Low-energy-consumption dynamic synchronous driving efficient indexing chuck control method

Abstract

The invention relates to the technical field of index chuck control, in particular to a low-energy-consumption dynamic synchronous driving efficient index chuck control method which comprises the following steps: collecting initial information, setting driving parameters, and initializing the index precision of a control system; chuck operation state data are collected through a sensor, and the load change trend is predicted in combination with an intelligent AI model; according to the prediction result, a driving signal is generated through a multi-axis synchronous driving algorithm, a plurality of driving units are controlled to operate in a coordinated mode, and the position, speed and torque distribution are dynamically adjusted; and meanwhile, driving parameters are dynamically optimized in combination with real-time energy consumption and indexing task requirements. The system energy consumption can be effectively reduced, the indexing precision and efficiency are improved, and the system is suitable for various industrial machining scenes.

Description

Technical Field

[0001] The present invention relates to the technical field of indexing chuck control, and particularly to an efficient indexing chuck control method for low-energy consumption dynamic synchronous drive. Background Art

[0002] As an important workpiece clamping and positioning device, the indexing chuck is widely used in precision machining fields such as numerical control machine tools and industrial automation devices. The traditional drive control method of the indexing chuck usually adopts fixed drive parameters and a simple open-loop control strategy, which is difficult to adapt to the dynamic load changes under complex working conditions. This method is prone to problems such as reduced control accuracy, increased energy consumption, and unstable equipment operation when the load is unbalanced or the working conditions change frequently. With the rapid development of industrial intelligence, multi-axis synchronous drive technology has gradually been introduced into the control system of the indexing chuck. However, the existing multi-axis synchronous drive technology is mainly designed for constant load conditions, and its ability to generate drive signals and adjust parameters is still insufficient for scenarios with large dynamic load changes. In addition, traditional drive control methods often ignore the optimal matching between real-time energy consumption and task requirements, and cannot achieve low-energy consumption operation while ensuring indexing accuracy, resulting in low operating efficiency of industrial equipment.

[0003] For example, the Chinese patent with the authorization announcement number CN108481086B discloses an indexing chuck, which includes a chuck body. An indexing shaft is rotatably arranged in the chuck body. A indexing block is arranged at the central position of one end of the indexing shaft. A first ejector rod and a second ejector rod are arranged on both sides of the indexing block. The first ejector rod and the second ejector rod are arranged in the chuck body so as to be movable up and down. The upper ends of the first ejector rod and the second ejector rod are close to each other, and the lower ends of the first ejector rod and the second ejector rod are far from each other. The feature is that a first indexing mating surface and a second indexing mating surface are arranged on the side of the indexing block. The first indexing mating surface and the second indexing mating surface are adjacent flat end faces, and the first indexing mating surface and the second indexing mating surface are perpendicular to each other. First indexing locking surfaces and second indexing locking surfaces perpendicular to the upper end surface of the chuck body are respectively arranged on the inner sides of the upper ends of the first ejector rod and the second ejector rod opposite to each other. The advantages are simple structure, suitable for machining right-angle joint parts, and high production efficiency.

[0004] The above patents all have the problems proposed in this background art: it is difficult to predict load changes in real time under complex working conditions and generate efficient drive signals. To solve the above problems, the present application designs an efficient indexing chuck control method for low-energy consumption dynamic synchronous drive. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide an efficient indexing chuck control method for low - energy - consumption dynamic synchronous drive in view of the deficiencies of the prior art. It collects initial information and sets drive parameters, and initializes the indexing accuracy of the control system; collects the operating state data of the chuck through sensors, and combines with an intelligent AI model to predict the load change trend; according to the prediction result, generates drive signals through a multi - axis synchronous drive algorithm, controls the coordinated operation of multiple drive units, and dynamically adjusts the position, speed and torque distribution; at the same time, combines the real - time energy consumption and the indexing task requirements to dynamically optimize the drive parameters. The modeling layer uses a method combining integrated learning and graph neural networks to extract load change characteristics, ensuring the prediction accuracy and robustness of the model under complex working conditions. The present invention can effectively reduce the system energy consumption, improve the indexing accuracy and efficiency, and is applicable to a variety of industrial processing scenarios.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] An efficient indexing chuck control method for low - energy - consumption dynamic synchronous drive, the method comprising:

[0008] Collect initial information, set initial drive parameters according to the initial information, initialize the indexing accuracy of the control system according to the initial drive parameters, and generate a first set of drive parameters;

[0009] After initialization is completed, collect the operating state data of the chuck through sensors, and predict the load change according to the operating state data;

[0010] According to the load change, generate drive signals through a multi - axis synchronous drive algorithm, and control the coordinated operation of multiple drive units;

[0011] Adjust the drive parameters according to the real - time energy consumption during the drive process and the indexing task requirements.

[0012] The setting of the initial drive parameters according to the initial information includes:

[0013] Obtain the initial information of the workpiece characteristics, indexing task requirements and equipment status;

[0014] Determine the indexing accuracy threshold of the control system according to the indexing angle range and the target accuracy;

[0015] Set the drive parameters of the drive motor according to the indexing accuracy threshold, workpiece weight and task target, wherein the drive parameters include the starting speed, acceleration range and minimum torque;

[0016] Set the initial energy consumption threshold according to the power limit;

[0017] Initialize the control system of the indexing chuck with the set drive parameters, generate a first set of drive parameters, and provide input for load prediction.

[0018] Predicting the load change trend based on the operating state data includes:

[0019] Collect the operating state data of the chuck during indexing through a sensor, and preprocess the operating state data. Among them, the operating state data includes the real-time speed and acceleration of the driving motor, the change data of the chuck load, and the energy consumption data. The preprocessing includes noise filtering, abnormal data elimination, and standardization;

[0020] Input the historical operating data and the operating state data into a preset intelligent AI model, and process the input parameters through the intelligent AI model to output a prediction result.

[0021] The intelligent AI model is configured with physical model parameters related to the operating characteristics of the chuck, including:

[0022] An input layer for receiving the historical operating data and the operating state data and normalizing the historical operating data and the operating state data;

[0023] A modeling layer for processing the historical operating data through an ensemble learning algorithm, calculating the load change characteristics, and modeling the operating state data according to a graph neural network to generate a graph structure, and propagating and adjusting the model according to the load change characteristics to output the feature representation of the graph structure;

[0024] A prediction layer for processing the feature representation through temporal embedding to generate a temporal feature vector, processing the temporal feature vector through a Transformer network, calculating the load prediction value, and calculating the temporal dependence relationship of the load through a self-attention mechanism, calculating the load change trend according to the temporal dependence relationship, and outputting the load prediction value and the load change trend as the load prediction result.

[0025] The modeling layer includes an ensemble learning sublayer, a graph neural network sublayer, and a fusion sublayer;

[0026] The ensemble learning sublayer is used to integrate multiple machine learning models to generate multiple preliminary learning submodels. Each submodel selects historical operating data for training according to the physical model parameters, outputs the predicted load change, calculates the weighted weight according to the hit rate of each submodel in historical predictions, and calculates the load change characteristics according to the weighted weight and the predicted load change;

[0027] The graph neural network sublayer is used to construct a graph structure containing sensor data and state information according to the spatial and temporal relationships of the operating state data and the sensors. Among them, each graph node represents a sensor or a data point, and each edge represents the dependence relationship between the nodes;

[0028] The fusion sub-layer is used to propagate the load change features and the adjustments of the graph structure output, update the internal parameters according to the adjustment results, and calculate the feature representation.

[0029] The graph neural network sub-layer further includes:

[0030] Propagate the load change features in the graph structure through the information propagation mechanism. Each node adjusts the load change features according to its environmental context during the propagation process, and locally adjusts the graph structure through the feature information between nodes.

[0031] The generation of the drive signal through the multi-axis synchronous drive algorithm includes:

[0032] Taking the load prediction result as the core, combining the real-time status data of each drive unit, and updating the drive parameter range, where the load prediction result includes the instantaneous load change value and the load distribution curve;

[0033] According to the drive parameter range, construct a multi-axis drive model, and output a drive signal according to the multi-axis drive model, where the multi-axis drive model is used to calculate the load balance and synchronism;

[0034] Adjust each drive unit according to the output drive signal, including position adjustment, speed adjustment, and torque distribution.

[0035] The multi-axis drive model includes:

[0036] A load balance calculation module, which is used to calculate the load distribution of each axis according to the load prediction results of each drive unit, generate load balance parameters, and combine the instantaneous load change value and the load distribution curve to adjust the load balance parameters through an optimization algorithm to ensure that the load differences of each axis are within the preset threshold range;

[0037] A synchronism calculation module, which is used to construct a synchronous equation for the multi-axis movement according to the real-time position and speed states between the multi-axes, calculate the movement deviation of each axis, and dynamically correct the movement deviation using an error feedback control algorithm;

[0038] A drive signal generation module, which is used to output a drive signal in real time according to the load balance parameters and the synchronism calculation results;

[0039] The drive signal includes the position, speed, and torque distribution schemes of each adjusted drive unit.

[0040] The adjustment of the drive parameters according to the real-time energy consumption during the drive process and the indexing task requirements includes:

[0041] Collect the power, current, and voltage data of the drive motor through an energy consumption monitoring sensor, and calculate the current instantaneous energy consumption;

[0042] Analyze the energy efficiency performance of the current drive parameters in combination with the current instantaneous energy consumption and the target requirements of the indexing task;

[0043] If it is found that the energy efficiency performance is greater than or equal to the set energy-saving threshold, adjust the parameters of the drive motor according to the energy consumption data.

[0044] The adjustment of the drive parameters according to the real-time energy consumption during the drive process and the indexing task requirements also includes adjusting the priority of the drive parameters according to the target angle, accuracy, and time limit of the indexing task.

[0045] Compared with the prior art, the beneficial effects of the present invention are:

[0046] 1. By introducing an intelligent AI model and a multi-axis synchronous drive algorithm, the present invention realizes the real-time prediction and efficient control of the operating state of the indexing chuck, can dynamically generate drive signals according to the load change trend, and improves the indexing accuracy and operating efficiency;

[0047] 2. The present invention combines real-time energy consumption data with the indexing task requirements, dynamically optimizes the drive parameters, and successfully achieves the goal of low-energy consumption operation. The control system minimizes energy consumption while ensuring accuracy and has broad industrial application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Other features, objects, and advantages of the present invention will become more apparent by reading the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0049] Figure 1 It is a schematic flowchart of the high-efficiency indexing chuck control method for low-energy consumption dynamic synchronization drive in Embodiment 1 of the present invention;

[0050] Figure 2 It is a structural diagram of the intelligent AI model in Embodiment 1 of the present invention;

[0051] Figure 3 It is a structural diagram of the modeling layer in Embodiment 1 of the present invention;

[0052] Figure 4 It is a structural diagram of the multi-axis drive model in Embodiment 1 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0053] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments.

[0054] Embodiment 1:

[0055] Please refer to Figure 1, An embodiment provided by the present invention: A high-efficiency indexing chuck control method for low-energy consumption dynamic synchronous drive, and the specific steps of the method are as follows:

[0056] S1: Collect initial information and initialize the control system according to the initial information;

[0057] In this step, the initial information of workpiece characteristics, indexing task requirements, and equipment status is collected through sensors, and this information is input into the control system for data processing. According to the collected information, the indexing accuracy threshold of the control system, the initial parameters of the drive motor, and the energy consumption limit value are set to complete the initial configuration of the system. Ensure that the control system can quickly adapt to different working conditions and enter the best operating state, and at the same time provide accurate initial conditions for subsequent load prediction and drive signal generation, effectively reducing energy consumption waste and control errors during operation.

[0058] S2: Collect the operating state data of the chuck and predict the load change according to the operating state data;

[0059] In this step, the state data during the operation of the indexing chuck is collected in real time through high-precision sensors, including the rotational speed and acceleration of the drive motor, the load change of the chuck, and the energy consumption data. The collected data is preprocessed and input into the intelligent AI model in combination with historical operation data. The AI model predicts the load change trend through time series analysis and ensemble learning algorithms, and outputs the instantaneous load change value and the future load distribution curve. It can effectively avoid control instability caused by sudden load changes and improve the stability and efficiency of the entire indexing process.

[0060] S3: Generate a drive signal through a multi-axis synchronous drive algorithm according to the load change;

[0061] In this step, with the predicted load change result as the core input, combined with the real-time state data of each drive unit, a drive signal is dynamically generated through a multi-axis synchronous drive algorithm, specifically including position adjustment, speed optimization, and torque distribution. Achieve coordinated operation between multiple axes, ensure motion consistency under load change conditions, and at the same time reduce the overload of a single drive unit through load balancing distribution, thereby improving efficiency and reliability.

[0062] S4: Adjust the drive parameters according to the real-time energy consumption during the drive and the indexing task requirements.

[0063] In this step, the drive parameters are dynamically adjusted according to the energy consumption data, including reducing unnecessary power output, optimizing speed and torque distribution. On the premise of meeting the indexing accuracy, the optimal energy consumption is prioritized. It can dynamically adapt to the working condition requirements and effectively reduce the overall energy consumption of the drive system, thereby achieving the energy-saving goal while ensuring high-efficiency indexing.

[0064] The specific steps of S1 are as follows:

[0065] S1.1: Obtain the initial information of workpiece characteristics, indexing task requirements, and equipment status;

[0066] Specifically, through the sensors and system input interfaces deployed around the indexing chuck, obtain the physical characteristic data of the workpiece, including the size, weight, shape, and material of the workpiece. These data are measured in real time by the sensors or provided by user input. At the same time, collect the indexing task requirement information, including the target indexing angle, indexing accuracy range, indexing period, and task priority. In addition, it is necessary to conduct a comprehensive inspection of the equipment operating status, collect the load data of the current control system, the no-load power of the drive motor, the initial clamping torque of the chuck, and the friction characteristics.

[0067] S1.2: Determine the indexing accuracy threshold of the control system according to the indexing angle range and target accuracy;

[0068] Specifically, utilize the collected indexing task requirement information, combine with the characteristic parameters of the workpiece, and determine the indexing accuracy threshold through the built-in calculation module. The control system comprehensively analyzes the target indexing angle error range and task priority, combines with the inherent accuracy of the equipment and possible load fluctuations, and generates the accuracy requirements for the indexing process.

[0069] Furthermore, the control system adopts a threshold setting algorithm to automatically assign different control strategies to tasks with different accuracy requirements. For example, tasks with high accuracy requirements will be allocated more computing resources and the speed will be reduced to ensure accuracy, while tasks with low accuracy requirements will prioritize efficiency optimization. It can ensure the precise completion of tasks without wasting resources, and at the same time avoid the increase in energy consumption caused by insufficient accuracy or over-control.

[0070] S1.3: Set the drive parameters of the drive motor according to the indexing accuracy threshold, workpiece weight, and task target, where the drive parameters include the starting speed, acceleration range, and minimum torque;

[0071] Specifically, estimate the inertial torque and friction resistance during the starting stage through the load calculation formula, set the initial speed and acceleration range of the motor according to the results, and the load calculation formula can be deduced through the initial manual of the control system to ensure that the requirements for smooth starting are met in the initial stage without excessive energy consumption. At the same time, combine with the task target angle and time requirements, dynamically adjust the acceleration and running time allocation to avoid problems such as excessive acceleration causing vibration or too slow acceleration delaying the task. It can achieve the smoothness of the starting stage and the efficiency of the running stage, while reducing the risk of overload or instability caused by improper parameter settings.

[0072] S1.4: Comprehensively considering the current motor power, chuck load distribution, and indexing accuracy requirements, different energy consumption ranges are set for different load conditions;

[0073] Exemplarily, in low-load conditions, the output power of the motor is reduced by lowering the upper limit of energy consumption, while in high-load situations, the power upper limit is allowed to be moderately increased to ensure indexing accuracy.

[0074] S1.5: Initialize the control system of the indexing chuck with the set drive parameters to generate a first set of drive parameters, providing an input for load prediction.

[0075] The specific steps of S2 are as follows:

[0076] S2.1: Collect the operating state data of the chuck during indexing through sensors, and preprocess the operating state data. Among them, the operating state data includes the real-time speed and acceleration of the drive motor, the change data of the chuck load, and the energy consumption data. The preprocessing includes noise filtering, abnormal data elimination, and standardization;

[0077] Specifically, the state data during the operation of the chuck is collected in real time through a variety of high-precision sensors arranged on the indexing chuck and the drive motor. These sensors include: an optical encoder for measuring the motor speed, an inertial sensor for measuring acceleration, a torque sensor for monitoring the change of the chuck load, and a current-voltage sensor for collecting energy consumption data. First, a filtering algorithm is used to eliminate noise signals caused by mechanical vibration, external interference, etc. Secondly, for possible abnormal data (such as short-term severe fluctuations or sudden invalid data), abnormal data is eliminated through statistical methods to ensure the validity of the data.

[0078] S2.2: Input the historical operation data and the operating state data into a preset intelligent AI model, and process the input parameters through the intelligent AI model to output a prediction result.

[0079] Please refer to Figure 2 , the structure diagram of the intelligent AI model in the embodiment of the present invention. The intelligent AI model is configured with physical model parameters related to the operating characteristics of the chuck, including:

[0080] An input layer for receiving the historical operation data and the operating state data and normalizing the historical operation data and the operating state data;

[0081] A modeling layer for processing the historical operation data through an ensemble learning algorithm, calculating the load change characteristics, modeling the operating state data according to a graph neural network to generate a graph structure, and propagating and adjusting the model according to the load change characteristics to output the feature representation of the graph structure;

[0082] The prediction layer is used to process the feature representation through temporal embedding to generate a temporal feature vector, process the temporal feature vector through a Transformer network, calculate the load prediction value, calculate the temporal dependence of the load through a self-attention mechanism, calculate the load change trend according to the temporal dependence, and output the load prediction value and the load change trend as the load prediction result.

[0083] Specifically, the architecture of the intelligent AI model includes an input layer, a modeling layer, and a prediction layer. The input layer first normalizes and extracts features from data from different sources, and extracts key features such as the amplitude of rotational speed fluctuation, the change rate of acceleration, the load change trend, and the real-time energy consumption fluctuation. The modeling layer uses algorithms based on ensemble learning and graph neural network technology. Through ensemble learning algorithms (such as random forest and gradient boosting tree), it deeply analyzes historical data to extract the historical laws of load changes; at the same time, it models the real-time operating state data through a graph neural network to generate a feature map structure based on multi-dimensional sensor data. The graph neural network can not only capture the complex associations between various types of data, but also adjust the feature weights through the propagation mechanism of the graph to further optimize the output performance of the model.

[0084] In the prediction layer, the model processes temporal data through a temporal embedding module and a Transformer network to generate an accurate prediction result of the load change trend. The temporal embedding module extracts the time dependence of the input data, enabling the model to predict the future load change curve and the instantaneous load change value; the Transformer network captures the long-term and short-term dependencies of the temporal data through a self-attention mechanism, improving the accuracy of the prediction and the adaptability to complex working conditions. The prediction results include the instantaneous load change value and the future load distribution curve, providing clear guidance for the generation of drive signals. Through the intelligent AI model, it is not only possible to dynamically respond to real-time load changes, but also to optimize the drive strategy in advance to avoid control instability caused by sudden load changes. Improve the operating efficiency and accuracy of the indexing chuck, while reducing energy consumption, thus achieving the goal of low-energy and high-efficiency indexing.

[0085] Please refer to Figure 3 , the structure diagram of the modeling layer in the embodiment of the present invention, the modeling layer includes an ensemble learning sub-layer, a graph neural network sub-layer, and a fusion sub-layer;

[0086] The ensemble learning sub-layer is used to integrate multiple machine learning models to generate multiple preliminary learning sub-models. Each sub-model selects historical operation data for training according to physical model parameters, outputs the predicted load change, calculates the weighted weight according to the hit rate of each sub-model in historical predictions, and calculates the load change feature according to the weighted weight and the predicted load change;

[0087] Specifically, the core idea of the ensemble learning sub-layer is to improve the accuracy and stability of predictions through multi-model ensemble. In this embodiment, the ensemble learning sub-layer combines multiple different types of machine learning models, including decision trees, support vector machines, and random forests, to generate multiple preliminary learning sub-models. During the training process of each sub-model, appropriate historical operation data is selected according to the physical model parameters for targeted training. For example, the decision tree model quickly evaluates the change trend under different load states by constructing splitting rules, while the support vector machine captures complex non-linear relationships in the form of high-dimensional space mapping, and the random forest achieves robust predictions by constructing multiple weak models. After preliminary training, each sub-model is weighted and evaluated based on the hit rate of historical predictions, and the weight allocation is based on the performance of the model in different scenarios. For example, in the case of high dynamic load changes, it may rely more on the random forest, while in the case of low dynamic changes, it relies more on the decision tree. By combining the weighted weights with the predicted load changes output by each sub-model, the load change characteristics are calculated through the ensemble strategy, and then a highly reliable prediction output is generated. This multi-model ensemble method effectively compensates for the prediction deviation problems that may occur in a single model under certain extreme conditions, thus significantly improving the accuracy and stability of load change prediction.

[0088] The graph neural network sub-layer is used to construct a graph structure containing sensor data and state information according to the spatial and temporal relationships of the operating state data and the sensors, where each graph node represents a sensor or a data point, and each edge represents the dependency relationship between the nodes.

[0089] The load change characteristics are propagated in the graph structure through the information propagation mechanism. Each node adjusts the load change characteristics according to its surrounding environmental context during the propagation process, and locally adjusts the graph structure through the feature information between the nodes.

[0090] Specifically, a holistic graph structure is constructed using the spatial and temporal relationships existing in the chuck operating state data to capture complex node dependencies. In the specific implementation, the real-time acquisition data of the sensors is regarded as graph nodes. For example, the states recorded by the speed sensor and the torque sensor are input as independent nodes respectively, and the edges between the nodes represent the physical dependencies between the data points, such as the coupling effect between speed and load. The sub-layer first normalizes the data through a preprocessing module to ensure the comparability of different data dimensions, and then propagates information in the graph structure based on the message passing mechanism in the graph neural network. For example, when the load of a certain node changes, this change will gradually spread to other related nodes through the edges, updating their feature representations, thereby achieving global adjustment. The characteristics of the graph neural network enable it to dynamically capture the implicit associations between multiple sensor data, such as the complex interaction relationships between position, speed, acceleration, and energy consumption, thus enhancing the depth and accuracy of load change feature extraction.

[0091] The fusion sub - layer is used to propagate the load change features and the adjustments of the graph structure output, update the internal parameters according to the adjustment results, and calculate the feature representation.

[0092] Specifically, the fusion sub - layer first refines the load change features generated by the ensemble learning sub - layer, aligns these features with the graph structure representation generated by the graph neural network, and ensures the dimensional consistency of the two types of data through feature space mapping. After alignment, the fusion sub - layer assigns weights to the feature importance through an attention mechanism. For example, when the dynamic load changes violently, it gives priority to the local features generated by the graph neural network, while when the load changes smoothly, it relies more on the long - term trend features of the ensemble learning. At the same time, the fusion sub - layer adopts a propagation algorithm based on adaptive parameter update, compares the output features of the previous stage with the current features layer by layer, and updates the internal parameters through iterative optimization. It can always maintain the accuracy and robustness of the feature representation under dynamic operating conditions. In addition, the final feature representation generated by the fusion sub - layer can provide unified high - dimensional data support for the entire modeling layer and provide optimal input conditions for the subsequent prediction layer calculation.

[0093] Furthermore, in this embodiment, the structure of the modeling layer can effectively solve the problem that traditional prediction methods are insufficient in responding to load changes under complex operating states. The ensemble learning sub - layer significantly improves the overall prediction accuracy of the model, the graph neural network sub - layer captures the deep - level spatial and temporal correlations, and the fusion sub - layer further enhances the adaptability of the model through dynamic adjustment. This multi - layer design ensures the high accuracy and stability of load prediction under multi - working conditions, providing strong technical support for realizing low - energy - consumption dynamic synchronous drive.

[0094] The specific steps of S3 are as follows:

[0095] S3.1: Taking the load prediction result as the core, combined with the real - time status data of each drive unit, update the drive parameter range, where the load prediction result includes the instantaneous load change value and the load distribution curve.

[0096] S3.2: According to the drive parameter range, construct a multi - axis drive model, and output a drive signal according to the multi - axis drive model, where the multi - axis drive model is used to calculate the load balance and synchrony.

[0097] S3.3: According to the output drive signal, adjust each drive unit, including position adjustment, speed adjustment, and torque distribution.

[0098] Please refer to Figure 4 , the structural diagram of the multi - axis drive model in the embodiment of the present invention. The multi - axis drive model includes:

[0099] A load balancing calculation module, which is used to calculate the load distribution of each axis according to the load prediction results of each driving unit, generate load balancing parameters, and combine the instantaneous load change value and the load distribution curve to adjust the load balancing parameters through an optimization algorithm to ensure that the load difference of each axis is within the preset threshold range;

[0100] A synchronism calculation module, which is used to construct a synchronism equation for the multi-axis movement according to the real-time position and speed states among multiple axes, calculate the movement deviation of each axis, and dynamically correct the movement deviation by using an error feedback control algorithm;

[0101] A drive signal generation module, which is used to output drive signals in real time according to the load balancing parameters and the synchronism calculation results;

[0102] The drive signals include the adjusted position, speed, and torque distribution schemes of each driving unit.

[0103] The specific steps of S4 are as follows:

[0104] S4.1: Collect the power, current, and voltage data of the drive motor through an energy consumption monitoring sensor, and calculate the current instantaneous energy consumption;

[0105] S4.2: Analyze the energy efficiency performance of the current drive parameters in combination with the current instantaneous energy consumption and the target requirements of the indexing task;

[0106] S4.3: If it is found that the energy efficiency performance is greater than or equal to the set energy-saving threshold, adjust the parameters of the drive motor according to the energy consumption data.

[0107] S4.4: Adjust the priority of the drive parameters according to the target angle, accuracy, and time limit of the indexing task.

[0108] Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. An efficient indexing chuck control method with low-energy consumption dynamic synchronous drive, characterized in that, The method includes: Collect initial information, set initial driving parameters according to the initial information, initialize the indexing accuracy of the control system according to the initial driving parameters, and generate a first set of driving parameters; After initialization is completed, collect the operating state data of the chuck through a sensor, and predict the load change according to the operating state data; According to the load change, generate a driving signal through a multi-axis synchronous driving algorithm to control the coordinated operation of multiple driving units; Adjust the driving parameters according to the real-time energy consumption during driving and the indexing task requirements.

2. The high-efficiency indexing chuck control method for low-energy consumption dynamic synchronous drive according to claim 1, wherein, The setting of the initial driving parameters according to the initial information includes: Obtain the initial information of the workpiece characteristics, indexing task requirements and equipment status; Determine the indexing accuracy threshold of the control system according to the indexing angle range and the target accuracy; Set the driving parameters of the driving motor according to the indexing accuracy threshold, workpiece weight and task target, where the driving parameters include the starting speed, acceleration range and minimum torque; Set the initial energy consumption threshold according to the power limit; Initialize the control system of the indexing chuck with the set driving parameters, generate a first set of driving parameters, and provide an input for load prediction.

3. The high-efficiency indexing chuck control method for low-energy-consumption dynamic synchronous drive according to claim 1, wherein The predicting the load change trend according to the operating state data includes: Collect the operating state data of the chuck during indexing through a sensor, and preprocess the operating state data, where the operating state data includes the real-time speed and acceleration of the driving motor, the change data of the chuck load and the energy consumption data, and the preprocessing includes noise filtering and abnormal data elimination; Input the historical operating data and the operating state data into a preset intelligent AI model, and process the input parameters through the intelligent AI model to output a prediction result.

4. The high-efficiency indexing chuck control method for low-energy-consumption dynamic synchronous drive according to claim 3, wherein The intelligent AI model is configured with physical model parameters related to the operating characteristics of the chuck, including: An input layer for receiving the historical operating data and the operating state data and normalizing the historical operating data and the operating state data; A modeling layer for processing the historical operating data through an ensemble learning algorithm, calculating the load change characteristics, modeling the operating state data according to a graph neural network to generate a graph structure, propagating and adjusting the model according to the load change characteristics, and outputting the feature representation of the graph structure; A prediction layer for processing the feature representation through a time series embedding to generate a time series feature vector, processing the time series feature vector through a Transformer network, calculating the load prediction value, calculating the time series dependence relationship of the load through a self-attention mechanism, calculating the load change trend according to the time series dependence relationship, and outputting the load prediction value and the load change trend as the load prediction result.

5. The high-efficiency indexing chuck control method for low-energy consumption dynamic synchronous drive according to claim 4, characterized in that, The modeling layer includes an ensemble learning sublayer, a graph neural network sublayer and a fusion sublayer; The ensemble learning sublayer is used to integrate multiple machine learning models to generate multiple preliminary learning submodels. Each submodel selects historical operating data for training according to the physical model parameters, outputs the predicted load change, calculates the weighted weight according to the hit rate of each submodel in the historical prediction, and calculates the load change characteristics according to the weighted weight and the predicted load change; The graph neural network sub-layer is used to construct a graph structure containing sensor data and status information according to the operating state data and the spatial and temporal relationships of the sensors. Each graph node represents a sensor or a data point, and each edge represents the dependency relationship between the nodes; The fusion sub-layer is used to propagate the load change characteristics and the adjustments output by the graph structure, update the internal parameters according to the adjustment results, and calculate the feature representation.

6. The high-efficiency indexing chuck control method for low-energy-consumption dynamic synchronous drive according to claim 5, wherein The graph neural network sub-layer further includes: Propagate the load change characteristics in the graph structure through the information propagation mechanism. Each node adjusts the load change characteristics according to the environmental context it is in during the propagation process, and locally adjusts the graph structure through the feature information between the nodes.

7. The high-efficiency indexing chuck control method for low-energy dynamic synchronous drive according to claim 1, characterized in that, Generating the drive signal through the multi-axis synchronous drive algorithm includes: Taking the load prediction result as the core, combining the real-time state data of each drive unit, and updating the drive parameter range, where the load prediction result includes the instantaneous load change value and the load distribution curve; According to the drive parameter range, construct a multi-axis drive model, and output a drive signal according to the multi-axis drive model, where the multi-axis drive model is used to calculate the load balance and synchronism; Adjust each drive unit according to the output drive signal, including position adjustment, speed adjustment, and torque distribution.

8. The high-efficiency indexing chuck control method for low-energy-consumption dynamic synchronous drive according to claim 7, wherein, The multi-axis drive model includes: A load balance calculation module, which is used to calculate the load distribution of each axis according to the load prediction results of each drive unit, generate load balance parameters, and combine the instantaneous load change value and the load distribution curve to adjust the load balance parameters through an optimization algorithm to ensure that the load differences between the axes are within the preset threshold range; A synchronism calculation module, which is used to construct a synchronous equation for multi-axis motion according to the real-time position and speed states between the multi-axes, calculate the motion deviation of each axis, and dynamically correct the motion deviation using an error feedback control algorithm; A drive signal generation module, which is used to output a drive signal in real time according to the load balance parameters and the synchronism calculation results; The drive signal includes the position, speed, and torque distribution schemes of each adjusted drive unit.

9. The high-efficiency indexing chuck control method for low-energy consumption dynamic synchronous drive according to claim 1, wherein, Adjusting the drive parameters according to the real-time energy consumption during the drive process and the indexing task requirements includes: Collect the power, current, and voltage data of the drive motor through an energy consumption monitoring sensor, and calculate the current instantaneous energy consumption; Combining the current instantaneous energy consumption and the target requirements of the indexing task, analyze the energy efficiency performance of the current drive parameters; If it is found that the energy efficiency performance is greater than or equal to the set energy-saving threshold, adjust the parameters of the drive motor according to the energy consumption data.

10. The high-efficiency indexing chuck control method for low-energy consumption dynamic synchronous drive according to claim 9, characterized in that, Adjusting the drive parameters according to the real-time energy consumption during the drive process and the indexing task requirements further includes adjusting the priority of the drive parameters according to the target angle, accuracy, and time limit of the indexing task.

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