A high-precision cylinder pneumatic hydraulic station control strategy optimization system

Abstract

This invention discloses a high-precision pneumatic hydraulic station control strategy optimization system, belonging to the field of high-precision pneumatic hydraulic station control technology. It includes a cloud platform and application terminals. The cloud platform includes an application configuration module and an optimization analysis module. The application configuration module is used to configure an optimization application library for each application terminal. The optimization analysis module summarizes various optimization problems and corresponding optimization methods based on terminal optimization data. It performs optimization simulation analysis on each optimization problem to obtain various optimized control strategies. The application terminals include an optimization feedback module, an optimization application library, and a control module. The optimization feedback module is used to feed back various terminal optimization data to the cloud platform. The control module is used for control management, acquiring operation monitoring data, performing real-time adaptation analysis on the operation monitoring data based on the optimization application library, determining applicable optimized control strategies, and controlling according to the optimized control strategies.

Description

Technical Field

[0001] This invention belongs to the field of high-precision pneumatic hydraulic station control technology, specifically a high-precision pneumatic hydraulic station control strategy optimization system. Background Technology

[0002] High-precision pneumatic hydraulic power units are core power units in high-end equipment manufacturing, aerospace precision drive, precision automated machining and other fields. With the advantages of fast pneumatic response, stable hydraulic output and high drive accuracy, they are widely used in high-precision working conditions such as precision displacement adjustment, precise pressure loading and high-frequency reciprocating operation.

[0003] Currently, the control optimization of pneumatic hydraulic power units mainly relies on the field experience of R&D and maintenance personnel. Optimization verification depends entirely on physical equipment debugging, which not only requires downtime and affects production efficiency but also causes wear and tear on precision components and increases maintenance costs. At the same time, technicians cannot conduct multi-condition simulation tests and performance quantitative comparisons of optimization strategies, making it difficult to predict the stability and adaptability of strategies under varying loads and operating conditions. Many innovative optimization solutions are shelved and buried because they cannot be effectively verified, evaluated for effectiveness, and risk-predicted, thus failing to demonstrate their application value. For example, in existing technologies, traditional techniques only use a single temperature parameter to establish a precision error compensation model, failing to distinguish the multi-field coupling effects of ambient temperature, oil temperature, and gas temperature. This makes them unsuitable for complex high and low temperature alternating operating conditions, and the fixed compensation amount cannot cover the entire temperature range, ultimately resulting in compensation lag and inaccurate compensation amounts under high and low temperature alternating operating conditions.

[0004] In order to solve the above problems, this invention provides a high-precision pneumatic hydraulic station control strategy optimization system. Summary of the Invention

[0005] To address the problems of the above solutions, this invention provides a high-precision pneumatic hydraulic station control strategy optimization system.

[0006] The objective of this invention can be achieved through the following technical solutions: A high-precision pneumatic hydraulic station control strategy optimization system includes a cloud platform and an application terminal; Furthermore, the cloud platform communicates with each application terminal; The cloud platform includes a storage repository, an application configuration module, and an optimization analysis module. The storage depot is used to store optimized control strategies applicable to various high-precision pneumatic hydraulic power units.

[0007] The application configuration module is used to configure and optimize the application library for each application terminal and to dynamically update the optimized application library.

[0008] Furthermore, optimize the configuration and updates of the application library, including: Obtain information about the pneumatic hydraulic station of the cylinder, match the information with the reserve in real time, and obtain various optimized control strategies. Configure the optimization application library in the application terminal and send the optimization control policy to the optimization application library for storage; Obtain the list of optimization control strategies stored in the optimization application library, calibrate the real-time matched optimization control strategies according to the list of optimization control strategies, obtain the optimization control strategies that need to be supplemented and updated in the optimization application library, and update the optimization application library according to the optimization control strategies.

[0009] Furthermore, the information from the pneumatic hydraulic station is matched with the reserve warehouse in real time, including: Extract the optimization adaptation features of each optimized control strategy in the reserve; extract the corresponding cylinder pneumatic hydraulic station features and basic strategy features based on the cylinder pneumatic hydraulic station information; integrate the cylinder pneumatic hydraulic station features and basic strategy features into matching condition data; The matching condition data and the optimization adaptation features of each optimization control strategy are respectively combined into input data and input into a preset strategy matching model for analysis to obtain each successfully matched optimization control strategy.

[0010] Furthermore, the expression for the policy matching model is: ; In the formula: (p, s i ) represents the input data, p represents the matching condition data, and s i The s represents the optimization adaptation characteristics of the corresponding optimized control strategy, where i represents the corresponding optimized control strategy, i = 1, 2, ..., n, and n is the number of optimized control strategies in the reserve; i →p represents the data where the corresponding optimized adaptation feature meets the matching condition; the output data is the policy matching value PU(p, s). i The strategy matching value is 1 or 0; When the strategy matching value is 1, the matching result is a successful match; When the strategy matching value is 0, the matching result is a failure.

[0011] The optimization analysis module is used to perform optimization feedback analysis, receive terminal optimization data sent by each application terminal in real time, and summarize various optimization problems and corresponding optimization methods based on the terminal optimization data. Each optimization problem is analyzed by simulation to obtain the target optimization problem, the target optimization method for each target optimization problem is determined, the corresponding optimization control strategy is formed based on the target optimization method, and the optimization control strategy is sent to the storage repository for storage.

[0012] Furthermore, optimization simulation analysis is performed on each optimization problem, including: A digital twin model of a high-precision pneumatic hydraulic station is established, and various simulation backgrounds are generated based on eliminating corresponding optimization problems. The simulation backgrounds are then simulated using the digital twin model to obtain optimized simulation data. Pre-set the evaluation items and weight coefficients of the cylinder pneumatic hydraulic station, obtain the basic control effect data of the cylinder pneumatic hydraulic station, evaluate the optimized simulation data and basic control effect data through each evaluation item, and obtain the single optimization ratio of the optimized simulation data relative to the basic control effect data in each evaluation item. The optimization simulation score is calculated based on the individual optimization ratio and weight coefficient of each evaluation item. Optimization problems whose optimization simulation scores are greater than the threshold X1 are marked as target optimization problems.

[0013] Furthermore, the optimization simulation score is calculated based on the individual optimization ratio and weight coefficient of each evaluation item, including: ; In the formula: YP is the optimization simulation score, j represents the corresponding evaluation item, j=1, 2, ..., m, and m is the number of evaluation items; δ j DP represents the weighting coefficient of the corresponding evaluation item. j This indicates the individual optimization ratio of the corresponding evaluation item.

[0014] Furthermore, before performing optimization simulation analysis on each optimization problem, optimization problems that are required by objective laws are first eliminated.

[0015] Furthermore, for target optimization problems without a target optimization method, the target optimization problem is marked as a problem to be optimized, and the associated simulation data of each problem to be optimized is obtained. The associated simulation data includes, but is not limited to, optimization simulation scores and optimization simulation data. The problems to be optimized and the associated simulation data are summarized and displayed according to a preset method.

[0016] The application terminal includes an optimization feedback module, a monitoring module, an optimization application library, and a control module; The optimization feedback module is used to send various terminal optimization data back to the cloud platform.

[0017] The monitoring module is used to perform real-time monitoring during the operation of the pneumatic hydraulic station and obtain operational monitoring data.

[0018] The control module is used for control management, acquiring operation monitoring data, performing real-time adaptation analysis on the operation monitoring data based on the optimization application library, determining the applicable optimized control strategy, and controlling according to the optimized control strategy; when there is no suitable optimized control strategy, no control optimization adjustment is performed.

[0019] Compared with the prior art, the beneficial effects of the present invention are: This invention replaces traditional on-site machine debugging with simulation analysis, eliminating the need for equipment downtime for optimization and verification. This avoids production line interruptions that lead to capacity losses and reduces wear and tear on precision components such as cylinders, valve assemblies, and sensors caused by repeated debugging, effectively lowering equipment maintenance and optimization costs and improving production continuity. It allows for multi-condition simulation testing and performance quantification comparison of various optimization ideas and improvement schemes proposed by personnel, accurately assessing the operational stability, adaptability, and potential operational risks of different optimization strategies under varying loads and operating conditions, and objectively determining the optimization potential of the scheme. This effectively solves the problem of innovative optimization schemes being difficult to verify and their value unproven in traditional models, thus being shelved and buried. It fully transforms the practical experience of frontline personnel, driving continuous iteration and upgrading of control strategies. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are 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.

[0021] Figure 1 This is a block diagram illustrating the principle of the present invention. Detailed Implementation

[0022] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0023] like Figure 1 As shown, a high-precision pneumatic hydraulic station control strategy optimization system includes a cloud platform and an application terminal; The cloud platform communicates with each application terminal. The cloud platform includes a storage repository, an application configuration module, an optimization analysis module, and a control module. The storage depot is used to store optimized control strategies applicable to various high-precision pneumatic hydraulic power units.

[0024] In one embodiment, the reserve can be pre-aggregated by the platform provider, which summarizes the optimized control strategies of various high-precision pneumatic hydraulic stations. The various high-precision pneumatic hydraulic stations refer to the optimized control strategies of the high-precision pneumatic hydraulic stations under the corresponding control strategies. Subsequent updates are made based on optimization feedback.

[0025] Based on this optimized control strategy, it is possible to determine how to optimize the original control strategy under the corresponding application conditions to achieve higher control.

[0026] For example, a precision pressure-holding condition (long-term pressure stabilization, micro-pressure fluctuation scenario): The original control strategy employed fixed-parameter PID closed-loop pressure control, using a single proportional, integral, and derivative parameter for pressure regulation throughout the entire process. Under prolonged precision pressure holding conditions, integral saturation was prone to occur, leading to slight overshoot of system pressure, large steady-state pressure fluctuations, and an inability to suppress pressure drift caused by minor oil leaks and air compressibility. This resulted in poor cylinder output pressure stability, affecting the accuracy of precision press-fitting and positioning operations.

[0027] Optimized control strategy: Segmented anti-integral saturation pressure stabilization optimization control strategy. The system automatically switches to this strategy after recognizing a long-term pressure-holding steady-state condition. During the rapid pressure build-up phase, a high-gain PID controller is used to improve response speed; when the pressure approaches the set threshold, the gain is automatically reduced, and the integral accumulation is limited to eliminate overshoot; during the steady-state pressure holding phase, micro-pressure differential dynamic compensation is activated to offset pressure deviations caused by hydraulic medium compression deformation, pipeline micro-leakage, and slight temperature changes in real time, keeping the equipment's steady-state pressure fluctuation rate within a precisely permissible range and significantly improving pressure holding accuracy.

[0028] Low-speed micro-motion precision positioning of cylinder: The original control strategy was a fixed-opening valve control output logic that controlled the proportional valve opening based on a preset fixed voltage signal. When the high-precision cylinder moved at low speed, it was easily affected by the difference between static and dynamic friction, resulting in low-speed crawling, motion jamming, and dead zones in micro-displacement. This led to large positioning deviations in small strokes and low repeatability, which could not meet the requirements of micron-level fine-tuning operations.

[0029] Optimized control strategy: Low-speed friction adaptive compensation optimized control strategy. After identifying low-speed, short-stroke, and precise positioning conditions, the system adaptively outputs a feedforward compensation signal from a small valve to offset the static friction resistance of the cylinder seal, eliminating micro-motion dead zones and crawling jams; at the same time, it dynamically fine-tunes the valve group output opening based on real-time displacement feedback to achieve smooth transition of low-speed movements, significantly improving the positioning accuracy and smoothness of the cylinder's micro-displacement.

[0030] The application configuration module is used to configure corresponding optimized application libraries for each application terminal and to dynamically update the optimized application libraries.

[0031] In one embodiment, optimizing the configuration and updates of the application library includes: Acquire information about the pneumatic hydraulic power unit, including basic overall information, cylinder body parameters, pneumatic system parameters, hydraulic system parameters, preset control strategies, and other relevant information. This information is used to match suitable optimized control strategies based on the pneumatic hydraulic power unit and the control strategy. The pneumatic hydraulic power unit information is then matched with the reserve database in real time to obtain various matched optimized control strategies. Configure the optimization application library in the application terminal and send the optimization control policy to the optimization application library for storage; Obtain the list of optimization control strategies stored in the optimization application library, calibrate the real-time matched optimization control strategies according to the list of optimization control strategies, obtain the optimization control strategies that need to be supplemented and updated in the optimization application library, and update the optimization application library according to the optimization control strategies.

[0032] In one embodiment, the information of the pneumatic hydraulic station of the cylinder is matched with the reserve in real time. Those with the same pneumatic hydraulic station of the cylinder and the same control strategy are considered to be successfully matched. That is, it is necessary to pre-set each optimized control strategy and apply the optimized control strategy in advance, so as to perform rapid matching.

[0033] In one embodiment, the information of the pneumatic hydraulic power unit is matched with the reserve in real time. Considering the variability of control strategies for different pneumatic hydraulic power unit applications, the matching is performed in the following manner, which can be combined with subsequent simulation evaluation; including: Extract the optimization adaptation features of each optimized control strategy in the reserve, such as the corresponding equipment accuracy level, equipment range, operating condition type, load range, speed range, control defects that can be solved, strategy gain characteristics, and adaptation threshold boundaries. The optimization adaptation features remain unchanged when the optimized control strategy remains unchanged. Based on the information of the pneumatic hydraulic power unit, extract the corresponding characteristics and basic strategy characteristics of the pneumatic hydraulic power unit. The characteristics of the pneumatic hydraulic power unit include equipment rated pressure, cylinder stroke, minimum stable speed, repeatability accuracy level, pneumatic-hydraulic linkage structure type, valve group response level, pipeline damping coefficient, medium compressibility characteristics, and equipment aging level. The basic strategy characteristics are the strategy characteristics corresponding to the original control strategy, such as the applicable operating condition range (load rate range, pressure holding duration range, reciprocating frequency range, micro-motion stroke range, ambient temperature fluctuation range, real-time pressure fluctuation rate range, displacement deviation range, load switching frequency range, etc.) and strategy application requirements (ensuring that the optimized control strategy can be applied). For the applicable operating condition range, any operating condition needs to be met to indicate that there is a background for optimized application. Integrate the characteristics of the pneumatic hydraulic power unit and the basic strategy characteristics into matching condition data. The matching condition data and the optimization adaptation features of each optimization control strategy are respectively composed of input data and input into a preset strategy matching model for analysis to obtain each successfully matched optimization control strategy.

[0034] In one embodiment, the policy matching model is established based on machine learning and deep learning algorithms; it is trained by labeling the corresponding training set according to the corresponding historical data. The training set includes input data and output data. The input data is the matching condition data and the optimization adaptation features of the corresponding optimized control strategy. The output data is the matching result of whether or not it matches. The corresponding training data can also be simulated based on subsequent simulation evaluation.

[0035] In one embodiment, the expression for the policy matching model is: ; In the formula: (p, s i ) represents the input data, p represents the matching condition data, and s i The s represents the optimization adaptation characteristics of the corresponding optimized control strategy, where i represents the corresponding optimized control strategy, i = 1, 2, ..., n, and n is the number of optimized control strategies in the reserve; i →p represents the data where the corresponding optimized adaptation feature meets the matching condition; the output data is the policy matching value PU(p, s). i The strategy matching value is 1 or 0; the training set is set according to the above embodiment for training, and the case where the optimized adaptation feature does not meet the matching condition data is regarded as an abnormal case.

[0036] When the strategy matching value is 1, the matching result is a successful match; When the strategy matching value is 0, the matching result is a failure.

[0037] The optimization analysis module is used to perform optimization feedback analysis, summarize terminal optimization data sent by each application terminal in real time, and summarize various optimization problems and corresponding optimization methods based on the terminal optimization data. Each optimization problem is analyzed by simulation to obtain the target optimization problem, the target optimization method for each target optimization problem is determined, the corresponding optimization control strategy is formed based on the target optimization method, and the optimization control strategy is sent to the storage repository for storage.

[0038] In one embodiment, optimization simulation analysis is performed on various optimization problems, including: A digital twin model of a high-precision pneumatic hydraulic station is established. The simulation background is set to eliminate the corresponding optimization problems. The simulation background is set according to each optimization problem, such as the temperature compensation problem. The simulation background with the predetermined temperature accuracy is generated according to the temperature compensation problem.

[0039] The simulation of various simulation backgrounds is carried out by digital twin models to obtain optimized simulation data. The optimized simulation data mainly includes relevant data such as the running effect of the optimization strategy when there is no optimization problem. The system pre-defines various evaluation items and weight coefficients for the pneumatic hydraulic power unit. By comparing the optimized simulation data with the original control strategy's operational effects (basic control effect data) across all dimensions, it achieves quantitative scoring and optimization potential assessment. All evaluation items are designed specifically for the precision control characteristics of the high-precision pneumatic hydraulic power unit. Examples include precision control accuracy evaluation items, operational stability evaluation items, dynamic response performance evaluation items, operating condition disturbance resistance evaluation items, operational consistency and repeatability evaluation items, and strategy adaptation and safety compatibility evaluation items, with weight coefficients of 0.25, 0.2, 0.15, 0.15, 0.15, and 0.1, respectively. Obtain the basic control effect data of the pneumatic hydraulic station of the cylinder, evaluate the optimized simulation data and the basic control effect data through various evaluation items, and obtain the single optimization ratio of the optimized simulation data relative to the basic control effect data in each evaluation item. For example, if the temperature drift error is reduced by 85% after optimization, the single optimization ratio is 0.85. The optimization simulation score is calculated based on the individual optimization ratio and weight coefficient of each evaluation item. Optimization problems whose optimization simulation scores are greater than the threshold X1 are marked as target optimization problems. The threshold X1 is set according to the platform's requirements, such as 60, 70, 80, etc.

[0040] In one embodiment, the optimization simulation score is calculated based on the individual optimization ratio and weight coefficient of each evaluation item, including: ; In the formula: YP is the optimization simulation score, j represents the corresponding evaluation item, j=1, 2, ..., m, and m is the number of evaluation items; δ j DP represents the weighting coefficient of the corresponding evaluation item. j This indicates the individual optimization ratio of the corresponding evaluation item.

[0041] For example, precision control accuracy (25 points, converted into corresponding scores according to weighting coefficients): After optimization, the temperature drift error is reduced by 85%, and the pressure holding accuracy and micro-positioning accuracy are greatly improved, with a score of 23 points.

[0042] Operational stability (20 points): Pressure fluctuation rate across the entire temperature range decreased significantly, and there was no temperature-induced accuracy degradation, scoring 18 points.

[0043] Dynamic response performance (15 points): There is no hysteresis in the pressure build-up and reset response under temperature change conditions, and there is basically no impact. Score: 13 points.

[0044] Operating condition disturbance resistance (15 points): It has the ability to dynamically suppress temperature disturbances and the anti-temperature drift performance has been greatly optimized, with a score of 14 points.

[0045] Operational consistency and repeatability (15 points): The operational accuracy is consistent under high and low temperature conditions, and the batch deviation dispersion is significantly reduced, with a score of 14 points.

[0046] Policy adaptation and security compatibility (10 points): Fully adapts to the original control architecture, signal timing, and security logic, with no conflicts, scoring 10 points.

[0047] Weighted sum total score: 23 + 18 + 13 + 14 + 14 + 10 = 92 points.

[0048] In one embodiment, before performing optimization simulation analysis on each optimization problem, optimization problems that are impossible to achieve, unrealistic, or contrary to scientific laws are eliminated, such as perpetual motion machines that save energy; and the impossible, unrealistic, and contrary to scientific laws are summarized into objective law requirements.

[0049] In one embodiment, a digital twin model can invoke an existing, established digital twin model.

[0050] In one embodiment, since the digital twin model is mainly used to determine the optimization simulation data and then the individual optimization ratio of each evaluation item, it can be estimated based on other simulation techniques, prediction techniques, etc., to determine the optimization simulation data or the individual optimization ratio.

[0051] In one embodiment, determining the objective optimization method for each objective optimization problem includes: The optimization methods corresponding to each target optimization problem are obtained. The optimization methods are simulated using a digital twin model, and those that cannot be implemented are eliminated. The remaining optimization methods are prioritized. For example, the optimization simulation score corresponding to each optimization method is calculated based on the optimization simulation score. The optimization simulation scores are ranked according to their priority. This priority ranking is presented to the relevant management personnel, and professionals conduct further evaluation, verification, and research to determine the target optimization method or the optimization method without a target.

[0052] In one embodiment, the optimization method may include not only the one fed back by the application terminal, but also the optimization method based on various existing technologies, as well as the application technology that may be implemented based on the application technology.

[0053] In one embodiment, for a target optimization problem without a target optimization method, the target optimization problem is marked as a problem to be optimized, and the associated simulation data of each problem to be optimized is obtained. The associated simulation data includes, but is not limited to, optimization simulation scores and optimization simulation data. The problems to be optimized and the associated simulation data are summarized and displayed in a preset manner, such as by using tables, charts, etc., and then displayed to the platform administrators as technical problems to be solved.

[0054] In one embodiment, the target optimization method for each target optimization problem is determined, and the target optimization method is developed and set by professionals based on the associated simulation data.

[0055] In one embodiment, a corresponding optimization control strategy is formed based on the target optimization method. When the target optimization method is determined, the hardware and software of the equipment are adjusted according to the target optimization method first, and then the corresponding optimization control strategy is formed. Generally, when the target optimization method is determined, it is set synchronously by the relevant professionals.

[0056] In one embodiment, the processing results of the optimization data from each terminal are fed back to the relevant personnel so that they can understand the analysis results of their own terminal optimization data.

[0057] The application terminal includes an optimization feedback module, a monitoring module, an optimization application library, and a control module; The optimization feedback module is used to send various terminal optimization data back to the cloud platform.

[0058] Terminal optimization data is compiled by various managers and technicians of the pneumatic hydraulic station based on optimization problems and solutions. It can include optimization problems and optimization methods, or only optimization problems, to facilitate the discovery of defects and enable technicians to provide timely and convenient feedback on terminal optimization data.

[0059] For example, in the equipment operating scenario: a high-precision cylinder pneumatic hydraulic station performs continuous precision pressure holding operations for a long time. As the ambient temperature gradually increases with the operation of the equipment, the viscosity of the hydraulic oil decreases, resulting in implicit pressure drift and a decrease in micro-positioning accuracy in the system.

[0060] Personnel feedback on optimization issues: The original control strategy was fixed parameter control and did not correlate with temperature variables. After the equipment temperature rises, the characteristics of the hydraulic medium change, and the pipeline leakage and medium compressibility change accordingly, resulting in a decrease in steady-state pressure holding accuracy and micron-level positioning offset. The greater the temperature fluctuation, the more obvious the precision control error.

[0061] The team proposed an optimization method: introducing a dynamic temperature compensation mechanism to collect hydraulic oil temperature and ambient temperature parameters in real time, and dynamically correcting the PID control parameters and pressure closed-loop compensation amount according to the temperature change gradient, so as to offset the accuracy error caused by temperature drift and achieve precise pressure stabilization and accurate positioning in the entire temperature range.

[0062] The optimization issues and optimization methods are integrated into terminal optimization data.

[0063] Equipment operating scenario: Dynamic processing conditions where the pneumatic hydraulic station of the cylinder rapidly switches between high and low loads, and frequent sudden load shock phenomena occur.

[0064] Personnel feedback optimization issues: When the equipment switches between light and heavy loads instantaneously, the original fixed pneumatic-hydraulic linkage timing cannot adapt to the sudden change in load conditions. The system will generate instantaneous pressure peak oscillations, causing slight overshoot of the cylinder piston rod. After multiple accumulations, it will cause deviations in the consistency of workpiece processing. However, the on-site operation and maintenance personnel cannot locate the specific timing defects or independently design corresponding optimization and control solutions.

[0065] Personnel optimization plan status: It can only accurately reflect the control defects and poor operating conditions of the equipment, but there are no feasible solutions or custom optimization methods.

[0066] At this point, the terminal optimization data only includes optimization issues.

[0067] The monitoring module is used to perform real-time monitoring during the operation of the pneumatic hydraulic station and obtain operational monitoring data, such as equipment pressure, cylinder displacement, operating speed, load, ambient temperature, and action sequence.

[0068] The optimization application library is used to store optimization control strategies.

[0069] The control module is used for control management, acquiring operation monitoring data, performing real-time adaptation analysis on the operation monitoring data based on the optimization application library, determining the applicable optimized control strategy, and controlling according to the optimized control strategy; when there is no suitable optimized control strategy, no control optimization adjustment is performed, that is, control management is still performed according to the original control strategy.

[0070] The control module is used for overall machine control and management. It acquires the operation monitoring data of the pneumatic hydraulic station of the cylinder; calls various optimized control strategies stored in the optimization application library; combines the operation monitoring data to conduct real-time working condition adaptation analysis; matches and calls the optimal optimized control strategy corresponding to the current working condition; and executes the optimized control logic. If no suitable optimized control strategy is found after traversing the optimization application library, the module does not perform strategy switching and parameter adjustment, and continues to use the original control strategy of the equipment to complete the control management.

[0071] In one embodiment, the optimized control strategy includes a temperature compensation strategy, wherein the temperature compensation strategy is: Step S1: Real-time acquisition and preprocessing of multiple temperature field parameters to obtain the effective ambient temperature, effective oil temperature, and effective gas temperature; High-precision digital temperature sensors were deployed at key points of the pneumatic-hydraulic power station to collect three sets of core temperature parameters in real time: ambient temperature, hydraulic system oil temperature, and cylinder drive gas temperature. To eliminate noise interference during data acquisition, the raw temperature data was preprocessed using a first-order moving average filter to obtain stable and effective temperature parameters.

[0072] For example, the formula for calculating the filter preprocessing is as follows: ; Explanation of each parameter in the formula: T i (n): The effective temperature value of the i-th type of temperature after filtering at time n, in °C; T 0i (j): The original temperature value collected by the sensor at time j for the i-th type of temperature, in °C; i: Temperature type identifier, i=1 is ambient temperature, i=2 is oil temperature, i=3 is gas temperature; k: Length of the sliding filter window, set to 5~10 according to the system response frequency; n: the current sampling time, j: the historical sampling time.

[0073] Step S2: Construct a multi-temperature field coupled static basic error model; Based on the temperature-precision error mechanism, the independent effects of ambient temperature, oil temperature, and gas temperature on cylinder positioning accuracy are quantified respectively. At the same time, multiple temperature coupling interference coefficients are introduced to construct a static basic error model with multiple temperature fields coupled, which replaces the traditional single-point static mapping model and accurately represents the original accuracy error under steady-state working conditions.

[0074] The formula for calculating the fundamental error of multi-temperature field coupling is as follows: ; In the formula: E0: Basic static error of multi-temperature field coupling, in μm, characterizing the original deviation of cylinder positioning; α1: Oil temperature error influence coefficient, obtained by calibration of oil viscosity-temperature characteristics, unit μm / ℃; α2: Gas temperature error influence coefficient, obtained by calibration of gas pressure-temperature characteristics, unit μm / ℃; α3: Environmental temperature structural deformation influence coefficient, obtained by thermal deformation test of equipment body, unit μm / ℃; T 10 T 20 T 30The system standard reference temperature refers to the rated operating temperature of the environment, oil, and gas, respectively, in °C. β: Coupling effect coefficient of multiple temperature fields, dimensionless, calibrated through orthogonal experiments; △T couple : Multiple temperature difference coupled variables, ΔT couple =|T2-T1|+|T3-T2|, representing the degree of temperature imbalance in the three fields, in °C.

[0075] This model breaks through the limitations of traditional single-temperature modeling, and simultaneously covers the independent influence of three temperature fields and the influence of coupled interference, thus achieving accurate quantification of steady-state operating condition errors.

[0076] Step S4: Establish a dynamic error correction model for high and low temperature differences; To address the different error generation mechanisms under low-temperature and high-temperature operating conditions, dynamic correction models are constructed to solve the problem that traditional models cannot adapt to operating conditions across the entire temperature range. The standard operating temperature range of the oil is used as a boundary to divide the operating conditions into low-temperature, normal-temperature, and high-temperature conditions, achieving differentiated correction for each condition.

[0077] Operating condition determination criteria: Low temperature operating condition: T2 <T L The oil viscosity surged, and motion resistance error became the dominant factor. High-temperature conditions: T2>T H Oil viscosity decreases, and leakage error becomes the dominant factor; Normal temperature operating conditions: T L ≤T2≤T H The error is mainly due to the basic coupling error, and no dynamic correction is required.

[0078] Wherein: T L Low temperature threshold, T H The high temperature threshold is calibrated based on the hydraulic oil type and equipment parameters.

[0079] S31: Dynamic correction model for low-temperature operating conditions; Under low-temperature conditions, the viscosity of the oil increases exponentially with decreasing temperature, leading to increased cylinder motion damping resistance and hysteresis error. A viscosity dynamic correction coefficient is introduced to construct the low-temperature error correction amount: ; Explanation of each parameter in the formula: E L Low-temperature resistance hysteresis correction error, in μm; η L Low-temperature viscosity reference error coefficient, in μm·s / mm, calibrated by low-temperature resistance test; λ L Low-temperature viscosity is a temperature-sensitive coefficient, dimensionless. v: Real-time cylinder movement speed, in mm / s; T2: Real-time oil temperature, in °C; e: natural constant.

[0080] S32: Dynamic correction model for high-temperature operating conditions; Under high-temperature conditions, oil dilution and leakage from sealing gaps increase linearly with increasing temperature, resulting in out-of-tolerance errors. A leakage correction coefficient is introduced to construct a high-temperature error correction amount: ; Explanation of each parameter in the formula: E H High-temperature leakage out-of-tolerance correction error, unit μm; η H High-temperature leakage error coefficient, in μm / MPa·℃, calibrated by high-temperature leakage test; P: Real-time working pressure of the hydraulic system, in MPa; T2: Real-time oil temperature, in °C.

[0081] Step S4: Iteratively calculate the real-time dynamic total compensation amount; By combining the basic coupling error and the dynamic correction error at high and low temperatures, the real-time total error of the system is calculated under different operating conditions. The accurate dynamic compensation amount is obtained through error closed-loop iteration, which completely solves the problems of lag and inaccurate compensation amount in traditional static compensation.

[0082] Formula for calculating total error and compensation: Total error under low temperature conditions: E total =E0+E L; Total error under high temperature conditions: E total =E0+E H Total error under normal temperature conditions: E total =E0 To eliminate the compensation lag problem, an incremental iterative correction algorithm is introduced to output the real-time dynamic compensation amount: ; Explanation of each parameter in the formula: △S(n): Real-time dynamic compensation at time n, in μm, used to correct cylinder stroke and pressure output; △S(n-1): Compensation amount at the previous moment, in μm; μ: Iterative convergence coefficient, dimensionless, ranging from 0.8 to 0.95, ensuring fast convergence of compensation without overshoot; Etota(n) and Etota(n-1): Total system error at the current and previous time steps, in μm.

[0083] Step S5: Adaptive control and optimization of the pneumatic-hydraulic station; The real-time dynamic compensation amount calculated in step S4 is input into the pneumatic hydraulic station controller, and differentiated control is performed according to the type of compensation amount: when the compensation amount is a positive error (insufficient stroke), the hydraulic output pressure is increased adaptively and the cylinder feed time is extended; when the compensation amount is a negative error (stroke exceeds tolerance), the system pressure is reduced adaptively and the braking is stopped in advance.

[0084] Through real-time iterative compensation, dynamic adaptive optimization of cylinder accuracy is achieved under alternating high and low temperatures and wide temperature range conditions, eliminating the accuracy drift problem caused by multi-field temperature coupling.

[0085] A computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of a high-precision cylinder pneumatic hydraulic station control strategy optimization system as described in the above embodiments.

[0086] The above formulas are all numerical calculations after removing dimensions. The formulas are obtained by software simulation based on a large amount of data and are closest to the real situation. The preset parameters and preset thresholds in the formulas are set by those skilled in the art according to the actual situation or obtained by simulation based on a large amount of data.

[0087] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A high-precision pneumatic hydraulic station control strategy optimization system, characterized in that, Including cloud platforms and application terminals; The cloud platform includes a storage repository, an application configuration module, and an optimization analysis module. The storage depot is used to store optimized control strategies applicable to various high-precision pneumatic hydraulic power units; The application configuration module is used to configure and optimize the application library for each application terminal and to dynamically update the optimized application library. The optimization analysis module is used to perform optimization feedback analysis, receive terminal optimization data sent by each application terminal in real time, and summarize various optimization problems and corresponding optimization methods based on the terminal optimization data. Optimization simulation analysis is performed on each optimization problem to obtain each target optimization problem, the target optimization method for each target optimization problem is determined, and the corresponding optimization control strategy is formed based on the target optimization method. The optimization control strategy is then sent to the storage repository for storage. The application terminal includes an optimization feedback module, a monitoring module, an optimization application library, and a control module; The optimization feedback module is used to feed back various terminal optimization data to the cloud platform; The monitoring module is used to perform real-time monitoring during the operation of the cylinder pneumatic hydraulic station and obtain operational monitoring data. The control module is used for control management, acquiring operation monitoring data, performing real-time adaptation analysis on the operation monitoring data based on the optimization application library, determining the applicable optimized control strategy, and controlling according to the optimized control strategy; when there is no suitable optimized control strategy, no control optimization adjustment is performed.

2. The high-precision pneumatic hydraulic station control strategy optimization system according to claim 1, characterized in that, The cloud platform communicates with each application terminal.

3. The high-precision pneumatic hydraulic station control strategy optimization system according to claim 1, characterized in that, Optimize application library configuration and updates, including: Obtain information about the pneumatic hydraulic station of the cylinder, match the information with the reserve in real time, and obtain various optimized control strategies. Configure the optimization application library in the application terminal and send the optimization control policy to the optimization application library for storage; Obtain the list of optimization control strategies stored in the optimization application library, calibrate the real-time matched optimization control strategies according to the list of optimization control strategies, obtain the optimization control strategies that need to be supplemented and updated in the optimization application library, and update the optimization application library according to the optimization control strategies.

4. The high-precision pneumatic hydraulic station control strategy optimization system according to claim 3, characterized in that, Real-time matching of cylinder pneumatic hydraulic station information with the reserve warehouse includes: Extract the optimization adaptation features of each optimized control strategy in the reserve; extract the corresponding cylinder pneumatic hydraulic station features and basic strategy features based on the cylinder pneumatic hydraulic station information; integrate the cylinder pneumatic hydraulic station features and basic strategy features into matching condition data; The matching condition data and the optimization adaptation features of each optimization control strategy are respectively combined into input data and input into a preset strategy matching model for analysis to obtain each successfully matched optimization control strategy.

5. The high-precision pneumatic hydraulic station control strategy optimization system according to claim 4, characterized in that, The expression for the policy matching model is: ； In the formula: (p, s i ) represents the input data, p represents the matching condition data, and s i The s represents the optimization adaptation characteristics of the corresponding optimized control strategy, where i represents the corresponding optimized control strategy, i = 1, 2, ..., n, and n is the number of optimized control strategies in the reserve; i →p represents the data where the corresponding optimized adaptation feature meets the matching condition; the output data is the policy matching value PU(p, s). i The strategy matching value is 1 or 0; When the strategy matching value is 1, the matching result is a successful match; When the strategy matching value is 0, the matching result is a failure.

6. The high-precision pneumatic hydraulic station control strategy optimization system according to claim 1, characterized in that, Optimization simulation analysis is performed on each optimization problem, including: A digital twin model of a high-precision pneumatic hydraulic station is established, and various simulation backgrounds are generated based on eliminating corresponding optimization problems. The simulation backgrounds are then simulated using the digital twin model to obtain optimized simulation data. Pre-set the evaluation items and weight coefficients of the cylinder pneumatic hydraulic station, obtain the basic control effect data of the cylinder pneumatic hydraulic station, evaluate the optimized simulation data and basic control effect data through each evaluation item, and obtain the single optimization ratio of the optimized simulation data relative to the basic control effect data in each evaluation item. The optimization simulation score is calculated based on the individual optimization ratio and weight coefficient of each evaluation item. Optimization problems whose optimization simulation scores are greater than the threshold X1 are marked as target optimization problems.

7. The high-precision pneumatic hydraulic station control strategy optimization system according to claim 6, characterized in that, The optimization simulation score is calculated based on the individual optimization ratio and weight coefficient of each evaluation item, including: ； In the formula: YP is the optimization simulation score, j represents the corresponding evaluation item, j=1, 2, ..., m, and m is the number of evaluation items; δ j DP represents the weighting coefficient of the corresponding evaluation item. j This indicates the individual optimization ratio of the corresponding evaluation item.

8. The high-precision pneumatic hydraulic station control strategy optimization system according to claim 6, characterized in that, Before performing optimization simulation analysis on each optimization problem, optimization problems that are required by objective laws should be eliminated.

9. The high-precision pneumatic hydraulic station control strategy optimization system according to claim 1, characterized in that, For target optimization problems without a target optimization method, the target optimization problem is marked as a problem to be optimized, and the associated simulation data of each problem to be optimized is obtained. The associated simulation data includes, but is not limited to, optimization simulation scores and optimization simulation data. The problems to be optimized and the associated simulation data are summarized and displayed according to a preset method.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of a high-precision cylinder pneumatic hydraulic station control strategy optimization system as described in any one of claims 1 to 9.

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