Iron roughneck oil cylinder hinge point driving optimization method, device, equipment, medium and product

Abstract

The invention relates to the field of engineering simulation, in particular to an iron roughneck oil cylinder hinge point driving optimization method and device, equipment, a medium and a product. According to the method, the basic simulation model capable of being regulated and controlled in a parameterized mode is established, the hinge point of the push-pull oil cylinder is set as a design variable, and the boom is subjected to flexible processing, so that the problem that vibration of the boom based on a rigid body cannot be simulated is solved, and driving function parameters can be dynamically adjusted along with the position of the hinge point; by initializing a parameter group and carrying out parallel simulation, the single optimization period is greatly shortened, and the driving parameters are prevented from being manually adjusted for multiple times; meanwhile, a dynamic graph is drawn in combination with simulation data and iterative data, and performance differences under different driving functions can be visually presented, so that the optimal solution of the oil cylinder force is accurately explored, and the defects that in the prior art, the optimization period is long, and the deviation between the result and the actual working condition is large are effectively overcome.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of engineering simulation, in particular to an iron roughneck cylinder hinge point driving optimization method, device, equipment, medium and product. BACKGROUND

[0002] In the field of oil drilling equipment, the iron roughneck is a key device, and the hinge point position of the push-pull cylinder and the driving function parameters directly affect the cylinder stress and the performance of the cantilever crane. At present, the dynamics simulation and optimization are mainly carried out through Adams software: first, the dynamics model of the iron roughneck is established, the hinge point position is defined as the design variable, and then the stroke-time function is set as the driving parameter to simulate the cylinder movement.

[0003] However, the preset stroke-time function in Adams cannot be dynamically adjusted with the hinge point position during optimization, and the driving parameters need to be adjusted manually multiple times, and the single optimization cycle is as long as 6-8 hours, which is low in work efficiency; at the same time, the existing optimization is based on the assumption of rigid body cantilever crane, which cannot simulate the vibration of the cantilever crane during operation, resulting in deviation between the optimization results and the actual working conditions. SUMMARY

[0004] Therefore, the embodiments of the present application provide an iron roughneck cylinder hinge point driving optimization method, device, equipment, medium and product to solve the problems that the preset stroke-time function in Adams cannot be dynamically adjusted with the hinge point position, the driving parameters need to be adjusted manually multiple times and the optimization cycle is long, and the optimization based on the rigid body cantilever crane cannot simulate the vibration of the cantilever crane, resulting in deviation between the optimization results and the actual working conditions.

[0005] In a first aspect, the embodiments of the present application provide an iron roughneck cylinder hinge point driving optimization method, which comprises: Obtaining a pre-established parameterized model, and setting the push-pull cylinder hinge point position as a design variable to obtain a parameterized controllable basic simulation model; Initializing the parameter group of the hinge point coordinates and the driving function based on the basic simulation model; According to the initialized parameter group, the peak thrust of the push-pull cylinder and the hinge point position are calculated through parallel simulation to obtain a plurality of simulation data; According to the plurality of simulation data and the corresponding iteration data of the plurality of simulation data, a dynamic diagram of the cylinder force and the hinge point position is drawn to obtain the output results under different driving functions.

[0006] Further, the initialization of the parameter group of the hinge point coordinates and the driving function based on the basic simulation model comprises: According to the design variable definition of the basic simulation model, the model is connected through script calling API to obtain a model parameter interaction interface; According to the model parameter interaction interface, input the preset hinge point coordinate initial value and assign it to the design variable to obtain an initialized hinge point coordinate parameter; According to the initialized hinge point coordinate parameter, define a driving function parameter and associate it to an arm support driving variable to obtain a parameter group.

[0007] Further, the initialized parameter group is used to calculate the peak thrust of the push-pull oil cylinder and the hinge point position through parallel simulation to obtain a plurality of simulation data, including: A plurality of independent simulation tasks are generated for the initialized parameter group through a multi-process library to obtain a parallel simulation task queue; According to the parallel simulation task queue, the basic simulation model is called to perform batch dynamics simulation to obtain original simulation results of each task; Based on the original simulation results, the peak thrust of the push-pull oil cylinder and the hinge point position data at each time are extracted and summarized to obtain a plurality of simulation data.

[0008] Further, the simulation data and the corresponding iterative data are used to draw an oil cylinder force and hinge point position dynamic diagram to obtain output results under different driving functions, including: The simulation data and the corresponding iterative data are used to filter the oil cylinder thrust and hinge point position core data and classify and associate the driving functions to obtain a regularized visual data source; According to the regularized visual data source, a dynamic chart framework is used to obtain a chart drawing template; According to the chart drawing template, the dynamic data corresponding to different driving functions is batch rendered to obtain an oil cylinder force and hinge point position dynamic diagram that intuitively displays performance differences.

[0009] Further, the method further includes: According to the plurality of simulation data, a target function of optimal oil cylinder force is set, and an optimization algorithm is called to optimize the driving function and the hinge point coordinate according to the target function to obtain optimized candidate values of the driving function and the hinge point coordinate; The basic simulation model is called to automatically replace the original driving function expression and the hinge point coordinate design variable according to the optimized candidate values to obtain a parameter updated basic simulation model; The optimized candidate values are verified according to the parameter updated basic simulation model to obtain iteratively updated driving function and hinge point coordinate parameters, and the iteratively updated driving function and hinge point coordinate parameters are used as the iterative data.

[0010] Further, the target function of optimal oil cylinder force according to the plurality of simulation data includes: The key features of the oil cylinder force are extracted according to the plurality of simulation data to obtain an oil cylinder force performance evaluation index; According to the oil cylinder force performance evaluation index, an optimization direction is determined, and a function core constraint is obtained; According to the function core constraint, an oil cylinder force optimal target function is constructed.

[0011] In a second aspect, an iron roughneck oil cylinder hinge point driving optimization device is provided, and the device comprises: An acquisition module is configured to acquire a pre-established parameterized model, set a push-pull oil cylinder hinge point position as a design variable, and obtain a parameterizable basic simulation model; A processing module is configured to initialize a hinge point coordinate and a parameter group of a driving function based on the basic simulation model; A simulation module is configured to calculate a push-pull oil cylinder peak thrust and a hinge point position through parallel simulation according to the initialized parameter group, and obtain a plurality of simulation data; An execution module is configured to draw an oil cylinder force and a hinge point position dynamic diagram according to the plurality of simulation data and corresponding iteration data, and obtain an output result under different driving functions.

[0012] In a third aspect, a computer device is provided, comprising a memory and a processor, which are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the method of the first aspect or any of the corresponding embodiments thereof.

[0013] In a fourth aspect, a computer readable storage medium is provided, which stores computer instructions, and the computer instructions are used to make a computer execute the method of the first aspect or any of the corresponding embodiments thereof.

[0014] In a fifth aspect, a computer program product is provided, which comprises computer instructions, and the computer instructions are used to make a computer execute the method of the first aspect or any of the corresponding embodiments thereof.

[0015] The present application establishes a parameterizable basic simulation model, sets a push-pull oil cylinder hinge point as a design variable, and performs flexible processing on the cantilever crane, which not only solves the problem that a rigid body cantilever crane cannot simulate vibration, but also enables driving function parameters to be dynamically adjusted according to hinge point positions; through initialization of a parameter group and parallel simulation, a single optimization cycle is greatly shortened, and manual adjustment of driving parameters is avoided multiple times; meanwhile, a dynamic diagram is drawn in combination with simulation data and iteration data, which can intuitively present performance differences under different driving functions, so as to accurately explore an oil cylinder force optimal solution, and effectively solve the defects of long optimization cycle and large deviation between results and actual working conditions in the prior art. BRIEF DESCRIPTION OF DRAWINGS

[0016] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating the optimization method for driving the hydraulic cylinder hinge point of a drill bit according to an embodiment of the present invention. Figure 2 This is a schematic diagram of a model of Adams, the iron driller, according to some embodiments of the present invention; Figure 3 This is a structural block diagram of the hydraulic cylinder hinge point drive optimization device according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] According to embodiments of the present invention, a method, apparatus, device, medium, and product for optimizing the hydraulic cylinder hinge point drive of a steel drill are provided. It should be noted that the steps shown in the flowcharts in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowcharts, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0020] This embodiment provides an optimization method for driving the hydraulic cylinder hinge point of a steel drill. Figure 1 This is a flowchart of an optimization method for driving the hinge point of a hydraulic cylinder for a steel drill according to an embodiment of the present invention, as follows: Figure 1 As shown, the process includes the following steps: Step S101: Obtain the pre-established parametric model and set the position of the push-pull cylinder hinge point as the design variable to obtain a basic simulation model that can be parametrically controlled.

[0021] In this embodiment, firstly, based on the actual structural characteristics of the iron drill (such as boom size, cylinder stroke, and pliers body connection relationship), a three-dimensional solid model is built in Adams multibody dynamics software, covering key components such as boom, push-pull cylinder, and pliers body. The motion relationship of each component is simulated through constraint pairs (such as rotary hinges and traverse hinges) to ensure that the model can truly reflect the dynamic behavior of the iron drill boom when it extends / retracts.

[0022] Since the boom is designed to be flexible, flexible body characteristics need to be introduced into the model: the boom components are converted into flexible bodies using Adams' flexible body module (e.g., by importing a modal neutral file based on a finite element model) to consider the impact of boom deformation on the cylinder force and hinge position, thereby improving simulation accuracy.

[0023] The key operation lies in parameter definition: the three-dimensional coordinates of the hinge points at both ends of the push-pull cylinder (cylinder hinge points X1, Y1, Z1 and piston rod hinge points X2, Y2, Z2) are set as design variables. Adams' parameter management tools are used to associate these coordinates with the component positions and constraints in the model. When the hinge point coordinates change, the cylinder length, boom posture, etc., are automatically updated without requiring manual adjustment of the model geometry. Simultaneously, reasonable value ranges are set for the design variables (such as limiting hinge point movement boundaries based on structural interference) to avoid physically infeasible parameter combinations in subsequent simulations.

[0024] The resulting basic simulation model has parameterized control capabilities, which can accurately simulate the kinematics and dynamics of the driller, and can also be flexibly adjusted by modifying the hinge point coordinates design variables, providing reliable underlying model support for subsequent Python-driven parameter optimization.

[0025] For example, such as Figure 2 As shown, based on the actual structure of the iron drill (including components such as the column, push-pull cylinder, flexible boom, and pliers), its geometric model is constructed in 3D modeling software. Then, the geometric model is imported into Adams software, and kinematic pairs (such as rotary hinges and traverse hinges) are added to define the motion relationships of each component. At the same time, the flexible boom is converted into a flexible body (by importing a modal neutral file). Finally, the position of the push-pull cylinder hinge point is defined as a design variable in Adams to complete the establishment of the parametric model, thereby obtaining a basic simulation model that can be parametrically controlled.

[0026] Step S102: Initialize the hinge point coordinates and the parameter set of the driving function based on the basic simulation model.

[0027] In this embodiment of the application, the parameter set of the hinge point coordinates and the driving function based on the basic simulation model includes: according to the design variable definition of the basic simulation model, connecting the model through the API by calling the script to obtain the model parameter interaction interface; according to the model parameter interaction interface, inputting the preset initial value of the hinge point coordinates and assigning it to the design variable to obtain the initialized hinge point coordinate parameters; according to the initialized hinge point coordinate parameters, defining the driving function parameters and associating them with the boom driving variable to obtain the parameter set.

[0028] First, the three-dimensional coordinates of the hinge points at both ends of the push-pull cylinder—the core design variables defined in the basic simulation model—need to be determined. This requires obtaining the unique identifiers, data types, and value constraints of these variables within the model. In the Python environment, configure the AdamsAPI development package, import the core modules, and establish communication with the Adams model using the `connect()` function to obtain the model operation handle. Write interface functions that include "read-modify-verify," allowing direct reading and writing of hinge point coordinate values ​​via variable names. An exception handling mechanism is also included to trigger alarms or retries for connection timeouts, missing variables, etc. The resulting model parameter interaction interface provides a stable and standardized channel for subsequent parameter passing, enabling direct manipulation of the Adams model by Python.

[0029] The initial values ​​of the hinge point coordinates need to be determined based on the iron drill design drawings, initial working conditions, and structural interference constraints, and organized into structured data in CSV or JSON format. A Python script calls the assignment function of the parameter interaction interface to iterate through the preset data, writing each set of hinge point 3D coordinates into the corresponding design variables of the model. After assignment, the data consistency is verified by immediately reading back, and values ​​exceeding the constraint range are automatically corrected and logged. For multi-working-condition requirements, multiple sets of differentiated initial parameters can be generated through loop logic, each set with an identifier and verification results, ensuring parameter traceability and compliance with model geometric constraints, providing a reliable benchmark for the definition of the driving function.

[0030] Based on the "acceleration-uniform speed-deceleration" motion characteristics of the iron drill boom, a suitable drive function model, such as a trapezoidal velocity curve, is selected. Key parameters such as acceleration coefficient, uniform speed, and total stroke are defined, with initial values ​​referenced from the equipment manual and adjustment space reserved. A Python script calls the parameter interaction interface to write the drive function expression into the drive attribute of the hydraulic cylinder moving pair, binding the function parameters to the boom drive variables and achieving real-time response to motion changes. Finally, following the "one-to-one correspondence" principle, the initial hinge point coordinates and drive function parameters are integrated into a parameter group with a unique identifier and stored in a standardized format file. This clarifies the parameter association logic and can be directly used as input for parallel simulation, completing the coordinated configuration of "geometry-motion" parameters.

[0031] Step S103: Based on the initialized parameter set, calculate the peak thrust and hinge position of the push-pull cylinder through parallel simulation to obtain multiple sets of simulation data.

[0032] In this embodiment of the application, the peak thrust and hinge position of the push-pull cylinder are calculated by parallel simulation based on the initialized parameter set to obtain multiple sets of simulation data. This includes: generating multiple independent simulation tasks for the initialized parameter set through a multi-process library to obtain a parallel simulation task queue; calling the basic simulation model to perform dynamic simulations in batches according to the parallel simulation task queue to obtain the original simulation results of each task; and extracting and summarizing the peak thrust and hinge position data of the push-pull cylinder at each moment based on the original simulation results to obtain multiple sets of simulation data.

[0033] First, it is necessary to clarify the core information contained in the initial parameter group: each parameter group consists of the coordinates of the push-pull cylinder hinge point (such as X1, Y1, Z1, X2, Y2, Z2) and the driving function parameters (such as motion curve coefficients and time intervals), and each parameter combination corresponds to a specific working condition of the iron drill (such as the boom extension speed and the difference in the initial posture).

[0034] In the Python environment, a process pool is created using the `multiprocessing` library (or the `concurrent.futures` module). The number of processes can be dynamically adjusted based on computer hardware performance (such as the number of CPU cores) to avoid resource overload. Next, the task generation logic is written: iterating through the initialized parameter groups, encapsulating an independent simulation task for each group—each task includes metadata such as parameter group identifier, basic simulation model path, simulation duration, and result output path, and then allocating tasks to the process pool using `Pool.map()` or the `Process` class.

[0035] To ensure task independence, each simulation task is assigned a unique temporary working directory to store a copy of the model, intermediate files, and result data, avoiding file conflicts between multiple tasks. Simultaneously, the task queue must include a priority mechanism. Tasks with parameter combinations closer to the optimization objective (such as "potential parameters" judged based on prior experience) can be assigned higher priority for execution, improving overall optimization efficiency. The resulting parallel simulation task queue is essentially a set of ordered, independently executable simulation instructions, providing a structured task scheduling scheme for subsequent batch calls to the basic simulation model.

[0036] Then, based on the metadata in the task queue, a batch execution command stream for Adams is generated using a Python script: for each task, the script automatically modifies the configuration file (such as the .adm file) of the basic simulation model, assigns the hinge coordinate parameters of the current task to the design variables of the model, and writes the driving function parameters into the motion constraints of the cylinder (such as updating the displacement function expression through the MODIFY command) to ensure that the model moves according to the settings of the current parameter set.

[0037] Subsequently, the modified model is called through Adams' command-line interface (such as adamsSolver) to perform dynamic simulation: During the simulation, the model will simulate the extension / retraction motion of the iron drill boom under flexible treatment, consider the influence of boom deformation on the force of the hydraulic cylinder, and record key physical quantities in real time through built-in sensors (such as force sensors and position sensors) - including the change of thrust / pull force of the push / pull hydraulic cylinder over time, the position coordinates of each hinge point in three-dimensional space, and the deformation of key nodes of the boom.

[0038] To ensure the stability of batch simulations, a monitoring mechanism needs to be added to the script: the subprocess module captures Adams's runtime logs to detect simulation anomalies in real time (such as calculation termination or convergence failure caused by structural interference). Abnormal tasks are automatically marked and retried. After the simulation is completed, each task generates raw simulation result files (such as .res binary files and .csv text files) containing time-series data (such as cylinder force and hinge point position recorded every 0.01 seconds). These files are stored in the corresponding temporary directory according to the task identifier, providing the original basis for subsequent data extraction.

[0039] Finally, for the original simulation result file of each task, the script calls Adams' data processing interface (such as the adams.post module) or directly parses the text file through Python script: For cylinder force data, the script will traverse the time series, identify and extract the maximum value (peak thrust) and minimum value (peak tension) of the thrust, and record the time point when the peak occurs (for analyzing the correlation between force and boom attitude); For hinge point position data, the script will extract the three-dimensional coordinates at each time point according to a preset time interval (such as 0.01 seconds consistent with the simulation step size) to form trajectory data of position changing over time.

[0040] Data verification is required during the extraction process: by comparing the numerical changes at adjacent time points, abnormal jumps in data (such as abrupt changes caused by calculation errors) are removed, and missing data is interpolated to ensure data continuity and accuracy. Subsequently, the extracted peak cylinder force and hinge point position trajectory are associated with the corresponding parameter sets (hinge point coordinates + drive function parameters) to form structured data with a "parameter-performance" mapping—each data set includes fields such as parameter identifier, peak thrust, peak tension, and hinge point position time series.

[0041] The pandas library is used to integrate the structured data of all tasks into a unified dataset (such as a DataFrame), and then classifies and stores it according to parameter group categories (such as grouping by driving function type). This dataset consists of multiple sets of simulation data, which not only fully preserve the performance characteristics of the iron drill under different parameter combinations, but also provide input samples for subsequent optimization algorithms. It is the core data support for realizing the "simulation-optimization" closed loop.

[0042] Step S104: Based on multiple sets of simulation data and the corresponding iterative data, plot the dynamic diagram of cylinder force and hinge position to obtain the output results under different driving functions.

[0043] In this embodiment of the application, a dynamic diagram of cylinder force and hinge point position is plotted based on multiple sets of simulation data and the corresponding iterative data to obtain the output results under different driving functions, including the following steps A1-A3: Step A1: Using multiple sets of simulation data and corresponding iterative data, core data such as cylinder push-pull force and hinge point position are selected and classified to associate driving functions, resulting in a regularized visual data source.

[0044] Specifically, firstly, the core components of the data source are clarified: multiple sets of simulation data include the time series of cylinder push-pull forces and the three-dimensional coordinate trajectory of the hinge point for each set of parameters; the corresponding iterative data covers the driving function parameters (such as acceleration coefficient and motion range), hinge point coordinate update values, and cylinder force optimization indicators (such as peak force change trend) for each iteration during the optimization process. These data are stored in CSV, JSON, and other format files, and need to be read and integrated in batches using Python's pandas and json libraries.

[0045] The data filtering process needs to focus on core indicators: For cylinder push-pull force data, remove transitional fluctuation data in the initial simulation stage (such as the starting impact value in the first 0.1 seconds), retain the force value sequence in the stable motion stage, and extract three key features: peak thrust, peak pull, and force fluctuation standard deviation; For hinge point position data, extract the X, Y, and Z coordinates at each moment according to the simulation time step (such as 0.01 seconds), and filter out abnormal jump values ​​caused by calculation errors (judged by the 3σ criterion). Simultaneously, the issue of missing data needs to be addressed: for a small number of missing force values ​​or coordinate data, linear interpolation should be used to complete them, ensuring the continuity of the time series.

[0046] Categorization and association are crucial steps: using the unique identifier of the driving function (e.g., function type + parameter combination number "Trapezoid_003") as an index, the filtered cylinder force characteristics and hinge point position trajectories are bound to the corresponding driving function parameters, forming a three-dimensional data structure of "driving function - force characteristic - position trajectory". For example, the "sine acceleration driving function" is associated and stored with the corresponding peak thrust of 8500N and hinge point Z-axis trajectory data. Finally, the integrated data is converted into a standardized format (e.g., a DataFrame table) containing fields such as "driving function ID, peak thrust, peak tension, force fluctuation value, hinge point X / Y / Z coordinate sequence", resulting in a standardized visualization data source. This data source ensures both the accuracy and completeness of the data and enables rapid retrieval of performance data under different driving functions through index association, providing structured input for subsequent chart creation.

[0047] Step A2: Based on the organized visual data source, use the visual design to create a dynamic chart framework and obtain the chart drawing template.

[0048] Specifically, firstly, the chart type combination was determined: considering that it is necessary to simultaneously display the time variation law of the cylinder force and the spatial motion trajectory of the hinge point, a "double-panel combination chart" design was adopted - the upper part is the "cylinder force-time" dynamic curve (including two sub-curves of thrust and pull), and the lower part is the "hinge point three-dimensional position trajectory diagram" (XY plane projection + Z-axis height change curve). At the same time, a key indicator label column (displaying peak force and fluctuation value) was added to the side of the chart.

[0049] The design of the chart framework needs to balance technical details and user experience: In Python, Matplotlib is used in conjunction with the FuncAnimation module to achieve dynamic effects. The subplot function is used to divide the chart area, and a uniform coordinate axis range is set (e.g., time axis 0-10 seconds, force axis 0-15000N, coordinate axes adaptive according to the hinge point movement range). To differentiate the results of different driving functions, distinct visual styles are defined: each driving function's corresponding curve is assigned a unique color and line style (e.g., a solid red line for sine wave drive, a dashed blue line for trapezoidal drive), and hinge point trajectory points are marked with different shapes (circles, squares).

[0050] Simultaneously, interactive and annotation features were added: a time slider was embedded in the chart, supporting manual control of the dynamic playback progress; when the mouse hovers over the curve, the specific force value or coordinate value at the current time point is displayed; and red markers and numerical labels are added at peak force locations. Finally, these designs were solidified into a chart drawing template—the template includes core elements such as chart layout, axis settings, visual style rules, and dynamic interaction logic, achieving the effect of "automatically applying formatting upon inputting data" through function encapsulation. This template ensures consistency across multiple sets of data visualizations and reduces the complexity of subsequent batch rendering through standardized design, ensuring that performance differences between different driving functions can be clearly compared.

[0051] Step A3: Based on the chart drawing template, batch render the dynamic data corresponding to different drive functions to obtain a dynamic diagram of cylinder force and hinge position that intuitively shows the performance differences.

[0052] Specifically, the first step is the batch data scheduling logic design: traverse the normalized visualization data source, extract the corresponding data (cylinder force time series, hinge point coordinate series) by grouping according to "driving function ID", and input each group of data into the chart drawing template one by one through a loop statement. For example, when processing the "Trapezoid_003" driving function, the template automatically calls its corresponding red solid line style and loads the thrust / tension data and hinge point trajectory data of that group.

[0053] The dynamic rendering process requires optimization of playback effects and efficiency: A reasonable frame rate (e.g., 10 frames per second) is set using the FuncAnimation module to ensure smooth curve playback; incremental rendering technology is employed, updating only the curve segment at the current time point to reduce redundant calculations and improve batch processing speed. For comparisons of multiple driving functions, two display modes are supported: "single chart switching mode" (dynamic results of different driving functions are switched via buttons within the same chart) and "multiple chart side-by-side mode" (multiple sub-charts are generated simultaneously, displaying the results of all driving functions horizontally), allowing users to choose according to their needs.

[0054] After rendering, the charts are optimized and exported: Each dynamic chart is given a title (including driving function type and parameter identifiers), axis labels (including units), and a legend to ensure complete information. The dynamic charts are exported as MP4 format (1920×1080 resolution) using the ffmpeg encoder, and static screenshots (PNG format) are generated for quick preview. The resulting dynamic charts of cylinder force and hinge point position visually demonstrate the performance differences under different driving functions—for example, the dynamic curves clearly show that the cylinder force fluctuation is smaller with the sinusoidal driving function, the hinge point movement is smoother with the trapezoidal driving function, and the peak force label directly reflects the optimization effect. These charts not only provide a visual basis for determining the optimal solution for cylinder force but also provide an intuitive reference for subsequent parameter adjustments, achieving a closed loop of "data-chart-decision".

[0055] In this embodiment of the application, the method further includes: Step B1: Based on multiple sets of simulation data, set the objective function for the optimal cylinder force, and call the optimization algorithm to optimize the driving function and hinge point coordinates according to the objective function, so as to obtain the optimized candidate values ​​of the driving function and hinge point coordinates.

[0056] In this embodiment of the application, the objective function for optimizing the cylinder force is set based on multiple sets of simulation data, including: extracting key features of the cylinder force based on multiple sets of simulation data to obtain cylinder force performance evaluation indicators; determining the optimization direction based on the cylinder force performance evaluation indicators to obtain the core constraints of the function; and constructing the objective function for optimizing the cylinder force based on the core constraints of the function.

[0057] First, the objective function needs to be constructed based on multiple sets of simulation data, implemented in three progressive layers: The first layer is to extract key features of the hydraulic cylinder force, selecting core indicators such as peak thrust, peak tension, force fluctuation amplitude (e.g., standard deviation), and force duration from the simulation data. These indicators directly reflect the rationality of the hydraulic cylinder's force application; for example, excessive peak thrust can lead to cylinder overload, and excessive force fluctuation can exacerbate boom fatigue. The second layer is to determine the optimization direction, transforming the core indicators into constraints based on engineering requirements, such as "peak thrust ≤ 10000N" and "force fluctuation amplitude ≤ 500N," while clarifying the optimization priority (e.g., prioritizing minimizing peak thrust, then reducing fluctuation amplitude). The third layer is to construct a mathematical expression, integrating the constraints into the objective function, for example, using a weighted summation form f(x) = ω1 × F_max + ω2 × σ_F (where F_max is the peak thrust, σ_F is the force fluctuation standard deviation, and ω1 and ω2 are weighting coefficients set according to the optimization priority), so that the smaller the objective function value, the better the hydraulic cylinder force performance.

[0058] In the optimization phase, a suitable intelligent optimization algorithm (such as particle swarm optimization or genetic algorithm) needs to be selected. The driving function parameters (such as acceleration coefficients and motion time) and hinge coordinates (X1, Y1, Z1, X2, Y2, Z2) are used as decision variables, and the objective function is used as the fitness function for iterative search. The initial population of the algorithm is randomly selected from parameters of multiple sets of simulation data. New parameter combinations are generated through "selection-crossover-mutation" operations, and the simulation model is called to evaluate the objective function value, gradually approaching the optimal solution. To improve efficiency, a premature convergence judgment mechanism can be added (if the objective function value does not improve after 5 consecutive generations, the algorithm parameters are adjusted). Finally, after a preset number of iterations, the algorithm outputs the parameter combination with the minimum objective function value, i.e., the optimized candidate values ​​of the driving function and hinge coordinates, providing direction for subsequent model updates.

[0059] Step B2: The basic simulation model is called up and the original driving function expression and hinge point coordinate design variables are automatically replaced according to the optimized candidate values ​​to obtain the basic simulation model with updated parameters.

[0060] First, a mapping relationship between optimization candidate values ​​and model parameters needs to be established: clearly define the model variables corresponding to each element in the optimization candidate values—for example, the first three parameters in the candidate values ​​correspond to the acceleration coefficient, uniform velocity, and deceleration time of the driving function, while the last six parameters correspond to the coordinates of the cylinder hinge points (X1, Y1, Z1) and piston rod hinge points (X2, Y2, Z2). This mapping relationship needs to be fixed through a configuration file (such as XML) to avoid incorrect parameter assignment.

[0061] Subsequently, the replacement operation is performed by calling the AdamsAPI via a Python script: the script reads the optimization candidate values ​​and mapping relationships, first calls the `get_design_variables()` method to obtain the current hinge point coordinate design variable values ​​in the basic simulation model, and then uses the `set_design_variable()` method to assign and replace the hinge point coordinates in the candidate values ​​one by one; for the drive function, the script locates the drive attribute of the hydraulic cylinder sliding joint, calls the `modify_drive_function()` method, substitutes the parameters in the candidate values ​​into the preset function expression (such as a, b, and c in S(t)=a×t²+b×t+c), and replaces the original function parameters. During the replacement process, the script monitors the model status in real time. If the parameters exceed the model constraint range (such as the hinge point coordinates causing the hydraulic cylinder length to be negative), the script automatically triggers the correction mechanism (such as truncation to the boundary value) and records the correction log.

[0062] After the replacement is complete, the script calls the `model_update()` method to automatically adjust the model geometry and constraint relationships as the parameters are updated. For example, after the hinge point coordinates change, the connection position between the cylinder and the boom is updated in real time to avoid structural interference. The final generated basic simulation model with updated parameters retains the dynamic characteristics of the original model while incorporating new parameters with optimized candidate values. It can be directly used for subsequent verification and simulation, achieving a seamless connection between the "optimization algorithm" and the "model parameters".

[0063] Step B3: Verify the optimization candidate values ​​based on the updated basic simulation model to obtain the iteratively updated driving function and hinge point coordinate parameters, and use the iteratively updated driving function and hinge point coordinate parameters as iterative data.

[0064] First, the verification must cover three core dimensions: First, geometric feasibility verification. This involves simulating the complete boom movement (extension + retraction) using a basic simulation model with updated parameters. The verification checks whether hinge points cause component interference (e.g., collision between the cylinder and the boom) and whether the cylinder stroke exceeds the design range (e.g., maximum extension exceeds cylinder length). Adams' "Interference Detection" module can automatically identify the collision time and location. Second, dynamic rationality verification. This involves running a dynamic simulation and analyzing the cylinder force curve to determine if there are abnormal peak values ​​(e.g., instantaneous force overload due to parameter mutations) and whether the force value consistently exceeds the material's allowable stress (e.g., continuously exceeding 12000N). Simultaneously, it checks whether the boom's flexible deformation is within a safe range (e.g., maximum deflection ≤ 5mm). Third, optimization target achievement verification. This involves comparing the objective function value of the verification simulation with the output value of the optimization algorithm. If the deviation exceeds 5% (e.g., algorithm prediction 8000N, actual simulation value 8500N), then the parameters are considered to have optimization errors.

[0065] For candidate optimization values ​​that pass verification, they are directly determined as the parameters after iterative updates. If they fail, a correction process is initiated: for geometric interference problems, the Z-axis height of the hinge point coordinates is fine-tuned (e.g., increased by 5mm to avoid collisions); for abnormal force values, the acceleration coefficient of the driving function is reduced (e.g., from 0.2m / s² to 0.15m / s²), and the corrected values ​​are re-substituted into the model for verification until all constraints are met. Finally, the parameters that pass verification (including driving function parameters and hinge point coordinates) are marked and stored according to the number of iterations (e.g., "iteration 10"), serving as iterative data for subsequent simulations and optimizations. This data ensures physical feasibility and continues the optimization algorithm's search direction, providing reliable initial parameters for the next iteration, forming a closed loop of "optimization-update-verification".

[0066] This embodiment also provides an optimized device for driving the hydraulic cylinder hinge point of a steel drill, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0067] This embodiment provides an optimized device for driving the hinge point of a hydraulic cylinder in a steel drill, such as... Figure 3 As shown, it includes: The acquisition module 301 is used to acquire a pre-established parametric model and set the position of the push-pull cylinder hinge point as a design variable to obtain a basic simulation model that can be parametrically controlled. Processing module 302 is used to initialize the hinge point coordinates and the parameter set of the driving function based on the basic simulation model; The simulation module 303 is used to calculate the peak thrust and hinge position of the push-pull cylinder through parallel simulation based on the initialized parameter set, and obtain multiple sets of simulation data. The execution module 304 is used to draw a dynamic diagram of cylinder force and hinge point position based on multiple sets of simulation data and the corresponding iterative data, and obtain the output results under different driving functions.

[0068] In this embodiment of the application, the processing module 302 is used to connect to the model through a script call API based on the design variable definition of the basic simulation model to obtain the model parameter interaction interface; according to the model parameter interaction interface, input the preset initial value of the hinge point coordinates and assign it to the design variable to obtain the initialized hinge point coordinate parameters; according to the initialized hinge point coordinate parameters, define the driving function parameters and associate them with the boom driving variables to obtain the parameter group.

[0069] In this embodiment of the application, the simulation module 303 is used to generate multiple independent simulation tasks for the initialized parameter group through a multi-process library to obtain a parallel simulation task queue; according to the parallel simulation task queue, the basic simulation model is called to perform dynamic simulation in batches to obtain the original simulation results of each task; based on the original simulation results, the peak thrust of the push-pull cylinder and the hinge point position data at each moment are extracted and summarized to obtain multiple sets of simulation data.

[0070] In this embodiment, the execution module 304 is used to use multiple sets of simulation data and corresponding iterative data to filter the core data of cylinder push-pull force and hinge point position, and classify and associate driving functions to obtain a regularized visual data source; based on the regularized visual data source, a dynamic chart framework is designed using visualization to obtain a chart drawing template; based on the chart drawing template, dynamic data corresponding to different driving functions are batch rendered to obtain a dynamic chart of cylinder force and hinge point position that intuitively shows the performance differences.

[0071] In this embodiment, the execution module 304 is used to set the objective function for the optimal cylinder force based on multiple sets of simulation data, and call the optimization algorithm to optimize the driving function and hinge point coordinates according to the objective function to obtain optimized candidate values ​​for the driving function and hinge point coordinates; call the basic simulation model to automatically replace the original driving function expression and hinge point coordinate design variables according to the optimized candidate values ​​to obtain the parameter-updated basic simulation model; verify the optimized candidate values ​​according to the parameter-updated basic simulation model to obtain the iteratively updated driving function and hinge point coordinate parameters, and use the iteratively updated driving function and hinge point coordinate parameters as iterative data.

[0072] In this embodiment, the execution module 304 is used to extract key features of the cylinder force based on multiple sets of simulation data to obtain cylinder force performance evaluation indicators; determine the optimization direction based on the cylinder force performance evaluation indicators to obtain core constraints of the function; and construct the objective function for the optimal cylinder force based on the core constraints of the function.

[0073] Please see Figure 4 , Figure 4 This is a schematic diagram of the structure of a computer device provided in an optional embodiment of the present invention, such as... Figure 4 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system).

[0074] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0075] The memory 20 stores instructions executable by at least one processor 10 to cause the at least one processor 10 to perform the method shown in the above embodiments.

[0076] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device as shown by a landing page for an app. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, which can be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0077] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0078] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0079] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0080] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. An optimization method for driving the hinge point of a hydraulic cylinder in a steel drill, characterized in that, The method includes: Obtain a pre-established parametric model and set the position of the push-pull cylinder hinge point as a design variable to obtain a basic simulation model that can be parametrically controlled; Initialize the hinge point coordinates and the parameter set of the driving function based on the basic simulation model; Based on the initialized parameter set, the peak thrust and hinge point position of the push-pull cylinder are calculated through parallel simulation to obtain multiple sets of simulation data; Based on multiple sets of simulation data and the corresponding iterative data, a dynamic graph of cylinder force and hinge point position is plotted to obtain the output results under different driving functions.

2. The method according to claim 1, characterized in that, The parameter set for initializing the hinge point coordinates and driving function based on the basic simulation model includes: Based on the design variable definitions of the basic simulation model, the model is connected via API calls through scripts to obtain the model parameter interaction interface; According to the model parameter interaction interface, input the preset initial values ​​of hinge point coordinates and assign them to the design variables to obtain the initialized hinge point coordinate parameters; Based on the initialized hinge point coordinate parameters, define the drive function parameters and associate them with the boom drive variables to obtain the parameter set.

3. The method according to claim 1, characterized in that, The peak thrust and hinge point position of the push-pull cylinder are calculated through parallel simulation based on the initialized parameter set, resulting in multiple sets of simulation data, including: The multi-process library generates multiple independent simulation tasks for the initialized parameter set, resulting in a parallel simulation task queue. Based on the parallel simulation task queue, the basic simulation model is called to perform dynamic simulations in batches to obtain the original simulation results of each task; Based on the original simulation results, the peak thrust of the push-pull cylinder and the hinge point position data at each moment were extracted and summarized to obtain multiple sets of simulation data.

4. The method according to claim 1, characterized in that, The process involves plotting a dynamic graph of cylinder force and hinge point position based on multiple sets of simulation data and corresponding iterative data, yielding output results under different driving functions, including: By using multiple sets of simulation data and corresponding iterative data, core data such as cylinder push-pull force and hinge point position are filtered and the driving functions are classified and associated to obtain a regularized visual data source. Based on the standardized visual data source, a dynamic chart framework is designed using visualization to obtain the chart drawing template; Based on the chart drawing template, batch render the dynamic data corresponding to different drive functions to obtain a dynamic diagram that intuitively shows the performance differences between the cylinder force and the hinge point position.

5. The method according to claim 4, characterized in that, The method further includes: Based on multiple sets of simulation data, the objective function for optimizing the cylinder force is set, and the optimization algorithm is called to optimize the driving function and hinge point coordinates according to the objective function, so as to obtain the optimized candidate values ​​of the driving function and hinge point coordinates. The basic simulation model is called and the original driving function expression and hinge point coordinate design variables are automatically replaced according to the optimized candidate values ​​to obtain the basic simulation model with updated parameters. The optimization candidate values ​​are verified based on the updated basic simulation model to obtain the iteratively updated driving function and hinge point coordinate parameters, and the iteratively updated driving function and hinge point coordinate parameters are used as the iterative data.

6. The method according to claim 5, characterized in that, The objective function for setting the optimal cylinder force based on multiple sets of simulation data includes: Key features of hydraulic cylinder force were extracted from multiple sets of simulation data to obtain hydraulic cylinder force performance evaluation indicators; The optimization direction is determined based on the hydraulic cylinder force performance evaluation index, and the core constraints of the function are obtained. Based on the core constraints of the function, construct the objective function for optimizing the cylinder force.

7. An optimized device for driving the hinge point of a hydraulic cylinder in a steel drill, characterized in that, The device includes: The acquisition module is used to acquire a pre-established parametric model and set the position of the push-pull cylinder hinge point as a design variable to obtain a basic simulation model that can be parameterized and controlled. The processing module is used to initialize the hinge point coordinates and the parameter set of the driving function based on the basic simulation model; The simulation module is used to calculate the peak thrust and hinge position of the push-pull cylinder through parallel simulation based on the initialized parameter set, and obtain multiple sets of simulation data. The execution module is used to draw a dynamic graph of cylinder force and hinge point position based on multiple sets of simulation data and the corresponding iterative data, and obtain the output results under different driving functions.

8. A computer device, characterized in that, include: A memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, the processor executing the computer instructions to perform the method of any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing a computer to perform the method of any one of claims 1 to 6.

10. A computer program product, characterized in that, Includes computer instructions that cause a computer to perform the method of any one of claims 1 to 6.

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