Automobile chassis wax spraying control system

Abstract

The invention discloses an automobile chassis wax spraying control system, and relates to the technical field of automobile machining. The system comprises a chassis scanning data acquisition module which acquires a chassis space point set; the structure coupling matrix generation module generates a corrosion potential degree in combination with the material sensitivity; the exposure coefficient generation module generates a structure exposure coefficient through neighborhood analysis; the path planning module generates wax spraying three-dimensional trajectory data; the spraying coupling degree prediction module generates a spraying coupling degree and wax layer thickness prediction result based on the trajectory data and the spraying parameters; the wax mask stability evaluation module calculates the wax mask deposition stability; the real-time deviation correction module adjusts spraying parameters and tracks according to the coordinates of the nozzle; and the closed-loop quality evaluation module generates a feedback factor based on the corrosion potential degree, forms overall spraying quality data and maps the overall spraying quality data into a wax spraying execution control signal. And through closed-loop quality evaluation and dynamic adjustment of execution parameters, stability improvement, corrosion protection reinforcement and overall spraying quality balance control in the spraying process are achieved.

Description

Technical Field

[0001] This invention relates to the field of automotive processing technology, specifically to an automotive chassis wax spraying control system. Background Technology

[0002] During long-term operation, the vehicle chassis is directly exposed to the road environment and is susceptible to corrosion from rainwater, mud, salt spray, and chemical media, making corrosion damage a common problem. To improve the chassis's protective performance, wax spraying has been widely used, forming a wax film layer on the chassis surface to isolate it from external media.

[0003] Existing automotive chassis waxing processes mainly rely on fixed nozzles or robotic arm trajectories to achieve wax film coverage on the chassis surface. These methods are typically based on empirical trajectories or simplified geometric modeling, lacking a systematic analysis of the complex geometric features of the chassis, its susceptibility to localized corrosion, and material differences. This results in insufficient uniformity of coating coverage and difficulty in guaranteeing localized deposition thickness.

[0004] In the path planning and parameter control of chassis wax spraying, existing technologies mostly use two-dimensional unfolded or simplified models for trajectory calculation, which cannot accurately reflect the true three-dimensional structural characteristics of the chassis. At the same time, the pressure, flow rate and trajectory parameters of the nozzle during the spraying process are coupled with the chassis surface in a complex manner, and traditional methods lack effective modeling for predicting dynamic deviations and wax layer thickness during the spraying process.

[0005] For the quality assessment of spraying results, existing solutions mostly rely on random sampling or overall appearance inspection, failing to establish a closed-loop quality assessment mechanism based on real-time measurement data, wax film deposition stability, and corrosion potential, making it difficult to achieve dynamic feedback adjustment of the spraying process.

[0006] Therefore, existing chassis wax spraying control technologies are insufficient in terms of scanning modeling, corrosion potential identification, spraying coupling degree prediction, real-time deviation correction, and closed-loop quality control, making it difficult to meet the requirements for wax spraying accuracy and consistency under complex structural conditions. Summary of the Invention

[0007] Based on the shortcomings of the prior art described above, the purpose of this invention is to provide an automotive chassis wax spraying control system to solve the aforementioned technical problems.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a car chassis wax spraying control system, comprising:

[0009] The chassis scanning data acquisition module is equipped with a high-precision three-dimensional scanning device to acquire discrete point data on the surface of the automobile chassis and form a set of chassis spatial points based on the discrete point data.

[0010] The structural coupling matrix generation module constructs a high-order geometric coupling matrix between chassis points based on the set of chassis spatial points, and generates the corrosion potential of sampling points by combining material sensitivity.

[0011] The exposure coefficient generation module generates the structural exposure coefficient based on a high-order geometric coupling matrix and corrosion potential through neighborhood relationship analysis.

[0012] The path planning module generates three-dimensional trajectory data for chassis waxing based on corrosion potential and structural exposure coefficient. The three-dimensional trajectory data includes trajectory coordinate functions and trajectory primitive parameters.

[0013] The spraying coupling degree prediction module generates prediction results for spraying coupling degree and wax layer thickness based on three-dimensional trajectory data and spraying parameter information through dynamic neighborhood feature analysis. The spraying parameter information includes nozzle position, speed and pressure.

[0014] The wax film stability assessment module calculates the wax film deposition stability based on the wax layer thickness prediction results, spraying coupling degree, high-order geometric coupling matrix and material sensitivity.

[0015] The real-time deviation correction module calculates the deviation based on the real-time coordinates and three-dimensional trajectory data of the nozzle, adjusts the spraying parameters and local trajectory according to the deviation, and forms the corrected spraying parameter information and three-dimensional trajectory data.

[0016] The closed-loop quality assessment module generates a closed-loop feedback factor based on the actual measurement data of wax layer thickness, wax film deposition stability, and corrosion potential. Based on the closed-loop feedback factor, it calculates the overall spraying quality data and maps the overall spraying quality data into wax spraying execution control signals to achieve dynamic adjustment of nozzle flow rate, pressure, oscillation angle, and spraying sequence.

[0017] The present invention is further configured such that the chassis scanning data acquisition module includes:

[0018] Generate a set of sampling point indexes based on the chassis geometry and scanning accuracy requirements;

[0019] A high-precision 3D scanning device is configured to obtain the coordinates of discrete points on the chassis surface based on the sampling point index, forming a set of discrete points.

[0020] Noise removal and local nonlinearity correction are performed on the discrete point set to generate a corrected discrete point set. The corrected discrete point set is then aggregated to form a chassis space point set.

[0021] The present invention is further configured such that the structural coupling matrix generation module includes:

[0022] Based on the set of spatial points on the chassis, a spatial coordinate difference mapping between sampling points is generated to form an initial distance information set;

[0023] The initial distance information set and the topological sensitivity parameters between regions are combined in multiple nonlinear layers to generate a high-order geometric coupling matrix, which expresses the geometric association strength and structural constraint relationship between points.

[0024] Based on the material sensitivity of each region, the region-level material sensitivity is mapped to the sampling point level, and point-level material sensitivity is generated by combining the local positional relationship of the points and the geometric characteristics of the region.

[0025] By combining a high-order geometric coupling matrix and point-level material sensitivity, the corrosion potential of each sampling point is generated, reflecting the potential corrosion risk of each sampling point on the chassis.

[0026] The present invention further specifies that the material sensitivity includes:

[0027] The material type, thickness, hardness, and surface treatment status of each area of ​​the chassis are encoded into material property vectors, and each vector clearly identifies the material characteristics and local property differences of the area.

[0028] A multidimensional nonlinear combination of material property vectors is performed to generate preliminary material sensitivity, which also reflects the asymmetric contribution relationship of material type, thickness, hardness and surface treatment state to the sensitivity.

[0029] By combining the preliminary material sensitivity of neighboring regions and the spatial distance between regions, a neighborhood enhancement coefficient is generated to reflect the influence of neighboring regions on the material sensitivity of the current region, and to distinguish between high-sensitivity and low-sensitivity neighboring regions.

[0030] The initial material sensitivity is combined with the neighborhood enhancement coefficient to generate the final material sensitivity, thus defining the material sensitivity level for each sampling point.

[0031] The present invention is further configured such that the exposure coefficient generation module includes:

[0032] Based on the high-order geometric coupling matrix and the corrosion potential, the neighborhood weighted coupling value is calculated by combining the neighborhood relationship of the sampling point, which reflects the coupling influence between the sampling point and its neighborhood.

[0033] The neighborhood weighted coupling values ​​are nonlinearly combined to generate preliminary structure exposure values;

[0034] By combining the local geometric sensitivity of the sampling points, the preliminary structural exposure value is adjusted to form a sampling point-level exposure coefficient, which reflects the sensitivity of the sampling points in the chassis structure.

[0035] The exposure coefficients at all sampling points are normalized to generate the final structural exposure coefficients.

[0036] The present invention is further configured such that the path planning module includes:

[0037] Based on corrosion potential and structural exposure coefficient, local path weights are generated by combining the neighborhood relationship of sampling points to reflect the waxing priority and local importance of each sampling point on the chassis.

[0038] Based on local path weights and neighborhood relationships, sample point trajectory primitive parameters are generated to describe the local control characteristics of the wax spray trajectory.

[0039] By combining trajectory primitive parameters and sampling point coordinates, a trajectory coordinate function is generated to form the three-dimensional trajectory data of chassis wax spraying.

[0040] The present invention is further configured such that the spraying coupling degree prediction module includes:

[0041] Based on the trajectory coordinate function and trajectory primitive parameters, combined with the chassis spatial point set, a dynamic neighborhood relationship between the nozzle trajectory points and the chassis surface sampling points is generated.

[0042] Based on dynamic neighborhood relationships and spraying parameter information, including nozzle position, velocity and pressure, local spraying energy coupling values ​​are generated to reflect the spatial distribution characteristics of nozzle spraying energy at various sampling points on the chassis.

[0043] Based on the local spraying energy coupling value and the structural coupling matrix, the spraying coupling degree is generated;

[0044] By combining information on spray coupling degree and neighborhood uniformity, a wax layer thickness prediction result is generated.

[0045] The present invention is further configured such that the wax film stability evaluation module includes:

[0046] Based on the wax layer thickness prediction results, spraying coupling degree, high-order geometric coupling matrix and material sensitivity, the neighborhood relationship of sampling points is established and the neighborhood stability contribution coefficient is generated.

[0047] Based on the neighborhood stability contribution coefficient, the local wax film deposition stability of each sampling point is generated, reflecting the nonlinear influence of spray coupling degree and wax layer thickness prediction results on local deposition.

[0048] By combining the spraying coupling degree and wax layer thickness prediction results, the local wax film deposition stability is corrected to form the corrected local deposition stability. The local deposition stability is then aggregated to form the overall wax film deposition stability of the chassis.

[0049] The present invention is further configured such that the real-time deviation correction module includes:

[0050] Based on the real-time coordinates and three-dimensional trajectory data of the nozzle, the spatial relationship between the nozzle and the trajectory points is established, and the deviation of the nozzle at each trajectory point is calculated.

[0051] Based on the deviation, the spraying parameter information is locally adjusted to generate corrected spraying parameter information.

[0052] By combining the corrected spraying parameters and deviations, the three-dimensional trajectory data is locally adjusted to form the corrected three-dimensional trajectory data.

[0053] The present invention is further configured such that the closed-loop quality assessment module includes:

[0054] Based on actual measurement data of wax layer thickness, wax film deposition stability and corrosion potential, a closed-loop feedback factor is formed for each sampling point;

[0055] Based on the closed-loop feedback factor and the neighborhood relationship of the sampling points, the overall spraying quality data is calculated to form a global spraying quality index;

[0056] The overall coating quality data is mapped into dynamic adjustment signals for nozzle flow rate, coating pressure, oscillation angle and coating timing, forming a wax spraying execution control signal;

[0057] Based on the actual measured thickness after spraying, the closed-loop deviation factor is updated to provide a basis for correction in the next waxing cycle, thus achieving continuous closed-loop optimization.

[0058] This invention provides a vehicle chassis wax spraying control system. The system utilizes a chassis scanning data acquisition module, equipped with a high-precision three-dimensional scanning device, to acquire discrete point data of the vehicle chassis surface, forming a chassis spatial point set based on the discrete point data. A structural coupling matrix generation module constructs a high-order geometric coupling matrix between chassis points based on the chassis spatial point set, and generates the corrosion potential of sampling points by combining material sensitivity. An exposure coefficient generation module generates a structural exposure coefficient based on the high-order geometric coupling matrix and corrosion potential through neighborhood relationship analysis. A path planning module generates three-dimensional trajectory data for chassis wax spraying based on the corrosion potential and structural exposure coefficient. The three-dimensional trajectory data includes trajectory coordinate functions and trajectory primitive parameters. A spraying coupling degree prediction module generates a spraying coupling degree prediction based on the three-dimensional trajectory data and spraying parameter information through dynamic neighborhood feature analysis. The system includes: a coating coupling degree and wax layer thickness prediction results; spraying parameter information including nozzle position, speed, and pressure; a wax film stability assessment module, which calculates wax film deposition stability based on wax layer thickness prediction results, coating coupling degree, high-order geometric coupling matrix, and material sensitivity; a real-time deviation correction module, which calculates deviation based on real-time nozzle coordinates and 3D trajectory data, and adjusts spraying parameters and local trajectories according to the deviation to form corrected spraying parameter information and 3D trajectory data; and a closed-loop quality assessment module, which generates a closed-loop feedback factor based on actual wax layer thickness measurement data, wax film deposition stability, and corrosion potential, calculates overall spraying quality data based on the closed-loop feedback factor, and maps the overall spraying quality data to wax spraying execution control signals to achieve dynamic adjustment of nozzle flow rate, pressure, oscillation angle, and spraying sequence. The beneficial effects include:

[0059] 1. Improved dynamic coating uniformity: The closed-loop quality assessment module provides comprehensive feedback on the actual measured data of wax layer thickness, wax film deposition stability and corrosion potential to form overall coating quality data, which is then mapped to the wax spraying execution control signal to achieve dynamic adjustment of nozzle flow rate, pressure, swing angle and spraying sequence, ensuring uniform coverage in different chassis areas during the wax spraying process.

[0060] 2. Enhanced stability of the spraying process: By introducing real-time feedback factors, the wax spraying execution unit can adjust according to the overall spraying quality data during operation, reducing uneven spraying caused by the complexity of the chassis structure and ensuring the continuous stability of the wax spraying process.

[0061] 3. Enhanced Durability of Protective Performance: By continuously monitoring and providing feedback on corrosion potential, the wax spraying control system can make targeted adjustments during the spraying process, reducing spraying defects in weak areas, extending the chassis's corrosion protection life, and thus improving the overall durability of protective performance.

[0062] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0063] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0064] Figure 1 The flowchart illustrates an automotive chassis wax spraying control system as an exemplary embodiment of the present invention. Detailed Implementation

[0065] The embodiments of the present invention will be described below with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention and not for limiting the scope of protection of the present invention.

[0066] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0067] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the invention. However, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the invention.

[0068] Example 1:

[0069] A car chassis wax spraying control system, such as Figure 1 As shown, it includes:

[0070] The chassis scanning data acquisition module is equipped with a high-precision three-dimensional scanning device to acquire discrete point data on the surface of the automobile chassis and form a set of chassis spatial points based on the discrete point data.

[0071] The structural coupling matrix generation module constructs a high-order geometric coupling matrix between chassis points based on the set of chassis spatial points, and generates the corrosion potential of sampling points by combining material sensitivity.

[0072] The exposure coefficient generation module generates the structural exposure coefficient based on a high-order geometric coupling matrix and corrosion potential through neighborhood relationship analysis.

[0073] The path planning module generates three-dimensional trajectory data for chassis waxing based on corrosion potential and structural exposure coefficient. The three-dimensional trajectory data includes trajectory coordinate functions and trajectory primitive parameters.

[0074] The spraying coupling degree prediction module generates prediction results for spraying coupling degree and wax layer thickness based on three-dimensional trajectory data and spraying parameter information through dynamic neighborhood feature analysis. The spraying parameter information includes nozzle position, speed and pressure.

[0075] The wax film stability assessment module calculates the wax film deposition stability based on the wax layer thickness prediction results, spraying coupling degree, high-order geometric coupling matrix and material sensitivity.

[0076] The real-time deviation correction module calculates the deviation based on the real-time coordinates and three-dimensional trajectory data of the nozzle, adjusts the spraying parameters and local trajectory according to the deviation, and forms the corrected spraying parameter information and three-dimensional trajectory data.

[0077] The closed-loop quality assessment module generates a closed-loop feedback factor based on the actual measurement data of wax layer thickness, wax film deposition stability, and corrosion potential. Based on the closed-loop feedback factor, it calculates the overall spraying quality data and maps the overall spraying quality data into wax spraying execution control signals to achieve dynamic adjustment of nozzle flow rate, pressure, oscillation angle, and spraying sequence.

[0078] The present invention is further configured such that the chassis scanning data acquisition module includes:

[0079] A set of sampling point indices is generated based on the chassis geometry and scanning accuracy requirements. Specifically, based on the chassis geometry and scanning accuracy requirements, the optimal set of sampling points is selected to cover the overall chassis structure and enhance key areas. Spatial coverage and key area priority are balanced by minimizing the inverse distance weighted sum, thus generating the scanning sampling point index set. Where H is the total number of sampling points, p h p k Let w be the three-dimensional spatial coordinates of the h-th, k-th candidate sampling point. h,k To assign importance weights to key areas such as thin walls, welds, and corners, high weights are given, Ω represents the entire set of candidate sampling points, and ||p h -p k || -1 The inverse distance function emphasizes dense coverage between points. It selects sampling points by minimizing the weighted inverse distance to achieve balanced spatial coverage of the point cloud, while also reinforcing the importance of key structural regions.

[0080] A high-precision 3D scanning device is configured to obtain the coordinates of discrete points on the chassis surface based on the sampling point index, forming a set of discrete points; specifically, the spatial coordinates p of the sampling points are obtained based on the high-precision 3D scanning device. h And generate a set of discrete points Where Ψ represents the measurement mapping function of the scanning device, based on the sampling point index. Scanner intrinsic parameter matrix Σ s With the scanner extrinsic matrix O t Calculate the actual spatial coordinates, Σ s Includes scanner focal length, pixel resolution, and ranging accuracy parameters, O t This step represents the position and attitude matrix of the scanner relative to the chassis. It maps the sampling point index to real space coordinate points, forming a discrete point set. The mapping function Ψ converts the index information into spatial points, ensuring that the sampling point coordinates accurately reflect the actual geometry of the chassis.

[0081] Noise removal and local nonlinearity correction are performed on the discrete point set to generate a corrected discrete point set. This corrected discrete point set is then aggregated to form the chassis space point set. Specifically, the discrete point set... Noise removal and accuracy correction are performed to generate a corrected set of points. in, Let α represent the neighborhood set of the h-th point. h,k This is the distance attenuation coefficient, dynamically adjusted based on the distance between points, with a value range of [0,1]. h,k The nonlinear weighting exponent is set based on local curvature and surface features, with a value range of [1,3]. The formula corrects the position of each sampling point through neighborhood nonlinear weighting, achieving local noise suppression and accuracy optimization while preserving key geometric features. The corrected set of discrete points is then used. As a set of chassis spatial points, it has the characteristics of balanced spatial coverage and weighted key structural areas, providing a chassis point cloud with high precision, balanced coverage and enhanced key areas.

[0082] The present invention is further configured such that the structural coupling matrix generation module includes:

[0083] Based on the chassis spatial point set, a spatial coordinate difference mapping between sampling points is generated to form an initial distance information set; specifically, the chassis spatial point set is: in, Let h represent the sampling point c in the local region on the chassis. The geometric distance between sampling points is measured using a fourth-power function to enhance the nonlinear influence of differences between distant points on the coupling relationship. An initial geometric distance matrix is ​​constructed, as shown in the formula. Where, x h,c ,y h,c ,z h,c Let h represent the three-dimensional coordinates of the sampling point c in the local region on the chassis. The fourth-order geometric distance between two points is represented by positive real numbers. h1 and h2 are region indices, and c1 and c2 are sampling point numbers within the region. The weight of distant points is enhanced by exponentiation to make the nonlinear effect of geometric relationships more prominent, ensuring that the high-order coupling matrix is ​​sensitive to both local and long-range structural relationships.

[0084] The initial distance information set and inter-region topological sensitivity parameters are combined in multiple nonlinear layers to generate a high-order geometric coupling matrix, which expresses the strength of geometric associations and structural constraints between points. Specifically, based on the initial distance matrix, inter-region geometric sensitivity factors are combined... Construct a high-order geometric coupling matrix to reflect the relationship between the coupling strength between points and structural constraints. in, It is a high-order geometric coupling matrix. This is a local geometric sensitivity factor, generated based on the region's topological adjacency relationships, and takes the value of a positive real number. It is a fourth-order geometric distance matrix. When the distance between points is large, it approaches zero, and the nearest neighbor points maintain significant values, highlighting local coupling while suppressing the influence of long-range redundancy, thus realizing the characterization of nonlinear structural coupling.

[0085] Based on the material sensitivity of each region, the region-level material sensitivity is mapped to the sampling point level. Combining the local positional relationships of the points and the geometric characteristics of the region, point-level material sensitivity is generated. Specifically, the region-level material sensitivity is mapped to the sampling point level, and a point index factor is introduced to achieve differentiation of point sensitivity within the region. Among them, M h,c For sampling point-level material sensitivity, μ h Let C be the material sensitivity of region h, taking the value of a positive real number. h Let h be the set of sampling point numbers within region h, and c be the sampling point number within the region. The mapping formula introduces a point number factor to make different sampling points within the same region exhibit slight differences in sensitivity, thereby enhancing the asymmetry and refinement of subsequent corrosion potential calculation.

[0086] By combining a high-order geometric coupling matrix and point-level material sensitivity, the corrosion potential of each sampling point is generated, reflecting the potential corrosion risk of each sampling point on the chassis. Specifically, by combining the high-order geometric coupling matrix and point-level material sensitivity, a hybrid square and cubic coupling method is used to generate the corrosion potential of each sampling point, highlighting the nonlinear amplification effect of geometrically tight regions and material-sensitive regions. Where, Φ h,c Sampling points Corrosion potential, G h,c；h',c' M is an element of the geometric coupling matrix. h',c' Let h' and c' be the material sensitivity of the sampling points, and h' and c' be the matrix indices. By traversing all points, the formula uses a hybrid coupling of square and cube to nonlinearly amplify the corrosion potential of geometrically close points and material-sensitive points, highlighting key corrosion risk areas.

[0087] The present invention further specifies that the material sensitivity includes:

[0088] The material type, thickness, hardness, and surface treatment status of each region of the chassis are encoded into material attribute vectors, with each vector clearly identifying the material characteristics and local attribute differences of the region; specifically, the material type, thickness, hardness, and surface treatment status of each region h are encoded into vector A. h This forms a computable multidimensional representation, A h =[τ h ,κ h ,σ h ,θ h ], where τ h κ represents the material hardness grade, indicating the hardness index, reflecting wear resistance and structural rigidity, with a value range of [0.1, 10]. hσ is the surface roughness index, representing the degree of microscopic undulation of the surface and affecting corrosion sensitivity. Its value ranges from [0,1]. h θ is a standardized index for regional thickness, representing the sensitivity to the effects of thin / thick walls, with a value range of [0.01, 1]. h Encoding the surface coating type identifies the surface treatment state. By vectorization, complex material properties are transformed into a unified and computable representation, which facilitates subsequent nonlinear sensitivity mapping.

[0089] A preliminary material sensitivity is generated by performing a multidimensional nonlinear combination of material property vectors, reflecting the asymmetric contribution of material type, thickness, hardness, and surface treatment state to the sensitivity. Specifically, a material property coupling function is constructed to nonlinearly combine different properties to generate the preliminary material sensitivity. The formula for calculating the preliminary material sensitivity is as follows: Among them, S h For preliminary material sensitivity, Enhanced hardness contribution, Magnify the effect of roughness. This reflects the nonlinear effect of thickness on sensitivity. The weight of coating type on sensitivity is displayed, and nonlinear coupling between hardness, roughness, thickness and coating type is achieved through combinations of square, cube and square root.

[0090] By combining the preliminary material sensitivity of neighboring regions and the spatial distance between regions, a neighborhood enhancement coefficient is generated to reflect the influence of neighboring regions on the material sensitivity of the current region, distinguishing between high-sensitivity and low-sensitivity neighboring regions. Specifically, by introducing the sensitivity and spatial distance of neighboring regions, a local enhancement effect is achieved through nonlinear attenuation design to generate the neighborhood enhancement coefficient, as shown in the formula below. Among them, E h N is the neighborhood enhancement coefficient, reflecting the nonlinear enhancement effect of neighboring high-sensitivity regions. h Given the neighborhood set of region h, determine the regions participating in the enhancement, d h,h' S represents the center distance between regions, used to adjust for the attenuation of neighboring influence. h' For initial sensitivity in the neighborhood, Amplify the contribution of highly sensitive neighborhoods. Nonlinear attenuation of distant neighbor influence: Through square and cubic attenuation design, the sensitivity of the current region is nonlinearly enhanced by the high-sensitivity neighboring region.

[0091] The initial material sensitivity is combined with the neighborhood enhancement coefficient to generate the final material sensitivity, thus defining the material sensitivity level for each sampling point. Specifically, the final material sensitivity μ is generated using a hybrid power-law calculation, combining the initial sensitivity and the neighborhood enhancement coefficient. h This reflects the nonlinear coupling effect between the local and neighboring regions. The final formula for calculating the material sensitivity is: Among them, S h ·E h Achieving nonlinear coupling between local sensitivity and neighborhood enhancement, This ensures that even with low neighborhood enhancement, the initial sensitivity still makes a fundamental contribution to the final sensitivity.

[0092] The present invention is further configured such that the exposure coefficient generation module includes:

[0093] Based on the high-order geometric coupling matrix and corrosion potential, a neighborhood-weighted coupling value is calculated by combining the neighborhood relationship of sampling points to reflect the coupling influence between sampling points and their neighbors. Specifically, the neighborhood-weighted coupling value is calculated based on the structural coupling relationship and corrosion potential between sampling points and their neighbors, highlighting the contribution of local high-risk points to the target point. Simultaneously, by attenuating the influence of distant neighbors through distance, the formula for calculating the neighborhood-weighted coupling value is as follows: Among them, W i,j G is the neighborhood weighted coupling value, describing the nonlinear coupling effect between point i and its neighboring point j. i,j Φ is a high-order geometric coupling matrix, representing the structural correlation strength between points. j d represents the potential for pitting corrosion in the vicinity. i,j N is the Euclidean distance between points, representing the spatial coordinate difference between sampling points. i For the neighborhood of the sampling point, The influence of the tightly coupled points of the reinforced structure Strengthen the weight of neighborhood points with high corrosion potential. Attenuate the influence of distant neighbors and highlight the local coupling effect;

[0094] The neighborhood weighted coupling values ​​are nonlinearly combined to generate preliminary structure exposure values; specifically, the neighborhood weighted coupling values ​​of sampling point i are nonlinearly aggregated to generate preliminary exposure values. Among them, E i 'This is the initial exposure value,' To suppress the excessive influence of low corrosion potential points on the aggregation value, ensure that the aggregation value is sensitive to high-risk neighborhood points, and control the numerical scale;

[0095] By combining the local geometric sensitivity of the sampling points, the initial structural exposure value is adjusted to form a sampling point-level exposure coefficient, which reflects the sensitivity of the sampling points in the chassis structure. Specifically, this is combined with the local geometric sensitivity factor Γ of sampling point i. i Adjust the initial exposure values ​​to generate exposure coefficients at the sampling point level. Among them, Γ i The exposure coefficient of geometrically sensitive points is determined based on the connection strength of the points in the higher-order coupling matrix or the complexity of the regional structure. The exposure coefficient is amplified exponentially, and the nonlinearity reflects the influence of the local structure.

[0096] The exposure coefficients at all sampling points are normalized to generate the final structural exposure coefficient. Specifically, the exposure coefficients E at all sampling points are normalized. i The final structure exposure coefficient is obtained by normalization: Among them, maximum value normalization ensures that the exposure coefficient is in the range of 0 to 1, and the constant 0.01 prevents division by zero anomalies.

[0097] The present invention is further configured such that the path planning module includes:

[0098] Based on corrosion potential and structural exposure coefficient, local path weights are generated by combining the neighborhood relationship of sampling points to reflect the waxing priority and local importance of each sampling point on the chassis. Specifically, local path weights are generated for each sampling point according to corrosion potential and structural exposure coefficient. Among them, W i Φ represents the local path weight, characterizing the wax spraying priority and local importance of the sampling points. i Corrosion potential at sampling points This is the normalized structural exposure coefficient. Amplifying the impact of high corrosion potential points, Emphasizing the importance of high exposure coefficient points, Suppress interference from low-corrosion neighboring points while introducing neighborhood structure information;

[0099] Based on local path weights and neighborhood relationships, trajectory primitive parameters for sampling points are generated to describe the local control characteristics of the wax spraying trajectory; specifically, trajectory primitive parameters θ are generated for each sampling point. i This reflects the direction, speed, and spacing of the localized wax spraying: Where, θ i W is the trajectory primitive parameter that controls the direction, speed, and spacing of the wax spray. j For the local weights of the neighborhood, d i,j The Euclidean distance between points is calculated using the difference in the three-dimensional coordinates of the sampling points. Indicates the local priority of this point. Introducing the neighborhood synergy effect, distance decay has a greater impact on nearest neighbors. To prevent excessive influence from distant neighbors while ensuring numerical stability;

[0100] By combining trajectory primitive parameters and sampling point coordinates, a trajectory coordinate function is generated to form the three-dimensional trajectory data of chassis wax spraying. Specifically, a continuous three-dimensional trajectory function is generated based on the trajectory primitive parameters and sampling point coordinates. Where T(s) is a continuous three-dimensional trajectory function, representing the coordinates of the wax spray trajectory, and p i Let θ be the coordinates of the sampling point, p(s) be the continuous coordinates of the trajectory, and θ be the coordinates of the sampling point. i For trajectory primitive parameters, Strengthen the influence of high-priority points on the trajectory, exp(-||p i -p(s)|| 1.5 The exponential decay highlights the impact of local points, while the contribution weakens at points farther away.

[0101] The present invention is further configured such that the spraying coupling degree prediction module includes:

[0102] Based on the trajectory coordinate function and trajectory primitive parameters, combined with the chassis spatial point set, a dynamic neighborhood relationship between the nozzle trajectory points and the sampling points on the chassis surface is generated; specifically, on the nozzle motion trajectory, the set of sampling points is denoted as Q = {q j (x,y,z)}, where q j Represents the three-dimensional coordinates of the trajectory sampling points, for each trajectory point q j Set of points in the chassis space Establish neighborhood relations and define neighborhood functions. Among them, Γ jh Representing point q j With point p h Geometric coupling metric, Υ j Using the neighborhood threshold, the interaction area between the nozzle trajectory point and the actual sampling point of the chassis is determined by spatial distance and geometric relationship, forming a local analysis domain for spraying calculation;

[0103] Based on dynamic neighborhood relationships and spraying parameter information, including nozzle position, velocity, and pressure, local spraying energy coupling values ​​are generated to reflect the spatial distribution characteristics of nozzle spray energy at various sampling points on the chassis. Specifically, nozzle parameters are defined as: position L. t =(x t ,y t ,z t ), speed V t Pressure F t For each neighborhood point p h With trajectory point q j Calculate the energy coupling value of local spraying: Among them, D jh =||q j -p h Represents spatial distance, α and β are energy decay exponents, and Θ j Path primitive parameters representing trajectory points Enhance the effect of nozzle pressure on energy transfer, (D jh +1) β Introducing nonlinear distance attenuation, the nearest neighbor points are greatly affected by the spraying process. Adjusting the nozzle speed and path characteristics to suppress energy transfer, the local spraying energy coupling value reflects the nonlinear effect of nozzle pressure and distance, while the speed V tIt will weaken energy transfer;

[0104] Based on the local spraying energy coupling value and structural coupling matrix, the spraying coupling degree is generated; specifically, the spraying coupling degree is defined as a nonlinear combination of the neighborhood point energy and geometric weights. Among them, C j Represents the trajectory point q j The coating coupling degree is determined by comprehensively considering local coating energy and geometric constraints. γ is the coupling enhancement coefficient, which increases the contribution of high-energy points to the coupling degree. jh The geometric coupling parameters between points are represented to reduce the coupling effect of geometrically restricted areas and reflect structural constraints. The coating coupling degree reflects the comprehensive distribution of the spray energy of the nozzle in the local neighborhood, taking into account both the nozzle dynamic parameters and the geometric constraints of the chassis points.

[0105] Combining spraying coupling degree and neighborhood uniformity information, a wax layer thickness prediction result is generated. Specifically, at trajectory point q... j Within the neighborhood of , the predicted value of the wax layer thickness is calculated: Among them, T j Representing point q j The predicted value of the wax layer thickness, where δ is the material deposition coefficient, η is the nonlinear deposition exponent, and Ω j This indicates the degree of energy imbalance between neighboring points. Thickness prediction is controlled by a combination of spraying coupling degree and energy balance, reflecting the synergistic effect of spray deposition characteristics and local geometric differences.

[0106] The present invention is further configured such that the wax film stability evaluation module includes:

[0107] Based on the wax layer thickness prediction results, spraying coupling degree, high-order geometric coupling matrix, and material sensitivity, a neighborhood relationship of sampling points is established, and a neighborhood stability contribution coefficient is generated; specifically, the neighborhood set of sampling points is defined as N. h ={p h' |G h,c；h',c' >Υ h}, where N h For sampling point p h The neighborhood set of G h,c；h',c' These are elements of a higher-order geometric coupling matrix, reflecting the degree of coupling between points, Υ h The neighborhood threshold determines the range of points included in the calculation. Within the neighborhood, the neighborhood stability contribution coefficient is calculated for each point. Among them, K h,h' C is the neighborhood stability contribution coefficient. h' ·T h' μ h' Reflecting the coupling degree of spraying C h' Thickness prediction T h' and material sensitivity μ h'The product effect, the higher-order geometric coupling matrix G h,c；h',c' Suppress excessive geometric coupling to prevent abnormal amplification of effects at certain points;

[0108] Based on the neighborhood stability contribution coefficient, the local wax film deposition stability of each sampling point is generated, reflecting the nonlinear influence of spray coupling degree and wax layer thickness prediction results on local deposition; specifically, the local wax film deposition stability is generated through a nonlinear combination of neighborhood contributions: in, For point p h The local wax film deposition stability, α and β are nonlinear adjustment exponents, controlling the contribution accumulation mode and distance attenuation intensity, D h,h' The spatial distance between points is such that the greater the distance, the weaker the contribution of the neighborhood.

[0109] Combining the spraying coupling degree and wax layer thickness prediction results, the local wax film deposition stability is corrected to form a corrected local deposition stability. The local deposition stability is then aggregated to form the overall chassis wax film deposition stability. Specifically, a global correction coefficient is generated by combining the global spraying coupling degree and thickness prediction. in, For the spraying coupling degree C j With thickness prediction T j The nonlinear superposition, with exponents η and ζ controlling the degree to which it amplifies stability, A global geometric coupling constraint term suppresses excessive coupling between different trajectory points, and a weighted correction is applied to the local wax film deposition stability using a global correction coefficient. S h To correct the wax film deposition stability, the correction reflects the influence of the global spraying state on the local deposition stability, taking into account the overall movement trajectory of the nozzle and the coupling characteristics of the neighborhood.

[0110] The present invention is further configured such that the real-time deviation correction module includes:

[0111] Based on the real-time coordinates and 3D trajectory data of the nozzle, the spatial relationship between the nozzle and the trajectory points is established, and the deviation of the nozzle at each trajectory point is calculated; specifically, the set of real-time coordinates of the nozzle, P, is defined. r ={p r (t k )|k=1,2,...,M}, trajectory data set T={p t (s i ),θ i |i=1,2,...,N}, spraying parameter set S={v i ,F i ,π i}, including nozzle speed v iSpraying pressure F i Spraying angle π i Calculate the spatial deviation between the real-time coordinates of the nozzle and the planned trajectory: Where, p r (t k ) represents the real-time coordinates of the nozzle, Δ i This indicates that the nozzle is at trajectory point p. t (s i The deviation amount, the exponential γ, δ nonlinear amplification of the deviation effect, and the neighborhood set N i Representing the points influencing the trajectory point, considering the spraying coupling degree, the higher-order geometric coupling matrix G. i,j Adjust the neighborhood contribution to ensure that the bias calculation is affected by the local structure;

[0112] Based on the deviation, the spraying parameters are locally adjusted to generate corrected spraying parameters; specifically, based on the deviation Δ... i Adjust the spray nozzle parameters: Where λ is the local adjustment coefficient, which controls the influence of the deviation on the spraying parameters; the exponent η provides nonlinear adjustment capability, making the correction more obvious when the local deviation is large; and ∈ is a small positive number for zero correction.

[0113] By combining the corrected spraying parameters and deviations, local adjustments are made to the 3D trajectory data to generate corrected 3D trajectory data. Specifically, local trajectory adjustments are generated by combining the corrected spraying parameters and deviations. The exponent κ provides a nonlinear amplification effect on trajectory offset, and the deviation of neighboring points jointly affects local trajectory correction, making the trajectory smoother and conforming to the spraying coupling law. The higher-order geometric coupling matrix G... i,j Constraining the influence of the neighborhood to prevent excessive trajectory adjustment, integrating the corrected spraying parameters and trajectory to form a closed-loop output: A set of nozzle correction parameters and trajectory information is formed to provide a basis for real-time closed-loop control of the spraying execution module; this ensures real-time adjustment of the wax spraying process and improves the uniformity and stability of wax film deposition.

[0114] The present invention is further configured such that the closed-loop quality assessment module includes:

[0115] Based on actual measured data of wax layer thickness, wax film deposition stability, and corrosion potential, a closed-loop feedback factor is formed for each sampling point; specifically, the closed-loop deviation factor between the actual wax layer thickness and the predicted thickness, stability, and corrosion potential is calculated: in, To represent the actual wax layer thickness measured at sampling point i, and thus the true coating result, T i The results are the predicted thickness of the wax layer. S represents the thickness difference, reflecting the magnitude of local deviation. hThe value represents the stability of the wax film deposition, ranging from 0 to 1. A higher value indicates more uniform deposition. Φ i The corrosion potential is 0 to 1. A higher value indicates a greater need for protection at that point. α, β, and γ are nonlinear amplification exponents, which cause the deviation to be amplified under high stability or high potential.

[0116] Based on the closed-loop feedback factor and the neighborhood relationship of sampling points, the overall spraying quality data is calculated to form a global spraying quality index; specifically, the overall spraying quality data is generated based on the closed-loop deviation factor. Where, N i Let w be the neighborhood set of sampling point i, representing the set of points that are geometrically or path-coupled with i. i,j The neighborhood weight, ranging from 0 to 1, is generated by the high-order geometric coupling matrix and the spraying path features. δ,∈ is the exponential adjustment factor, which nonlinearly amplifies the deviation contribution to ensure that the neighborhood deviation is not diluted by the linear average. It couples the local factors with the neighborhood effect to form the overall spraying quality data Q, which measures the global spraying uniformity and consistency.

[0117] The overall coating quality data is mapped to dynamic adjustment signals for nozzle flow rate, spray pressure, oscillation angle, and spray timing, forming the wax spraying execution control signal; specifically, the overall coating quality is mapped to dynamically adjusted nozzle control parameters: Among them, S i b represents the original spraying execution parameters. i Q is the local closed-loop factor, ζ and η are the overall spraying quality index, ζ and η are the nonlinear adjustment indexes, which control the influence ratio of local deviation and global quality, and ∈ is a small positive number to avoid the denominator being zero. The quality data is converted into a dynamic adjustment signal to realize the closed-loop correction of nozzle flow, pressure, swing angle and spraying sequence.

[0118] Based on the actual measured thickness after spraying, the closed-loop deviation factor is updated to provide a basis for correction in the next waxing cycle, achieving continuous closed-loop optimization. Specifically, based on the actual thickness measurement after spraying, the closed-loop feedback factor is updated to the next waxing cycle. λ is the closed-loop update coefficient, which takes the value of a positive real number and controls the feedback convergence speed. Corrections are made based on the difference between measured and predicted values; the larger the difference, the stronger the correction. The updated closed-loop feedback factor will be passed to the next wax spraying cycle, ensuring that the system is gradually optimized in continuous cycles, achieving dynamic self-adaptation and steady-state convergence.

[0119] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An automotive chassis waxing control system, characterized by, The application relates to a wax spraying method and device for vehicle chassis, which comprises the following modules: a chassis scanning data acquisition module configured with a high-precision three-dimensional scanning device to obtain discrete point data of a vehicle chassis surface and form a chassis space point set according to the discrete point data; a structure coupling matrix generation module for constructing a high-order geometric coupling matrix between chassis points based on the chassis space point set and generating corrosion potential of sampling points in combination with material sensitivity; an exposure coefficient generation module for generating a structure exposure coefficient through neighborhood relationship analysis based on the high-order geometric coupling matrix and the corrosion potential; a path planning module for generating three-dimensional trajectory data of chassis wax spraying based on the corrosion potential and the structure exposure coefficient, wherein the three-dimensional trajectory data comprises a trajectory coordinate function and a trajectory primitive parameter; a spraying coupling degree prediction module for generating a spraying coupling degree and a wax layer thickness prediction result through dynamic neighborhood feature analysis based on the three-dimensional trajectory data and spraying parameter information, wherein the spraying parameter information comprises a nozzle position, a speed and a pressure; a wax film stability evaluation module for calculating a wax film deposition stability based on the wax layer thickness prediction result, the spraying coupling degree, the high-order geometric coupling matrix and the material sensitivity; a real-time deviation correction module for calculating a deviation amount based on real-time coordinates of a nozzle and the three-dimensional trajectory data, adjusting spraying parameters and local trajectories according to the deviation amount, and forming corrected spraying parameter information and three-dimensional trajectory data; a closed-loop quality evaluation module for generating a closed-loop feedback factor based on actual measurement data of the wax layer thickness, the wax film deposition stability and the corrosion potential, calculating overall spraying quality data according to the closed-loop feedback factor, mapping the overall spraying quality data into a wax spraying execution control signal and realizing dynamic adjustment of nozzle flow, pressure, swing angle and spraying timing.

2. The automotive chassis wax application control system of claim 1, wherein, The chassis scanning data acquisition module comprises the following steps: generating a sampling point index set according to a chassis geometric profile and scanning accuracy requirements; configuring a high-precision three-dimensional scanning device to obtain discrete point coordinates of a chassis surface according to the sampling point index and form a discrete point set; performing noise elimination and local nonlinear accuracy correction on the discrete point set to generate a corrected discrete point set, and aggregating the corrected discrete point set to form a chassis space point set.

3. The automotive chassis wax application control system of claim 1, wherein, The structure coupling matrix generation module comprises the following steps: generating a spatial coordinate difference mapping between sampling points according to the chassis space point set to form an initial distance information set; performing multilayer nonlinear combination on the initial distance information set and regional topological sensitivity parameters to generate a high-order geometric coupling matrix for expressing geometric correlation strength and structure constraint relationship between points; mapping regional material sensitivity to a sampling point level according to material sensitivity of each region, combining local position relationship of the point and regional geometric characteristics to generate point-level material sensitivity; combining the high-order geometric coupling matrix and the point-level material sensitivity to generate corrosion potential of each sampling point, reflecting potential corrosion risk of each sampling point of the chassis.

4. The automotive chassis wax application control system of claim 3, wherein, The material sensitivity comprises the following steps: encoding material types, thicknesses, hardnesses and surface treatment states of each region of the chassis into material attribute vectors, and each vector explicitly identifies regional material characteristics and local attribute differences; performing multidimensional nonlinear combination on the material attribute vectors to generate preliminary material sensitivity, and simultaneously reflecting the asymmetric contribution relationship of the material types, thicknesses, hardnesses and surface treatment states in the sensitivity; The neighborhood enhancement coefficient is generated by combining the preliminary material sensitivity of the adjacent region and the spatial distance between regions, and reflects the influence of the neighborhood region on the material sensitivity of the current region, and distinguishes between high-sensitivity regions and low-sensitivity regions in the neighborhood; The final material sensitivity is generated by combining the preliminary material sensitivity and the neighborhood enhancement coefficient, and the material sensitivity level of each sampling point is determined.

5. The automotive chassis wax application control system of claim 3, wherein, The exposure coefficient generation module includes: Based on the high-order geometric coupling matrix and the corrosion potential, the neighborhood weighted coupling value is calculated based on the neighborhood relationship of the sampling point, reflecting the coupling influence between the sampling point and the neighborhood; The neighborhood weighted coupling value is combined non-linearly to generate a preliminary structure exposure value; Combined with the local geometric sensitivity of the sampling point, the preliminary structure exposure value is adjusted to form a sampling point level exposure coefficient, reflecting the sensitivity of the sampling point in the chassis structure; All sampling point level exposure coefficients are normalized to generate the final structure exposure coefficient.

6. The automotive chassis wax application control system of claim 1, wherein, The path planning module includes: Based on the corrosion potential and the structure exposure coefficient, the local path weight is generated based on the neighborhood relationship of the sampling point, reflecting the priority and local importance of the sampling points of the chassis for wax spraying; According to the local path weight and the neighborhood relationship, the sampling point trajectory primitive parameters are generated to describe the local control characteristics of the wax spraying trajectory; Combined with the trajectory primitive parameters and the sampling point coordinates, the trajectory coordinate function is generated to form the three-dimensional trajectory data of the chassis wax spraying.

7. A system for controlling the application of a wax to the underside of a vehicle as claimed in claim 6, wherein, The spraying coupling degree prediction module includes: Based on the trajectory coordinate function and the trajectory primitive parameters, the dynamic neighborhood relationship between the trajectory point of the spray head and the surface sampling point of the chassis is generated by combining the spatial point set of the chassis; According to the dynamic neighborhood relationship and the spraying parameter information, including the position, speed and pressure of the spray head, the local spraying energy coupling value is generated, reflecting the spatial distribution characteristics of the spray energy of the spray head at each sampling point of the chassis; Based on the local spraying energy coupling value and the structure coupling matrix, the spraying coupling degree is generated; Combined with the spraying coupling degree and the neighborhood balance information, the wax layer thickness prediction result is generated.

8. The automotive chassis wax application control system of claim 1, wherein, The wax film stability evaluation module includes: Based on the wax layer thickness prediction result, the spraying coupling degree, the high-order geometric coupling matrix and the material sensitivity, the neighborhood relationship of the sampling point is established to generate the neighborhood stability contribution coefficient; Based on the neighborhood stability contribution coefficient, the local wax film deposition stability of each sampling point is generated, reflecting the non-linear influence of the spraying coupling degree and the wax layer thickness prediction result on the local deposition; Combined with the spraying coupling degree and the wax layer thickness prediction result, the local wax film deposition stability is corrected to form the corrected local deposition stability, and the local deposition stability is aggregated to form the overall wax film deposition stability of the chassis.

9. The automotive chassis wax application control system of claim 1, wherein, The real-time deviation correction module includes: Based on the real-time coordinates of the spray head and the three-dimensional trajectory data, the spatial relationship between the spray head and the trajectory point is established, and the deviation amount of the spray head at each trajectory point is calculated; Based on the deviation amount, the spraying parameter information is locally adjusted to generate the corrected spraying parameter information; Combined with the corrected spraying parameter information and the deviation amount, the three-dimensional trajectory data is locally adjusted to form the corrected three-dimensional trajectory data.

10. The automotive chassis wax application control system of claim 1, wherein, The closed-loop quality evaluation module includes: Based on the actual measurement data of the wax layer thickness, the wax film deposition stability and the corrosion potential, the closed-loop feedback factor of each sampling point is formed. According to the closed-loop feedback factor and the sampling point neighborhood relationship, the overall spraying quality data is calculated to form a global spraying quality index; The overall spraying quality data is mapped to the dynamic adjustment signals of the nozzle flow, spraying pressure, swing angle and spraying timing to form a spraying execution control signal; According to the actual measured thickness after spraying execution, the closed-loop deviation factor is updated to provide a correction basis for the next spraying cycle, and continuous closed-loop optimization is realized.

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