Predictive compensation method and system for hydraulic cylinder seal groove geometric machining error

Abstract

The present application provides a hydraulic cylinder sealing groove geometric processing error predictive compensation method and system, relates to the hydraulic equipment processing technical field, the method comprises the following steps: by acquiring the geometric parameter set of the target sealing groove in the hydraulic cylinder, simulation processing and error analysis are carried out, the geometric error prediction module is constructed, the real-time processing state parameters are combined for prediction, the geometric processing flow information is called, the predicted value is matched with the geometric processing flow information, the compensation feedback data is generated, and the error predictive compensation of the target sealing groove is updated multiple processing compensation instructions for geometric processing. The technical problems that the existing hydraulic cylinder sealing groove processing process is difficult to predict and accurately compensate the processing error in real time, which leads to the decline of the sealing performance of the hydraulic cylinder. Real-time accurate prediction and dynamic compensation of the processing error are realized, and the technical effects of improving the processing precision and sealing performance of the hydraulic cylinder sealing groove are achieved.

Description

Technical Field

[0001] This invention relates to the field of hydraulic equipment processing technology, specifically to a predictive compensation method and system for geometric machining errors of hydraulic cylinder sealing grooves. Background Technology

[0002] In the field of hydraulic cylinder manufacturing, the geometric accuracy of the sealing groove plays a crucial role in the sealing performance and service life of the hydraulic cylinder. In traditional machining methods, the machining accuracy of the sealing groove mainly relies on the machining accuracy of the machine tool and the experience of the operator. However, due to various uncertainties during the machining process, such as machine tool thermal deformation, tool wear, and changes in material properties, the actual geometric parameters of the machined sealing groove often deviate from the design values, leading to machining errors. This, in turn, affects the sealing performance of the hydraulic cylinder, resulting in leakage and failure of the hydraulic system.

[0003] There is a technical problem in the current hydraulic cylinder sealing groove machining process that makes it difficult to predict and accurately compensate for machining errors in real time, leading to a decline in the sealing performance of the hydraulic cylinder. Summary of the Invention

[0004] This application provides a predictive compensation method and system for geometric machining errors of hydraulic cylinder sealing grooves, which is used to solve the technical problem that it is difficult to predict and accurately compensate machining errors in real time during the existing hydraulic cylinder sealing groove machining process, resulting in a decrease in the sealing performance of the hydraulic cylinder.

[0005] In view of the above problems, this application provides a predictive compensation method and system for the geometric machining error of hydraulic cylinder sealing grooves.

[0006] The first aspect of this application provides a predictive compensation method for geometric machining errors of a hydraulic cylinder sealing groove. The method includes: acquiring a set of geometric parameters for a target sealing groove within the hydraulic cylinder; performing simulated machining of the target sealing groove based on the set of geometric parameters; obtaining simulation machining parameters and backtracking them to the set of geometric parameters for error analysis; constructing a geometric error prediction module; performing real-time acquisition of machining data based on the target sealing groove; using the geometric error prediction module in conjunction with real-time machining status parameters to predict and obtain a predicted value for the geometric machining error; retrieving geometric machining process information; matching the predicted value for the geometric machining error with the geometric machining process information to generate multiple machining compensation commands for error compensation; generating compensation feedback data; and updating the multiple machining compensation commands based on the compensation feedback data to perform predictive compensation for the geometric machining errors of the target sealing groove.

[0007] Optionally, a geometric parameter set of the target sealing groove inside the hydraulic cylinder is obtained, and the target sealing groove is simulated and machined according to the geometric parameter set. The simulation machining parameters are then backtracked to the geometric parameter set for error analysis, and a geometric error prediction module is constructed. The method includes: performing analysis based on the geometric parameter set, setting geometric sealing constraints, performing three-dimensional analysis according to the geometric sealing constraints, and constructing a three-dimensional digital model; configuring a virtual machining environment based on the three-dimensional digital model using digital twin analysis, and constructing three-dimensional digital twin environment parameters; performing multi-physics coupling simulation machining based on the three-dimensional digital twin environment parameters to obtain simulation machining parameters; backtracking the simulation machining parameters to the geometric parameter set for traversal identification, and drawing a simulation error distribution map; performing error source analysis based on the simulation error distribution map, and constructing the geometric error prediction module based on the error source analysis results.

[0008] Optionally, the simulation processing parameters are traced back to the set of geometric parameters for traversal identification to draw a simulation error distribution map. The method includes: traversing the simulation processing parameters and decomposing and labeling them according to the processing flow to obtain multi-stage processing labels; associating the multi-stage processing labels with the simulation processing parameters to construct a processing stage-geometric parameter association mapping table; traversing the simulation processing parameters back to the processing stage-geometric parameter association mapping table to calculate geometric deviations and obtain cumulative geometric parameter deviation values; traversing the cumulative geometric parameter deviation values ​​to identify abrupt changes and determine multiple deviation abrupt change nodes; and performing three-dimensional thermodynamic conversion on the multiple deviation abrupt change nodes to construct the simulation error distribution map.

[0009] Optionally, error source analysis is performed based on the simulation error distribution map, and the geometric error prediction module is constructed based on the error source analysis results. The method includes: retrieving historical processing record data of the target sealing groove, aligning the simulation processing parameters with the historical processing record data in time and space to determine matching process feature points; traversing the simulation error distribution map according to the matching process feature points to perform error evaluation and obtain error contribution; performing parameter sensitivity analysis based on the error contribution and constructing a geometric parameter sensitivity matrix; and performing error source analysis according to the geometric parameter sensitivity matrix to obtain error source analysis results.

[0010] Optionally, the real-time acquisition of machining data based on the target sealing groove includes: acquiring real-time machining initial state parameters based on the target sealing groove; performing time-frequency domain analysis on the real-time machining initial state parameters according to machining stability to extract time-frequency domain features; performing anti-interference processing on the real-time machining initial state parameters according to the time-frequency domain features to generate a process state feature vector; and updating the real-time machining initial state parameters according to the process state feature vector to determine the real-time machining state parameters.

[0011] Optionally, based on real-time data acquisition during the processing of the target sealing groove, the geometric error prediction module is used to predict the geometric processing error value in conjunction with real-time processing status parameters. The method includes: synchronizing the real-time processing status parameters to the geometric error prediction module for time-series inference to generate a geometric error time-series inference result; identifying key values ​​according to the geometric error time-series inference result to determine key set size deviation values; setting a confidence interval for the predicted deviation value; performing confidence analysis on the key set size deviation value according to the confidence interval to generate a deviation value confidence level; verifying the key set size deviation value based on the deviation value confidence level; and when the verification is successful, predicting based on the key set size deviation value to obtain the geometric processing error prediction value.

[0012] Optionally, the real-time processing status parameters are synchronized to the geometric error prediction module for time-series inference to generate a geometric error time-series inference result. The method includes: synchronizing the real-time processing status parameters to the geometric error prediction module for time-series inference: S1: Based on the real-time processing status parameters, perform processing cycle analysis to determine N processing step lengths, and extract processing feature vector sequences according to the N processing step lengths, where N is a positive integer greater than 1; S2: Construct a dual-channel network architecture, which includes a first channel and a second channel; S3: Continuously capture the processing feature vector sequence through the first channel to obtain multi-cycle process change parameters; S4: Perform abrupt change analysis on the multi-cycle process change parameters through the second channel to determine abnormal impact events; dynamically allocate weights based on the abnormal impact events to determine multiple influence weight coefficients; fuse the first channel and the second channel according to the multiple influence weight coefficients to generate the geometric error time-series inference result.

[0013] Optionally, geometric machining process information is retrieved, and multiple machining compensation instructions are generated by matching the predicted geometric machining error value with the geometric machining process information. The method includes: dynamically mapping the predicted geometric machining error value with the geometric machining process information to construct a process-error mapping relationship table; performing prediction deviation analysis based on the process-error mapping relationship table to determine the prediction deviation critical point; locating the target sealing groove according to the prediction deviation critical point to determine the machining process node to be compensated; performing multi-layer analysis based on the machining process node to be compensated to set a layered compensation instruction set, and adding the layered compensation instruction set to the multiple machining compensation instructions.

[0014] Optionally, the method for predictive compensation of geometric machining errors of the target sealing groove based on the compensation feedback data and updating the plurality of machining compensation commands includes: deconstructing the compensation feedback data, extracting key performance indicators, and constructing a three-dimensional performance initial matrix according to the key performance indicators; performing compensation quantization based on the three-dimensional performance initial matrix to determine the compensation correction efficiency; constructing a working condition feature fingerprint database of the target sealing groove, performing similarity calculation based on the working condition feature fingerprint database to obtain working condition similarity; dynamically adjusting the three-dimensional performance initial matrix according to the working condition similarity to obtain a three-dimensional performance matrix, wherein the three-dimensional performance matrix contains machining working condition features; associating the compensation correction efficiency with the machining working condition features, reprioritizing the plurality of machining compensation commands according to the association results, and constructing a compensation strategy decision tree; and performing predictive compensation of geometric machining errors of the target sealing groove based on the compensation strategy decision tree.

[0015] A second aspect of this application provides a predictive compensation system for geometric machining errors of hydraulic cylinder sealing grooves. The system includes: a simulation machining and error analysis module, used to acquire a set of geometric parameters of a target sealing groove within the hydraulic cylinder, perform simulated machining of the target sealing groove based on the set of geometric parameters, obtain simulation machining parameters, backtrack to the set of geometric parameters for error analysis, and construct a geometric error prediction module; a machining error prediction module, used to collect real-time machining data based on the target sealing groove, and predict the geometric machining error by combining the geometric error prediction module with real-time machining status parameters; a compensation instruction generation module, used to retrieve geometric machining process information, match the predicted geometric machining error with the geometric machining process information, generate multiple machining compensation instructions for error compensation, and generate compensation feedback data; and an error compensation module, used to update the multiple machining compensation instructions based on the compensation feedback data to perform predictive compensation for the geometric machining errors of the target sealing groove.

[0016] One or more technical solutions provided in this application have at least the following technical effects or advantages: The method provided in this application acquires a set of geometric parameters for a target sealing groove inside a hydraulic cylinder. Based on this set, the target sealing groove is simulated and machined. The simulated machining parameters are then back-analyzed using the geometric parameter set to perform error analysis, thus constructing a geometric error prediction module. Real-time machining data is collected based on the target sealing groove, and the geometric error prediction module, combined with real-time machining status parameters, is used to predict the geometric machining error. Geometric machining process information is retrieved, and the predicted geometric machining error is matched with the geometric machining process information to generate multiple machining compensation commands for error compensation, generating compensation feedback data. Based on the compensation feedback data, the multiple machining compensation commands are updated to perform predictive compensation for the geometric machining error of the target sealing groove. This method, based on simulated machining and real-time monitoring, achieves real-time accurate prediction and dynamic compensation of machining errors, improving the machining accuracy and sealing performance of the hydraulic cylinder sealing groove. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating the predictive compensation method for the geometric machining error of the hydraulic cylinder sealing groove provided in this application.

[0019] Figure 2 A schematic diagram of the structure of the predictive compensation system for the geometric machining error of the hydraulic cylinder seal groove provided in this application.

[0020] Figure labeling: Simulation machining and error analysis module 10, machining error prediction module 20, compensation instruction generation module 30, error compensation module 40. Detailed Implementation

[0021] This application provides a predictive compensation method and system for geometric machining errors in hydraulic cylinder sealing grooves. It addresses the technical problem of difficulty in real-time prediction and accurate compensation of machining errors during existing hydraulic cylinder sealing groove machining processes, leading to a decline in hydraulic cylinder sealing performance. Based on simulation machining and real-time monitoring, the predictive compensation for geometric machining errors achieves real-time accurate prediction and dynamic compensation of machining errors, thereby improving the machining accuracy and sealing performance of hydraulic cylinder sealing grooves.

[0022] The technical solutions of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. It should be understood that the present invention is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention. It should also be noted that, for ease of description, only the parts related to the present invention are shown in the accompanying drawings, not all of them.

[0023] Example 1, as Figure 1 As shown, this application provides a predictive compensation method for the geometric machining error of hydraulic cylinder seal grooves, which specifically includes the following steps: Obtain the geometric parameter set of the target sealing groove inside the hydraulic cylinder, perform simulation machining on the target sealing groove based on the geometric parameter set, obtain the simulation machining parameters and backtrack to the geometric parameter set for error analysis, and construct a geometric error prediction module.

[0024] Specifically, a hydraulic cylinder is an actuator in a hydraulic system that converts hydraulic energy into mechanical energy. A sealing groove is a specific structure inside the hydraulic cylinder used to install seals, such as sealing rings and gaskets. Its main function is to provide a stable mounting position for the seals, enabling them to withstand pressure and friction during cylinder operation and maintain sealing performance. Insufficient geometric precision in the sealing groove can lead to insecure seal installation or incomplete sealing, resulting in problems such as hydraulic oil leakage and reduced cylinder efficiency.

[0025] To achieve high-precision and high-efficiency geometric machining error control of hydraulic cylinder sealing grooves, it is first necessary to obtain the geometric parameter set of the target sealing groove inside the hydraulic cylinder. The geometric parameter set refers to the geometric characteristic parameters of the target sealing groove, including dimensions, shape, and position. This can be directly extracted from design files using design software such as SolidWorks and UG, or obtained by measuring the machined sealing groove using a coordinate measuring machine (CMM). For example, if the target sealing groove is a rectangular groove, its geometric parameter set might include a length of 50mm, a width of 10mm, and a depth of 5mm. Then, the actual machining process is simulated using a computer, performing machining operations according to the actual machining process and the geometric parameter set. After the simulation, a series of simulation machining parameters corresponding to the geometric parameter set are generated. The simulation machining parameters are then backtracked to the geometric parameter set for error analysis, comparing the simulation machining parameters with the original geometric parameter set to analyze whether the machined dimensions and shape meet the design requirements. For example, the comparison reveals that the length of the simulated rectangular groove is 50.2mm, the width is 10.1mm, and the depth is 4.9mm, which deviates from the design values. The error data is then processed and analyzed to construct a geometric error prediction model. This model is used to predict geometric errors that occur during the machining process and can provide reliable data for subsequent machining error compensation.

[0026] Furthermore, a geometric parameter set of the target sealing groove inside the hydraulic cylinder is obtained. The target sealing groove is then simulated and machined based on the geometric parameter set. The simulation machining parameters are then backtracked to the geometric parameter set for error analysis, and a geometric error prediction module is constructed. The method includes: analyzing the geometric parameter set, setting geometric sealing constraints, performing three-dimensional analysis according to the geometric sealing constraints, and constructing a three-dimensional digital model; configuring a virtual machining environment based on the three-dimensional digital model using digital twin analysis, and constructing three-dimensional digital twin environment parameters; performing multiphysics coupling simulation machining based on the three-dimensional digital twin environment parameters to obtain simulation machining parameters; backtracking the simulation machining parameters to the geometric parameter set for traversal identification, and drawing a simulation error distribution map; performing error source analysis based on the simulation error distribution map, and constructing the geometric error prediction module based on the error source analysis results.

[0027] Specifically, computer-aided design software, such as SolidWorks, UG, or AutoCAD, is used to analyze the geometric parameter set in detail, extracting key parameters such as the size, shape, and location of the sealing groove. For example, information such as the length, width, depth, and surface roughness requirements of the sealing groove are read from the design file. Then, based on the type of seal and sealing requirements, geometric sealing constraints are set according to industry standards. Geometric sealing constraints refer to the sealing-related requirements that the sealing groove must meet during design and manufacturing, such as dimensional tolerances, shape tolerances, and surface roughness, to ensure sealing performance. For example, for a rectangular sealing groove, its width tolerance is set to ±0.05mm, its depth tolerance to ±0.03mm, and its surface roughness to Ra1.6, to ensure that the seal can fit tightly and maintain good sealing performance during operation. A 3D analysis is performed based on geometric sealing constraints. A 3D digital model of the sealing groove is constructed using CAD software. Then, specialized digital twin software, such as Siemens NX and DELMIA, is used to load the constructed 3D digital model of the sealing groove and configure a virtual machining environment to generate 3D digital twin environment parameters. These parameters, such as machine tool parameters, cutting tool parameters, material properties, and machining process parameters, correspond to the actual machining conditions within the virtual machining environment. In the configured virtual machining environment, multiphysics coupling simulation machining is performed. Multiphysics coupling simulation machining considers the influence of multiple physical factors, such as heat, force, and fluid dynamics, on the machining process to more accurately simulate actual machining conditions. Specifically, the simulation considers multiple physical factors simultaneously, including mechanical stress (such as cutting force and tool-workpiece contact force), thermal effects (such as cutting heat and heat conduction), and fluid dynamics (such as coolant flow and chip removal). This allows the simulation machining parameters obtained in the virtual environment to match those obtained after geometric parameter-based simulation. The geometric parameter set describes the dimensions, shape, and location of the sealing groove during design, while the simulation machining parameters reflect the machining results under simulation. The parameters generated during simulation machining are compared and analyzed against the original design's geometric parameter set to assess the deviation between the machining results and design requirements. Data visualization tools are used to display the deviation data graphically, creating a simulation error distribution map. This map visually identifies the error distribution in different areas during machining. Finally, error source analysis is performed to identify the root causes of the errors.The tracing results are then combined with historical processing data to form a dataset, which is divided into a training set and a test set. Machine learning algorithms are used to train the training set and establish a mathematical model between the input parameters and the processing error. Taking linear regression as an example, the scikit-learn library of Python is used to load the data, extract features and target variables, create and train the geometric error prediction module, and use the test set to evaluate the model performance. When the model prediction performance reaches the preset standard, the constructed geometric error prediction module is obtained.

[0028] Furthermore, the simulation processing parameters are traced back to the set of geometric parameters for traversal identification, and a simulation error distribution map is drawn. The method includes: traversing the simulation processing parameters and decomposing and labeling them according to the processing flow to obtain multi-stage processing labels; associating the multi-stage processing labels with the simulation processing parameters to construct a processing stage-geometric parameter association mapping table; traversing the simulation processing parameters back to the processing stage-geometric parameter association mapping table to calculate geometric deviations and obtain cumulative geometric parameter deviation values; traversing the cumulative geometric parameter deviation values ​​to identify abrupt changes and determine multiple deviation abrupt change nodes; and performing three-dimensional thermodynamic conversion on the multiple deviation abrupt change nodes to construct the simulation error distribution map.

[0029] Specifically, the simulation machining parameters are traversed using machining process decomposition tools, such as the simulation log parsing module of Vericut or NXCAM, by time series or process node. These parameters are decomposed into multi-stage machining labels, each corresponding to a specific geometric feature of the machining process. These multi-stage machining labels describe the machining characteristics and state of each stage. Then, a matching algorithm based on time series or process nodes is used to associate the multi-stage machining labels with the simulation machining parameters, obtaining a machining stage-geometric parameter association mapping table. This table clearly shows the changes in geometric parameters for each machining stage. The machining simulation parameters are then returned to the machining stage-geometric parameter association mapping table. The single-stage geometric deviation is calculated according to the formula: deviation = |machining simulation parameter - geometric parameter|. Finally, a cumulative deviation algorithm is used, such as weighted summation or variance superposition of the deviations for each stage according to the machining sequence, to accumulate the single-point deviations of all stages step by step, ultimately generating a cumulative geometric parameter deviation value covering the entire machining process. Furthermore, by traversing the cumulative deviation values ​​of these geometric parameters, abrupt changes are identified to pinpoint nodes where significant changes in geometric parameter deviations occur during processing. These nodes indicate problems or areas requiring special attention during processing. Finally, these abrupt change nodes are subjected to three-dimensional thermodynamic transformation to construct a simulation error distribution map. Three-dimensional thermodynamic transformation visualizes deviation data in three-dimensional space as a heatmap, where color intensity represents the magnitude of the deviation. For example, the Matplotlib library in MATLAB or Python can be used to plot the simulation error distribution map. In the simulation error distribution map, darker areas represent larger deviations, and lighter areas represent smaller deviations. This method visually displays the spatial distribution of processing errors, thereby improving the accuracy of error analysis and optimization.

[0030] Furthermore, error source analysis is performed based on the simulation error distribution map, and the geometric error prediction module is constructed based on the error source analysis results. The method includes: retrieving historical processing record data of the target sealing groove, aligning the simulation processing parameters with the historical processing record data in time and space to determine matching process feature points; traversing the simulation error distribution map according to the matching process feature points to perform error evaluation and obtain error contribution; performing parameter sensitivity analysis based on the error contribution and constructing a geometric parameter sensitivity matrix; and performing error source analysis according to the geometric parameter sensitivity matrix to obtain error source analysis results.

[0031] Specifically, historical machining records of the target sealing groove are retrieved from a database or file containing the geometry of the hydraulic cylinder sealing groove. This data includes machining time, process nodes, target geometric parameters, and the actual geometric parameters after machining. Then, the simulated machining parameters are spatiotemporally aligned with the historical machining records. Spatiotemporal alignment means matching the simulated data and historical machining records according to time and process nodes, ensuring consistency in both. Using the spatiotemporally aligned data, matching process feature points are identified. These are record points in the simulated and historical data that share the same time and process node. Then, based on the matching process feature points, the simulation error distribution map is traversed, the error of each feature point is evaluated, and the error contribution of each feature point is calculated. The error contribution refers to the proportion of each feature point's contribution to the total error. Specifically, it is calculated by dividing the error value of the feature point by the sum of the error values ​​of all feature points. The error contribution can identify the degree of influence of multiple feature points on the overall error. Finally, the error contribution is sorted from largest to smallest, and parameter sensitivity analysis is performed to determine which parameters contribute more significantly to the error. The results of these sensitivity analyses were then compiled into a matrix, namely the geometric parameter sensitivity matrix. The rows of the matrix represent different machining parameter stages, and the columns represent the corresponding error contribution sensitivity values. This matrix visually demonstrates the sensitivity of each machining parameter to errors. Finally, by analyzing the sensitivity matrix, the key machining stages with the greatest impact on errors were identified. For example, if the matrix shows that the cutting force stage has the highest sensitivity value, it can be inferred that the cutting force is the main factor causing machining errors. Furthermore, combining actual machining scenarios and experience, the specific performance and changes of these key parameters during the machining process were analyzed. For example, it was checked whether the cutting force increased abnormally due to tool wear or improper cutting parameter settings, thereby obtaining error source tracing results. This provides a clear direction and basis for subsequent machining process optimization and error compensation, improving the sealing performance of hydraulic cylinders.

[0032] Based on the real-time acquisition of data during the processing of the target sealing groove, the geometric error prediction module is used to predict the geometric processing error value in combination with the real-time processing status parameters.

[0033] Furthermore, the method for real-time acquisition of machining data based on the target sealing groove includes: acquiring real-time machining initial state parameters based on the target sealing groove; performing time-frequency domain analysis on the real-time machining initial state parameters according to machining stability to extract time-frequency domain features; performing anti-interference processing on the real-time machining initial state parameters according to the time-frequency domain features to generate a process state feature vector; and updating the real-time machining initial state parameters according to the process state feature vector to determine the real-time machining state parameters.

[0034] Specifically, high-precision sensor arrays, such as triaxial force sensors, laser displacement sensors, and acoustic emission sensors, are used to acquire real-time data on the machining process of the target sealing groove, obtaining real-time initial machining state parameters. These parameters include, but are not limited to, basic information such as tool position, cutting force, and vibration frequency. Subsequently, considering the temporal characteristics of these initial machining state parameters, a joint time-frequency domain analysis method is employed: the time-domain signal is converted into a time-frequency spectrum using Short-Time Fourier Transform (STFT), extracting time-frequency domain features. These features include both time-domain and frequency-domain characteristics. Time-domain features include mean, variance, and peak factor, while frequency-domain features include dominant frequency energy and bandwidth entropy. The STFT reveals the frequency distribution of the signal at different time points. Then, based on these time-frequency domain features, signal processing algorithms, such as wavelet transform or adaptive filters, are used to perform anti-interference processing on the real-time initial machining state parameters, effectively removing noise and abnormal data while retaining signal components that reflect the essential characteristics of the machining process, thereby generating a process state feature vector. For example, wavelet transform can decompose the signal into different frequency components, removing high-frequency noise components and retaining effective low-frequency signals. Finally, based on the process state feature vector, a weighted fusion algorithm, such as the entropy weight method, is used to calculate the weight of each feature and dynamically update the real-time processing initial state parameters. Parameters that are significantly disturbed are smoothed according to the stability index in the feature vector, while parameters that reflect the true processing state are kept at their original values. The updated real-time processing state parameters retain the physical meaning of the original data and eliminate the influence of environmental and system noise, which can more realistically reflect the actual state of the current processing process, thereby achieving accurate prediction and compensation of the geometric processing error of the hydraulic cylinder sealing groove.

[0035] Furthermore, based on real-time data acquisition during the processing of the target sealing groove, the geometric error prediction module, combined with real-time processing status parameters, is used to predict the geometric processing error. The method includes: synchronizing the real-time processing status parameters to the geometric error prediction module for time-series inference, generating a geometric error time-series inference result; identifying key values ​​according to the geometric error time-series inference result to determine key set size deviation values; setting a confidence interval for the predicted deviation value, performing confidence analysis on the key set size deviation value according to the confidence interval, generating a deviation value confidence level; verifying the key set size deviation value based on the deviation value confidence level, and when the verification passes, predicting based on the key set size deviation value to obtain the geometric processing error prediction value.

[0036] Specifically, real-time machining status parameters are synchronously input to a pre-trained geometric error prediction module via industrial Ethernet. This module performs temporal inference based on the time-series data of the real-time machining status parameters, generating temporal inference results for the geometric error. Temporal inference refers to the process of reasoning and predicting based on time-series data. Then, for key feature points in the temporal inference results, a sliding window peak detection algorithm is used to identify key values. The key parameter values ​​or dimensional deviation values ​​with the greatest impact on the error are identified from the temporal inference results, forming a key set of dimensional deviation values. Confidence intervals for the predicted deviation values ​​are set. Confidence intervals are a statistical concept used to represent the reliability of the predicted values ​​and can be determined based on the model's prediction error distribution. For example, if the standard deviation of the model's prediction error is 0.02 mm, a 95% confidence interval can be set to ±0.04 mm. Confidence analysis is performed on the key set of dimensional deviation values ​​according to the confidence intervals to generate deviation value confidence scores. Deviation value confidence scores refer to the probability that the key set of dimensional deviation values ​​fall within the confidence interval; a higher confidence score indicates a more reliable prediction result. If the critical deviation value falls within the confidence interval, the prediction result is considered reliable with a high confidence level. Finally, the critical set dimension deviation value is verified based on the deviation value's confidence level. If the verification passes (i.e., the deviation value is within the confidence interval and the confidence level meets the requirements), a prediction is made based on the critical set dimension deviation value to obtain the predicted geometric machining error value. For example, if the critical deviation value is 0.03 mm and the confidence level is 95%, the predicted geometric error under the current machining state is 0.03 mm. In this way, real-time and accurate prediction of geometric errors during machining can be achieved, thereby improving the reliability and accuracy of error compensation and machining optimization.

[0037] Furthermore, the real-time processing status parameters are synchronized to the geometric error prediction module for time-series inference to generate a geometric error time-series inference result. The method includes: synchronizing the real-time processing status parameters to the geometric error prediction module for time-series inference: S1: Based on the real-time processing status parameters, perform processing cycle analysis to determine N processing steps, and extract processing feature vector sequences according to the N processing steps, where N is a positive integer greater than 1; S2: Construct a dual-channel network architecture, which includes a first channel and a second channel; S3: Continuously capture the processing feature vector sequence through the first channel to obtain multi-cycle process change parameters; S4: Perform abrupt change analysis on the multi-cycle process change parameters through the second channel to determine abnormal impact events; dynamically allocate weights based on the abnormal impact events to determine multiple influence weight coefficients; fuse the first channel and the second channel according to the multiple influence weight coefficients to generate the geometric error time-series inference result.

[0038] Specifically, real-time processing status parameters are synchronized to the geometric error prediction module for time-series inference. First, based on the real-time processing status parameters, time-series analysis methods, such as Fourier transform or wavelet transform, are used to analyze the time-series data of the processing process, identify the periodic characteristics of the processing process, and determine N processing step lengths. A processing step length refers to the time length of each stage in the processing cycle. For example, a processing cycle may be divided into N step lengths, each corresponding to a specific processing stage. Then, processing feature vector sequences containing both time-domain and frequency-domain features are extracted according to these step lengths. Each vector contains the key processing parameters within that step length. A dual-channel network architecture is constructed, consisting of a first channel and a second channel operating in parallel. The first channel is a temporal continuity capture channel, employing a bidirectional long short-term memory network. Its bidirectional structure can simultaneously capture the forward dependencies of the machining feature vector sequence, such as the temperature rise effect of the cutting force accumulating with cutting time, and the backward feedback, such as the influence of vibration on the adjustment of subsequent cutting parameters. Through the gating mechanism between hidden layer nodes, the input gate, forget gate, and output gate dynamically filter key temporal features and output multi-cycle process change parameters, which reflect the gradual evolution of the machining state. The second channel is an abnormal event detection channel, employing a hybrid structure of convolutional neural network and attention mechanism. The convolutional neural network extracts local time-frequency features through different convolutional kernels, while the attention mechanism assigns weights to the output feature map of the convolutional neural network. Finally, abnormal impact events are identified through a fully connected layer. In the dual-channel feature fusion stage, dynamic weight allocation is performed based on the abnormal impact events detected by the second channel. A dynamic weight calculation algorithm maps the severity and frequency of abnormal events to influence weight coefficients. These coefficients, normalized using the Softmax function, are applied to the multi-cycle process variation parameters output by the first channel and the abnormal feature vector output by the second channel, respectively. Weighted summation achieves dual-channel fusion, generating a geometric error temporal inference result that considers both the continuity and abrupt change sensitivity of the fused temporal sequence. This geometric error temporal inference result not only includes normal changes in the processing but also considers the impact of abnormal events, thus providing more accurate information for subsequent error prediction and compensation.

[0039] Retrieve geometric machining process information, match the predicted geometric machining error value with the geometric machining process information, generate multiple machining compensation commands to perform error compensation, and generate compensation feedback data.

[0040] Furthermore, the geometric machining process information is retrieved, and the predicted geometric machining error value is matched with the geometric machining process information to generate multiple machining compensation instructions. The method includes: dynamically mapping the predicted geometric machining error value with the geometric machining process information to construct a process-error mapping relationship table; performing prediction deviation analysis based on the process-error mapping relationship table to determine the prediction deviation critical point; locating the target sealing groove according to the prediction deviation critical point to determine the machining process node to be compensated; performing multi-layer analysis based on the machining process node to be compensated to set a layered compensation instruction set, and adding the layered compensation instruction set to the multiple machining compensation instructions.

[0041] Specifically, the geometric machining process information of the target sealing groove is first retrieved from the machining process document or database. This information includes the complete process chain of the target sealing groove, such as rough milling, semi-finish milling, finish milling, and honing, as well as the name, machining parameters, and machining sequence of each process. The predicted geometric machining error is then dynamically mapped to the geometric machining process information: through a process feature association algorithm, the geometric elements corresponding to each process are extracted, and a process-error mapping relationship table is constructed according to the process-element dimension. This table uses the process number as the row index and the geometric element as the column index. The cell content represents the predicted error value and its direction; positive deviation indicates exceeding the standard, and negative deviation indicates insufficient error. This is used to establish a quantitative correlation between error and machining processes.

[0042] Predictive error analysis is performed based on the process-error mapping table: A dynamic threshold judgment method is used, combining the tolerance zone defined in the process specification with actual requirements, to set the machining error range and calculate whether the predicted error value of each process exceeds this range. If it does, the process is considered a predicted error critical point. Then, the target sealing groove is located according to the predicted error critical point to determine the machining process node to be compensated. For example, the predicted error value of rough milling is 0.05mm, exceeding the allowable range; therefore, the rough milling process is considered a critical point and belongs to the node to be compensated. Multi-layer analysis is performed based on the machining process node to be compensated, considering factors such as error magnitude, machining sequence, and compensation feasibility. The causes of error are analyzed from three dimensions: equipment layer (e.g., machine tool rigidity, spindle thermal deformation), process layer (e.g., cutting parameter optimization), and control layer (e.g., servo system response delay). A layered compensation instruction set is then set. For example, for tool wear error in semi-rough milling, the equipment layer compensation instruction is to replace the tool; the process layer compensation instruction is to reduce the cutting speed by 10% to reduce cutting heat; and the control layer compensation instruction is to enable the tool radius dynamic compensation function with a compensation value of +0.005mm. These compensation instruction sets are then added to multiple machining compensation instructions to form complete compensation feedback data. By matching the predicted geometric machining error value with the geometric machining process information and generating multiple machining compensation instructions, the process from error prediction to compensation execution response is realized, thereby improving the machining accuracy and efficiency of hydraulic cylinder seal grooves.

[0043] Based on the compensation feedback data, the multiple processing compensation commands are updated to perform error prediction compensation for the geometric processing of the target sealing groove.

[0044] Furthermore, the method for predictive compensation of geometric machining errors of the target sealing groove based on the compensation feedback data and updating the multiple machining compensation commands includes: deconstructing the compensation feedback data, extracting key performance indicators, and constructing a three-dimensional performance initial matrix according to the key performance indicators; performing compensation quantization based on the three-dimensional performance initial matrix to determine the compensation correction efficiency; constructing a working condition feature fingerprint database of the target sealing groove, performing similarity calculation based on the working condition feature fingerprint database to obtain working condition similarity; dynamically adjusting the three-dimensional performance initial matrix according to the working condition similarity to obtain a three-dimensional performance matrix, wherein the three-dimensional performance matrix contains machining working condition features; associating the compensation correction efficiency with the machining working condition features, repricing the multiple machining compensation commands according to the association results, and constructing a compensation strategy decision tree; and performing predictive compensation of geometric machining errors of the target sealing groove based on the compensation strategy decision tree.

[0045] Specifically, data processing tools are used to deconstruct and filter the compensation feedback data, extracting key performance indicators (KPIs). These KPIs are important indicators extracted from the compensation feedback data that reflect the compensation effect, including post-compensation accuracy, stability, and response time. A three-dimensional initial performance matrix is ​​constructed based on these KPIs. This matrix records and analyzes the compensation effect from multiple dimensions, such as accuracy, stability, and response time. Based on the three-dimensional initial performance matrix, a weighted comprehensive evaluation method is used to linearly weight and sum the key performance indicators such as compensation accuracy, stability, and response time according to preset process weights, such as accuracy 0.6, stability 0.3, and response time 0.1, to calculate the compensation correction efficiency, i.e., the actual contribution of the compensation measures to error correction. Simultaneously, a working condition feature fingerprint database of the target sealing groove is constructed. This database contains feature data of the sealing groove under different processing conditions, including processing parameters, error values, and compensation measures. Similarity calculation methods, such as Euclidean distance and cosine similarity, are used to compare the current processing condition with data in the condition feature fingerprint database to obtain the condition similarity, thereby assessing the similarity between the current condition and other known conditions. Based on the condition similarity, the initial three-dimensional performance matrix is ​​dynamically adjusted to generate a three-dimensional performance matrix that incorporates processing condition features. This matrix integrates compensation and correction efficiency with condition features; for example, if the current condition has a high similarity to a condition in the database, the compensation and correction efficiency of that condition can be adjusted into the three-dimensional performance matrix. Then, the compensation and correction efficiency is correlated with the processing condition features, and multiple processing compensation instructions are prioritized based on this correlation result to construct a compensation strategy decision tree. Specifically, by analyzing the correlation strength between compensation and correction efficiency and processing condition features, such as using grey relational analysis to calculate feature weights, and using the correlation strength as the decision tree splitting criterion, compensation instructions are hierarchically sorted according to condition features, ultimately generating a tree-like decision model with condition features as nodes and compensation instruction priorities as branches. The compensation strategy decision tree can intelligently select the optimal compensation strategy based on different condition features and correction efficiencies. Finally, error prediction compensation for the geometric machining of the target sealing groove is performed based on the compensation strategy decision tree. By implementing the optimized compensation strategy, the machining accuracy is improved, ensuring that the geometric machining error of the sealing groove is effectively controlled and compensated, thereby improving the sealing performance and reliability of the hydraulic cylinder and reducing the risk of hydraulic cylinder leakage caused by machining errors.

[0046] Example 2, based on the same inventive concept as the predictive compensation method for the geometric machining error of the hydraulic cylinder seal groove in the foregoing examples, such as... Figure 2 As shown, this application provides a predictive compensation system for the geometric machining error of hydraulic cylinder seal grooves, the system comprising: The simulation machining and error analysis module 10 is used to acquire the geometric parameter set of the target sealing groove inside the hydraulic cylinder, perform simulation machining on the target sealing groove based on the geometric parameter set, obtain simulation machining parameters, backtrack to the geometric parameter set for error analysis, and construct a geometric error prediction module. The machining error prediction module 20 is used to collect real-time machining data based on the target sealing groove, and predict the geometric machining error by combining the geometric error prediction module with real-time machining status parameters. The compensation instruction generation module 30 is used to retrieve geometric machining process information, match the geometric machining error prediction value with the geometric machining process information, generate multiple machining compensation instructions for error compensation, and generate compensation feedback data. The error compensation module 40 is used to update the multiple machining compensation instructions based on the compensation feedback data to perform predictive compensation for the geometric machining error of the target sealing groove.

[0047] Furthermore, the simulation processing and error analysis module 10 is also used to perform the following: based on the geometric parameter set, perform analysis, set geometric sealing constraints, perform three-dimensional analysis according to the geometric sealing constraints, and construct a three-dimensional digital model; perform digital twin analysis based on the three-dimensional digital model to configure a virtual processing environment and construct three-dimensional digital twin environment parameters; perform multi-physics coupling simulation processing based on the three-dimensional digital twin environment parameters to obtain simulation processing parameters; trace the simulation processing parameters back to the geometric parameter set for traversal identification and draw a simulation error distribution map; perform error source analysis based on the simulation error distribution map, and construct the geometric error prediction module based on the error source analysis results.

[0048] Furthermore, the simulation processing and error analysis module 10 is also used to perform the following: traversing the simulation processing parameters and decomposing and labeling them according to the processing flow to obtain multi-stage processing labels; associating the multi-stage processing labels with the simulation processing parameters to construct a processing stage-geometric parameter association mapping table; backtracking the simulation processing parameters to the processing stage-geometric parameter association mapping table to calculate geometric deviations and obtain cumulative geometric parameter deviation values; traversing the cumulative geometric parameter deviation values ​​to identify abrupt changes and determine multiple deviation abrupt change nodes; and performing three-dimensional thermodynamic conversion on the multiple deviation abrupt change nodes to construct the simulation error distribution map.

[0049] Furthermore, the simulation processing and error analysis module 10 is also used to perform the following: retrieve historical processing record data of the target sealing groove, align the simulation processing parameters with the historical processing record data in time and space, and determine matching process feature points; traverse the simulation error distribution map according to the matching process feature points to perform error evaluation and obtain error contribution; perform parameter sensitivity analysis based on the error contribution and construct a geometric parameter sensitivity matrix; and perform error source tracing analysis according to the geometric parameter sensitivity matrix to obtain error source tracing results.

[0050] Furthermore, the machining error prediction module 20 is also used to perform: real-time acquisition of machining based on the target sealing groove to obtain real-time machining initial state parameters; time-frequency domain analysis based on the real-time machining initial state parameters according to machining stability to extract time-frequency domain features; anti-interference processing of the real-time machining initial state parameters according to the time-frequency domain features to generate a process state feature vector; and updating the real-time machining initial state parameters according to the process state feature vector to determine the real-time machining state parameters.

[0051] Furthermore, the machining error prediction module 20 is also used to perform the following: synchronizing the real-time machining status parameters to the geometric error prediction module for temporal inference, generating a geometric error temporal inference result; identifying key values ​​according to the geometric error temporal inference result, determining key set size deviation values; setting a confidence interval for the predicted deviation value, performing confidence analysis on the key set size deviation value according to the confidence interval, generating a deviation value confidence level; verifying the key set size deviation value based on the deviation value confidence level, and when the verification is successful, predicting based on the key set size deviation value to obtain the geometric machining error prediction value.

[0052] Furthermore, the machining error prediction module 20 is also used to perform: synchronizing the real-time machining status parameters to the geometric error prediction module for time-series inference: S1: performing machining cycle analysis based on the real-time machining status parameters, determining N machining step lengths, and extracting machining feature vector sequences according to the N machining step lengths, where N is a positive integer greater than 1; S2: constructing a dual-channel network architecture, the dual-channel network architecture including a first channel and a second channel; S3: continuously capturing the machining feature vector sequence through the first channel to obtain multi-cycle process change parameters; S4: performing abrupt change analysis on the multi-cycle process change parameters through the second channel to determine abnormal impact events; dynamically allocating weights based on the abnormal impact events to determine multiple influence weight coefficients; and fusing the first channel and the second channel according to the multiple influence weight coefficients to generate the geometric error time-series inference result.

[0053] Furthermore, the compensation instruction generation module 30 is also used to perform: retrieving geometric machining process information, matching the predicted geometric machining error value with the geometric machining process information, and generating multiple machining compensation instructions. The method includes: dynamically mapping the predicted geometric machining error value with the geometric machining process information to construct a process-error mapping relationship table; performing prediction deviation analysis based on the process-error mapping relationship table to determine the prediction deviation critical point; locating the target sealing groove according to the prediction deviation critical point to determine the machining process node to be compensated; performing multi-layer analysis based on the machining process node to be compensated, setting a layered compensation instruction set, and adding the layered compensation instruction set to the multiple machining compensation instructions.

[0054] Furthermore, the error compensation module 40 is also used to perform the following: deconstruct the compensation feedback data, extract key performance indicators, and construct a three-dimensional performance initial matrix according to the key performance indicators; perform compensation quantization based on the three-dimensional performance initial matrix to determine the compensation correction efficiency; construct a working condition feature fingerprint database of the target sealing groove, perform similarity calculation based on the working condition feature fingerprint database to obtain working condition similarity; dynamically adjust the three-dimensional performance initial matrix according to the working condition similarity to obtain a three-dimensional performance matrix, the three-dimensional performance matrix containing processing working condition features; associate the compensation correction efficiency with the processing working condition features, reorder the priority of the multiple processing compensation instructions according to the association result, and construct a compensation strategy decision tree; and perform error predictive compensation for geometric processing of the target sealing groove based on the compensation strategy decision tree.

[0055] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0056] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A predictive compensation method for geometric machining errors of hydraulic cylinder sealing grooves, characterized in that, The method includes: Obtain the geometric parameter set of the target sealing groove inside the hydraulic cylinder, perform simulation processing on the target sealing groove based on the geometric parameter set, obtain the simulation processing parameters and backtrack to the geometric parameter set for error analysis, and construct a geometric error prediction module; Based on the real-time acquisition of the target sealing groove, the geometric error prediction module is combined with the real-time processing status parameters to predict the geometric processing error value. Retrieve geometric machining process information, match the predicted geometric machining error value with the geometric machining process information, generate multiple machining compensation instructions for error compensation, and generate compensation feedback data; Based on the compensation feedback data, the multiple processing compensation commands are updated to perform error prediction compensation for the geometric processing of the target sealing groove.

2. The predictive compensation method for geometric machining errors of hydraulic cylinder sealing grooves as described in claim 1, characterized in that, Obtain the geometric parameter set of the target sealing groove inside the hydraulic cylinder, perform simulation machining on the target sealing groove based on the geometric parameter set, obtain the simulation machining parameters and backtrack to the geometric parameter set for error analysis, and construct a geometric error prediction module. The method includes: Based on the set of geometric parameters, the geometric sealing constraints are set, and a three-dimensional analysis is performed according to the geometric sealing constraints to construct a three-dimensional digital model. Based on the three-dimensional digital model, perform digital twin analysis to configure the virtual processing environment and construct the three-dimensional digital twin environment parameters; Based on the three-dimensional digital twin environment parameters, perform multiphysics coupling simulation processing to obtain simulation processing parameters; The simulation processing parameters are traced back to the set of geometric parameters for traversal identification, and a simulation error distribution map is plotted. Error source analysis is performed based on the simulated error distribution map, and the geometric error prediction module is constructed based on the error source analysis results.

3. The predictive compensation method for geometric machining errors of hydraulic cylinder sealing grooves as described in claim 2, characterized in that, The method involves traversing and identifying the simulation processing parameters back to the set of geometric parameters, and then plotting the simulation error distribution map. The simulated processing parameters are traversed and decomposed and labeled according to the processing flow to obtain multi-stage processing labels; The multi-stage processing labels are associated with the simulation processing parameters to construct a processing stage-geometric parameter association mapping table; The simulated machining parameters are traced back to the machining stage-geometric parameter association mapping table to calculate the geometric deviation and obtain the cumulative geometric parameter deviation value. By iterating through the cumulative deviation values ​​of the geometric parameters, abrupt changes are identified, and multiple deviation abrupt change nodes are determined. The multiple deviation mutation nodes are subjected to three-dimensional thermodynamic conversion to construct the simulation error distribution map.

4. The predictive compensation method for geometric machining errors of hydraulic cylinder sealing grooves as described in claim 2, characterized in that, Based on the simulated error distribution map, error source analysis is performed, and based on the error source analysis results, the geometric error prediction module is constructed. The method includes: Retrieve historical processing record data of the target sealing groove, and align the simulation processing parameters with the historical processing record data in time and space to determine matching process feature points; Error assessment is performed by traversing the simulation error distribution map according to the matching process feature points to obtain the error contribution. Based on the error contribution, parameter sensitivity analysis is performed to construct a geometric parameter sensitivity matrix; Error source analysis was performed based on the aforementioned geometric parameter sensitivity matrix to obtain the error source analysis results.

5. The predictive compensation method for geometric machining errors of hydraulic cylinder sealing grooves as described in claim 1, characterized in that, Real-time data acquisition based on the target sealing groove processing method includes: Real-time data acquisition is performed based on the target sealing groove to obtain real-time initial processing state parameters; Based on the real-time initial state parameters of the machining, time-frequency domain analysis is performed according to the machining stability, and time-frequency domain features are extracted. The real-time processing initial state parameters are subjected to anti-interference processing according to the time-frequency domain characteristics to generate a process state feature vector. The real-time processing initial state parameters are updated based on the process state feature vector to determine the real-time processing state parameters.

6. The predictive compensation method for geometric machining errors of hydraulic cylinder sealing grooves as described in claim 1, characterized in that, Based on real-time data acquisition during the processing of the target sealing groove, the geometric error prediction module is used to predict the geometric processing error value in conjunction with real-time processing status parameters. The method includes: The real-time processing status parameters are synchronized to the geometric error prediction module for time-series inference, generating geometric error time-series inference results. Based on the geometric error time-series reasoning results, key values ​​are identified, and key set size deviation values ​​are determined. Set a confidence interval for the predicted deviation value, perform a confidence analysis on the key set size deviation value according to the confidence interval, and generate the confidence level of the deviation value; The deviation value of the key set size is verified based on the confidence level of the deviation value. When the verification is successful, the prediction value of the geometric processing error is obtained based on the deviation value of the key set size.

7. The predictive compensation method for geometric machining errors of hydraulic cylinder sealing grooves as described in claim 6, characterized in that, The method of synchronizing the real-time processing status parameters to the geometric error prediction module for timing inference and generating geometric error timing inference results includes: The real-time machining status parameters are synchronized to the geometric error prediction module for timing inference. S1: Based on the real-time processing status parameters, perform processing cycle analysis to determine N processing steps, and extract processing feature vector sequences according to the N processing steps, where N is a positive integer greater than 1; S2: Construct a dual-channel network architecture, which includes a first channel and a second channel; S3: Continuously capture the processing feature vector sequence through the first channel to obtain multi-cycle process change parameters; S4: Perform abrupt change analysis on the multi-cycle process variation parameters through the second channel to determine abnormal impact events; Based on the aforementioned abnormal impact event, dynamic weight allocation is performed to determine multiple influencing weight coefficients; The first channel and the second channel are fused together according to the multiple influence weighting coefficients to generate the geometric error time-series inference result.

8. The predictive compensation method for geometric machining errors of hydraulic cylinder sealing grooves as described in claim 1, characterized in that, Retrieving geometric machining process information, matching the predicted geometric machining error value with the geometric machining process information, and generating multiple machining compensation commands, the method includes: The predicted values ​​of geometric machining errors are dynamically mapped to the geometric machining process information to construct a process-error mapping relationship table; Based on the process-error mapping table, predictive error analysis is performed to determine the predictive error critical point. The target sealing groove is located according to the predicted out-of-tolerance critical point, and the processing node to be compensated is determined. Based on the processing node to be compensated, a multi-layer analysis is performed, a layered compensation instruction set is set, and the layered compensation instruction set is added to the multiple processing compensation instructions.

9. The predictive compensation method for geometric machining errors of hydraulic cylinder sealing grooves as described in claim 1, characterized in that, The method for predictive compensation of geometric machining errors of the target sealing groove based on the compensation feedback data and updating the multiple machining compensation commands includes: The compensation feedback data is deconstructed to extract key performance indicators, and a three-dimensional performance initial matrix is ​​constructed according to the key performance indicators. Compensation quantization is performed based on the aforementioned three-dimensional performance initial matrix to determine the compensation correction efficiency; Construct a working condition feature fingerprint database of the target sealing trench, and perform similarity calculation based on the working condition feature fingerprint database to obtain the working condition similarity. The initial three-dimensional performance matrix is ​​dynamically adjusted based on the similarity of the working conditions to obtain a three-dimensional performance matrix, which includes processing working condition features. The compensation and correction efficiency is correlated with the processing condition characteristics, and the multiple processing compensation instructions are reordered according to the correlation results to construct a compensation strategy decision tree. Error prediction compensation for geometric processing of the target sealing groove based on the compensation strategy decision tree.

10. A predictive compensation system for geometric machining errors of hydraulic cylinder sealing grooves, characterized in that, The steps for implementing the method according to any one of claims 1 to 9 include: The simulation machining and error analysis module is used to obtain the geometric parameter set of the target sealing groove inside the hydraulic cylinder, perform simulation machining on the target sealing groove according to the geometric parameter set, obtain the simulation machining parameters and backtrack to the geometric parameter set for error analysis, and construct a geometric error prediction module. The machining error prediction module is used to collect real-time machining data based on the target sealing groove. The geometric error prediction module is combined with real-time machining status parameters to predict the geometric machining error value. The compensation instruction generation module is used to retrieve geometric machining process information, match the geometric machining error prediction value with the geometric machining process information, generate multiple machining compensation instructions for error compensation, and generate compensation feedback data. An error compensation module is used to predictively compensate for errors in the geometric machining of the target sealing groove by updating the plurality of machining compensation instructions based on the compensation feedback data.

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