Energy-saving driving method and system for machine tool and electronic equipment

Abstract

The invention provides an energy-saving driving method and system for a machine tool and electronic equipment, and relates to the technical field of hydraulic oil cylinders, a dynamic load parameter set of a machine tool oil cylinder is obtained through real-time monitoring to predict oil cylinder driving demand information so as to carry out control analysis, an energy-saving control instruction is generated and executed, and real-time working condition information of the oil cylinder is synchronously recorded; an energy recovery mode is started in the oil cylinder return stroke braking stage to convert braking kinetic energy into hydraulic energy to be stored in an energy accumulator; and dynamic control analysis is carried out according to the pressure data of the energy accumulator and the real-time working condition information of the oil cylinder, and cooperative control parameters are generated for energy-saving driving. The technical problems that in the prior art, due to the fact that a machine tool oil cylinder lacks a dynamic sensing and feedback mechanism for the real-time load and the energy state, a driving control strategy is disjointed with the actual working condition of the oil cylinder, and return stroke kinetic energy cannot be effectively recycled are solved, and the technical effect of improving energy-saving driving accuracy and energy recycling efficiency is achieved.

Description

Technical Field

[0001] This application relates to the field of hydraulic cylinder technology, and more specifically to energy-saving drive methods, systems and electronic devices for machine tools. Background Technology

[0002] In modern manufacturing equipment, hydraulic cylinders are a core technology for achieving high-load, high-response force drive in machine tools, and are widely used in heavy-duty operation scenarios such as various CNC machine tools, presses, and forging equipment. As a power actuator, the hydraulic cylinder relies on an electric motor to drive a hydraulic pump to provide constant or variable frequency flow, thereby realizing pushing and pulling actions.

[0003] Existing energy-saving drive methods for machine tools mainly include frequency conversion control, load-sensitive control, and return stroke energy recovery technology. These methods often employ experience-based control strategies to adjust the driving pressure and flow of the hydraulic cylinders, or recover and reuse some energy during the cylinder's no-load return stroke through throttle valves or hydraulic accumulators. However, due to a lack of awareness of real-time load changes and energy status, existing methods still suffer from shortcomings in drive control, such as control response lag, insufficient energy utilization, and a lack of dynamic adaptability in energy-saving strategies. They cannot achieve precise drive and energy optimization based on the actual needs of the machine tool under different operating conditions, thus hindering further improvements in energy-saving performance. Summary of the Invention

[0004] This application provides an energy-saving drive method, system, and electronic equipment for machine tools. It solves the technical problem in the prior art that the lack of a dynamic perception and feedback mechanism for real-time load and energy status in machine tool cylinders leads to a disconnect between the drive control strategy and the actual working conditions of the cylinders, resulting in the failure to effectively recover and utilize return kinetic energy. It achieves the technical effect of constructing a closed-loop energy control mechanism based on real-time working condition analysis and energy status linkage, thereby improving the accuracy of energy-saving drive and the efficiency of energy recovery and utilization in machine tools.

[0005] In view of the above problems, in a first aspect, this application provides an energy-saving drive method for machine tools, the method comprising: real-time monitoring of the load pressure and movement direction of the machine tool cylinder, generating a dynamic load parameter set to predict the drive of the machine tool cylinder, and determining the cylinder drive demand information; performing control analysis according to the cylinder drive demand information, generating energy-saving control commands, executing the energy-saving control commands and recording synchronously to obtain real-time operating information of the cylinder; when the machine tool cylinder is in the cylinder return braking stage, activating an energy recovery mode to convert braking kinetic energy into hydraulic energy and storing it in an accumulator, the accumulator containing the pressure data of the machine tool cylinder; performing dynamic control analysis based on the pressure data of the accumulator and the real-time operating information of the cylinder to generate collaborative control parameters, and using the collaborative control parameters to perform energy-saving drive of the machine tool cylinder to form a closed-loop energy-saving drive strategy.

[0006] Preferably, the method for real-time monitoring of the load pressure and movement direction of the machine tool cylinder to generate a dynamic load parameter set includes: acquiring the load pressure of the machine tool cylinder in real time through pressure sensors at the front and rear ends of the cylinder to obtain bidirectional pressure values; sensing the extension and retraction displacement of the piston rod inside the machine tool cylinder to obtain real-time displacement data and movement speed data; aligning the bidirectional pressure values ​​and the real-time displacement data according to a time series to construct a pressure-displacement phase relationship matrix; traversing the pressure-displacement phase relationship matrix to perform operational calculations on the machine tool cylinder to obtain the instantaneous output power of the cylinder; performing gradient analysis based on the bidirectional pressure values ​​to determine the pressure gradient value; calibrating the movement direction based on the real-time displacement data and the movement speed data; identifying the power waveform information based on the instantaneous output power of the cylinder; and integrating the power waveform information with the pressure gradient value and the movement direction to determine the dynamic load parameter set.

[0007] Preferably, the method for predicting the drive demand information of the machine tool cylinder includes: recording the changes in the pressure gradient value according to the power waveform information to obtain the pressure gradient change rate; retrieving the machining process data of the machine tool cylinder and matching it with the pressure gradient change rate to determine the machining load characteristic information; when the pressure gradient change rate exceeds a preset threshold, activating a high-frequency sampling mode, and sampling the pressure of the machine tool cylinder according to the machining load characteristic information to obtain pressure pulsation spectrum characteristics; identifying the load mutation type based on the pressure pulsation spectrum characteristics and performing drive prediction according to the machining cycle to determine the cylinder drive demand information.

[0008] Preferably, the method for generating energy-saving control commands by performing control analysis based on the cylinder drive demand information includes: parsing the cylinder drive demand information to determine the target driving force; performing control constraint analysis based on the target driving force to determine the control parameter constraint set; performing multi-objective optimization calculations on the machine tool cylinder according to the control parameter constraint set to generate a hydraulic pump displacement solution set; setting a feasible region for the proportional valve opening; filtering the hydraulic pump displacement solution set according to the feasible region for the proportional valve opening to construct a displacement adjustment curve; formulating a valve opening sequence based on the displacement adjustment curve; and adding the valve opening sequence to the energy-saving control command.

[0009] Preferably, the method for synchronously recording the energy-saving control command to obtain real-time cylinder operating information includes: synchronously recording the machine tool cylinder by executing the energy-saving control command to obtain multiple real-time control data, including real-time pump outlet pressure data, real-time valve core displacement data, and real-time cylinder acceleration data; integrating the real-time pump outlet pressure data, real-time valve core displacement data, and real-time cylinder acceleration data according to the machine tool cylinder's running sequence identifier timestamp to obtain a real-time cylinder operating log; comparing and analyzing the real-time cylinder operating log with the cylinder drive demand information to generate a real-time operating feedback parameter set; and dynamically compensating the real-time cylinder operating log according to the real-time operating feedback parameter set to obtain the real-time cylinder operating information.

[0010] Preferably, the process of determining that the machine tool cylinder is in the cylinder return braking stage includes: calculating the decrease in piston displacement speed of the machine tool cylinder based on the real-time acceleration data of the cylinder to obtain the speed decrease rate; calculating the difference between the pressure value at the front end of the cylinder and the pressure value at the rear end of the cylinder based on the bidirectional pressure value to generate a pressure difference between the front and rear chambers, wherein the pressure difference includes pressure difference gradient data, wherein the pressure difference gradient data can be positive gradient data or negative gradient data; when the speed decrease rate exceeds a preset critical threshold and the pressure difference gradient data is negative gradient data, the machine tool cylinder is determined to be in the cylinder return braking stage.

[0011] Preferably, when the machine tool cylinder is in the return braking phase, an energy recovery mode is activated to convert braking kinetic energy into hydraulic energy and store it in an accumulator. The accumulator contains pressure data of the machine tool cylinder. The method includes: when the machine tool cylinder is in the return braking phase, activating the energy recovery mode, closing the main oil supply line solenoid valve and switching to the energy recovery circuit through the energy recovery mode, allowing hydraulic oil in the rod chamber to be injected into the accumulator through a one-way valve group; collecting the pressure data of the accumulator in real time through a pressure sensor inside the accumulator, calculating the energy storage capacity of the accumulator based on the pressure data; when the pressure data reaches a first critical value, switching to bypass overflow state, and prioritizing the use of the hydraulic energy stored in the accumulator to drive the machine tool cylinder in the initial stage of the next working stroke.

[0012] Preferably, the method for generating collaborative control parameters by performing dynamic control analysis based on the pressure data of the accumulator and the real-time operating information of the hydraulic cylinder includes: constructing a four-dimensional state space based on the pressure data of the accumulator and the real-time operating information of the hydraulic cylinder; calculating multidimensional correlation coefficients according to the four-dimensional state space; performing correlation analysis based on the multidimensional correlation coefficients to extract multiple strongly correlated dimension pairs; performing principal component analysis using the multiple strongly correlated dimension pairs; performing orthogonal mapping based on the analysis results to construct a pressure-operating condition coupling matrix; performing dynamic programming based on the pressure-operating condition coupling matrix to solve for the energy control allocation ratio of the machine tool hydraulic cylinder; and generating the collaborative control parameters based on the energy control allocation ratio.

[0013] Secondly, this application also provides an energy-saving drive system for machine tools, the system comprising: a drive demand prediction module, used to monitor the load pressure and movement direction of the machine tool cylinder in real time, generate a dynamic load parameter set to predict the drive of the machine tool cylinder, and determine the cylinder drive demand information; an energy-saving control analysis module, used to perform control analysis according to the cylinder drive demand information, generate energy-saving control commands, execute the energy-saving control commands and record synchronously to obtain real-time cylinder operating condition information; an energy recovery module, used to activate an energy recovery mode when the machine tool cylinder is in the cylinder return braking stage to convert braking kinetic energy into hydraulic energy and store it in an accumulator, the accumulator containing the pressure data of the machine tool cylinder; and a dynamic control analysis module, used to perform dynamic control analysis based on the pressure data of the accumulator and the real-time cylinder operating condition information, generate cooperative control parameters, and use the cooperative control parameters to perform energy-saving drive of the machine tool cylinder to form a closed-loop energy-saving drive strategy.

[0014] Thirdly, this application also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described energy-saving drive method for machine tools.

[0015] One or more technical solutions provided in this application have at least the following beneficial effects: By monitoring the load pressure and direction of motion of the machine tool cylinder in real time, a dynamic load parameter set is constructed to provide data support for subsequent control and realize intelligent prediction of drive demand, thereby determining the cylinder drive demand information and improving control accuracy. Control analysis is performed according to the cylinder drive demand information, and the prediction results are converted into actual energy-saving control commands. The execution of these commands is synchronously recorded to obtain real-time cylinder operating information. When the machine tool cylinder is in the return braking phase, an energy recovery mode is activated to convert braking kinetic energy into hydraulic energy and store it in an accumulator, realizing the recovery and storage of kinetic energy. Dynamic control analysis is performed based on the pressure data from the accumulator and the real-time cylinder operating information to generate collaborative control parameters. These parameters are used to drive the machine tool cylinder in an energy-saving manner, forming a closed-loop energy-saving drive strategy. This ensures that the drive strategy is continuously optimized according to the latest operating conditions, achieving intelligent energy-saving control throughout the entire lifecycle.

[0016] In summary, this application realizes the transformation of machine tool cylinder from static control to dynamic intelligent energy-saving control, improves the accuracy of drive response and energy recovery efficiency, and effectively improves the operating efficiency and green manufacturing level of machine tool cylinder under complex working conditions.

[0017] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0018] Figure 1 This is a schematic flowchart of an energy-saving drive method for machine tools provided in an embodiment of this application.

[0019] Figure 2 This is a schematic diagram of the structure of an energy-saving drive system for machine tools provided in an embodiment of this application.

[0020] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0021] Explanation of reference numerals in the attached diagram: Drive demand prediction module 10, energy-saving control analysis module 20, energy recovery module 30, dynamic control analysis module 40, bus 300, receiver 301, processor 302, transmitter 303, memory 304, bus interface 305. Detailed Implementation

[0022] This application provides an energy-saving drive method, system, and electronic device for machine tools. It solves the technical problem in the prior art where the drive control strategy is disconnected from the actual working condition of the cylinder due to the lack of a dynamic perception and feedback mechanism for real-time load and energy status of the machine tool cylinder, resulting in the failure to effectively recover and utilize the return kinetic energy. It achieves the technical effect of constructing a closed-loop energy control mechanism based on real-time working condition analysis and energy status linkage, thereby improving the accuracy of energy-saving drive and the efficiency of energy recovery and utilization of machine tools.

[0023] Example 1, as Figure 1 As shown in the embodiment of this application, an energy-saving drive method for machine tools is provided, the method comprising: Step S100: Monitor the load pressure and movement direction of the machine tool cylinder in real time, generate a dynamic load parameter set to predict the drive of the machine tool cylinder, and determine the cylinder drive demand information.

[0024] Specifically, load pressure refers to the pressure generated by the working resistance experienced by the machine tool cylinder during operation, collected by pressure sensors at both ends of the cylinder. Movement direction refers to the current direction of movement of the machine tool cylinder piston rod (extending or retracting), which can be determined by combining displacement and speed changes. The dynamic load parameter set is a collection of time-related information such as pressure, displacement, and speed, reflecting the dynamic characteristics of the cylinder's operating load. Cylinder drive demand information is the predicted driving force and speed required by the cylinder in future work cycles, used to guide the cylinder's control strategy.

[0025] High-precision pressure sensors installed in the front and rear chambers of the machine tool cylinder are used to collect real-time pressure data at the front and rear ends. Combined with piston displacement and velocity trend data collected by displacement sensors, the current direction of cylinder movement is identified. These load pressure and displacement data are time-aligned to construct a dynamic pressure-displacement-time matrix. From this matrix, parameters such as instantaneous output power, pressure gradient, and power waveform of the cylinder are calculated, generating a dynamic load parameter set. This dynamic load parameter set is input into a drive prediction model. By comparing it with preset machining process data or analyzing it based on a machine learning prediction model, it can predict upcoming load changes and movement trends in advance, thereby outputting cylinder drive demand information, such as target pressure, target speed, or target flow rate.

[0026] This step enables real-time perception and trend prediction of the load status of the machine tool cylinder, providing a feedforward data basis for subsequent energy-saving control, improving the cylinder response speed and control accuracy, avoiding blind pressure or flow supply, and improving drive efficiency.

[0027] Step S200: Perform control analysis according to the cylinder drive demand information, generate energy-saving control instructions, execute the energy-saving control instructions and record synchronously to obtain real-time operating information of the cylinder.

[0028] Specifically, energy-saving control commands are a set of instructions used to control the hydraulic pump and proportional directional valve to perform energy-saving actions, including target flow rate, pressure value, and valve spool opening. Real-time cylinder operating status information is obtained through synchronous recording of the cylinder's status during real-time operation, used for feedback control and optimization of control strategies.

[0029] The cylinder drive demand information output in step S100 is parsed into a target driving force, thereby determining the hydraulic pump output displacement and proportional valve orifice opening range that meet this target driving force. Through multi-objective optimization calculations, considering energy consumption, responsiveness, and mechanical limitations, a set of optimal control parameters is selected. Subsequently, energy-saving control commands are generated and executed to control the hydraulic pump to adjust its output flow and pressure, while simultaneously controlling the valve core displacement of the proportional directional valve to precisely regulate the oil flow direction and speed. During this process, multiple control quantities, such as the hydraulic pump outlet pressure, the proportional directional valve core position, and the cylinder acceleration, are recorded synchronously in real time to generate real-time cylinder operating condition information for subsequent feedback analysis and control compensation.

[0030] This step achieves integrated operation of energy-saving control and operating condition identification through precise control based on predictive information and real-time synchronous recording, significantly improving the responsiveness and adaptability to actual operating conditions, and providing necessary data support for the formation of closed-loop control.

[0031] Step S300: When the machine tool cylinder is in the cylinder return braking stage, the energy recovery mode is activated to convert the braking kinetic energy into hydraulic energy and store it in the accumulator. The accumulator contains the pressure data of the machine tool cylinder.

[0032] Specifically, the return braking phase refers to the braking process when the piston rod returns to its initial position after the hydraulic cylinder has completed its working stroke. At this time, the piston still retains a certain amount of kinetic energy. The energy recovery mode is a working mode that converts the kinetic energy of the hydraulic cylinder during the return braking phase into hydraulic energy and stores it. In this mode, the control system cuts off the main hydraulic circuit and guides the hydraulic oil in the cylinder to the accumulator for energy storage. The accumulator is a device used to store hydraulic energy, and its forms include pneumatic or spring-loaded types, equipped with internal pressure sensors.

[0033] The system determines whether the cylinder has entered the return braking state by calculating the cylinder speed descent rate and the pressure difference gradient between the front and rear chambers. When the machine tool cylinder is in the return braking stage, the energy recovery mode is immediately activated, the main oil supply solenoid valve is closed, and the hydraulic passage to the accumulator is opened simultaneously. At this time, the hydraulic oil in the rod chamber enters the accumulator through the check valve under the push of the piston's retraction inertia, realizing the conversion of kinetic energy into hydraulic energy. The pressure sensor inside the accumulator records the energy storage pressure in real time to calculate its current available energy capacity, and prioritizes the release of this energy in subsequent working stages to achieve energy reuse.

[0034] This step effectively utilizes the residual kinetic energy during the cylinder's return stroke, realizing the recovery and reuse of hydraulic energy, avoiding energy waste in the form of heat, and improving overall energy efficiency.

[0035] Step S400: Perform dynamic control analysis based on the pressure data of the accumulator and the real-time operating information of the hydraulic cylinder to generate collaborative control parameters, and use the collaborative control parameters to drive the machine tool hydraulic cylinder in an energy-saving manner to form a closed-loop energy-saving drive strategy.

[0036] Specifically, the coordinated control parameters are a set of parameters for drive control derived from multiple information sources (such as pressure data and operating condition data). These parameters coordinate the operation of the hydraulic pump and proportional directional valve to achieve energy-saving drive. Pressure data collected by the pressure sensor in the accumulator is fused with real-time operating information of the cylinder to construct a four-dimensional state space including accumulator pressure data, piston speed, load pressure, and unit displacement. Within this four-dimensional state space, the multidimensional correlation coefficients between variables are calculated, key variable combinations (strongly correlated dimension pairs) are selected, and principal component analysis is used to orthogonally map high-dimensional information to a low-dimensional coupling matrix to extract the main control factors. A dynamic programming algorithm is then used to calculate the energy distribution ratio in this matrix, such as how to switch power supply between the pump and accumulator or coordinate the adjustment of the proportional valve opening, thereby generating the final coordinated control parameters. Energy-saving drive of the machine tool cylinder is achieved through these coordinated control parameters, forming a closed-loop energy-saving drive strategy that coordinates and regulates both energy and operating states.

[0037] This step deeply integrates the energy supply status with the cylinder operating conditions, realizes pump-valve coordinated control, and constructs a closed-loop drive system, enabling the machine tool cylinder to achieve dynamic adaptive energy-saving control throughout the entire cycle, significantly improving overall energy efficiency and operational stability.

[0038] Furthermore, in step S100, the load pressure and direction of movement of the machine tool cylinder are monitored in real time to generate a dynamic load parameter set, including: Step S110: The load pressure of the machine tool cylinder is collected in real time by the pressure sensor at the front end of the cylinder and the pressure sensor at the rear end of the cylinder to obtain bidirectional pressure values.

[0039] Step S120: Perform telescopic displacement sensing on the piston rod inside the machine tool cylinder to obtain real-time displacement data and motion speed data.

[0040] Step S130: Align the bidirectional pressure value with the real-time displacement data according to the time series to construct a pressure-displacement phase relationship matrix.

[0041] Step S140: Traverse the pressure-displacement phase relationship matrix to perform operation calculations on the machine tool cylinder and obtain the instantaneous output power of the cylinder.

[0042] Step S150: Perform gradient analysis based on the bidirectional pressure values ​​to determine the pressure gradient values, and calibrate the motion direction based on the real-time displacement data and the motion speed data.

[0043] Step S160: Based on the instantaneous output power identification power waveform information of the hydraulic cylinder, the dynamic load parameter set is determined by integrating the power waveform information with the pressure gradient value and the direction of motion.

[0044] Specifically, pressure sensors installed at the front (rod chamber) and rear (rodless chamber) of the hydraulic cylinder are used to measure the pressure of the hydraulic oil on both sides of the cylinder in real time. The front pressure sensor monitors the pressure when the piston rod extends, and the rear pressure sensor monitors the pressure when the piston rod retracts. The sampling frequency can be set to 50Hz or higher to ensure the timeliness and continuity of the data. Bidirectional pressure values, including both front and rear pressure values, are obtained as time series data.

[0045] The extension and retraction position of the piston rod in the hydraulic cylinder is measured with high precision in real time by a linear encoder or magnetostrictive displacement sensor installed in the cylinder. Real-time displacement data is obtained, which is the displacement value of the piston rod in real time operation, usually in millimeters (mm). Then, the motion speed data is obtained by calculating the time derivative of the displacement data, which is the speed value of the piston rod in real time operation, usually in millimeters per second (mm / s).

[0046] The collected bidirectional pressure values ​​and real-time displacement data are aligned according to a time series. Specifically, the pressure and displacement data are matched according to timestamps to ensure that the pressure and displacement values ​​correspond at each time point. Then, a pressure-displacement phase relationship matrix is ​​constructed, where the rows of the matrix represent the time series and the columns represent the pressure and displacement values. This matrix structure allows for a direct observation of the dynamic relationship between pressure and displacement.

[0047] By iterating through the pressure-displacement phase relationship matrix for each time point, the pressure and displacement values ​​are calculated, and the instantaneous output power of the cylinder is determined using the hydraulic power formula. An example of the hydraulic power formula is as follows: P(t) = (F1(t) - F2(t)) × A × V(t), where A is the effective piston area, V(t) is the instantaneous velocity, F1(t) is the front-end pressure value, and F2(t) is the rear-end pressure value. By iterating through all time points, a power waveform in time series form is obtained.

[0048] Gradient analysis is performed on the bidirectional pressure values. The first derivative of the pressure time series of the front and rear pressure values ​​is calculated as the pressure gradient value. Taking the front pressure value F1(t) as an example, the formula for calculating the front chamber pressure gradient is as follows: ∇F1(t)=dF1(t) / dt. The formula for calculating the rear pressure gradient is similar. Simultaneously, the direction of piston rod movement is determined based on real-time displacement and velocity data. A positive velocity value indicates the piston rod is extending forward; a negative velocity value indicates the piston rod is retracting; and a velocity value close to zero indicates a stationary or oscillating state. When calculating the pressure gradient value, a pressure source representing the load is selected for gradient analysis based on the direction of movement. The front pressure value is selected when the piston rod extends forward, and the rear pressure value is selected when the piston rod retracts. The pressure gradient value and the direction of movement are injected into the aforementioned pressure-displacement phase relationship matrix to form structured dynamic monitoring data.

[0049] The power waveform is analyzed for its dominant frequency, peak value, and volatility. Combined with the pressure gradient and motion direction information at the corresponding time, a composite feature vector is formed to construct a dynamic load parameter set, which includes, but is not limited to, power feature values, pressure gradient feature values, and motion direction feature values.

[0050] Furthermore, in step S100, the drive prediction of the machine tool hydraulic cylinder is performed, and the hydraulic cylinder drive demand information is determined, including: Step S170: Based on the pressure gradient value, record the changes according to the power waveform information to obtain the pressure gradient change rate.

[0051] Step S180: Retrieve the machining process data of the machine tool cylinder, and match it with the pressure gradient change rate to determine the machining load characteristic information.

[0052] Step S190: When the pressure gradient change rate exceeds a preset threshold, activate the high-frequency sampling mode, and use the high-frequency sampling mode to sample the pressure of the machine tool cylinder according to the processing load characteristic information to obtain the pressure pulsation spectrum characteristics.

[0053] Step S1X0: Based on the pressure pulsation spectrum characteristics, identify and extract the load mutation type, and determine the cylinder drive demand information by driving prediction according to the processing cycle.

[0054] Specifically, the pressure gradient change rate refers to the magnitude of change in the pressure gradient value over time, used to characterize the drastic nature of load changes, and is expressed as the first derivative (i.e., the second derivative) of the gradient. After calculating the pressure gradient, the pressure gradient value and power waveform information are simultaneously sampled and compared. Using a sliding window method, the degree of gradient value change over time is statistically analyzed to calculate the pressure gradient change rate. For example, using a 100ms periodic window, the gradient change trend is compared before and after, and the acceleration of pressure change is extracted. This process helps detect the response characteristics during drastic load changes (such as tool contact, cutting impact, etc.). Combining this with the time periods of power rise, fall, or fluctuation in the power waveform allows for more accurate location of the load transition range. For example, within a certain sampling period: if the pressure gradient increases from 0.8 MPa / s to 2.4 MPa / s, the change rate is 1.6 MPa / s. 2 The power waveform shows a sudden jump from a low, stable value to a peak value, indicating that the machine tool has entered a high-load segment; based on this, it can be inferred that it is entering the main machining stage.

[0055] Machine tool cylinder machining process data refers to the standard load patterns, action sequences, and drive characteristic parameters of the machine tool under different machining tasks, such as feed rate and depth of cut. The machining load characteristic information is the result of load pattern recognition obtained after matching and analyzing the current actual load change trend with the standard process. Based on the currently collected pressure gradient change rate, it is compared with pre-stored machine tool cylinder machining process data. Each machining program (such as roughing, finishing, rapid traverse, and rapid retraction) corresponds to a different pressure-time change template. Through pattern matching algorithms (such as DTW or cosine similarity-based comparison), the process template closest to the current change rate is found, and the load characteristic type (such as steady-state cutting, intermittent impact, and no-load transition) is identified. This load type will be used as a reference for setting drive control parameters. For example, when the pressure gradient change rate is detected to be at a medium level and matches the standard template "roughing start segment," the load characteristic is marked as the "gradual pressurization, stable output" mode. By combining historical process data for load trend recognition, the drive control gains the ability to understand the task context, significantly improving the rationality and robustness of predictive control.

[0056] The preset threshold is a critical value for the rate of change of pressure gradient, set based on experimental or historical data experience, used to determine whether the hydraulic cylinder is currently operating during a period of drastic load fluctuation. Pressure pulsation spectrum characteristics refer to the frequency domain indicators of the pressure signal obtained through Fourier transform, including the dominant frequency, peak amplitude, and spectral energy distribution. The system monitors the changing trend of the pressure gradient value in real time. When the rate of change of pressure gradient exceeds the preset threshold, a high-frequency sampling mode is triggered, increasing the sampling frequency to 2-10 times higher than the normal mode. This allows for sampling of the machine tool hydraulic cylinder pressure signal data at a higher frequency, capturing rapidly changing pressure signals. Subsequently, an appropriate window length, such as 200ms, is selected based on the processing load characteristics. A sampling segment is extracted based on this window length, and a Fast Fourier Transform is performed on the pressure signal within it to obtain the pressure spectrum. Pressure pulsation spectrum characteristics, including the dominant frequency peak, spectral energy density, and frequency offset, are then extracted.

[0057] The pressure pulsation spectrum features are input into a pre-trained classification model (such as a support vector machine or random forest) for identification, determining the type of load mutation. The load mutation type is a classification label for abnormal processing events or load fluctuations, such as "rapid loading," "backlash peak," or "short-term overpressure." Based on the identified mutation type and historical operating trends, a regression prediction model (such as a long short-term memory network or random forest regressor) trained on historical data is invoked to predict the required driving pressure and flow rate of the cylinder in the next 1-2 cycles, outputting cylinder driving demand information, including target pressure, flow rate, and movement speed. This information will be used to generate subsequent energy-saving control parameters, achieving efficient and responsive energy distribution control.

[0058] The training dataset for the classification model is constructed based on historical sampled data, containing labeled spectrum-event pairs. The specific training process is as follows: First, pressure signal data under multiple actual processing conditions are collected, and a Fast Fourier Transform is performed to extract spectral features such as dominant frequency, amplitude spectrum, and spectral energy, forming a feature vector set. Based on manual annotation or processing records, samples are labeled with load mutation types such as "normal," "rapid loading," "recoil peak," and "short-term overpressure," constructing a training label set. Subsequently, the feature set is standardized using the scikit-learn library in Python, and SVM and random forest classification models are trained separately. The model accuracy is evaluated using cross-validation, and the best-performing model is selected as the classification model.

[0059] An example of the regression prediction model training process is as follows: Extract historical pressure and flow data (e.g., 5 seconds of data sampled at 10Hz) from the training dataset of the above classification model within a few seconds before and after each load mutation event. Construct a sliding window as the input sequence for the Long Short-Term Memory (LSTM) network, and use the actual pressure and flow within the next 1-2 processing cycles as the prediction target. Build a standard LSTM network using TensorFlow or PyTorch, setting up three layers of LSTM network units and adding a fully connected output layer. Train the model using the minimum mean squared error (MSE) as the loss function. After training, the model can be used in online systems to dynamically drive demand prediction using a sliding window approach. For resource-constrained or response-time-sensitive scenarios, a Random Forest Regressor (RF Regression) can also be used as a lightweight alternative.

[0060] Furthermore, in step S200, control analysis is performed according to the cylinder drive demand information to generate energy-saving control commands, including: Step S210: Based on the hydraulic cylinder drive demand information, analyze the data to determine the target driving force, perform control constraint analysis according to the target driving force, and determine the control parameter constraint set.

[0061] Step S220: Perform multi-objective optimization calculations on the machine tool cylinder according to the control parameter constraint set to generate a hydraulic pump displacement solution set.

[0062] Step S230: Set the feasible region of proportional valve opening, filter the hydraulic pump displacement solution set according to the feasible region of proportional valve opening, construct the displacement adjustment curve, and formulate the valve opening sequence based on the displacement adjustment curve.

[0063] Step S240: Add the valve opening sequence to the energy-saving control command.

[0064] Specifically, key elements in the hydraulic cylinder drive demand information are analyzed to extract parameters such as target pressure, target flow rate, and holding time, and the target driving force required by the hydraulic cylinder is calculated. For example, when the predicted output pressure is 7.5 MPa and the flow rate is 12 L / min, the actual required thrust can be calculated by combining the cylinder diameter and piston area. Subsequently, a set of control parameter constraints is formed by combining the current hydraulic cylinder status (minimum displacement of the hydraulic pump, maximum power limit, response delay under the influence of current oil temperature, etc.), including pump speed limit, minimum throttling pressure difference, response time limit, etc.

[0065] Using the driving objective as the optimization objective function, with energy saving as the main objective (such as minimum input energy or energy consumption per unit thrust), and the aforementioned set of control parameter constraints as the boundary, a mathematical optimization model is established. This model can be solved using optimization algorithms such as linear programming, particle swarm optimization, or mixed integer programming. The main model variables include hydraulic pump displacement and driving duration. The output is a set of hydraulic pump displacement solutions that meet the conditions, representing multiple pump control schemes that can achieve the target driving force within the constraints.

[0066] Based on the hydraulic pump displacement solution set and considering the structural limitations and response characteristics of the proportional directional valve, a feasible region for the proportional valve opening is defined. This region represents the safe and effective operating range of the proportional directional valve, such as the linear response range between 0% and 85%. The obtained hydraulic pump displacement solution set is then filtered based on this feasible region, eliminating solutions that exceed it. A displacement adjustment curve is constructed based on the filtered displacement values ​​and corresponding proportional valve openings. This curve reflects the dynamic relationship between the hydraulic pump displacement and the proportional valve opening. Finally, based on the displacement adjustment curve, a valve opening sequence is established, defining the opening values ​​of the proportional directional valve at different time points or operating stages.

[0067] Finally, the valve opening sequence is combined with the corresponding pump displacement adjustment value to form a complete energy-saving control command, which includes the following format: start timestamp, pump displacement setpoint (variable over time), and target valve opening sequence (executed periodically). This energy-saving control command is transmitted to the pump control unit and proportional valve unit via a field controller or industrial bus to execute the energy-saving drive process of the hydraulic cylinder.

[0068] Furthermore, in step S200, the energy-saving control command is executed synchronously to obtain real-time operating information of the hydraulic cylinder, including: Step S250: Execute the energy-saving control command to synchronously record the machine tool cylinder and obtain multiple real-time control data, including real-time pressure data at the pump outlet, real-time displacement data of the valve core, and real-time acceleration data of the cylinder.

[0069] Step S260: Integrate the real-time pressure data of the pump outlet, the real-time displacement data of the valve core, and the real-time acceleration data of the cylinder according to the timestamp of the machine tool cylinder's running sequence identifier to obtain the real-time operating log of the cylinder.

[0070] Step S270: Based on the comparison and analysis of the real-time operating log of the hydraulic cylinder and the hydraulic cylinder drive demand information, a set of real-time operating condition feedback parameters is generated.

[0071] Step S280: Perform dynamic compensation on the real-time operating log of the hydraulic cylinder according to the real-time operating condition feedback parameter set to obtain the real-time operating information of the hydraulic cylinder.

[0072] Specifically, after executing the generated energy-saving control command, a real-time data synchronization mechanism is activated to collect and mark the operating status of key components of the hydraulic cylinder at high frequency, forming a real-time operating condition log of the hydraulic cylinder operation process. By comparing and analyzing this log with drive demand information, a set of operating condition feedback parameters is further formed, realizing data closed-loop compensation and control effect evaluation.

[0073] During the execution of energy-saving control commands, key operational data during the control process are collected through sensor devices: real-time pressure data at the pump outlet is obtained through a pressure sensor installed at the hydraulic pump outlet to reflect the current driving pressure of the hydraulic cylinder; real-time displacement data of the valve core is collected through a displacement sensor integrated inside the proportional directional valve to monitor the actual response behavior of the valve core; and real-time acceleration data of the hydraulic cylinder is collected through a high-sensitivity acceleration sensor fixed on the piston rod or cylinder body of the machine tool hydraulic cylinder to reflect the dynamic response state of the hydraulic cylinder. All of the above data are collected synchronously at a uniform period (e.g., every 5 milliseconds) to form multiple real-time control data sets.

[0074] The collected real-time pump outlet pressure data, valve core displacement data, and cylinder acceleration data are aligned according to the same time base and marked with a unified system timestamp to construct a real-time cylinder operating log, which fully records the cylinder's operating status under energy-saving control commands. This real-time cylinder operating log supports a sliding window storage mechanism and can be updated in real time for online analysis.

[0075] The real-time operating logs of the hydraulic cylinders are compared item by item with the predicted hydraulic cylinder drive demand information. Differences are analyzed, and pressure deviation, valve core displacement deviation, and acceleration response deviation are calculated to obtain a real-time operating condition feedback parameter set. Here, pressure deviation is the difference between the target pressure and the actual pump outlet pressure; valve core displacement deviation is the response deviation between the predicted valve position and the actual valve core displacement; and acceleration response deviation is the error or hysteresis behavior between the predicted hydraulic cylinder acceleration and the actual acceleration. The predicted valve position is obtained by mapping the target flow rate to a known flow-valve position mapping table; the predicted hydraulic cylinder acceleration is calculated based on the target pressure using the motion equation (F=m*a, where F is the target driving force in Newtons, m is the effective mass in kilograms, including the mass of the cylinder piston, connecting parts, and part of the driven object; a is the predicted hydraulic cylinder acceleration in square meters per second). The feedback parameters are dynamically generated in function form, recording the degree of matching between the system response and the control command over different time periods.

[0076] Based on the real-time operating condition feedback parameter set, data segments with errors in the real-time operating condition log of the hydraulic cylinder are compensated and corrected. Specifically, this includes: smoothing the pressure curve using the exponential moving average method to correct the trend of pressure deviation; correcting the valve position response delay by backtracking the response time to fill in the actual valve control execution time; and suppressing noise and performing feature regression on abrupt data in the acceleration signal to enhance the responsiveness to actual load dynamics. The compensated dataset constitutes the final real-time operating condition information of the hydraulic cylinder, which will serve as an important basis for subsequent energy efficiency analysis and collaborative control strategy formulation in closed-loop control.

[0077] Furthermore, the process of determining whether the machine tool cylinder is in the return braking phase includes: Step 1: Calculate the decrease in piston displacement speed of the machine tool cylinder based on the real-time acceleration data of the cylinder to obtain the speed decrease rate.

[0078] Step 2: Based on the bidirectional pressure value, the pressure value at the front end of the cylinder and the pressure value at the rear end of the cylinder are subtracted to generate a pressure difference between the front and rear chambers. The pressure difference between the front and rear chambers includes pressure difference gradient data, which can be positive gradient data or negative gradient data.

[0079] Step 3: When the speed decrease rate exceeds the preset critical threshold and the pressure difference gradient data is negative gradient data, it is determined that the machine tool cylinder is in the cylinder return braking stage.

[0080] Specifically, the change in piston displacement velocity is calculated using real-time acceleration data from the hydraulic cylinder. Acceleration data is acquired in real-time by an accelerometer mounted on the piston rod, and the piston displacement velocity is then calculated through integration. The velocity descent rate is obtained by calculating the time derivative of the velocity data. The acceleration signal of the machine tool hydraulic cylinder piston is acquired by the accelerometer and converted into a velocity change trend. The piston velocity sequence is calculated using first-order numerical integration, and the velocity descent rate is obtained by derivative processing. This velocity descent rate reflects the deceleration characteristics of the hydraulic cylinder piston over a short period and is used to initially determine whether there is a return deceleration trend.

[0081] Pressure values ​​are collected in real time by pressure sensors at the front and rear ends of the hydraulic cylinder. The pressure difference between the front and rear chambers is calculated by subtracting the pressure values ​​at the front and rear ends. The pressure difference gradient data is obtained by calculating the time derivative of the pressure difference. The gradient (rate of change) of this pressure difference sequence is calculated to obtain the pressure difference gradient data. If the pressure difference decreases with time, it is a negative gradient, indicating that the pressure in the front chamber is decreasing while the pressure in the rear chamber is increasing; conversely, if the pressure difference increases with time, it is a positive gradient, indicating that the pressure in the front chamber is increasing while the pressure in the rear chamber is decreasing. If the rate of decrease in speed is greater than a preset critical threshold and the pressure difference gradient at the current moment is a negative gradient, the machine tool hydraulic cylinder is determined to be in the return braking phase.

[0082] Furthermore, step S300 includes: Step S310: When the machine tool cylinder is in the cylinder return braking stage, the energy recovery mode is activated. The main oil supply pipeline solenoid valve is closed and switched to the energy recovery circuit through the energy recovery mode, so that the hydraulic oil in the rod chamber is injected into the accumulator through the one-way valve group.

[0083] Step S320: Collect pressure data of the accumulator in real time through the pressure sensor inside the accumulator, and calculate the energy storage capacity of the accumulator based on the pressure data.

[0084] Step S330: When the pressure data reaches the first critical value, switch to bypass overflow state, and use the hydraulic energy stored in the accumulator to drive the machine tool cylinder in the early stage of the next working stroke.

[0085] Specifically, when the machine tool cylinder is determined to be in the return braking phase, the energy recovery mode is activated, converting the hydraulic kinetic energy originally consumed during the braking process into reusable hydraulic energy. In this mode, the main oil supply line solenoid valve is closed, while the energy recovery branch is opened, allowing the hydraulic oil in the rod chamber of the cylinder to be conducted to the accumulator through the one-way valve assembly. This process does not rely on an external hydraulic pump for power; instead, it utilizes the high-pressure fluid flow formed by the piston compressing the hydraulic oil due to inertia to achieve passive reinjection.

[0086] During energy recovery, pressure sensors installed inside the accumulator or on the inlet pipeline continuously collect the accumulator pressure value. Combining the accumulator's internal volume and the accumulator's gas pre-charge pressure P0, the current energy storage capacity is estimated based on an isothermal compression model. The calculation formula is as follows: ,in, Let V be the energy storage capacity at time t, Pacc(t) be the accumulator pressure at time t, V be the accumulator internal volume, P0 be the accumulator gas pre-charge pressure, and k be the polytropic index of the compression process, with a typical value of 1.2 (approximately isothermal).

[0087] When the pressure sensor detects that the accumulator pressure has reached the preset first critical value, it is considered that the energy storage has reached the upper limit allowed by the accumulator. At this time, the system switches to bypass overflow mode, allowing the return fluid to be discharged through the overflow valve to prevent overpressure damage to the accumulator. In the next processing cycle (when the next main cylinder working stroke begins), the stored hydraulic energy is preferentially released from the accumulator. Through the bypass circuit control logic, the main cylinder is assisted in driving the operation, reducing the load on the hydraulic pump, thereby achieving energy saving and consumption reduction.

[0088] Furthermore, step S400 includes: Step S410: Construct a four-dimensional state space based on the pressure data of the accumulator and the real-time operating information of the hydraulic cylinder.

[0089] Step S420: Calculate the multidimensional correlation coefficient according to the four-dimensional state space, perform correlation analysis based on the multidimensional correlation coefficient, and extract multiple strongly correlated dimension pairs.

[0090] Step S430: Perform principal component analysis using the multiple strongly correlated dimensions, perform orthogonal mapping based on the analysis results, and construct the pressure-operating condition coupling matrix.

[0091] Step S440: Perform dynamic programming based on the pressure-operating condition coupling matrix to solve for the energy control allocation ratio of the machine tool cylinder, and generate the cooperative control parameters based on the energy control allocation ratio.

[0092] Specifically, a four-dimensional state space is constructed based on the accumulator pressure data and the real-time operating information of the hydraulic cylinder to describe the operating dynamics of the hydraulic cylinder, including accumulator pressure data (MPa), piston speed (mm / s), hydraulic cylinder load pressure (MPa), and unit displacement (mm). The accumulator pressure data and the real-time operating information of the hydraulic cylinder at each time point constitute a four-dimensional point, forming a sample point in the four-dimensional state space.

[0093] Pearson correlation coefficients are calculated for each pair of data in the state space to obtain a multidimensional correlation coefficient matrix. Dimension pairs corresponding to all multidimensional correlation coefficients greater than a preset threshold in the multidimensional correlation coefficient matrix are extracted as strongly correlated dimension pairs, such as (accumulator pressure, load pressure) and (piston speed, unit displacement).

[0094] Principal component analysis (PCA) was used to reduce the dimensionality of the strongly correlated dimensions mentioned above. The first two principal components were extracted and orthogonally mapped to form a two-dimensional coupling matrix, namely the pressure-operating condition coupling matrix. This pressure-operating condition coupling matrix reflects the "operating condition-pressure" coupling mode under different state combinations.

[0095] An energy consumption cost function is introduced based on the pressure-operating condition coupling matrix: Where J represents the total energy consumption cost, indicating the total cost or energy expenditure of the system's power supply during the entire working cycle (time step t=1 to T), and t represents a discrete time point, such as sampling once every 10ms, for a total of T sampling points. Q1(t) is the hydraulic pump's oil supply flow rate at time t, representing the instantaneous flow rate supplied by the hydraulic pump to the cylinder alone. The pump flow rate is usually controlled by a servo motor or variable displacement mechanism, and the energy consumption is roughly proportional to the square of the flow rate, so a square term is used to represent its consumption cost. Q2(t) is the accumulator's oil supply flow rate at time t, representing the conversion of hydraulic energy released from the accumulator into flow rate, also represented by a square term to represent the accumulator's consumption cost. α is the hydraulic pump flow rate cost weight; the larger the value, the more the cylinder tends to reduce pump power supply and prioritize the use of the accumulator to reduce electrical energy consumption. β is the accumulator flow rate cost weight; a value less than α indicates that accumulator power supply is a low-cost operation. The dynamic programming (DP) algorithm is used to optimize the energy supply ratio between the hydraulic pump and the accumulator in each cycle, obtain the energy control allocation ratio, and generate the cooperative control parameters accordingly.

[0096] In summary, the energy-saving drive method for machine tools provided in this application has the following beneficial effects: This application combines multi-source sensing acquisition, energy recovery, and collaborative control to significantly improve the energy efficiency and response accuracy of machine tool cylinders. First, the real-time load status of the machine tool cylinder is acquired through bidirectional pressure sensors and displacement and velocity sensors. A pressure-displacement phase relationship matrix is ​​constructed, instantaneous output power is calculated, and a dynamic load parameter set is extracted through pressure gradient and power waveform to achieve accurate modeling and drive prediction of cylinder load behavior. Then, a preset threshold for the pressure gradient change rate is set to determine whether a high-frequency sampling mode is triggered. Fast Fourier Transform is used to extract pressure pulsation spectrum features to further identify the type of load abrupt changes. Combined with the machining cycle, future drive requirements are predicted to obtain cylinder drive demand information. Based on this cylinder drive demand information, control parameter analysis and multi-objective optimization are performed to generate a hydraulic pump displacement solution set and valve opening sequence, forming energy-saving control commands. By synchronously recording pump pressure, valve position, and cylinder acceleration data in real time, a real-time cylinder operating condition log is constructed. Then, through time-series calibration and difference analysis, a real-time operating condition feedback parameter set is obtained to achieve dynamic compensation for the cylinder's operating state. The system determines whether the hydraulic cylinder is in the return braking phase by analyzing the speed drop rate and pressure difference gradient. During this phase, an energy recovery mode is activated, storing the braking kinetic energy hydraulically in an accumulator. When the accumulator pressure reaches a critical value, it switches to bypass power supply to prioritize energy utilization. A four-dimensional state space is constructed using accumulator pressure, piston speed, load pressure, and unit displacement. A pressure-operating condition coupling matrix is ​​generated through correlation analysis and principal component analysis. Based on this matrix, dynamic programming is performed to solve for the energy control allocation ratio between the hydraulic pump and the accumulator, generating pump-valve coordinated control parameters to achieve closed-loop energy-saving drive. Overall, this embodiment of the application realizes the transformation of machine tool hydraulic cylinders from static control to dynamic intelligent energy-saving control, improving the accuracy of drive response and energy recovery efficiency, and effectively enhancing the operating efficiency and green manufacturing level of machine tool hydraulic cylinders under complex operating conditions.

[0097] Example 2, as Figure 2 As shown, based on the same inventive concept as in Embodiment 1 above, this application provides an energy-saving drive system for machine tools, the system comprising: The drive demand prediction module 10 is used to monitor the load pressure and movement direction of the machine tool cylinder in real time, generate a dynamic load parameter set to predict the drive of the machine tool cylinder, and determine the cylinder drive demand information.

[0098] The energy-saving control analysis module 20 is used to perform control analysis according to the cylinder drive demand information, generate energy-saving control commands, execute the energy-saving control commands and record synchronously to obtain real-time operating information of the cylinder.

[0099] The energy recovery module 30 is used to activate the energy recovery mode when the machine tool cylinder is in the return braking stage to convert the braking kinetic energy into hydraulic energy and store it in the accumulator. The accumulator contains the pressure data of the machine tool cylinder.

[0100] The dynamic control analysis module 40 is used to perform dynamic control analysis based on the pressure data of the accumulator and the real-time operating information of the hydraulic cylinder, generate collaborative control parameters, and use the collaborative control parameters to perform energy-saving drive on the machine tool hydraulic cylinder to form a closed-loop energy-saving drive strategy.

[0101] Furthermore, in this embodiment of the application, the demand forecasting module 10 is also used to perform the following steps: The load pressure of the machine tool hydraulic cylinder is collected in real time by pressure sensors at the front and rear ends of the cylinder to obtain bidirectional pressure values. The piston rod inside the cylinder is sensed for extension and retraction displacement to obtain real-time displacement and velocity data. The bidirectional pressure values ​​and the real-time displacement data are aligned according to a time series to construct a pressure-displacement phase relationship matrix. The hydraulic cylinder is then traversed through the pressure-displacement phase relationship matrix to perform operational calculations and obtain the instantaneous output power. Gradient analysis is performed based on the bidirectional pressure values ​​to determine the pressure gradient value. The direction of motion is calibrated based on the velocity data and the real-time displacement data. The power waveform information is identified based on the instantaneous output power of the cylinder. The dynamic load parameter set is determined by integrating the power waveform information with the pressure gradient value and the direction of motion.

[0102] Furthermore, in this embodiment of the application, the demand forecasting module 10 is also used to perform the following steps: Based on the pressure gradient value, the changes are recorded according to the power waveform information to obtain the pressure gradient change rate; the machine tool cylinder machining process data is retrieved, and the pressure gradient change rate is matched with the machine tool cylinder machining process data to determine the machining load characteristic information; when the pressure gradient change rate exceeds a preset threshold, a high-frequency sampling mode is activated, and the machine tool cylinder is pressure sampled according to the machining load characteristic information through the high-frequency sampling mode to obtain the pressure pulsation spectrum characteristics; based on the pressure pulsation spectrum characteristics, the load mutation type is identified and the cylinder drive demand information is determined by driving prediction according to the machining cycle.

[0103] Furthermore, in this embodiment of the application, the energy-saving control analysis module 20 is also used to perform the following steps: Based on the cylinder drive demand information, the target driving force is determined through analysis. Control constraint analysis is then performed according to the target driving force to determine the control parameter constraint set. Multi-objective optimization calculations are then performed on the machine tool cylinder according to the control parameter constraint set to generate a hydraulic pump displacement solution set. A feasible region for the proportional valve opening is set, and the hydraulic pump displacement solution set is filtered according to this region to construct a displacement adjustment curve. A valve opening sequence is then formulated based on the displacement adjustment curve. Finally, the valve opening sequence is added to the energy-saving control command.

[0104] Furthermore, in this embodiment of the application, the energy-saving control analysis module 20 is also used to perform the following steps: The energy-saving control command is executed to synchronously record the machine tool cylinder, obtaining multiple real-time control data, including real-time pump outlet pressure data, real-time valve core displacement data, and real-time cylinder acceleration data. The real-time pump outlet pressure data, valve core displacement data, and cylinder acceleration data are integrated according to the machine tool cylinder's runtime sequence identifier timestamp to obtain a real-time cylinder operating log. Based on the comparison and analysis between the real-time cylinder operating log and the cylinder drive demand information, a real-time operating feedback parameter set is generated. The real-time operating log is dynamically compensated according to the real-time operating feedback parameter set to obtain the real-time cylinder operating information.

[0105] Furthermore, in this embodiment of the application, the energy recovery module 30 is also used to perform the following steps: Based on the real-time acceleration data of the hydraulic cylinder, the piston displacement speed of the machine tool hydraulic cylinder is calculated to obtain the speed decrease rate; based on the bidirectional pressure value, the pressure value at the front end of the hydraulic cylinder and the pressure value at the rear end of the hydraulic cylinder are subtracted to generate the pressure difference between the front and rear chambers. The pressure difference between the front and rear chambers includes pressure difference gradient data, which can be positive or negative gradient data; when the speed decrease rate exceeds a preset critical threshold and the pressure difference gradient data is negative gradient data, it is determined that the machine tool hydraulic cylinder is in the hydraulic cylinder return braking stage.

[0106] Furthermore, in this embodiment of the application, the energy recovery module 30 is also used to perform the following steps: When the machine tool cylinder is in the return braking phase, the energy recovery mode is activated. The main oil supply line solenoid valve is closed and switched to the energy recovery circuit, allowing hydraulic oil in the rod chamber to be injected into the accumulator through the one-way valve group. The pressure data of the accumulator is collected in real time by the pressure sensor inside the accumulator, and the energy storage capacity of the accumulator is calculated based on the pressure data. When the pressure data reaches the first critical value, the bypass overflow state is switched, and the hydraulic energy stored in the accumulator is used preferentially to drive the machine tool cylinder at the beginning of the next working stroke.

[0107] Furthermore, in this embodiment of the application, the dynamic control analysis module 40 is also used to perform the following steps: A four-dimensional state space is constructed based on the pressure data of the accumulator and the real-time operating information of the hydraulic cylinder. Multidimensional correlation coefficients are calculated according to the four-dimensional state space, and correlation analysis is performed based on these coefficients to extract multiple strongly correlated dimension pairs. Principal component analysis is then performed using these strongly correlated dimension pairs, and orthogonal mapping is performed based on the analysis results to construct a pressure-operating condition coupling matrix. Dynamic programming is then performed based on the pressure-operating condition coupling matrix to solve for the energy control allocation ratio of the machine tool hydraulic cylinder, and the collaborative control parameters are generated based on the energy control allocation ratio.

[0108] Through the foregoing detailed description of the energy-saving drive method for machine tools, those skilled in the art can clearly understand that the energy-saving drive system for machine tools in this embodiment corresponds to the system disclosed in Embodiment 2, and has corresponding functional modules and beneficial effects as it is similar to the method disclosed in Embodiment 1. For relevant details, please refer to the description in the method section.

[0109] In embodiment three, based on the same inventive concept as the energy-saving drive method for machine tools in embodiment one, this application also provides an electronic device, including: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the steps of the method described in embodiment one.

[0110] like Figure 3 As shown, the bus architecture is represented by bus 300. Bus 300 may include any number of interconnected buses and bridges, connecting various circuits including one or more processors represented by processor 302 and memory represented by memory 304. Bus 300 may also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. Bus interface 305 provides an interface between bus 300 and receiver 301 and transmitter 303. Receiver 301 and transmitter 303 may be the same element, i.e., a transceiver, providing a unit for communicating with various other devices over a transmission medium. Processor 302 is responsible for managing bus 300 and general processing, while memory 304 can be used to store data used by processor 302 during operation.

[0111] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An energy-saving drive method for machine tools, characterized in that, The method includes: Real-time monitoring of the load pressure and movement direction of the machine tool hydraulic cylinder generates a dynamic load parameter set to predict the drive of the machine tool hydraulic cylinder and determine the hydraulic cylinder drive requirements. Based on the cylinder drive demand information, control analysis is performed to generate energy-saving control commands. The energy-saving control commands are executed and synchronously recorded to obtain real-time operating information of the cylinder. When the machine tool cylinder is in the return braking stage, the energy recovery mode is activated to convert the braking kinetic energy into hydraulic energy and store it in the accumulator. The accumulator contains the pressure data of the machine tool cylinder. Dynamic control analysis is performed based on the pressure data of the accumulator and the real-time operating information of the hydraulic cylinder to generate collaborative control parameters. These collaborative control parameters are then used to drive the machine tool hydraulic cylinder in an energy-saving manner, forming a closed-loop energy-saving drive strategy.

2. The energy-saving drive method for machine tools as described in claim 1, characterized in that, Real-time monitoring of the load pressure and direction of motion of machine tool hydraulic cylinders to generate a dynamic load parameter set, including the following methods: The load pressure of the machine tool cylinder is collected in real time by pressure sensors at the front and rear ends of the cylinder to obtain bidirectional pressure values. The piston rod inside the machine tool cylinder is subjected to telescopic displacement sensing to obtain real-time displacement data and motion speed data. Align the bidirectional pressure values ​​with the real-time displacement data according to the time series to construct a pressure-displacement phase relationship matrix; The machine tool cylinder is operated by traversing the pressure-displacement phase relationship matrix to obtain the instantaneous output power of the cylinder; Gradient analysis is performed based on the bidirectional pressure values ​​to determine the pressure gradient values, and the direction of motion is calibrated based on the real-time displacement data and the motion speed data. Based on the instantaneous output power waveform information of the hydraulic cylinder, the dynamic load parameter set is determined by integrating the power waveform information with the pressure gradient value and the direction of motion.

3. The energy-saving drive method for machine tools as described in claim 2, characterized in that, Methods for predicting the drive requirements of machine tool hydraulic cylinders include: Based on the pressure gradient value, the change is recorded according to the power waveform information to obtain the pressure gradient change rate; Retrieve the machining process data of the machine tool hydraulic cylinder, and match it with the pressure gradient change rate to determine the machining load characteristic information; When the pressure gradient change rate exceeds a preset threshold, a high-frequency sampling mode is activated. The high-frequency sampling mode is used to sample the pressure of the machine tool cylinder according to the processing load characteristic information to obtain the pressure pulsation spectrum characteristics. Based on the pressure pulsation spectrum characteristics, the load mutation type is extracted and the cylinder drive demand information is determined by driving prediction according to the processing cycle.

4. The energy-saving drive method for machine tools as described in claim 1, characterized in that, Based on the cylinder drive demand information, control analysis is performed to generate energy-saving control commands. The method includes: Based on the hydraulic cylinder drive demand information, the target driving force is determined by analysis, and the control constraint analysis is performed according to the target driving force to determine the set of control parameter constraints. Perform multi-objective optimization calculations on the machine tool cylinder according to the control parameter constraint set to generate a hydraulic pump displacement solution set. A feasible region for the proportional valve opening is defined, and the hydraulic pump displacement set is filtered according to the feasible region for the proportional valve opening to construct a displacement adjustment curve. Based on the displacement adjustment curve, a valve opening sequence is determined. Add the valve opening sequence to the energy-saving control command.

5. The energy-saving drive method for machine tools as described in claim 2, characterized in that, The method for executing the energy-saving control command and synchronously recording to obtain real-time operating information of the hydraulic cylinder includes: The energy-saving control command is executed to synchronously record the machine tool cylinder, and multiple real-time control data are obtained. The multiple real-time control data include real-time pump outlet pressure data, real-time valve core displacement data, and real-time cylinder acceleration data. The real-time pressure data at the pump outlet, the real-time displacement data of the valve core, and the real-time acceleration data of the hydraulic cylinder are integrated according to the timestamp of the machine tool hydraulic cylinder's running sequence identifier to obtain the real-time operating log of the hydraulic cylinder. Based on the comparison and analysis of the real-time operating log of the hydraulic cylinder and the hydraulic cylinder drive demand information, a set of real-time operating condition feedback parameters is generated. The real-time operating log of the hydraulic cylinder is dynamically compensated according to the real-time operating condition feedback parameter set to obtain the real-time operating condition information of the hydraulic cylinder.

6. The energy-saving drive method for machine tools as described in claim 5, characterized in that, The process of determining whether a machine tool hydraulic cylinder is in the return braking phase includes the following methods: Based on the real-time acceleration data of the hydraulic cylinder, the decrease in piston displacement speed of the machine tool hydraulic cylinder is calculated to obtain the speed decrease rate; Based on the bidirectional pressure value, the pressure value at the front end of the cylinder and the pressure value at the rear end of the cylinder are subtracted to generate a pressure difference between the front and rear chambers. The pressure difference between the front and rear chambers includes pressure difference gradient data, which can be positive gradient data or negative gradient data. When the rate of decrease in speed exceeds a preset critical threshold and the pressure difference gradient data is negative gradient data, it is determined that the machine tool cylinder is in the cylinder return braking stage.

7. The energy-saving drive method for machine tools as described in claim 1, characterized in that, When the machine tool cylinder is in the return braking phase, an energy recovery mode is activated to convert braking kinetic energy into hydraulic energy and store it in an accumulator. The accumulator contains pressure data of the machine tool cylinder. The method includes: When the machine tool cylinder is in the cylinder return braking stage, the energy recovery mode is activated. The main oil supply line solenoid valve is closed and switched to the energy recovery circuit through the energy recovery mode, so that the hydraulic oil in the rod chamber is injected into the accumulator through the one-way valve group. The pressure data of the energy storage device is collected in real time by the pressure sensor inside the energy storage device, and the energy storage capacity of the energy storage device is calculated based on the pressure data. When the pressure data reaches the first critical value, the system switches to bypass overflow mode, and in the early stage of the next working stroke, the hydraulic energy stored in the accumulator is used first to drive the machine tool cylinder.

8. The energy-saving drive method for machine tools as described in claim 1, characterized in that, Dynamic control analysis is performed based on the pressure data of the accumulator and the real-time operating information of the hydraulic cylinder to generate coordinated control parameters. The method includes: A four-dimensional state space is constructed based on the pressure data of the accumulator and the real-time operating information of the hydraulic cylinder. Calculate the multidimensional correlation coefficient according to the four-dimensional state space, perform correlation analysis based on the multidimensional correlation coefficient, and extract multiple strongly correlated dimension pairs; Principal component analysis was performed using the aforementioned multiple strongly correlated dimensions, and orthogonal mapping was performed based on the analysis results to construct the pressure-operating condition coupling matrix. Dynamic programming is performed based on the pressure-operating condition coupling matrix to solve for the energy control allocation ratio of the machine tool cylinder, and the coordinated control parameters are generated based on the energy control allocation ratio.

9. An energy-saving drive system for machine tools, characterized in that, The system is used to execute the energy-saving drive method for machine tools according to any one of claims 1-8, comprising: The drive demand prediction module is used to monitor the load pressure and movement direction of the machine tool cylinder in real time, generate a dynamic load parameter set to predict the drive of the machine tool cylinder, and determine the cylinder drive demand information. The energy-saving control analysis module is used to perform control analysis according to the cylinder drive demand information, generate energy-saving control commands, execute the energy-saving control commands and record synchronously to obtain real-time operating information of the cylinder. An energy recovery module is used to activate the energy recovery mode when the machine tool cylinder is in the return braking phase, converting the braking kinetic energy into hydraulic energy and storing it in an accumulator. The accumulator contains the pressure data of the machine tool cylinder. The dynamic control analysis module is used to perform dynamic control analysis based on the pressure data of the accumulator and the real-time operating information of the hydraulic cylinder, generate collaborative control parameters, and use the collaborative control parameters to perform energy-saving drive on the machine tool hydraulic cylinder to form a closed-loop energy-saving drive strategy.

10. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the energy-saving drive method for a machine tool as described in any one of claims 1-8.

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