laser profile scanner rotates around the axis of the polishing rod and emits a laser beam to scan the inner wall surface of the fire extinguisher, and record the time difference between the emission and return of the laser beam.

[0092] 1032. Convert the time difference into the height value of each scanning point on the inner wall surface of the fire extinguisher, and calculate the difference in height values ​​between adjacent scanning points and the arc distance between scanning points. Based on the difference and the arc distance between scanning points, generate the height change rate value.

[0093] Step 1032 may specifically include the following processes: converting the time difference into a propagation distance value of the laser beam in the air, the propagation distance value being obtained through a preset physical characteristic of the speed of light; correcting the propagation distance value according to a preset refractive index characteristic of the fire extinguisher canister material to generate height values ​​for each scanning point on the inner wall surface of the fire extinguisher; locating adjacent scanning points and calculating the difference in height values ​​between adjacent scanning points as a height change; obtaining the distance value corresponding to the arc length rotated by the rotating platform between adjacent scanning points, and generating a height change rate value describing the degree of height change per unit arc length distance based on the proportional relationship between the height change and the distance value.

[0094] 1033. Map the height change rate value to a thermal color level value, and arrange the thermal color level values ​​according to the spatial position of the scanning point to form a real-time morphological thermal map that identifies geometric change regions. The geometric change regions are continuous color block regions where the thermal color level values ​​exceed a preset change threshold.

[0095] In the above scheme, the real-time morphology heatmap refers to the thermal distribution map reflecting the surface morphology. The geometrical abrupt change region refers to the region where the geometric shape changes abruptly. The axis refers to the center line of rotation. The laser beam refers to the laser beam. The time difference refers to the time difference between laser emission and return. The height value refers to the numerical height of a point. The height change rate value refers to the rate at which the height changes. The arc length refers to the length of the arc. The propagation distance value refers to the distance the laser travels. The physical characteristics of the speed of light refer to the characteristics of the speed of light. The refractive index characteristics refer to the refractive characteristics of the material. The height change amount refers to the numerical change in height. The thermal color gradation value refers to the color value representing the thermal intensity. The preset abrupt change threshold refers to the critical value for judging abrupt changes. The continuous color patch region refers to a continuous color patch region.

[0096] In this embodiment, the system first activates the adjusted rotating laser profile scanner to perform a scanning operation: the scanning control module drives the optimized rotating laser profile scanner to rotate at a constant speed around the polishing rod axis, synchronously triggering the laser emitter to project a focused laser beam onto the inner wall surface of the fire extinguisher. The time recording unit accurately captures the time difference between the laser beam's emission and its reflection back from the inner wall (i.e., the laser's round-trip flight time). This data is recorded at microsecond resolution by a high-precision timing circuit, providing the raw input for subsequent distance calculation. This process relies on rotation synchronization technology to ensure that the scanning trajectory covers the entire circumferential area of ​​the inner wall.

[0097] Subsequently, the system converts the time difference into a height value and calculates the height change rate: the distance solver converts the time difference into the linear propagation distance of the laser in air based on the physical properties of the speed of light (a constant value). The refraction correction module corrects the propagation distance value based on the preset refractive index characteristics of the fire extinguisher canister material (such as the refractive deviation coefficient of metal to laser), outputting an accurate height value (quantifying the radial depth of each point on the inner wall). The neighbor point analysis engine locates adjacent scanning points according to the scanning sequence and calculates the difference in height values ​​between the two points as the height change (vertical deformation gradient). The arc length converter calculates the arc length distance (horizontal path length) between scanning points based on the angular displacement of the rotating platform and the inner wall radius. The change rate generator divides the height change by the arc length distance to generate a height change rate value (the rate of height change per unit arc length), which directly reflects the steepness of the surface.

[0098] Finally, the system maps the height change rate value to a thermal color level and generates a real-time morphology heatmap: the color level mapper converts the height change rate value into a thermal color level value using a linear normalization algorithm (e.g., high change rate maps to red, low change rate maps to blue). The spatial arrangement engine fills the color level value into the two-dimensional image matrix according to the original spatial coordinates (axial position + circumferential angle) of the scan points, forming an initial grayscale heatmap. The mutation identification module traverses the heatmap pixels, merging consecutive pixel areas whose thermal color level values ​​exceed a preset mutation threshold (such as the material deformation safety threshold) into color block areas, and enhances the contours using an edge detection algorithm. The visualization generator finally outputs a real-time morphology heatmap, where highlighted color blocks identify geometric mutation areas such as inner wall depressions and protrusions, providing intuitive defect location for polishing process adjustments.

[0099] In practical applications, during the automatic polishing of the inner wall of a fire extinguisher tank, the operator installs an adjusted rotating laser profile scanner at the end of the polishing rod, activates the scanner to rotate uniformly around the axis of the polishing rod, and simultaneously emits a laser beam towards the inner wall surface of the fire extinguisher. The laser beam returns after impacting the inner wall, and the scanner precisely records the time difference between emission and return for each scanning point. Based on the physical properties of the speed of light, the system converts the time difference into the propagation distance of the laser beam in the air. This distance value is then corrected according to the preset refractive index characteristics of the fire extinguisher tank material, ultimately generating precise height values ​​for each scanning point on the inner wall surface of the fire extinguisher. Subsequently, the system locates adjacent scanning points, calculates the difference between their height values ​​as the height change, and simultaneously acquires the distance value corresponding to the arc length traversed by the rotating platform between adjacent scanning points. The height change rate value per unit arc length is generated through the proportional relationship between the height change and the distance value. The height change rate values ​​of all scanning points are mapped in real-time to preset thermodynamic color scale values ​​(e.g., high change rate corresponds to red, low change rate corresponds to blue), and arranged according to the spatial position of the scanning points to form a real-time morphological thermal map covering the entire inner wall surface. The thermal map visually identifies areas of geometric abrupt change (i.e., areas where the thermal color gradation value exceeds a preset abrupt change threshold) through continuous color blocks, such as continuous color blocks with a high-temperature hue at the weld seam of the tank. Based on the geometric abrupt change areas located by the thermal map, the polishing system automatically adjusts the pressure and trajectory of the polishing head to perform targeted polishing on those areas until the abrupt color blocks disappear as shown on the real-time thermal map, and the roughness of the inner wall reaches a uniform state.

[0100] The scheme described in step 103 above enables real-time monitoring and visualization of the surface morphology during the polishing process. An innovative dynamic thermographic characterization method for morphology changes was constructed using an optimized laser scanning system. This technology employs an analysis process combining time-difference-height conversion and rate-of-change calculation to achieve real-time quantification of surface geometric features. An innovative color-gradient mapping algorithm transforms abstract deformation data into an intuitive thermographic map, accurately identifying the spatial distribution of regions experiencing geometric abrupt changes. This real-time morphology monitoring technology provides timely data feedback for the dynamic adjustment of the polishing process.

[0101] 104. Combining the surface roughness matrix and the real-time morphology heat map, calculate the pressure correlation value between the geometric features of the inner wall of the fire extinguisher and the polishing pressure, and determine the correlation vector between the geometric change region and the polishing pressure through the pressure correlation value.

[0102] Optionally, step 104 may specifically include the following steps:

[0103] 1041. Extract the concavity and convexity feature values ​​from the surface concavity and convexity matrix, simultaneously extract the thermal color level values ​​of the geometric abrupt change regions from the real-time morphology heat map, and combine the concavity and convexity feature values ​​with the thermal color level values ​​to generate a comprehensive geometric feature value.

[0104] 1042. Combine the geometric feature composite value with a preset pressure conversion coefficient to generate a pressure correlation value that describes the relationship between the geometric feature and the required polishing pressure.

[0105] 1043. Locate each spatial location point within the geometric change region, and arrange the pressure correlation values ​​of the spatial location points in a preset spatial coordinate order to form a pressure correlation value sequence.

[0106] 1044. Calculate the average value of the pressure correlation values ​​of all spatial locations within the geometric abrupt change region as the polishing pressure reference value for the geometric abrupt change region.

[0107] 1045. Bind the polishing pressure reference value to the spatial coordinates of the geometric change region to form an association vector between the geometric change region and the polishing pressure.

[0108] In the above scheme, the pressure correlation value refers to the degree of correlation between geometric features and pressure. The correlation vector is a vector reflecting the correlation relationship. The geometric feature composite value is the composite numerical value of the geometric features. The pressure conversion coefficient is the proportional coefficient for pressure conversion. The polishing pressure reference value is the reference pressure value for polishing. Spatial position coordinates are the spatial coordinates of the location. A spatial position point is a point in space. The pressure correlation value sequence is a sequence of pressure correlation values. The average value is the average of the numerical values. The preset spatial coordinate order is a pre-defined coordinate order.

[0109] In this embodiment, the system first extracts the concavity / convexity feature values ​​from the surface concavity / convexity matrix and simultaneously acquires the thermal color-gradient values ​​of geometric abrupt change regions from the real-time morphology heatmap. The data fusion engine reads the concavity / convexity feature values ​​of each grid cell from the surface concavity / convexity matrix generated in step 101 (quantifying the degree of local deformation of the inner wall), and simultaneously calls the real-time morphology heatmap output in step 103, using spatial coordinate matching technology to locate the thermal color-gradient values ​​of points at the same location (reflecting the intensity level of geometric abrupt change). The feature integration module merges the two types of parameters into a comprehensive geometric feature value through a weighted superposition algorithm. This value integrates the concavity / convexity amplitude and the abrupt change gradient, forming a comprehensive index characterizing the local geometric anomalies of the inner wall.

[0110] Subsequently, the system combines the geometric feature composite value with a preset pressure conversion coefficient to generate a pressure correlation value: the parameter mapper calls the pre-calibrated pressure conversion coefficient (a scaling factor set based on material hardness and the mechanical properties of the polishing tool), and multiplies it with the geometric feature composite value through a linear transformation model. The correlation calculator outputs the product as the pressure correlation value, which describes the polishing pressure intensity required for a specific geometric feature (e.g., increased pressure for high-uneven areas and decreased pressure for smooth areas), establishing a quantitative correlation between geometric deformation and polishing force.

[0111] Next, the system locates spatial points within the geometrically abrupt change region and arranges pressure correlation values: the region scanner traverses the coordinates of all spatial points within the geometrically abrupt change region and extracts the pressure correlation value corresponding to each point using topological indexing technology. The sequence generator, based on a preset axial or circumferential spatial coordinate order (e.g., from the bottom of the tank to the opening), linearly arranges the pressure correlation values ​​into a pressure correlation value sequence, forming a continuous mapping chain between spatial location and pressure demand.

[0112] Then, the system calculates the average of all pressure correlation values ​​within the geometric abrupt change region as the polishing pressure baseline value: the baseline solver performs an arithmetic mean operation on all values ​​in the pressure correlation value sequence to generate a polishing pressure baseline value (scalar value) representing the overall pressure requirement of the region. The outlier filter eliminates outlier interference through standard deviation analysis, ensuring that the baseline value accurately reflects the common polishing requirements of the geometric abrupt change region, providing a unified reference standard for subsequent pressure regulation.

[0113] Finally, the system binds the polishing pressure reference value with the spatial coordinates of the geometric abrupt change region to form an association vector: the vector synthesis module encapsulates the polishing pressure reference value and the boundary coordinates of the geometric abrupt change region (such as axial start / end position, circumferential angle range) into structured data units. The spatial mapper integrates all data units into an association vector in coordinate order according to the physical distribution topology of the region (such as the spatial relationship of multiple independent abrupt change regions). Each element contains a binding relationship of "spatial position - reference pressure", forming a set of instructions that can drive the polishing equipment to apply targeted pressure.

[0114] In practical applications, a fire equipment factory uses an automated polishing system to treat the inner wall surface of fire extinguisher tanks. During the inspection phase, the system acquires three-dimensional topographic data of the tank's inner wall using a high-precision laser scanner, constructing a surface roughness matrix that quantifies the undulation characteristics at various locations on the inner wall. Simultaneously, the system generates a real-time topographic heatmap, visually displaying areas of abrupt changes in surface height (such as weld remnants or corrosion pits) through color gradients. Upon entering the polishing pressure control phase, the system first extracts roughness feature values ​​(such as the rate of curvature change) from the surface roughness matrix, and simultaneously locates geometrically abrupt change areas (such as red highlighted areas in the heatmap) from the real-time topographic heatmap, extracting their thermal color gradation values ​​(corresponding to the color gradation encoding of surface height). Combining these two values, the system generates a comprehensive geometric feature value that fully characterizes the surface geometry. Subsequently, the system combines the comprehensive geometric feature value with a preset pressure conversion coefficient (set based on material hardness and polishing tool characteristics) to calculate a pressure correlation value describing the required polishing pressure intensity for that area. Next, the system locates all spatial points within the geometrically abrupt change region and arranges the pressure correlation values ​​of each point in the order of their axial and circumferential spatial coordinates, forming a pressure correlation value sequence. Based on this sequence, the system calculates the average value of the pressure correlation values ​​of all spatial points within the entire geometrically abrupt change region, using it as the polishing pressure benchmark value for that region. Finally, the system binds the polishing pressure benchmark value to the central spatial coordinates of the geometrically abrupt change region, forming a correlation vector between that region and the dynamic polishing pressure, and transmits this vector to the robotic arm control system. During the actual polishing process, when the robotic arm carrying the polishing head moves to this geometrically abrupt change region, the system automatically increases the polishing pressure to the benchmark value according to the correlation vector. This ensures that higher pressure is applied to protruding parts to eliminate burrs, while reducing pressure on recessed parts to avoid excessive wear. This achieves adaptive and precise control of the inner wall roughness, improving the sealing and pressure resistance of the fire extinguisher tank.

[0115] The scheme described in step 104 above achieves intelligent correlation modeling between geometric features and polishing pressure. Through the fusion analysis of surface morphology data and real-time thermal images, an innovative method for calculating pressure correlation values ​​is constructed. This technology employs a strategy of eigenvalue weighting and spatial location binding to establish a precise correspondence between geometrically abrupt regions and polishing pressure. The innovative correlation vector generation method transforms the complex morphology-pressure relationship into executable process parameters, providing a scientific basis for precise polishing control. This multi-data source fusion analysis method significantly improves the adaptability and accuracy of the polishing process.

[0116] 105. Based on the aforementioned correlation vector, adjust the axial movement path and rotation speed of the polishing head to eliminate roughness differences in geometrically abrupt regions on the inner wall of the fire extinguisher tank.

[0117] Optionally, step 105 may specifically include the following steps:

[0118] 1051. Analyze the coordinates of the geometric change region in the correlation vector and the polishing pressure reference value, and combine the polishing pressure reference value with the preset axial speed conversion coefficient to generate the axial movement speed adjustment value.

[0119] 1052. The polishing pressure reference value is combined with a preset rotation speed conversion coefficient to generate a rotation speed adjustment value, which corresponds to the change in the rotation speed of the polishing head.

[0120] 1053. Plan the axial movement path of the polishing head according to the coordinates of the geometric change region, so that the axial movement path passes through the geometric change region.

[0121] 1054. According to the axial movement speed adjustment value, the axial drive motor controller of the polishing head changes the axial movement speed of the polishing head according to the movement speed adjustment value.

[0122] 1055. According to the rotation speed adjustment value, the rotary motor controller of the polishing head changes the rotation speed of the polishing head according to the rotation speed adjustment value to eliminate the roughness difference in the geometrically abrupt area on the inner wall of the fire extinguisher tank.

[0123] In the above scheme, axial movement path refers to the axial movement path. Rotational speed refers to the rate of rotation. Roughness difference refers to the difference in surface roughness. Axial speed conversion coefficient refers to the proportional coefficient for axial speed conversion. Rotational speed conversion coefficient refers to the proportional coefficient for rotational speed conversion. Axial movement speed adjustment value refers to the adjustment amount of axial speed. Rotational speed adjustment value refers to the adjustment amount of rotational speed. Axial drive motor controller refers to the device that controls the axial motor. Rotational motor controller refers to the device that controls the rotational motor. Rotational speed change refers to the change in rotational speed. Coordinates of the geometric change region refer to the position coordinates of the geometric change region. Polishing pressure reference value refers to the reference pressure value for polishing.

[0124] In this embodiment, the system first analyzes the coordinates of the geometrically abrupt change region and the polishing pressure reference value in the associated vector: the vector analysis module extracts the boundary coordinates (such as the axial start and end points, and the circumferential angle range) and the corresponding polishing pressure reference value (a scalar value that quantifies the required polishing intensity of the region) of the geometrically abrupt change region from the associated vector generated in step 104. The parameter fusion engine multiplies the polishing pressure reference value with a pre-calibrated axial speed conversion coefficient (a mapping factor set based on material removal efficiency and equipment dynamics characteristics) through a linear proportional model to generate an axial movement speed adjustment value that controls the axial feed rate. This process achieves coordinated adjustment of polishing pressure and axial speed through dynamic parameter mapping technology, ensuring that high-roughness regions obtain lower feed rates to extend the polishing contact time.

[0125] Subsequently, the system combines the polishing pressure reference value with the rotational speed conversion coefficient: the speed solver calls the same polishing pressure reference value, combines it with a predefined rotational speed conversion coefficient (a proportional factor reflecting the pressure-speed correlation), and calculates the rotational speed adjustment value through a nonlinear conversion algorithm. This adjustment value directly corresponds to the change in the polishing head's rotational speed, providing precise speed control command input to the rotary motor. This process ensures that higher rotational speeds are synchronously obtained in high polishing pressure areas through a pressure-speed coupling model, enhancing material removal efficiency.

[0126] Next, the system plans axial movement paths based on the coordinates of the geometrically abrupt change regions: the path planner generates a continuous trajectory covering all abrupt change regions using a spatial interpolation algorithm, based on the coordinate set of these regions (such as discrete point clouds or continuous boundaries). The topology optimization module discretizes the trajectory into a sequence of axial movement nodes, ensuring that the axial movement path traverses the center position of each geometrically abrupt change region and avoids non-abnormal regions to reduce ineffective polishing. This process utilizes targeted path generation technology to achieve precise placement of polishing resources in key defect areas.

[0127] Then, the system controls the axial drive motor to change its moving speed according to the adjustment value: the command converter converts the axial moving speed adjustment value into a pulse frequency signal for the servo motor. After receiving this signal, the axial drive motor controller dynamically adjusts the rotation frequency of the motor output shaft through frequency conversion speed regulation technology, driving the lead screw or linear module to advance the polishing head at the target rate. This process uses a closed-loop feedback mechanism to calibrate the deviation between the actual feed speed and the set value in real time, ensuring low-speed fine polishing in high-pressure areas and high-speed rapid passage in low-pressure areas.

[0128] Finally, the system controls the rotary motor to change its speed to eliminate roughness differences: after receiving the rotational speed adjustment value, the rotary motor controller adjusts the current frequency and voltage amplitude of the motor windings through voltage-frequency coordination control. The torque balancing unit synchronously increases the output torque as the speed increases to avoid stalling due to sudden load changes. The dynamic polishing actuator applies uniform shear force to the geometrically abrupt areas through a high-speed rotating polishing head, smoothing out protrusions and filling depressions, ultimately eliminating roughness differences. This process utilizes the proportional relationship between rotational speed and material removal rate to achieve surface texture homogenization.

[0129] In practical applications, an adaptive polishing system is used on the fire extinguisher tank inner wall in the fire equipment production line of an automobile manufacturing plant. After the system determines the coordinates of a geometrically abrupt change area (such as a weld protrusion) and the polishing pressure reference value through an association vector, it first analyzes the coordinates of the geometrically abrupt change area and the polishing pressure reference value in the association vector. The polishing pressure reference value is then combined with a preset axial speed conversion coefficient to generate an axial movement speed adjustment value; simultaneously, the polishing pressure reference value is combined with a preset rotational speed conversion coefficient to generate a rotational speed adjustment value, which corresponds to the change in the polishing head's rotational speed. Subsequently, the system replans the axial movement path of the polishing head based on the coordinates of the geometrically abrupt change area, ensuring that the axial movement path passes through the geometrically abrupt change area. During the execution phase, the system controls the axial drive motor controller according to the axial movement speed adjustment value, adjusting the axial movement speed of the polishing head accordingly; simultaneously, it controls the rotary motor controller according to the rotational speed adjustment value, increasing the rotational speed of the polishing head accordingly. When the polishing head enters the weld protrusion area along the modified axial movement path, the axial movement speed is reduced to extend the local polishing time, while the rotation speed is increased simultaneously to enhance the grinding frequency per unit area. The two work together to efficiently remove the metal burrs on the protrusion, ultimately eliminating the roughness difference between this geometrically abrupt area and the surrounding plane, ensuring that the overall smoothness of the inner wall of the tank meets the high-pressure sealing requirements.

[0130] The scheme described in step 105 above achieves closed-loop dynamic optimization of polishing process parameters. Based on intelligent analysis of correlation vectors, an innovative collaborative control scheme for axial movement and rotational speed is constructed. This technology employs a dynamic matching mechanism between the speed conversion coefficient and the reference value to achieve precise control of the polishing path and parameters. An innovative multi-motor collaborative control algorithm ensures optimized motion parameters of the polishing head in areas of geometric abrupt changes, effectively eliminating surface roughness differences. This technical approach, which directly transforms detection and analysis into control commands, significantly improves the polishing quality and consistency of the inner wall of the fire extinguisher tank.

[0131] The following are specific examples for steps 101 to 105:

[0132] When fire extinguisher manufacturers use an adaptive polishing system to address weld protrusion defects on the inner wall of fire extinguisher tanks, the system first acquires 3D point cloud data of the tank's inner wall using a high-precision laser scanner. After dividing the data into a preset spatial grid, it calculates the local height deviation and curvature gradient characteristic values ​​for each grid zone, integrating them to generate a surface roughness matrix that quantifies the surface undulation characteristics. Subsequently, a rotating laser profile scanner mounted at the front end of the polishing rod dynamically adjusts the scanning frequency and angle based on the roughness characteristic values ​​of this surface roughness matrix (i.e., the weighted sum of local height deviation and curvature gradient characteristic values), combined with preset frequency and angle conversion coefficients, to focus the scan on high-curvature areas.

[0133] During the polishing process, the adjusted rotating laser profile scanner rotates around the polishing rod axis. It calculates the height value of each scanning point by recording the time difference between laser beam emission and return, and generates a height change rate value based on the height difference between adjacent scanning points and the arc length distance. The system maps this value to thermal color scale values, arranging them according to spatial location to form a real-time morphological heat map. Continuous color block areas in the image where the thermal color scale value exceeds the abrupt change threshold are identified as geometric abrupt change areas (such as weld protrusions). Next, the system extracts the concavity / convexity feature values ​​of the corresponding locations from the surface concavity / convexity matrix, combines them with the thermal color scale values ​​of the geometric abrupt change areas in the real-time morphological heat map to generate a comprehensive geometric feature value, and then calculates the pressure correlation value using a preset pressure conversion coefficient. Within the geometric abrupt change area, the system locates all spatial points and arranges their pressure correlation values ​​to form a pressure correlation value sequence. The average value of this sequence is calculated as the polishing pressure reference value. Finally, this reference value is bound to the spatial coordinates of the geometric abrupt change area to form a correlation vector.

[0134] Based on the correlation vector, the system analyzes the coordinates of the weld protrusion area and the polishing pressure reference value, combines them with a preset axial speed conversion coefficient to generate an axial movement speed adjustment value, and simultaneously combines them with a rotational speed conversion coefficient to generate a rotational speed adjustment value. The axial movement path of the polishing head is replanned according to the coordinates of the geometrically abrupt change area, ensuring it passes through the weld area. The axial drive motor controller executes the axial movement speed adjustment value to reduce the movement speed, extending the local polishing time, while the rotational motor controller increases the rotational speed according to the rotational speed adjustment value to enhance the grinding frequency. When the polishing head reaches the weld protrusion along the corrected path, the synergistic effect of low speed and high rotation effectively eliminates the roughness difference between the protrusion and the surrounding wall surface, ensuring that the overall surface finish of the inner wall meets the sealing requirements.

[0135] Figure 2 This application provides a schematic diagram of the structure of an automatic polishing roughness control system for the inner wall of a fire extinguisher tank, as shown in the embodiment. Figure 2 As shown, the system includes:

[0136] The quantization module 21 is used to acquire three-dimensional point cloud data of the inner wall of the fire extinguisher tank, and perform curvature analysis on the three-dimensional point cloud data to generate local height deviation feature values ​​and curvature gradient feature values ​​of the inner wall of the fire extinguisher tank, and quantize the local height deviation feature values ​​and curvature gradient feature values ​​to obtain the surface concavity matrix.

[0137] The adjustment module 22 is used to adjust the scanning frequency and scanning angle of the rotating laser profile scanner based on the surface concavity matrix, using a rotating laser profile scanner installed at the front end of the polishing rod.

[0138] The scanning module 23 is used to scan the inner wall surface of the fire extinguisher during the polishing process using the adjusted rotating laser profile scanner to generate a real-time morphological thermal map, which is used to identify geometric abrupt change areas on the inner wall surface.

[0139] The calculation module 24 is used to combine the surface roughness matrix and the real-time topography heat map to calculate the pressure correlation value between the geometric features of the inner wall of the fire extinguisher and the polishing pressure, and to determine the correlation vector between the geometric change region and the polishing pressure through the pressure correlation value.

[0140] Elimination module 25 is used to adjust the axial movement path and rotation speed of the polishing head based on the correlation vector to eliminate roughness differences in geometrically abrupt regions on the inner wall of the fire extinguisher tank.

[0141] Figure 2 The aforementioned automatic polishing roughness control system for the inner wall of a fire extinguisher tank can perform... Figure 1 The implementation principle and technical effects of the automatic polishing roughness control method for the inner wall of a fire extinguisher tank as described in the above embodiment will not be repeated here. The specific operation methods of each module and unit in the automatic polishing roughness control system for the inner wall of a fire extinguisher tank in the above embodiment have been described in detail in the embodiments related to this method, and will not be elaborated upon here.

[0142] In one possible design, Figure 2 The automatic polishing roughness control system for the inner wall of a fire extinguisher tank, as shown in the embodiment, can be implemented as a computing device, such as... Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0143] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are invoked and executed by the processing component 32.

[0144] The processing component 32 is used for the above Figure 1 The embodiment describes an automatic polishing roughness control method for the inner wall of a fire extinguisher tank.

[0145] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above-described method. Alternatively, the processing component may be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described method.

[0146] Storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0147] Of course, computing devices may also include other components, such as input / output interfaces, display components, communication components, etc.

[0148] Input / output interfaces provide interfaces between processing components and peripheral interface modules, which can be output devices, input devices, etc.

[0149] The communication components are configured to facilitate wired or wireless communication between computing devices and other devices.

[0150] The computing device can be a physical device or an elastic computing host provided by a cloud computing platform. In this case, the computing device can refer to a cloud server, and the aforementioned processing components, storage components, etc., can be basic server resources rented or purchased from the cloud computing platform.

[0151] This application also provides a computer storage medium storing a computer program, which, when executed by a computer, can perform the above-described functions. Figure 1 The embodiment shown illustrates an automatic polishing roughness control method for the inner wall of a fire extinguisher tank.

[0152] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0153] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0154] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0155] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for controlling the roughness of the inner wall of a fire extinguisher tank through automatic polishing, characterized in that, include: Three-dimensional point cloud data of the inner wall of the fire extinguisher tank is acquired, and curvature analysis is performed on the three-dimensional point cloud data to generate local height deviation feature values ​​and curvature gradient feature values ​​of the inner wall of the fire extinguisher tank. The local height deviation feature values ​​and curvature gradient feature values ​​are then quantified to obtain the surface concavity and convexity matrix. Using a rotary laser profile scanner mounted at the front end of a polishing rod, the scanning frequency and scanning angle of the rotary laser profile scanner are adjusted according to the surface concavity / convexity matrix; The adjusted rotating laser profile scanner scans the inner wall surface of the fire extinguisher during the polishing process to generate a real-time morphological thermal map, which is used to identify geometric abrupt change areas on the inner wall surface. By combining the surface roughness matrix and the real-time topography thermal map, the pressure correlation value between the geometric features of the inner wall of the fire extinguisher and the polishing pressure is calculated, and the correlation vector between the geometric change region and the polishing pressure is determined by the pressure correlation value. Based on the correlation vector, the axial movement path and rotation speed of the polishing head are adjusted to eliminate roughness differences in geometrically abrupt regions on the inner wall of the fire extinguisher tank.

2. The method according to claim 1, characterized in that, By combining the surface roughness matrix and the real-time topographic thermal map, a pressure correlation value between the geometric features of the fire extinguisher's inner wall and the polishing pressure is calculated. The correlation vector between the geometrically abrupt regions and the polishing pressure is then determined using this pressure correlation value, including: Extract concavity and convexity feature values ​​from the surface concavity and convexity matrix, simultaneously extract thermal color level values ​​of geometrically abrupt regions from the real-time morphology heat map, and combine the concavity and convexity feature values ​​with the thermal color level values ​​to generate a comprehensive geometric feature value. The geometric feature composite value is combined with a preset pressure conversion coefficient to generate a pressure correlation value that describes the relationship between the geometric feature and the required polishing pressure. Within the geometric abrupt change region, locate each spatial location point and arrange the pressure correlation values ​​of the spatial location points according to a preset spatial coordinate order to form a pressure correlation value sequence; The average value of the pressure correlation values ​​at all spatial locations within the geometric abrupt change region is calculated as the polishing pressure reference value for the geometric abrupt change region. The polishing pressure reference value is bound to the spatial coordinates of the geometric change region to form an association vector between the geometric change region and the polishing pressure.

3. The method according to claim 1, characterized in that, The adjusted rotating laser profile scanner scans the inner wall surface of the fire extinguisher during the polishing process to generate a real-time topographic thermal map. This real-time topographic thermal map is used to identify geometrically abrupt changes in the inner wall surface, including: The adjusted rotating laser profile scanner is activated so that it rotates around the axis of the polishing rod and emits a laser beam to scan the inner wall surface of the fire extinguisher, and the time difference between the emission and return of the laser beam is recorded. The time difference is converted into the height value of each scanning point on the inner wall surface of the fire extinguisher. At the same time, the difference in height values ​​of adjacent scanning points and the arc distance between scanning points are calculated. Based on the difference and the arc distance between scanning points, the height change rate value is generated. The height change rate value is mapped to a thermal color level value, and the thermal color level values ​​are arranged according to the spatial position of the scan point to form a real-time morphological heat map that identifies geometric change regions. The geometric change regions are continuous color block regions where the thermal color level values ​​exceed a preset change threshold.

4. The method according to claim 3, characterized in that, The time difference is converted into the height value of each scanning point on the inner wall surface of the fire extinguisher. Simultaneously, the difference in height values ​​between adjacent scanning points and the arc distance between scanning points are calculated. Based on the difference and the arc distance between scanning points, a height change rate value is generated, including: The time difference is converted into the propagation distance of the laser beam in the air, and the propagation distance is obtained through a preset physical characteristic of the speed of light. The propagation distance value is corrected according to the preset refractive index characteristics of the fire extinguisher canister material to generate the height value of each scanning point on the inner wall surface of the fire extinguisher. Locate adjacent scan points and calculate the difference in height values ​​between adjacent scan points as the height change; Obtain the distance value corresponding to the arc length rotated by the rotating platform between adjacent scanning points, and generate a height change rate value describing the degree of height change in a unit arc length distance based on the proportional relationship between the height change amount and the distance value.

5. The method according to claim 1, characterized in that, Based on the correlation vector, the axial movement path and rotational speed of the polishing head are adjusted to eliminate roughness differences in geometrically abrupt regions on the inner wall of the fire extinguisher tank, including: The coordinates of the geometric abrupt change region in the correlation vector and the polishing pressure reference value are analyzed, and the polishing pressure reference value is combined with the preset axial speed conversion coefficient to generate the axial movement speed adjustment value. The polishing pressure reference value is combined with a preset rotation speed conversion coefficient to generate a rotation speed adjustment value, which corresponds to the change in the rotation speed of the polishing head. The axial movement path of the polishing head is planned according to the coordinates of the geometric change region, so that the axial movement path passes through the geometric change region; The axial drive motor controller of the polishing head changes the axial movement speed of the polishing head according to the axial movement speed adjustment value. According to the rotation speed adjustment value, the rotary motor controller of the polishing head changes the rotation speed of the polishing head according to the rotation speed adjustment value to eliminate the roughness difference in the geometrically abrupt area on the inner wall of the fire extinguisher tank.

6. The method according to claim 1, characterized in that, Using a rotary laser profile scanner mounted at the front end of a polishing rod, the scanning frequency and scanning angle of the rotary laser profile scanner are adjusted according to the surface roughness matrix, including: A rotating laser profile scanner is installed at the front end of the polishing rod to extract the concavity and convexity feature values ​​of each grid partition from the surface concavity and convexity matrix. The concavity and convexity feature values ​​are the weighted sum of the local height deviation feature values ​​and the curvature gradient feature values. The concave and convex feature values ​​are combined with a preset frequency conversion coefficient to generate an adjustment value for the scanning frequency. The adjustment value for the scanning frequency corresponds to the change in the laser pulse emission frequency of the rotating laser contour scanner to achieve scanning frequency adjustment. The concave and convex feature values ​​are combined with a preset angle conversion coefficient to generate an adjustment value for the scanning angle. The adjustment value of the scanning angle corresponds to the change in the deflection angle of the rotating platform of the rotating laser contour scanner, so as to achieve the adjustment of the scanning angle.

7. The method according to claim 1, characterized in that, Three-dimensional point cloud data of the inner wall of the fire extinguisher tank is acquired, and curvature analysis is performed on the three-dimensional point cloud data to generate local height deviation feature values ​​and curvature gradient feature values ​​of the inner wall of the fire extinguisher tank. The local height deviation feature values ​​and curvature gradient feature values ​​are then quantified to obtain a surface concavity / convexity matrix, including: Acquire three-dimensional point cloud data of the inner wall of the fire extinguisher tank, and divide the three-dimensional point cloud data into grids according to a preset spatial grid. Calculate the height value of the 3D point cloud data points within the grid partition, and calculate the average value of the height values ​​as the reference height value of the grid partition; The difference between the height value of each 3D point cloud data point within the measured grid partition and the reference height value is used as the local height deviation characteristic value of the grid partition; Within the grid partition, locate adjacent 3D point cloud data points, calculate the straight-line distance and height difference between adjacent 3D point cloud data points, and use the ratio of the height difference to the straight-line distance as the curvature gradient feature value. Integrate the local height deviation feature values ​​and curvature gradient feature values ​​of all grid partitions, and then arrange the local height deviation feature values ​​and curvature gradient feature values ​​according to the preset spatial grid to form a surface concavity and convexity matrix.

8. An automatic polishing roughness control system for the inner wall of a fire extinguisher tank, characterized in that, include: The quantization module is used to acquire three-dimensional point cloud data of the inner wall of the fire extinguisher tank, and to perform curvature analysis on the three-dimensional point cloud data to generate local height deviation feature values ​​and curvature gradient feature values ​​of the inner wall of the fire extinguisher tank, and to quantize the local height deviation feature values ​​and curvature gradient feature values ​​to obtain the surface concavity and convexity matrix. An adjustment module is used to adjust the scanning frequency and scanning angle of a rotary laser profile scanner mounted at the front end of a polishing rod, based on the surface roughness matrix. The scanning module is used to scan the inner wall surface of the fire extinguisher during the polishing process using an adjusted rotating laser profile scanner to generate a real-time morphological thermal map, which is used to identify geometric abrupt change areas on the inner wall surface. The calculation module is used to combine the surface roughness matrix and the real-time topography heat map to calculate the pressure correlation value between the geometric features of the inner wall of the fire extinguisher and the polishing pressure, and to determine the correlation vector between the geometric change region and the polishing pressure through the pressure correlation value. The elimination module is used to adjust the axial movement path and rotation speed of the polishing head based on the correlation vector to eliminate roughness differences in geometrically abrupt regions on the inner wall of the fire extinguisher tank.

9. A computing device, characterized in that, It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement the automatic polishing roughness control method for the inner wall of a fire extinguisher tank as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that, The device contains a computer program that, when executed by a computer, implements an automatic polishing roughness control method for the inner wall of a fire extinguisher tank as described in any one of claims 1 to 7.

Images