Dynamic jet lubrication control method and system based on trajectory optimization

Abstract

The invention discloses a dynamic jet lubrication control method and system based on trajectory optimization, and relates to the technical field of jet control, and the method comprises the steps: carrying out design parameter networking calling according to the model information of a jet object; calling local injection quality constraints of a plurality of areas to be injected, performing injection control optimization, and positioning a plurality of local injection tracks and a plurality of local injection parameter adjustment sequences; track jump analysis is executed, and a target dynamic injection track is positioned; and the displacement of the injection valve is operated through the target dynamic injection track, injection control dynamic parameter adjustment is conducted through the multiple local injection parameter adjustment sequences, and lubricating oil injection coating is completed. The technical problems that an existing injection control method lacks dynamic adjustment capacity for different parts, is difficult to cope with complex injection scenes and is poor in injection efficiency and quality are solved, regional dynamic injection control is carried out for different parts, the injection efficiency and quality are improved, and the injection cost is reduced. And the flexibility and adaptability of the system are enhanced.

Description

Technical Field

[0001] This invention relates to the field of injection control technology, and more specifically to a dynamic injection lubrication control method and system based on trajectory optimization. Background Technology

[0002] With the rapid development of industrial automation and intelligent manufacturing technologies, higher demands are being placed on the precise control of the lubricating oil injection process. As a crucial device for achieving lubricating oil injection, the injection valve faces the critical challenge of ensuring efficient and uniform lubrication coverage on diverse mechanical component surfaces. Existing injection control methods are often static, lacking the ability to dynamically adjust to different component surface characteristics and lubrication requirements, making it difficult to handle complex injection scenarios. Summary of the Invention

[0003] This application provides a dynamic injection lubrication control method and system based on trajectory optimization, which solves the technical problems of existing injection control methods lacking dynamic adjustment capabilities for different components, making it difficult to cope with complex injection scenarios, and resulting in poor injection efficiency and quality.

[0004] The first aspect of this application provides a dynamic injection lubrication control method based on trajectory optimization. The method includes: accessing design parameters via a network based on the model information of the object to be sprayed, obtaining component structural parameters and injection design parameters, wherein the injection design parameters include injection area design and injection quality parameters; constructing an injection contact model based on the component structural parameters and injection area design, wherein the injection contact model includes multiple areas to be sprayed; obtaining multiple local injection quality constraints for the multiple areas to be sprayed from the injection quality parameters; optimizing injection control in the multiple areas to be sprayed based on the multiple local injection quality constraints, locating multiple local injection trajectories and multiple local injection parameter adjustment sequences; performing trajectory jump analysis on the multiple local injection trajectories to locate a target dynamic injection trajectory; after clamping and positioning the object to be sprayed, dynamically adjusting the injection control parameters of the injection valve using the multiple local injection parameter adjustment sequences during the displacement process of the injection valve using the target dynamic injection trajectory, thereby completing the lubricating oil spray coating of the object to be sprayed.

[0005] A second aspect of this application provides a dynamic injection lubrication control system based on trajectory optimization. The system includes: a design parameter network retrieval module, used to retrieve design parameters based on the model information of the injection object to obtain component structural parameter information and injection design parameters, wherein the injection design parameters include injection area design and injection quality parameters; an injection contact model construction module, used to construct an injection contact model based on the component structural parameter information and the injection area design, wherein the injection contact model includes multiple areas to be injected; and a local injection quality constraint retrieval module, used to retrieve the multiple injection quality constraints from the injection quality parameters. The system includes: a spraying quality constraint module for multiple local spraying areas; a spraying control optimization module for optimizing spraying control in multiple spraying areas based on the multiple local spraying quality constraints, locating multiple local spraying trajectories and multiple local spraying parameter adjustment sequences; a trajectory jump analysis module for performing trajectory jump analysis on the multiple local spraying trajectories to locate the target dynamic spraying trajectory; and a spray coating execution module for clamping and positioning the spraying object, and then, during the displacement of the spraying valve using the target dynamic spraying trajectory, dynamically adjusting the spraying control of the spraying valve using the multiple local spraying parameter adjustment sequences to complete the lubricating oil spray coating of the spraying object.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages: The dynamic injection lubrication control method and system based on trajectory optimization provided in this application pertain to the field of injection control technology. By utilizing the model information of the injection object, it calls upon local injection quality constraints of multiple areas to be sprayed for injection control optimization, locates multiple local injection trajectories and multiple local injection parameter adjustment sequences, performs trajectory jump analysis, locates the target dynamic injection trajectory, and uses multiple local injection parameter adjustment sequences to perform dynamic parameter adjustment of injection control, thereby completing lubricant spraying and coating. This solves the technical problems of existing injection control methods lacking dynamic adjustment capabilities for different components, struggling to cope with complex injection scenarios, and exhibiting poor injection efficiency and quality. It achieves regional dynamic injection control for different components, improving injection efficiency and quality, and enhancing the system's flexibility and adaptability. Attached Figure Description

[0007] 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 accompanying 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.

[0008] Figure 1 A schematic flowchart of the dynamic jet lubrication control method based on trajectory optimization provided in this application embodiment; Figure 2 This is a flowchart illustrating the process of locating multiple local injection trajectories and multiple local injection parameter tuning sequences in the dynamic injection lubrication control method based on trajectory optimization provided in this application embodiment. Figure 3 This is a schematic diagram of the dynamic jet lubrication control system based on trajectory optimization provided in an embodiment of this application.

[0009] Figure labeling: 11 Design parameter network call module, 12 Spray contact model construction module, 13 Local spray quality constraint call module, 14 Spray control optimization module, 15 Trajectory jump analysis module, 16 Spray coating execution module. Detailed Implementation

[0010] This application provides a dynamic injection lubrication control method and system based on trajectory optimization, which solves the technical problems of existing injection control methods lacking dynamic adjustment capabilities for different components, making it difficult to cope with complex injection scenarios, and resulting in poor injection efficiency and quality.

[0011] 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 a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0012] It should be noted that the terms "first," "second," etc., in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules not explicitly listed or inherent to such processes, methods, products, or devices.

[0013] Example 1, as Figure 1 As shown, this application provides a dynamic injection lubrication control method based on trajectory optimization, the method comprising: P10: Based on the model information of the spraying object, the design parameters are called up online to obtain the component structure parameter information and spraying design parameters, wherein the spraying design parameters include the spraying area design and spraying quality parameters.

[0014] Specifically, through a networked system, design parameters are retrieved based on the model information of the object being sprayed, and detailed structural parameters and spray design parameters related to the object being sprayed (i.e., the mechanical parts that need to be lubricated) are obtained.

[0015] First, the model information of the object to be sprayed is input through the networked system. Model information refers to the code or name used to identify a specific mechanical component, helping the system locate the component's specific design parameters in the database. During network access, the system retrieves the component's structural parameters and spraying design parameters from the database based on this model information. For example, the system first matches the model information of the object to be sprayed against standard component models in the database to retrieve the corresponding component structural parameters, including geometry, dimensions, and surface roughness. Then, based on the component's structural characteristics, the spraying area design and spraying quality parameters are extracted from the spraying design parameter library.

[0016] The component structural parameter information is a detailed description of the mechanical component, including its geometry, dimensions, material, and surface properties. These parameters are crucial for accurately constructing the spray contact model because they directly affect the coverage and distribution uniformity of the lubricating oil. For example, the surface roughness of the component may affect the adhesion of the lubricating oil, while the material of the component may affect the penetration of the lubricating oil.

[0017] The injection design parameters include injection zone design and injection quality parameters. Injection zone design refers to detailed planning for the lubrication needs of specific areas of a mechanical component, determining which parts require lubrication and their relative positions and ranges. Injection quality parameters involve the injection quantity, injection pressure, injection angle, and oil film thickness of the lubricating oil, ensuring the desired lubrication effect is achieved in each specified area. These parameters are used in subsequent injection optimization processes to achieve optimal lubrication performance.

[0018] P20: Based on the component structural parameter information and the design of the spray area, a spray contact model is constructed, wherein the spray contact model includes multiple spray areas.

[0019] Furthermore, step P20 in this embodiment of the application also includes: P21: Call the standard component model according to the component name of the spraying object; P22: Adjust the model parameters of the standard component model based on the component structure parameter information to obtain the spraying geometry model; P23: Fit the spraying area to the spraying geometry model according to the spraying area design to obtain the spraying contact model, wherein the spraying contact model includes multiple areas to be sprayed.

[0020] It should be understood that a spray contact model is constructed based on component structural parameter information and spray area design. The construction of this model is crucial for achieving precise lubricant spraying, as it can clearly identify multiple areas to be sprayed, thereby guiding subsequent spray optimization and control processes.

[0021] First, the system retrieves standard component models based on the component names of the spraying object. This step utilizes a pre-established standardized component library, which contains 3D models of various common components and their associated attributes. Through precise matching of component names, the system can quickly retrieve the corresponding standard component models, providing a basic framework for subsequent model parameter tuning.

[0022] Next, the standard component model is tuned based on the component's structural parameters. Model tuning refers to adjusting the standard model based on the actual component's structural parameters to more accurately reflect the actual mechanical component. High-precision model tuning techniques, such as feature-based model modification algorithms and parametric design methods, are used to generate a highly accurate injection geometry model. For example, if the actual dimensions of the component differ slightly from the standard model, the standard model is scaled, stretched, or subjected to other geometric transformations based on the component's actual dimensions to generate an injection geometry model that matches the actual component.

[0023] Finally, the spray area is fitted onto the spray geometry model according to the spray area design. By utilizing CAD technology and simulation analysis software, the specific requirements of the spray area design are translated into specific area divisions on the model. For example, based on the structural parameter information of the component, a three-dimensional geometric model of the component is established using CAD modeling software (such as SolidWorks or AutoCAD), ensuring that the geometry, size, and surface roughness of each component are accurately reproduced. Subsequently, finite element analysis software (such as ANSYS) is used to fit the spray area onto the surface of the component, simulating the coverage effect of the sprayed liquid in different areas, precisely delineating multiple areas to be sprayed in the spray contact model, ensuring the uniformity and accuracy of the spray coverage. Through precise geometric calculations and simulation analysis, the specific location, shape, and size of each area to be sprayed are determined, generating the final spray contact model. This model contains multiple areas to be sprayed, each area meticulously divided and labeled according to spray requirements, providing clear guidance for subsequent spray control optimization.

[0024] P30: Obtain multiple local spray quality constraints for the multiple areas to be sprayed from the spray quality parameters.

[0025] Optionally, information can be precisely retrieved from the injection quality parameters to set local injection quality constraints for each area to be sprayed, ensuring that the spraying process meets specific lubrication requirements. First, using the injection contact model built in the previous step, the system has clearly defined the location and shape of each area to be sprayed. Next, based on the specific requirements of each area, relevant information is extracted from the pre-set injection quality parameters. Injection quality parameters are quantitative descriptions of the lubricating oil spraying effect, typically including injection quantity, injection pressure, injection angle, oil film thickness, and injection coverage. These parameters directly affect the distribution and adhesion of lubricating oil on the surface of mechanical parts.

[0026] After invoking these injection quality parameters, the corresponding local injection quality constraints are calculated for each area to be sprayed. Local injection quality constraints refer to the injection conditions that must be met within a specific area to ensure that the lubrication in that area meets the expected standards. For example, for an area requiring a thicker oil film, local injection quality constraints may require higher injection volume and injection pressure; while for a small area requiring fine spraying, more precise injection angle and coverage may be required.

[0027] To determine these constraints, the geometry, material properties, and expected lubrication effect of each area to be sprayed are comprehensively considered. For example, when dealing with components with complex shapes, the system may need to adjust the spray parameters to ensure that the lubricant can adequately cover all uneven surfaces. In this process, local spray quality constraints play a crucial role, providing clear standards and objectives for subsequent spray optimization.

[0028] By defining local injection quality constraints, we can not only ensure that each area to be sprayed receives adequate lubricant coverage, but also optimize the spraying process, resulting in more uniform and effective lubrication. This lays the foundation for overall injection optimization and ensures that the injection valve can dynamically adjust injection parameters in different areas to achieve the best lubrication effect.

[0029] P40: Based on the multiple local injection quality constraints, perform injection control optimization in the multiple areas to be injected, locate multiple local injection trajectories and multiple local injection parameter tuning sequences.

[0030] Furthermore, such as Figure 2 As shown, step P40 in this embodiment further includes: P41: Interact to obtain the specification information of the lubricating oil to be used, and based on the specification information, call the injection data network to obtain sample injection application information; P42: Construct the injection control optimization space based on the sample injection application information; P43: In the injection control optimization space, optimize the injection parameters based on the multiple local injection quality constraints, and based on the optimization results, fit the injection trajectory in the multiple areas to be injected, and locate the multiple local injection trajectories and multiple local injection parameter adjustment sequences.

[0031] In one possible embodiment of this application, based on the multiple local injection quality constraints determined in the previous step, injection control optimization is performed on multiple areas to be sprayed to locate the optimal local injection trajectory and local injection parameter adjustment sequence. That is, through fine-tuning the injection parameters, it is ensured that each area achieves the expected lubrication effect.

[0032] First, the specifications of the lubricating oil to be used are obtained through an interactive interface or automated interface. This information includes, but is not limited to, the physicochemical properties of the lubricating oil such as viscosity, density, flash point, and pour point, which directly affect the injection performance and effect of the lubricating oil. Based on the specifications, corresponding sample injection application information is retrieved through a network or local database. This sample information comes from previous injection experiments or actual production cases and contains valuable data such as injection parameter settings and injection effect evaluations for different specifications of lubricating oil under different conditions.

[0033] Next, the injection control optimization space is constructed using sample injection application information. This space is a multi-dimensional set of parameters, containing all variables that may affect the injection effect, such as injection pressure, injection speed, injection angle, nozzle position, and nozzle movement path. By reasonably combining and adjusting these variables, the system can explore the optimal injection control scheme. To construct this space, various techniques such as mathematical modeling, simulation, and statistical analysis can be used to ensure the accuracy and completeness of the optimization space.

[0034] Finally, in the injection control optimization space, injection parameter tuning is performed based on multiple local injection quality constraints. This process may involve complex optimization algorithms, such as genetic algorithms, particle swarm optimization algorithms, and simulated annealing algorithms, to quickly search for the optimal or near-optimal solution that satisfies all local injection quality constraints in a vast parameter space. For example, in the injection control optimization process, the particle swarm optimization (PSO) algorithm is used to perform multi-dimensional optimization of multiple local injection quality constraints. Multiple objective functions are set based on parameters such as injection quantity, pressure, angle, and injection speed in the injection area to ensure that quality control requirements are met in each injection area. The optimization objective for each injection area generates corresponding local injection trajectories and parameter tuning sequences. These parameter tuning sequences ensure that the preset lubrication effect is achieved in each area, and the optimal parameter combination is automatically generated through the optimization algorithm. After finding the optimal parameter combination, injection trajectory fitting is performed in multiple areas to be injected, i.e., the movement path of the nozzle and the corresponding injection parameter settings in each area are determined based on the optimization results. Finally, multiple local injection trajectories and multiple local injection parameter tuning sequences are located, providing precise guidance for subsequent injection execution.

[0035] Furthermore, step P42 in this embodiment of the application also includes: P42-1: Preset a set of spray correlation indicators, wherein the set of spray correlation indicators includes spray distance indicators, spray pressure indicators, spray flow rate indicators, and spray speed indicators; P42-2: Decompose the sample spray application information based on the set of spray correlation indicators to obtain multiple sets of sample spray records and multiple sample spray qualities, wherein each set of sample spray records includes sample spray distance, sample spray pressure, sample spray flow rate, and sample spray speed, and each sample spray quality includes sample coverage and sample spray uniformity; P42-3: Construct the spray control optimization space based on the set of spray correlation indicators; P42-4: Locate multiple original particle points in the spray control optimization space according to the multiple sets of sample spray records, and fill the data of the multiple original particle points using the multiple sample spray qualities to complete the data update of the spray control optimization space.

[0036] Optionally, the specific process for constructing the injection control optimization space can be as follows: First, a set of injection-related indicators is preset. This set of indicators refers to a group of key indicators used to describe and quantify different aspects of the injection process, including injection distance, injection pressure, injection flow rate, and injection speed. These indicators reflect the most important physical parameters during the injection process, directly affecting the injection effect and final coverage uniformity of the lubricating oil. For example, the injection distance indicator indicates the distance between the nozzle and the surface of the object being sprayed, while the injection pressure indicator describes the pressure of the lubricating oil during injection.

[0037] Next, based on a pre-defined set of spray correlation indicators, the sample spray application information is decomposed to obtain multiple sets of sample spray records and multiple sample spray qualities. Sample spray application information refers to the parameters and effect data recorded during historical spraying processes. After decomposition, this information is broken down into more specific indicator data. Each set of sample spray records includes specific information such as spray distance, spray pressure, spray flow rate, and spray speed, while each sample spray quality includes result parameters such as spray coverage and spray uniformity. This process helps the system extract more detailed parameter information from macroscopic spray application information, laying the foundation for subsequent optimization space construction.

[0038] Furthermore, based on these decomposed injection-related index sets, an injection control optimization space is constructed. This optimization space is a multi-dimensional parameter space, where each dimension corresponds to an injection-related index, encompassing all possible parameter combinations during the injection process. By constructing this optimization space, the system can comprehensively explore and optimize injection parameters to find the optimal parameter settings and ensure the best lubricant injection effect.

[0039] Then, multiple initial particle points are located in the jet control optimization space based on multiple sets of sample jet records. These initial particle points refer to the initial parameter combinations in the optimization space; they are located based on historical data and represent initial guesses of the jet parameters. After locating these particle points, data is populated using multiple sample jet masses. By combining historical jet result data with these particle points, the optimization space is updated, resulting in a more accurate jet parameter model. This process ensures that the jet control optimization space not only reflects the theoretical optimal solution but can also be adjusted and optimized based on actual data.

[0040] Furthermore, step P43 in this embodiment of the application also includes: P43-1: Obtain the first local injection quality constraint and the first region to be injected from the multiple local injection quality constraints and the multiple regions to be injected; P43-2: Obtain M original particle points whose injection uniformity satisfies the first local injection quality constraint from the injection control optimization space, where M is a positive integer; P43-3: Obtain the M sample coverage ranges of the M original particle points, and use the M sample coverage ranges to fit the injection trajectory in the first region to be injected to obtain multiple backup injection trajectories and multiple backup injection parameter tuning sequences; P43-4: Perform injection control reliability evaluation on the multiple backup injection trajectories and multiple backup injection parameter tuning sequences, and select the first local injection trajectory and the first local injection parameter tuning sequence based on the evaluation results; P43-5: And so on, locate the multiple local injection trajectories and multiple local injection parameter tuning sequences.

[0041] Specifically, for each local spray quality constraint and spray area, the optimal spray trajectory and spray parameter tuning sequence are determined through trajectory optimization. This process fully considers the impact of different spray parameters on the spray coverage area, and how the spray coverage area further affects the determination of the spray trajectory.

[0042] First, from the previously determined multiple local injection quality constraints and multiple areas to be injected, a mapping call is made to obtain the first local injection quality constraint and the first area to be injected. This mapping call refers to the system extracting the local information that needs optimization from the overall injection quality constraints and areas to be injected, according to pre-defined mapping rules. The first local injection quality constraint defines the injection requirements of the current area, while the first area to be injected is the specific area the system is currently focusing on.

[0043] Next, M original particle points that satisfy the first local injection quality constraints (especially the injection uniformity requirement) are invoked in the injection control optimization space. These particle points represent possible combinations of injection parameters and form the basis for subsequent trajectory fitting. M is a positive integer representing the number of particle points invoked, and its size can be adjusted according to actual conditions and computational resources. The construction of the injection control optimization space is accomplished by mapping sample injection application information (including data such as injection distance, pressure, and flow rate) into a multi-dimensional space. For example, injection application information of lubricating oil samples of different specifications is collected, including parameters such as coverage, uniformity, and injection speed during injection. Through this historical data, a multi-dimensional parameter space for the injection control optimization space can be constructed.

[0044] Then, the M sample coverage areas corresponding to these M original particle points are invoked. These sample coverage areas, obtained through previous experiments or simulations, reflect the spraying effect under different combinations of spraying parameters. Using these sample coverage areas, a spray trajectory is fitted in the first area to be sprayed, and multiple alternative spray trajectories and multiple alternative spray parameter tuning sequences are simulated using mathematical models or simulation algorithms. For example, for each alternative spray trajectory, reliability is evaluated based on the following indicators: trajectory displacement distance, spray duration, update frequency, and spray quality uniformity. First, the total displacement distance of each alternative trajectory is calculated, and the total duration of the spraying process is evaluated. Then, the trajectory update frequency is statistically analyzed, and its impact on the spraying process is evaluated. Finally, the uniformity of spray quality is determined by calculating the Euclidean distance between adjacent data, and a spraying reliability coefficient is calculated based on these parameters. Based on the calculated reliability coefficient, the optimal alternative spray trajectory and parameter tuning sequence are selected. These alternative spray trajectories and parameter tuning sequences provide a rich selection for subsequent screening.

[0045] Subsequently, the reliability of the injection control is evaluated for multiple backup injection trajectories and multiple backup injection parameter tuning sequences. This evaluation process may involve multiple aspects, such as the stability of the injection trajectory, the feasibility of the injection parameters, and the prediction of the injection effect. Based on the evaluation results, the system will select the optimal first local injection trajectory and the first local injection parameter tuning sequence to ensure that they can meet the local injection quality constraints and have good reliability.

[0046] Finally, a similar method is used to handle the remaining local injection quality constraints and the area to be injected. All local injection trajectories and local injection parameter tuning sequences are located iteratively to ensure the efficiency and accuracy of the entire injection process.

[0047] Furthermore, step P43-3 in the embodiments of this application also includes: P43-31: Call the first sample coverage area from the M sample coverage areas; P43-32: Randomly select the first trajectory starting point in the first area to be sprayed, and generate a spiral progressive trajectory with the first sample coverage area as a constraint until the coverage areas overlap and trigger trajectory updates, thus obtaining the first stage trajectory; P43-33: Randomly call the second sample coverage area, which is smaller than the first sample coverage area, from the M sample coverage areas; P43-34: Take the trajectory ending point of the first stage trajectory as the starting point of the second trajectory, and generate a spiral progressive trajectory with the second sample coverage area as a constraint until the coverage areas overlap and trigger trajectory updates, thus obtaining the second stage trajectory. Segment trajectory; P43-35: Continue in this manner until the first spray area is fully covered, obtaining K stage trajectories; P43-36: Based on trajectory updates, stitch together the K stage trajectories to obtain the first backup spray trajectory; P43-37: According to the K sample coverage ranges corresponding to the K stage trajectories, retrieve K sets of sample spray records from the M original particle point mappings, and stitch together the K sets of sample spray records according to the first backup spray trajectory to obtain the first backup spray parameter tuning sequence; P43-38: In this manner, use the M sample coverage ranges to perform spray trajectory fitting in the first spray area to obtain the multiple backup spray trajectories and multiple backup spray parameter tuning sequences.

[0048] It should be understood that within the first area to be sprayed, a spiral-progressive trajectory is generated using the coverage of M samples to fit multiple alternative spray trajectories and their corresponding spray parameter tuning sequences. This process, through a phased and gradual coverage approach, ensures the accuracy of the spray trajectory and the uniformity of the spray effect.

[0049] First, the first sample coverage area is retrieved from the M sample coverage areas. This coverage area serves as a constraint for the current stage of trajectory generation, ensuring that the spray trajectory covers a sufficient area. Next, a point is randomly selected within the first area to be sprayed as the starting point of the first trajectory. Using the first sample coverage area as a constraint, a spiral-progressive trajectory is generated. This trajectory generation method ensures that the spray trajectory moves uniformly and continuously within the coverage area, while avoiding spray blind spots. When the trajectory coverage area overlaps with an already covered area, the system triggers a trajectory update mechanism to adjust the trajectory's direction or speed to avoid repeated spraying. Through this process, the first-stage trajectory is obtained.

[0050] Subsequently, trajectory generation continues using the remaining sample coverage area. To more precisely control the spraying process, a second sample coverage area smaller than the first sample coverage area is randomly selected from the M sample coverage areas. Then, the endpoint of the first-stage trajectory is used as the starting point of the second trajectory, and a spiral progressive trajectory is generated with the second sample coverage area as a constraint until the coverage areas overlap. At this point, the system triggers a trajectory update again, generating the second-stage trajectory. This process is repeated until all sample coverage areas are utilized and the first area to be sprayed is completely covered, generating K stage trajectories that cover every part of the area to be sprayed. During each trajectory update, the system records the trajectory of the current stage and the corresponding spraying parameters for subsequent stitching and mapping.

[0051] Furthermore, through trajectory update and splicing mechanisms, the K stage trajectories are merged into a complete first backup spray trajectory. This trajectory covers all important parts of the first spray area and ensures the uniformity and continuity of the spray. Next, based on the K sample coverage areas corresponding to the K stage trajectories, K sets of sample spray records are retrieved from M original particle points. These records contain detailed spray parameters for each stage trajectory. Then, according to the order of the first backup spray trajectory, these sample spray records are spliced ​​to obtain the first backup spray parameter tuning sequence. This sequence provides precise parameter guidance for subsequent spray execution.

[0052] Finally, a similar method was used to process the remaining sample coverage area, performing multiple injection trajectory fittings within the first injection region. Through continuous iteration and updates, multiple alternative injection trajectories and multiple alternative injection parameter tuning sequences were ultimately obtained. These alternative schemes provide a wealth of options for subsequent injection control reliability evaluation and optimization.

[0053] Furthermore, step P43-4 of the embodiments of this application also includes: P43-41: Based on the K stage trajectories, obtain K trajectory displacement distances, and sum the K trajectory displacement distances to obtain the first backup displacement distance; P43-42: Use the K sets of sample spray records and the K trajectory displacement distances to calculate the spraying time of the K stage trajectories to obtain K spraying durations; P43-43: Sum the K spraying durations to obtain the first backup spraying time; P43-44: Perform trajectory update counting on the K stage trajectories to obtain the first backup update frequency; P43-45: Calculate the Euclidean distance between adjacent data of the K sets of sample spray records, and average the calculation results. Calculate and obtain the first backup update span; P43-46: Introduce the first backup displacement distance, the first backup injection time, the first backup update frequency, and the first backup update span into the pre-constructed injection control evaluation function to obtain the first injection reliability coefficient; P43-47: Similarly, perform injection control reliability evaluation on the multiple backup injection trajectories and multiple backup injection parameter tuning sequences to obtain multiple injection reliability coefficients; P43-48: Serialize the multiple injection reliability coefficients and call the backup injection trajectory and backup injection parameter tuning sequence corresponding to the extreme values ​​to obtain the first local injection trajectory and the first local injection parameter tuning sequence.

[0054] The injection control evaluation function is as follows: ;in, For the injection reliability coefficient, For backup displacement distance, For backup injection time, As a backup update frequency, This is for backup update span.

[0055] Specifically, a refined reliability evaluation of the generated backup injection trajectories and parameter tuning sequences is conducted. By extracting key parameters, calculating index values, and introducing a pre-constructed injection control evaluation function, the reliability of each backup scheme is scientifically assessed, thereby selecting the optimal local injection trajectory and parameter tuning sequence.

[0056] First, K trajectory displacement distances are extracted from the K stage trajectories, and these distances are summed to obtain the first backup displacement distance. This distance reflects the total length of the injection trajectory and is one of the important indicators for evaluating injection efficiency. Specifically, the trajectory displacement distance refers to the actual distance traveled by the nozzle from the starting point to the ending point in each stage trajectory.

[0057] Next, using K sets of sample injection records and K trajectory displacement distances, the injection time for the K stages of the trajectory is calculated. By simulating the injection process or based on experimental data, the injection duration for each stage of the trajectory is estimated and summed to obtain the first backup injection time. This time consumption indicator is directly related to efficiency in actual production and is an important basis for evaluating the reliability of injection control.

[0058] Then, trajectory update counts are performed on the K-stage trajectories to obtain the first backup update frequency. This frequency reflects the number of times the injection trajectory needs adjustment during generation and is an important indicator for evaluating trajectory stability and reliability. Simultaneously, the Euclidean distance between adjacent data is calculated for the K sets of sample injection records, and the average is taken to obtain the first backup update span. This span indicator reflects the magnitude of change in injection parameters during trajectory generation and is also an important aspect of evaluating injection control stability.

[0059] Subsequently, four key parameters—first backup displacement distance, first backup injection time, first backup update frequency, and first backup update span—are introduced into a pre-constructed injection control evaluation function. This function is a mathematical model that integrates multiple evaluation dimensions and can objectively reflect the reliability level of the injection control. Its formula is as follows: ;in, For the injection reliability coefficient, For backup displacement distance, For backup injection time, As a backup update frequency, This serves as a backup update span. The first injection reliability coefficient is calculated using a formula; the larger this coefficient, the higher the reliability of the corresponding backup injection trajectory and parameter tuning sequence.

[0060] Finally, a similar method was used to evaluate the reliability of injection control for multiple backup injection trajectories and injection parameter tuning sequences, resulting in multiple injection reliability coefficients. These coefficients were then serialized, and the backup injection trajectory and parameter tuning sequence corresponding to the extreme values ​​were selected as the first local injection trajectory and the first local injection parameter tuning sequence. This ensures that the selected trajectory and parameter sequence possess optimal reliability and operational performance in practical applications.

[0061] P50: Perform trajectory jump analysis on the multiple local spray trajectories to locate the target's dynamic spray trajectory.

[0062] Optionally, trajectory jump analysis can be performed on the multiple local injection trajectories generated in the previous steps to locate the final target dynamic injection trajectory. The purpose of this step is to optimize the injection path, making the transition between different areas of the injection valve smoother and more efficient, thereby improving the overall injection effect.

[0063] The multiple local injection trajectories were obtained through spiral progressive trajectory generation and injection control reliability evaluation. Each trajectory corresponds to a specific injection area and parameter settings. However, these trajectories are generated in segments, which may result in insufficient connection between trajectories or low injection efficiency. Therefore, the system needs to integrate and optimize these trajectories.

[0064] Trajectory jump analysis involves identifying and optimizing key transition points between different local injection trajectories to ensure that the injection valve can quickly and smoothly transition between regions without affecting injection quality. For example, the endpoint of each local injection trajectory and the starting point of the next trajectory are first identified. These points, called jump points, determine how the injection valve transitions from one region to the next. After identifying the jump points, the connection paths between these points are optimized. This optimization process considers factors such as the injection valve's physical movement capabilities, injection speed requirements, and the continuity of injection quality. The goal is to find the shortest and smoothest path that allows the injection valve to quickly switch between different regions without interrupting the injection process. Furthermore, to ensure injection quality during trajectory jumps, injection parameters are dynamically adjusted. For example, when turning to the next injection region, the injection pressure or speed may need to be slightly changed to adapt to the new injection environment. These dynamic adjustments are calculated and applied as part of the trajectory jump analysis.

[0065] Ultimately, through jump analysis and optimization, the system is able to integrate a complete target dynamic spray trajectory. This trajectory not only connects the local spray trajectories of all areas to be sprayed, but also ensures the continuity of spray quality and the efficiency of the spray process during jumps between areas.

[0066] P60: After clamping and positioning the spraying object, during the displacement of the spray valve using the target dynamic spray trajectory, the spray control of the spray valve is dynamically adjusted using the multiple local spray parameter adjustment sequences to complete the lubricating oil spray coating on the spraying object.

[0067] It should be understood that the actual operation stage, namely the application of lubricating oil to the object being sprayed, begins with a high-precision clamping mechanism that stably and reliably holds and positions the object. This process ensures that the object's position and orientation remain unchanged during spraying, providing a foundation for subsequent precise spraying. The clamping mechanism may employ various drive methods such as pneumatic, electric, or hydraulic, combined with sophisticated sensors and control systems to achieve precise control over the object's position and orientation.

[0068] Next, as the injection valve begins to move according to the target dynamic injection trajectory, a dynamic parameter adjustment mechanism for injection control is simultaneously activated. This mechanism adjusts the injection parameters of the injection valve in real time based on the requirements of the target dynamic injection trajectory and the actual state of the object being sprayed. These parameters may include injection pressure, injection speed, and injection angle, which together determine the shape and distribution of the jet stream, thus affecting the coating effect of the lubricating oil on the surface of the object being sprayed.

[0069] To achieve precise dynamic parameter tuning for spray control, multiple local spray parameter tuning sequences obtained through trajectory jump analysis are employed. These sequences contain optimal combinations of spray parameters for different spray stages and spray areas, ensuring optimal coating results at each stage of the spraying process. Based on the current position and velocity of the target dynamic spray trajectory, the corresponding local spray parameter tuning sequence is automatically selected and applied, enabling real-time dynamic control of the spray valve.

[0070] In addition, to ensure the stability and reliability of the spraying process, the system can also employ some advanced technical support measures. For example, a closed-loop feedback control system can be used to monitor and adjust the spraying parameters in real time to deal with various disturbances that may occur during the spraying process; or image recognition and processing technology can be used to monitor and analyze the surface condition of the sprayed object in real time to evaluate the effect of lubricant coating and adjust the spraying parameters accordingly.

[0071] Ultimately, under precise clamping and positioning, guided by the target dynamic injection trajectory, and with real-time dynamic control of multiple local injection parameter adjustment sequences, the injection valve can accurately spray lubricating oil to the designated location of the object being sprayed, forming a uniform coating layer. This process not only improves the accuracy and efficiency of lubricating oil injection but also ensures the lubrication performance and service life of the object being sprayed.

[0072] In summary, the embodiments of this application have at least the following technical effects: This application uses the model information of the object to be sprayed to call the local spray quality constraints of multiple areas to be sprayed, optimizes the spray control, locates multiple local spray trajectories and multiple local spray parameter adjustment sequences, performs trajectory jump analysis, locates the target dynamic spray trajectory, and uses multiple local spray parameter adjustment sequences to perform dynamic parameter adjustment of spray control, thereby completing the lubricating oil spray coating.

[0073] This technology achieves the effect of regional dynamic injection control for different components, improving injection efficiency and quality, and enhancing the system's flexibility and adaptability.

[0074] Example 2, based on the same inventive concept as the trajectory optimization-based dynamic injection lubrication control method in the previous examples, such as... Figure 3As shown, this application provides a dynamic injection lubrication control system based on trajectory optimization. The system and method embodiments in this application are based on the same inventive concept. The system includes: The design parameter network call module 11 is used to call design parameters online according to the model information of the spraying object, and obtain component structure parameter information and spraying design parameters. The spraying design parameters include spraying area design and spraying quality parameters.

[0075] The jet contact model construction module 12 is used to construct a jet contact model based on the component structural parameter information and the jet area design, wherein the jet contact model includes multiple jet areas.

[0076] The local injection quality constraint calling module 13 is used to call multiple local injection quality constraints of the multiple areas to be injected from the injection quality parameters.

[0077] The injection control optimization module 14 is used to optimize the injection control in the multiple areas to be sprayed according to the multiple local injection quality constraints, and to locate multiple local injection trajectories and multiple local injection parameter adjustment sequences.

[0078] The trajectory jump analysis module 15 is used to perform trajectory jump analysis on the multiple local spray trajectories and locate the target dynamic spray trajectory.

[0079] The spray coating execution module 16 is used to clamp and position the spray object, and during the displacement of the spray valve using the target dynamic spray trajectory, the spray valve is dynamically adjusted using the multiple local spray parameter adjustment sequences to complete the spray coating of lubricating oil onto the spray object.

[0080] Furthermore, the jet contact model construction module 12 is also used to perform the following steps: A standard component model is called based on the component name of the object to be sprayed; the model parameters of the standard component model are adjusted based on the component structure parameter information to obtain a spray geometry model; the spray area is fitted to the spray geometry model according to the spray area design to obtain the spray contact model, wherein the spray contact model includes multiple areas to be sprayed.

[0081] Furthermore, the injection control optimization module 14 is also used to perform the following steps: The system interactively obtains the specifications of the lubricating oil to be used, and uses the specifications to call up the injection data network to obtain sample injection application information. Based on the sample injection application information, it constructs an injection control optimization space. In the injection control optimization space, it optimizes the injection parameters based on the multiple local injection quality constraints, and uses the optimization results to fit the injection trajectory in the multiple areas to be injected, thereby locating the multiple local injection trajectories and multiple local injection parameter adjustment sequences.

[0082] Furthermore, the injection control optimization module 14 is also used to perform the following steps: A preset set of spray correlation indicators is established, including spray distance, spray pressure, spray flow rate, and spray speed indicators. Based on this set, the sample spraying application information is decomposed to obtain multiple sets of sample spraying records and multiple sample spraying qualities. Each set of sample spraying records includes sample spraying distance, sample spraying pressure, sample spraying flow rate, and sample spraying speed. Each sample spraying quality includes sample coverage and sample spraying uniformity. A spraying control optimization space is constructed based on the set of spray correlation indicators. Multiple original particle points are located in the spraying control optimization space according to the multiple sets of sample spraying records, and the data of these original particle points is filled using the multiple sample spraying qualities to complete the data update of the spraying control optimization space.

[0083] Furthermore, the injection control optimization module 14 is also used to perform the following steps: The first local injection quality constraint and the first region to be injected are obtained by mapping and calling the multiple local injection quality constraints and the multiple regions to be injected. M original particle points whose injection uniformity satisfies the first local injection quality constraint are obtained from the injection control optimization space, where M is a positive integer. M sample coverage ranges of the M original particle points are obtained, and the injection trajectory is fitted in the first region to be injected using the M sample coverage ranges to obtain multiple alternative injection trajectories and multiple alternative injection parameter tuning sequences. The injection control reliability is evaluated on the multiple alternative injection trajectories and multiple alternative injection parameter tuning sequences, and the first local injection trajectory and the first local injection parameter tuning sequence are obtained based on the evaluation results. This process is repeated to locate the multiple local injection trajectories and multiple local injection parameter tuning sequences.

[0084] Furthermore, the injection control optimization module 14 is also used to perform the following steps: The system retrieves a first sample coverage area from the M sample coverage areas; randomly selects a first trajectory starting point in the first area to be sprayed, and generates a spiral progressive trajectory with the first sample coverage area as a constraint until the coverage areas overlap and trigger a trajectory update, thus obtaining a first-stage trajectory; randomly retrieves a second sample coverage area smaller than the first sample coverage area from the M sample coverage areas; takes the trajectory endpoint of the first-stage trajectory as the starting point of the second trajectory, and generates a spiral progressive trajectory with the second sample coverage area as a constraint until the coverage areas overlap and trigger a trajectory update, thus obtaining a second-stage trajectory; and so on, until the first area to be sprayed is fully covered, obtaining K stage trajectories; stitches the K stage trajectories based on the trajectory update to obtain a first backup spray trajectory; retrieves K sets of sample spray records from the M original particle point mappings according to the K sample coverage areas corresponding to the K stage trajectories, and stitches the K sets of sample spray records according to the first backup spray trajectory to obtain a first backup spray parameter tuning sequence; and so on, uses the M sample coverage areas to perform spray trajectory fitting in the first area to be sprayed to obtain multiple backup spray trajectories and multiple backup spray parameter tuning sequences.

[0085] Furthermore, the injection control optimization module 14 is also used to perform the following steps: K trajectory displacement distances are obtained based on the K stage trajectory extraction. These K trajectory displacement distances are summed to obtain a first backup displacement distance. The K sets of sample injection records and the K trajectory displacement distances are used to calculate the injection time for the K stage trajectories, resulting in K injection durations. These K injection durations are summed to obtain a first backup injection time. Trajectory update counts are performed on the K stage trajectories to obtain a first backup update frequency. Euclidean distances between adjacent data are calculated for the K sets of sample injection records, and the mean of the calculation results is taken to obtain a first backup update span. The first backup displacement distance, first backup injection time, first backup update frequency, and first backup update span are introduced into a pre-constructed injection control evaluation function to obtain a first injection reliability coefficient. Similarly, injection control reliability evaluation is performed on the multiple backup injection trajectories and multiple backup injection parameter tuning sequences to obtain multiple injection reliability coefficients. The multiple injection reliability coefficients are serialized, and the backup injection trajectories and backup injection parameter tuning sequences corresponding to extreme values ​​are called to obtain the first local injection trajectory and the first local injection parameter tuning sequence.

[0086] Furthermore, the injection control optimization module 14 is also used to perform the following steps: The injection control evaluation function is as follows: ; in, For the injection reliability coefficient, For backup displacement distance, For backup injection time, As a backup update frequency, This is for backup update span.

[0087] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0088] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0089] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application intends to include such modifications and variations.

Claims

1. A dynamic jet lubrication control method based on trajectory optimization, characterized in that, The method includes: Based on the model information of the object to be sprayed, the design parameters are called up online to obtain the component structure parameter information and the spraying design parameters, wherein the spraying design parameters include the spraying area design and the spraying quality parameters; Based on the component structural parameter information and the design of the spray area, a spray contact model is constructed, wherein the spray contact model includes multiple spray areas; Multiple local spray quality constraints for the multiple areas to be sprayed are obtained from the spray quality parameters. Based on the multiple local injection quality constraints, the injection control is optimized in the multiple areas to be injected, and multiple local injection trajectories and multiple local injection parameter tuning sequences are located; Perform trajectory jump analysis on the multiple local spray trajectories to locate the target's dynamic spray trajectory; After clamping and positioning the spraying object, during the displacement of the spray valve using the target dynamic spray trajectory, the spray control of the spray valve is dynamically adjusted using the multiple local spray parameter adjustment sequences to complete the lubricating oil spray coating on the spraying object.

2. The method as described in claim 1, characterized in that, The method further includes constructing a jet contact model based on the component structural parameters and jet design parameters. Call the standard component model based on the component name of the sprayed object; Based on the component structural parameter information, the model parameters of the standard component model are adjusted to obtain the injection geometry model; Based on the design of the spray area, the spray area is fitted to the spray geometry model to obtain the spray contact model, wherein the spray contact model includes multiple spray areas.

3. The method as described in claim 1, characterized in that, Based on the multiple local injection quality constraints, injection control optimization is performed in the multiple areas to be injected, locating multiple local injection trajectories and multiple local injection parameter tuning sequences. The method further includes: The specification information of the lubricating oil to be used is obtained interactively, and the injection data is called online based on the specification information to obtain sample injection application information; Based on the sample jetting application information, a jetting control optimization space is constructed; In the injection control optimization space, injection parameter tuning optimization is performed based on the multiple local injection quality constraints, and injection trajectory fitting is performed in the multiple areas to be injected based on the optimization results to locate the multiple local injection trajectories and multiple local injection parameter tuning sequences.

4. The method as described in claim 3, characterized in that, The method further includes constructing a jet control optimization space based on the sample jet application information, and the method also includes: A preset set of injection-related indicators is provided, wherein the set of injection-related indicators includes injection distance indicator, injection pressure indicator, injection flow rate indicator, and injection speed indicator; Based on the set of spraying correlation indicators, the sample spraying application information is decomposed to obtain multiple sets of sample spraying records and multiple sample spraying qualities. Each set of sample spraying records includes sample spraying distance, sample spraying pressure, sample spraying flow rate and sample spraying speed. Each sample spraying quality includes sample coverage area and sample spraying uniformity. The injection control optimization space is generated based on the injection correlation index set. Based on the multiple sets of sample jet records, multiple original particle points are located in the jet control optimization space, and the data of the multiple original particle points is filled using the multiple sample jet quality to complete the data update of the jet control optimization space.

5. The method as described in claim 4, characterized in that, In the injection control optimization space, injection parameter tuning optimization is performed based on the multiple local injection quality constraints, and injection trajectory fitting is performed on the multiple areas to be injected based on the optimization results to locate the multiple local injection trajectories and multiple local injection parameter tuning sequences. The method further includes: The first local injection quality constraint and the first area to be injected are obtained by mapping and calling the plurality of local injection quality constraints and the plurality of areas to be injected. In the injection control optimization space, M original particle points whose injection uniformity satisfies the first local injection quality constraint are obtained by calling M, where M is a positive integer; The M sample coverage areas of the M original particle points are obtained by calling the method, and the M sample coverage areas are used to fit the spray trajectory in the first spray area to obtain multiple alternative spray trajectories and multiple alternative spray parameter tuning sequences. The reliability of injection control is evaluated for the multiple backup injection trajectories and multiple backup injection parameter tuning sequences, and the first local injection trajectory and the first local injection parameter tuning sequence are selected based on the evaluation results. Similarly, the multiple local injection trajectories and multiple local injection parameter tuning sequences are located.

6. The method as described in claim 5, characterized in that, The method further includes: obtaining M sample coverage areas of the M original particle points, and using the M sample coverage areas to fit the spray trajectory in the first area to be sprayed, thereby obtaining multiple alternative spray trajectories and multiple alternative spray parameter tuning sequences; The first sample coverage range is retrieved from the coverage ranges of the M samples; In the first area to be sprayed, a first trajectory starting point is randomly selected, and a spiral progressive trajectory is generated with the coverage range of the first sample as a constraint until the coverage range overlaps and triggers trajectory update to obtain the first stage trajectory. A second sample coverage range smaller than the first sample coverage range is randomly selected from the M sample coverage ranges; The endpoint of the first stage trajectory is taken as the starting point of the second trajectory, and the coverage area of ​​the second sample is used as a constraint to generate a spiral progressive trajectory until the coverage areas overlap and trigger trajectory updates to obtain the second stage trajectory. This process continues until the first area to be sprayed is fully covered, resulting in K stages of trajectory. Based on the trajectory update, the K-stage trajectories are stitched together to obtain the first backup injection trajectory; K sets of sample jet records are obtained from the M original particle point mapping based on the K sample coverage ranges corresponding to the K stage trajectories, and the K sets of sample jet records are spliced ​​together according to the first backup jet trajectory to obtain the first backup jet parameter tuning sequence. Similarly, the spray trajectory is fitted in the first spray area using the coverage of the M samples to obtain the multiple backup spray trajectories and multiple backup spray parameter tuning sequences.

7. The method as described in claim 6, characterized in that, The method further includes: evaluating the reliability of injection control for the multiple backup injection trajectories and multiple backup injection parameter tuning sequences, and selecting the first local injection trajectory and the first local injection parameter tuning sequence based on the evaluation results; Based on the K stage trajectory extraction, K trajectory displacement distances are obtained, and the K trajectory displacement distances are summed to obtain the first backup displacement distance; The spraying time of the K stages of the trajectory is calculated by using the K sets of sample spraying records and K trajectory displacement distances to obtain K spraying durations; The first backup injection time is obtained by summing the K injection durations; The trajectory update count is performed on the K stages of the trajectory to obtain the first backup update frequency; The Euclidean distance between adjacent data is calculated for the K groups of sample spray records, and the mean of the calculation results is obtained to obtain the first backup update span; The first backup displacement distance, the first backup injection time, the first backup update frequency, and the first backup update span are introduced into a pre-constructed injection control evaluation function to obtain the first injection reliability coefficient. Similarly, the reliability of injection control is evaluated for the multiple backup injection trajectories and multiple backup injection parameter adjustment sequences to obtain multiple injection reliability coefficients; The multiple injection reliability coefficients are serialized, and the backup injection trajectory and backup injection parameter tuning sequence corresponding to the extreme values ​​are called to obtain the first local injection trajectory and the first local injection parameter tuning sequence.

8. The method as described in claim 7, characterized in that, The injection control evaluation function is as follows: ； in, For the injection reliability coefficient, For backup displacement distance, For backup injection time, As a backup update frequency, This is for backup update span.

9. A dynamic injection lubrication control system based on trajectory optimization, characterized in that, The system includes: The design parameter network call module is used to call design parameters online according to the model information of the spraying object, and obtain component structure parameter information and spraying design parameters. The spraying design parameters include spraying area design and spraying quality parameters. A jet contact model construction module is used to construct a jet contact model based on the component structural parameter information and the jet area design, wherein the jet contact model includes multiple jet areas; A local injection quality constraint invocation module is used to retrieve multiple local injection quality constraints for the multiple areas to be sprayed from the injection quality parameters. The injection control optimization module is used to optimize the injection control in the multiple areas to be sprayed based on the multiple local injection quality constraints, and to locate multiple local injection trajectories and multiple local injection parameter tuning sequences. The trajectory jump analysis module is used to perform trajectory jump analysis on the multiple local spray trajectories to locate the target dynamic spray trajectory; The spray coating execution module is used to clamp and position the spraying object, and during the displacement of the spray valve using the target dynamic spray trajectory, dynamically adjust the spray control of the spray valve using the multiple local spray parameter adjustment sequences to complete the spray coating of lubricating oil onto the spraying object.

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