Energy-saving driving method, system and electronic device for machine tool

Abstract

The application provides an energy-saving driving method, system and electronic equipment for a machine tool, relates to the technical field of hydraulic oil cylinders, and predicts oil cylinder driving demand information for control analysis by acquiring a machine tool oil cylinder dynamic load parameter set through real-time monitoring, generates and executes energy-saving control instructions, and synchronously records oil cylinder real-time working condition information; an energy recovery mode is started in the oil cylinder return braking stage to convert braking kinetic energy into hydraulic energy and store the hydraulic energy to an accumulator; dynamic control analysis is performed according to pressure data of the accumulator and the oil cylinder real-time working condition information, and collaborative control parameters are generated for energy-saving driving. The application solves the technical problems that, due to the lack of a dynamic perception and feedback mechanism for real-time load and energy state of the machine tool oil cylinder in the prior art, the driving control strategy is disconnected from the actual working condition of the oil cylinder, and return kinetic energy cannot be effectively recovered and utilized, and achieves the technical effects of improving the precision of energy-saving driving and the efficiency of energy recovery and utilization.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of hydraulic cylinders, in particular to an energy-saving driving method, system and electronic device for machine tools. BACKGROUND

[0002] In modern manufacturing equipment, hydraulic cylinders are widely used in various numerical control machine tools, presses, forging equipment and other heavy load operation scenes as the core technology for realizing high load and high response force driving in machine tools. As a power execution element, hydraulic cylinders rely on motor-driven hydraulic pumps to provide constant or variable frequency flow to realize push-pull action.

[0003] The existing energy-saving driving method for machine tools mainly includes frequency conversion control, load-sensitive control and return energy recovery technology. These methods mostly use control strategies based on experience to adjust the driving pressure and flow of the cylinder, or to recover and reuse part of the energy through a throttle valve or a hydraulic accumulator during the cylinder empty return stage. However, due to the lack of real-time load change and energy state sensing ability, the existing methods still have the problems of control response lag, insufficient energy utilization, and non-dynamic adaptability of energy-saving strategies in the driving control process, which cannot realize precise driving and energy optimization according to the actual needs of machine tools under different working conditions, restricting the further improvement of energy-saving effect. SUMMARY

[0004] The present application provides an energy-saving driving method, system and electronic device for machine tools, which solves the technical problems that the existing technology lacks dynamic sensing and feedback mechanism of real-time load and energy state of machine tool cylinders, leading to disconnection between driving control strategy and actual working condition of the cylinder, and ineffective recovery and utilization of return kinetic energy, and achieves the technical effects of constructing a closed-loop energy-saving control mechanism based on real-time working condition analysis and energy state linkage, and improving the precision of machine tool energy-saving driving and energy recovery and utilization efficiency.

[0005] In view of the above problems, in a first aspect, the present application provides an energy-saving driving method for machine tools, which comprises: monitoring the load pressure and movement direction of the machine tool cylinder in real time, generating a dynamic load parameter set for driving prediction of the machine tool cylinder, and determining cylinder driving demand information; performing control analysis according to the cylinder driving demand information, generating energy-saving control instructions, executing the energy-saving control instructions for synchronous recording, and obtaining real-time working condition information of the cylinder; when the machine tool cylinder is in the cylinder return braking stage, starting the energy recovery mode to convert the braking kinetic energy into hydraulic energy and store it in the accumulator, the accumulator containing pressure data of the machine tool cylinder; performing dynamic control analysis according to the pressure data of the accumulator and the real-time working condition information of the cylinder, generating cooperative control parameters, and performing energy-saving driving of the machine tool cylinder through the cooperative control parameters to form a closed-loop energy-saving driving strategy.

[0006] Preferably, the load pressure and movement direction of the machine tool oil cylinder are monitored in real time to generate a dynamic load parameter set, the method comprising: collecting the load pressure of the machine tool oil cylinder in real time through the front-end pressure sensor and the rear-end pressure sensor of the oil cylinder to obtain bidirectional pressure values; sensing the extension and retraction displacement of the piston rod in the machine tool oil cylinder to obtain real-time displacement data and movement speed data; aligning the bidirectional pressure values and the real-time displacement data according to time sequence to construct a pressure-displacement phase relationship matrix; traversing the pressure-displacement phase relationship matrix to calculate the operation of the machine tool oil cylinder to obtain the instantaneous output power of the oil cylinder; performing gradient analysis based on the bidirectional pressure values to determine the pressure gradient value, and calibrating the movement direction based on the real-time displacement data according to the movement speed data; identifying power waveform information based on the instantaneous output power of the oil cylinder, integrating the pressure gradient value, the movement direction according to the power waveform information, and determining the dynamic load parameter set.

[0007] Preferably, the driving prediction of the machine tool oil cylinder is performed to determine the oil cylinder driving demand information, the method comprising: recording the change of the pressure gradient value according to the power waveform information to obtain a pressure gradient change rate; calling the machine tool oil cylinder processing process data, matching the pressure gradient change rate with the machine tool oil cylinder processing process data to determine the processing 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 oil cylinder according to the processing load characteristic information through the high-frequency sampling mode to obtain pressure pulsation spectrum characteristics; identifying based on the pressure pulsation spectrum characteristics, extracting the load mutation type, and performing driving prediction according to the processing cycle to determine the oil cylinder driving demand information.

[0008] Preferably, control analysis is performed according to the oil cylinder driving demand information to generate an energy-saving control instruction, the method comprising: analyzing the oil cylinder driving demand information to determine the target driving force, performing control limit analysis according to the target driving force to determine a control parameter constraint condition set; performing multi-objective optimization calculation on the machine tool oil cylinder according to the control parameter constraint condition set to generate a hydraulic pump displacement solution set; setting a proportional valve opening degree feasible region, screening the hydraulic pump displacement solution set according to the proportional valve opening degree feasible region to construct a displacement adjustment curve, and formulating a valve port opening sequence based on the displacement adjustment curve; adding the valve port opening sequence to the energy-saving control instruction.

[0009] Preferably, the energy-saving control instruction is executed to record synchronously to obtain the real-time working condition information of the oil cylinder, and the method comprises: executing the energy-saving control instruction to record synchronously the oil cylinder of the machine tool to obtain a plurality of real-time control data, wherein the plurality of real-time control data comprises real-time pressure data of a pump outlet, real-time displacement data of a valve core, and real-time acceleration data of the oil cylinder; integrating 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 oil cylinder according to a time stamp of a running time sequence of the oil cylinder to obtain an oil cylinder real-time working condition log; comparing and analyzing the oil cylinder real-time working condition log and the oil cylinder driving demand information to generate a real-time working condition feedback parameter set; and dynamically compensating the oil cylinder real-time working condition log according to the real-time working condition feedback parameter set to obtain the real-time working condition information of the oil cylinder.

[0010] Preferably, the process of determining that the oil cylinder of the machine tool is in the oil cylinder return braking stage comprises: calculating the piston displacement speed of the oil cylinder of the machine tool based on the real-time acceleration data of the oil cylinder to obtain a speed drop rate; generating a front and rear cavity pressure difference by subtracting the oil cylinder front end pressure value from the oil cylinder rear end pressure value based on the bidirectional pressure value, wherein the front and rear cavity pressure difference comprises pressure difference gradient data, and the pressure difference gradient data can be positive gradient data or negative gradient data; and determining that the oil cylinder of the machine tool is in the oil cylinder return braking stage when the speed drop rate exceeds a preset critical threshold and the pressure difference gradient data is negative gradient data.

[0011] Preferably, when the oil cylinder of the machine tool is in the oil cylinder return braking stage, an energy recovery mode is started to convert braking kinetic energy into hydraulic energy and store the hydraulic energy in an accumulator, and the accumulator comprises pressure data of the oil cylinder of the machine tool, and the method comprises: starting the energy recovery mode when the oil cylinder of the machine tool is in the oil cylinder return braking stage, closing the main oil supply pipeline electromagnetic valve and switching to the energy recovery circuit through the energy recovery mode, so that the rod cavity hydraulic oil is injected into the accumulator through the one-way valve group; real-time acquisition of pressure data of the accumulator through the pressure sensor in the accumulator, calculation of the energy storage capacity of the accumulator according to the pressure data; and switching to a bypass overflow state when the pressure data reaches a first critical value, and preferentially using the hydraulic energy stored in the accumulator to drive the oil cylinder of the machine tool at the beginning of the next working stroke.

[0012] Preferably, the pressure data of the accumulator and the real-time working condition information of the oil cylinder are dynamically controlled and analyzed to generate the cooperative control parameters, and the method comprises the following steps: constructing a four-dimensional state space based on the pressure data of the accumulator and the real-time working condition information of the oil cylinder; calculating a multi-dimensional correlation coefficient according to the four-dimensional state space, performing correlation analysis according to the multi-dimensional correlation coefficient, and extracting a plurality of strong correlation dimension pairs; performing principal component analysis on the plurality of strong correlation dimension pairs, performing orthogonal mapping according to the analysis result, and constructing a pressure-working condition coupling matrix; performing dynamic programming based on the pressure-working condition coupling matrix, solving the energy control distribution ratio of the machine tool oil cylinder, and generating the cooperative control parameters according to the energy control distribution ratio.

[0013] In a second aspect, the application further provides an energy-saving driving system for a machine tool, which comprises: a driving demand prediction module for monitoring the load pressure and the movement direction of the machine tool oil cylinder in real time, generating a dynamic load parameter set to predict the driving of the machine tool oil cylinder, and determining the oil cylinder driving demand information; an energy-saving control analysis module for performing control analysis according to the oil cylinder driving demand information, generating an energy-saving control instruction, executing the energy-saving control instruction to perform synchronous recording, and obtaining the real-time working condition information of the oil cylinder; an energy recovery module for starting an energy recovery mode to convert the braking kinetic energy into hydraulic energy and store it in an accumulator when the machine tool oil cylinder is in the oil cylinder backstroke braking stage, the accumulator containing the pressure data of the machine tool oil cylinder; a dynamic control analysis module for dynamically controlling and analyzing the pressure data of the accumulator and the real-time working condition information of the oil cylinder to generate cooperative control parameters, and performing energy-saving driving on the machine tool oil cylinder through the cooperative control parameters to form a closed-loop energy-saving driving strategy.

[0014] In a third aspect, the application further provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps in the above-mentioned energy-saving driving method for a machine tool when executing the computer program.

[0015] One or more technical solutions provided in the application have at least the following beneficial effects:

[0016] By monitoring the load pressure and movement direction of the machine tool oil cylinder in real time, a dynamic load parameter set is constructed to provide data support for subsequent control and realize intelligent prediction of driving demand, determine the oil cylinder driving demand information, and improve control accuracy. According to the oil cylinder driving demand information, control analysis is performed, the prediction result is converted into actual energy-saving control instructions, the energy-saving control instructions are executed for synchronous recording, and the oil cylinder real-time working condition information is obtained; when the machine tool oil cylinder is in the oil cylinder return braking stage, the energy recovery mode is started to convert the braking kinetic energy into hydraulic energy and store it in the accumulator, realizing energy recovery and storage of kinetic energy. According to the pressure data of the accumulator and the real-time working condition information of the oil cylinder, dynamic control analysis is performed to generate collaborative control parameters, and the machine tool oil cylinder is driven by the collaborative control parameters to form a closed-loop energy-saving driving strategy, ensuring that the driving strategy is continuously optimized according to the latest working condition, realizing intelligent energy-saving control in the whole cycle.

[0017] In summary, the present application realizes the transformation of machine tool oil cylinder from static control to dynamic intelligent energy-saving control, improves the accuracy of driving response and energy recovery and utilization efficiency, and effectively improves the operation efficiency and green manufacturing level of machine tool oil cylinder under complex working conditions.

[0018] The above description is only a summary of the technical solutions of the present application. In order to more clearly understand the technical means of the present application, the following specific embodiments of the present application can be implemented according to the content of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the following specific embodiments of the present application are described. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 The flowchart of the energy-saving driving method for machine tool provided by the embodiment of the present application.

[0020] Figure 2 The structural schematic diagram of the energy-saving driving system for machine tool provided by the embodiment of the present application.

[0021] Figure 3 The structural schematic diagram of an electronic device provided by the embodiment of the present application.

[0022] Explanation of reference numerals: driving 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 DESCRIPTION

[0023] 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.

[0024] 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:

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Specifically, the energy-saving control instruction is a set of instructions for controlling the hydraulic oil pump and the proportional directional valve to perform energy-saving actions, including target flow, pressure value, and valve core opening. The real-time working condition information of the oil cylinder is the state information of the oil cylinder in real-time operation obtained by synchronous recording, which is used for feedback control and optimization of control strategy.

[0031] The oil cylinder driving demand information output in step S100 is parsed into target driving force, and then the hydraulic pump output displacement and proportional valve port opening range that meet the target driving force are determined. Through multi-objective optimization calculation, the optimal control parameters are selected by considering energy consumption, responsiveness and mechanical limitations, and then the energy-saving control instruction is generated and executed to control the hydraulic oil pump to adjust its output flow and pressure, and to control the displacement of the proportional directional valve core to accurately adjust the oil flow and speed. In this process, the hydraulic oil pump outlet pressure, proportional directional valve core position, oil cylinder acceleration and other control quantities are recorded in real time to generate oil cylinder real-time working condition information for subsequent feedback analysis and control compensation.

[0032] This step realizes the integrated operation of energy-saving control and working condition identification through accurate control based on prediction information and real-time synchronous recording, significantly improves the response ability and adaptability to the actual running state, and provides necessary data support for forming a closed loop control.

[0033] Step S300: When the machine tool oil cylinder is in the oil cylinder return braking stage, start the energy recovery mode to convert the braking kinetic energy into hydraulic energy and store it in the accumulator, which contains the pressure data of the machine tool oil cylinder.

[0034] Specifically, the return braking stage refers to the braking process of the piston rod returning to the initial position after completing the working stroke, at which time the piston still has some kinetic energy. The energy recovery mode is a working mode that converts the kinetic energy of the oil cylinder in the return braking stage into hydraulic energy and stores it. In this mode, the control system cuts off the main oil circuit and directs the hydraulic oil in the oil cylinder to the accumulator for energy storage. The accumulator is a device for storing hydraulic energy, which can be in the form of air bag or spring, and is equipped with a pressure sensor inside.

[0035] The oil cylinder speed drop rate and the pressure difference gradient between the front and rear cavities are calculated to determine whether the oil cylinder has entered the return braking state. When the machine tool oil cylinder is in the oil cylinder return braking stage, the energy recovery mode is started immediately, the main oil supply electromagnetic valve is closed, and the hydraulic passage to the accumulator is opened. At this time, the hydraulic oil in the rod cavity enters the accumulator through the check valve under the push of the piston retraction inertia, realizing the conversion of kinetic energy to hydraulic energy. The pressure sensor inside the accumulator records the energy storage pressure in real time, which is used to calculate the current available energy capacity, and these energies are preferentially released in the subsequent working stage to realize energy reuse.

[0036] This step effectively utilizes the residual kinetic energy during the return stroke of the oil cylinder, recovers and reuses hydraulic energy, avoids energy waste in the form of heat energy, and improves overall energy efficiency.

[0037] Step S400: Dynamic control analysis is performed according to the pressure data of the accumulator and the real-time working condition information of the oil cylinder to generate a cooperative control parameter, and the machine tool oil cylinder is driven by the cooperative control parameter to form a closed-loop energy-saving driving strategy.

[0038] Specifically, the cooperative control parameter is a parameter set obtained by integrating multiple information sources (such as pressure data and working condition data) for driving control, which is used to coordinate the work of the hydraulic pump and the proportional directional valve to achieve energy-saving driving. The pressure data collected by the pressure sensor in the accumulator and the real-time working condition information of the oil cylinder are multi-dimensionally fused to construct a four-dimensional state space including accumulator pressure data, piston speed, load pressure, and unit displacement. In this four-dimensional state space, the multi-dimensional correlation coefficients between variables are calculated, the key variable combinations (strongly correlated dimensions) are selected, and the principal component analysis method is used to orthogonally map high-dimensional information into a low-dimensional coupling matrix to extract the main control factors. The energy distribution ratio is calculated in the matrix by a dynamic programming algorithm, such as how to switch energy supply between the pump and the accumulator or cooperatively adjust the opening degree of the proportional valve, and then the final cooperative control parameter is generated. The machine tool oil cylinder is driven by the cooperative control parameter to form a closed-loop energy-saving driving strategy, which realizes the coordinated regulation of energy state and working state.

[0039] This step deeply integrates the energy supply state and the working condition of the oil cylinder, realizes pump-valve cooperative control, constructs a closed-loop driving system, and enables the machine tool oil cylinder to realize dynamic adaptive energy-saving control in the whole cycle, significantly improving overall energy efficiency and operation stability.

[0040] Further, in step S100, the load pressure and the movement direction of the machine tool oil cylinder are monitored in real time to generate a dynamic load parameter set, including:

[0041] Step S110: The load pressure of the machine tool oil cylinder is collected in real time by the front-end pressure sensor and the rear-end pressure sensor of the oil cylinder to obtain bidirectional pressure values.

[0042] Step S120: The piston rod in the machine tool oil cylinder is sensed for extension and retraction displacement to obtain real-time displacement data and movement speed data.

[0043] Step S130: Align the bidirectional pressure values and the real-time displacement data in time sequence to construct a pressure-displacement phase relationship matrix.

[0044] Step S140: Traverse the pressure-displacement phase relationship matrix to perform operation calculation on the machine tool oil cylinder to obtain the instantaneous output power of the oil cylinder.

[0045] Step S150: Based on the bidirectional pressure value, gradient analysis is performed to determine the pressure gradient value, and based on the real-time displacement data, the motion direction is calibrated according to the motion speed data.

[0046] Step S160: Based on the instantaneous output power of the oil cylinder, power waveform information is identified, and the power waveform information is integrated according to the pressure gradient value and the motion direction to determine the dynamic load parameter set.

[0047] Specifically, by installing pressure sensors at the front end (rod cavity) and the rear end (rodless cavity) of the oil cylinder, the pressures of the hydraulic oil on both sides of the oil cylinder are measured in real time. The front-end pressure sensor monitors the pressure when the piston rod extends, and the rear-end pressure sensor monitors the pressure when the piston rod retracts. The sampling frequency can be set to 50 Hz or higher to ensure the timeliness and continuity of the data. Bidirectional pressure values including two time series of front-end pressure values and rear-end pressure values are obtained.

[0048] The extension and retraction positions of the oil cylinder piston rod are measured in real time with high precision by installing a linear encoder or a magnetostrictive displacement sensor in the oil cylinder, and real-time displacement data, i.e., the displacement values of the piston rod in real-time operation, are obtained, usually in millimeters (mm). Then, the time derivative of the displacement data is calculated to obtain motion speed data, i.e., the speed values of the piston rod in real-time operation, usually in millimeters per second (mm / s).

[0049] The collected bidirectional pressure values and real-time displacement data are aligned according to the time sequence. Specifically, the pressure data and displacement data are matched according to the time stamp to ensure that the pressure value and displacement value at each time point correspond. Then, a pressure-displacement phase relationship matrix is constructed, with the rows representing the time sequence and the columns representing the pressure values and displacement values. Through this matrix structure, the dynamic change relationship between pressure and displacement can be observed intuitively.

[0050] The pressure values and displacement values at each time point in the pressure-displacement phase relationship matrix are traversed, and the instantaneous output power of the oil cylinder is calculated according to the hydraulic power formula. An example of the hydraulic power formula is as follows: P(t) = (F1(t) - F2(t)) x A x V(t), where A is the effective area of the piston, V(t) is the instantaneous speed, F1(t) is the front-end pressure value, and F2(t) is the rear-end pressure value. All time points are traversed to obtain the power waveform in time sequence.

[0051] Gradient analysis is performed on the bidirectional pressure values, and the first-order derivative is calculated as the pressure gradient value by numerically differentiating the pressure time series of the front-end pressure value and the rear-end pressure value. Taking the front-end pressure value F1(t) as an example, the front cavity pressure gradient calculation formula is as follows: ∇F1(t) = dF1(t) / dt, and the rear-end pressure gradient calculation formula is similar. At the same time, according to the real-time displacement data and the motion speed data, the motion direction of the piston rod is determined. If the speed value is positive, it indicates that the piston rod is extending forward; if the speed value is negative, it indicates that the piston rod is retracting backward; and if the speed value is close to zero, it indicates that it is in a static or oscillation state. When calculating the pressure gradient value, the pressure source representing the load is selected for gradient analysis according to the motion direction. The front-end pressure value is selected when the piston rod is extending, and the rear-end pressure value is selected when the piston rod is retracting. The pressure gradient value and the motion direction are marked in the aforementioned pressure-displacement phase relationship matrix to form structured dynamic monitoring data.

[0052] The main frequency, peak value, fluctuation rate and other characteristic quantities of the power waveform are analyzed, and the pressure gradient and motion direction information at the corresponding moment are combined to form a composite feature vector, and a dynamic load parameter set is constructed. The dynamic load parameter set includes but is not limited to power characteristic values, pressure gradient characteristic values and motion direction characteristic values.

[0053] Further, the driving prediction of the machine tool oil cylinder in step S100 determines the oil cylinder driving demand information, which includes:

[0054] Step S170: Based on the pressure gradient value, record the change according to the power waveform information, and obtain the pressure gradient change rate.

[0055] Step S180: Retrieve the machine tool oil cylinder processing process data, and match the pressure gradient change rate with the machine tool oil cylinder processing process data to determine the processing load characteristic information.

[0056] Step S190: When the pressure gradient change rate exceeds a preset threshold, activate a high-frequency sampling mode, and sample the pressure of the machine tool oil cylinder according to the processing load characteristic information through the high-frequency sampling mode to obtain the pressure pulsation spectrum feature.

[0057] Step S1X0: Based on the pressure pulsation spectrum feature, identify and extract the load mutation type, and perform driving prediction according to the processing period to determine the oil cylinder driving demand information.

[0058] Specifically, the pressure gradient change rate refers to the change amplitude of the pressure gradient value over time, which is used to characterize the severity of load change and is expressed as the first derivative of the gradient (i.e., the second derivative). After completing the pressure gradient calculation, the pressure gradient value is synchronized with the power waveform information for sampling and comparison. Through the sliding window method, the variation degree of the gradient value over time is calculated to obtain the pressure gradient change rate. For example, with a 100ms period window, the gradient change trend before and after is compared, and the pressure change acceleration is extracted. This process helps to detect the response characteristics when the load changes sharply (such as tool contact, cutting impact, etc.). Combined with the time period of power rise, fall or fluctuation in the power waveform, the load transition interval can be more accurately located. For example, in a certain sampling period: the pressure gradient increases from 0.8MPa / s to 2.4MPa / s, and the change rate is 1.6MPa / s 2 ; the power waveform shows a sudden jump from a low stable value to a peak value, indicating that the machine tool enters the high load section; accordingly, it can be inferred that the main machining phase is being entered.

[0059] Machine tool cylinder machining process data refers to the standard load mode, action sequence and driving characteristic parameters of the machine tool under different machining tasks, such as feed speed, cutting depth, etc. The machining load characteristic information is the load mode recognition result obtained after matching analysis between the current actual load change trend and the standard process. According to the current collected pressure gradient change rate, it is compared with the pre-stored machine tool cylinder machining process data. Each machining program (such as rough machining, finishing, fast forward, and fast backward) corresponds to a different pressure-time change template. Through a pattern matching algorithm (such as DTW or cosine similarity-based comparison), the closest process template to the current change rate is found, and the load characteristic type (such as steady cutting, intermittent impact, and idle transition, etc.) is identified. This load type will be used as a reference for parameter setting in driving control. For example, when the pressure gradient change rate is detected at a medium level and matches the standard template "rough machining starting section", the load characteristic is marked as "gradual pressurization, stable output" mode. By combining historical process data for load trend recognition, the driving control has the ability to understand the task context, significantly improving the rationality and robustness of predictive control.

[0060] The preset threshold is a critical value of pressure gradient change rate experienced according to experimental or historical data, which is used to determine whether the current operation of the oil cylinder is in a period of severe load variation. The pressure pulsation spectrum feature refers to the frequency domain index of the pressure signal obtained by Fourier transform, including the main frequency, peak amplitude, spectral energy distribution, etc. The change trend of the pressure gradient value is monitored in real time, and when the pressure gradient change rate exceeds the preset threshold, the high-frequency sampling mode is triggered, and the sampling frequency is increased to more than 2-10 times of the normal mode, so that the pressure signal data of the machine tool oil cylinder is sampled at a higher frequency to capture the rapidly changing pressure signal. Subsequently, a suitable window length, such as 200 ms, is selected according to the processing load characteristics, and the sampling section is extracted according to the window length, and the pressure signal in the sampling section is subjected to fast Fourier transform to obtain a pressure spectrum diagram, and pressure pulsation spectrum features including main frequency peak value, spectral energy density, frequency offset, etc. are extracted.

[0061] The pressure pulsation spectrum features are input into a pre-trained classification model (such as a support vector machine or a random forest) for recognition to determine which load mutation type it belongs to. Among them, the load mutation type is a classification label for abnormal processing events or load fluctuations, such as "rapid loading", "rebound peak", "short-term overpressure", etc. Based on the identified mutation type and historical running trend, a regression prediction model (such as a long short-term memory network or a random forest regressor) trained based on historical data is called to predict the driving pressure and flow required by the oil cylinder in the next 1-2 cycles, and the oil cylinder driving demand information is output, including: target pressure, flow, and movement speed. This information will be used for subsequent energy-saving control parameter generation to achieve efficient and responsive energy allocation control.

[0062] The training data set of the classification model is constructed based on historical sampling samples, containing labeled spectrum-event pairs. The specific training process is as follows: first, pressure signal data under multiple actual processing conditions is collected, and fast Fourier transform is performed on the data to extract spectral features such as main frequency, amplitude spectrum, and spectral energy, forming a feature vector set. According to manual labeling or processing records, the samples are labeled as "normal", "rapid loading", "rebound peak", "short-term overpressure", etc. Load mutation types are constructed to form a training label set. Then, the scikit-learn library in Python is used to standardize the feature set, and SVM and random forest classification models are trained respectively, and the model accuracy is evaluated by cross-validation method, and the model with the best performance is selected as the classification model.

[0063] An example of the regression prediction model training process is as follows: extract the pressure and flow historical data within a few seconds before and after each load mutation event in the above classification model training data set (such as 5 seconds of data under 10 Hz sampling), construct a sliding window as the input sequence of the long short-term memory network, and at the same time, the actual pressure and flow in the subsequent 1-2 processing periods are taken as the prediction target. Use TensorFlow or PyTorch to build a standard long short-term memory network, set three layers of long short-term memory network units and add a fully connected output layer, use the mean square error (MSE) as the loss function for training, and after the training is completed, the model can be used in the online system to dynamically drive the demand prediction in the sliding window mode. For resource-limited or response time-sensitive occasions, a random forest regressor (RF regression) can also be used as a lightweight alternative.

[0064] Further, in step S200, control analysis is performed according to the oil cylinder driving demand information to generate energy-saving control instructions, including:

[0065] Step S210: Based on the oil cylinder driving demand information, the target driving force is determined, the control parameter constraint condition set is determined according to the control restriction analysis of the target driving force.

[0066] Step S220: According to the control parameter constraint condition set, multi-objective optimization calculation is performed on the machine tool oil cylinder to generate a hydraulic pump displacement solution set.

[0067] Step S230: Set the proportional valve opening feasible region, filter the hydraulic pump displacement solution set according to the proportional valve opening feasible region, construct the displacement adjustment curve, and formulate the valve port opening sequence based on the displacement adjustment curve.

[0068] Step S240: Add the valve port opening sequence to the energy-saving control instruction.

[0069] Specifically, the key elements in the oil cylinder driving demand information are analyzed, and parameters such as target pressure, target flow, and maintenance time are extracted to calculate the target driving force required by the oil cylinder. For example, when the predicted output pressure is 7.5 MPa and the flow is 12 L / min, the actual required thrust can be calculated in combination with the oil cylinder diameter and piston area. Then, a set of control parameter constraint conditions are formed in combination with the current oil cylinder state (minimum displacement of 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.

[0070] The driving target is taken as the optimization objective function, with energy saving as the main target (such as minimum input energy or energy consumption per unit thrust), and the above-mentioned set of control parameter constraints as the boundary, to establish a mathematical optimization model. The model can be solved by using optimization algorithms such as linear programming, particle swarm optimization or mixed integer programming. The model variables mainly include the hydraulic pump displacement and the driving duration, and the output is a set of hydraulic pump displacement solutions that meet the conditions, which represents multiple pump control schemes that can achieve the target driving force within the constraints.

[0071] According to the hydraulic pump displacement solution set, combined with the structural constraints and response characteristics of the proportional directional valve, a proportional valve opening feasible region is set, i.e. the range in which the proportional directional valve can operate safely and effectively, such as the linear response section between 0% and 85%. The obtained hydraulic pump displacement solution set is filtered according to the proportional valve opening feasible region, and solutions that exceed the proportional valve opening feasible region are excluded. According to the filtered displacement values and the corresponding proportional valve opening, a displacement adjustment curve is constructed, which 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 developed, which defines the opening value of the proportional directional valve at different time points or working stages.

[0072] Finally, the valve opening sequence and the corresponding pump displacement adjustment value are combined to form a complete energy-saving control instruction, which includes the following format: start timestamp, pump displacement set value (variable over time), valve opening target sequence (executed periodically). The energy-saving control instruction is transmitted to the pump control unit and the proportional valve unit through the field controller or the industrial bus, and the energy-saving driving process of the oil cylinder is executed.

[0073] Further, the energy-saving control instruction is executed in step S200 for synchronous recording, and real-time working condition information of the oil cylinder is obtained, including:

[0074] Step S250: The energy-saving control instruction is executed for synchronous recording of the machine tool oil cylinder, and a plurality of real-time control data is obtained, including real-time pressure data of the pump outlet, real-time displacement data of the valve core, and real-time acceleration data of the oil cylinder.

[0075] Step S260: The pump outlet real-time pressure data, the valve core real-time displacement data and the oil cylinder real-time acceleration data are integrated according to the running time sequence identification timestamp of the machine tool oil cylinder, to obtain an oil cylinder real-time working condition log.

[0076] Step S270: Based on the comparison and analysis of the oil cylinder real-time working condition log and the oil cylinder driving demand information, a real-time working condition feedback parameter set is generated.

[0077] Step S280: The oil cylinder real-time working condition log is dynamically compensated according to the real-time working condition feedback parameter set, to obtain the oil cylinder real-time working condition information.

[0078] Specifically, after executing the generated energy-saving control instruction, a real-time data synchronization mechanism is started to collect and mark the operating state of the key components of the oil cylinder at a high frequency, forming a real-time working condition log of the oil cylinder operation process. By comparing and analyzing with the driving demand information, a working condition feedback parameter set is further formed to realize data closed-loop compensation and control effect evaluation.

[0079] During the execution of the energy-saving control instruction, key operating data in the control execution process is collected through a sensor device: real-time pressure data of the pump outlet is obtained through a pressure sensor installed at the outlet of the hydraulic pump, which is used to reflect the current driving pressure of the oil cylinder; real-time displacement data of the valve core is collected through a displacement sensor integrated in the proportional directional valve, which is used to monitor the actual response behavior of the valve core; real-time acceleration data of the oil cylinder is collected through a high-sensitivity acceleration sensor fixed on the piston rod or cylinder body of the machine tool oil cylinder, which is used to reflect the dynamic response state of the oil cylinder. The above data is collected synchronously at a uniform period (such as every 5 milliseconds) to form multiple real-time control data.

[0080] The collected pump outlet real-time pressure data, valve core real-time displacement data, and oil cylinder real-time acceleration data are aligned according to the same time reference and marked with a unified system timestamp to construct an oil cylinder real-time working condition log, which records the operating state of the oil cylinder under the energy-saving control instruction. The oil cylinder real-time working condition log supports a sliding window storage mechanism and can be updated in real time for online analysis.

[0081] The above oil cylinder real-time working condition log is compared with the predicted oil cylinder driving demand information item by item, the differences are analyzed, and the pressure deviation, valve core displacement deviation, and acceleration response deviation are calculated to obtain a real-time working condition feedback parameter set. The pressure deviation is the difference between the target pressure and the actual pump outlet pressure; the valve core displacement deviation is the response deviation between the predicted valve position and the actual valve core displacement; and the acceleration response deviation is the error or hysteresis behavior between the predicted oil cylinder acceleration and the actual acceleration. The predicted valve position is obtained by mapping the target flow rate according to the known flow rate-valve position mapping table; and the predicted oil cylinder acceleration is calculated based on the target pressure through the motion equation (F=m*a, where F is the target driving force, the unit is Newton, m is the effective mass, the unit is kilogram, including the mass of the oil cylinder piston, connecting components, and part of the driven object, and a is the predicted oil cylinder acceleration, the unit is square meter per second). The feedback parameters are dynamically generated in a function form to record the response matching degree between the system response and the control instruction in different time periods.

[0082] Based on the real-time working condition feedback parameter set, the data segment with error in the real-time working condition log of the oil cylinder is compensated and corrected, specifically including: smoothing the pressure curve by using the exponential moving average method to correct the pressure deviation trend; the response time backtracking correction is performed on the valve position response delay to fill in the actual execution time of the valve control; the noise suppression and feature regression are performed on the mutation data in the acceleration signal to enhance the response ability to the actual load dynamics. The compensated data set is the final formed oil cylinder real-time working condition information, which will be used as an important basis for subsequent closed-loop control, energy efficiency analysis and collaborative control strategy development.

[0083] Further, the process of determining that the machine tool oil cylinder is in the oil cylinder return braking stage includes:

[0084] Step one: based on the real-time acceleration data of the oil cylinder, the piston displacement speed of the machine tool oil cylinder is calculated to obtain the speed drop rate.

[0085] Step two: based on the bidirectional pressure value, the front end pressure value of the oil cylinder and the rear end pressure value of the oil cylinder are subtracted to generate the front and rear cavity pressure difference, and the front and rear cavity pressure difference contains pressure difference gradient data, wherein the pressure difference gradient data can be positive gradient data or negative gradient data.

[0086] Step three: when the speed drop rate exceeds the preset critical threshold and the pressure difference gradient data is negative gradient data, it is determined that the machine tool oil cylinder is in the oil cylinder return braking stage.

[0087] Specifically, the change of the piston displacement speed is calculated through the real-time acceleration data of the oil cylinder. The acceleration data is collected by the acceleration sensor installed on the piston rod, and then the piston displacement speed is calculated by integration. The speed drop rate is obtained by calculating the time derivative of the speed data. The acceleration signal of the machine tool oil cylinder piston is collected by the acceleration sensor, and is converted into speed change trend. The piston speed sequence is calculated by using first-order numerical integration, and the derivative processing is performed to obtain the speed drop rate. The speed drop rate reflects the deceleration characteristics of the oil cylinder piston in a short time, and is used for preliminary judgment of whether there is a return deceleration trend.

[0088] The pressure difference between the front end and the rear end of the oil cylinder is calculated by subtracting the pressure value at the front end of the oil cylinder from the pressure value at the rear end of the oil cylinder through real-time acquisition of the bidirectional pressure values by the pressure sensors at the front end and the rear end of the oil cylinder. The pressure difference gradient data is obtained by calculating the time derivative of the pressure difference. The gradient (change rate) of the pressure difference sequence is calculated by taking the derivative of the pressure difference sequence, and the pressure difference gradient data is obtained. If the pressure difference decreases with time, it is negative gradient data, indicating that the pressure in the front chamber decreases and the pressure in the rear chamber increases; otherwise, if the pressure difference increases with time, it is positive gradient data, indicating that the pressure in the front chamber increases and the pressure in the rear chamber decreases. If the speed drop rate is greater than the preset critical threshold value and the pressure difference gradient at the current time is negative gradient data, it is determined that the machine tool oil cylinder is in the oil cylinder return braking stage.

[0089] Further, step S300 comprises:

[0090] Step S310: When the machine tool oil cylinder is in the oil cylinder return braking stage, the energy recovery mode is started, the main oil supply pipeline electromagnetic valve is closed and switched to the energy recovery circuit through the energy recovery mode, so that the hydraulic oil in the rod cavity is injected into the accumulator through the one-way valve group.

[0091] Step S320: The pressure data of the accumulator is acquired in real time by the pressure sensor in the accumulator, and the energy storage capacity of the accumulator is calculated according to the pressure data.

[0092] Step S330: When the pressure data reaches a first critical value, switch to a bypass overflow state, and preferentially use the hydraulic energy stored in the accumulator to drive the machine tool oil cylinder at the beginning of the next working stroke.

[0093] Specifically, when it is determined that the machine tool oil cylinder is in the oil cylinder return braking stage, the energy recovery mode is started, the hydraulic kinetic energy originally consumed in the braking process is converted into reusable hydraulic energy, and in this mode, the main oil supply pipeline electromagnetic valve is closed and the energy recovery branch is opened, so that the hydraulic oil in the oil cylinder rod cavity is conducted to the accumulator through the one-way valve group. This process does not depend on external hydraulic pump power, but uses the high-pressure liquid flow formed by the compression of hydraulic oil by the piston due to inertia to achieve passive backflow.

[0094] During energy recovery, the pressure sensor arranged in the accumulator or on the inlet pipeline continuously acquires the pressure value of the accumulator. According to the isothermal compression model, the current energy storage capacity is estimated in combination with the accumulator inner cavity volume and the accumulator gas pre-charging pressure P0, and the calculation formula is as follows: wherein, is the energy storage capacity at time t, Pacc(t) is the pressure value of the accumulator at time t, V is the volume of the accumulator inner cavity, P0 is the pre-charging pressure of the accumulator gas, k is the polytropic index of the compression process, and the typical value is 1.2 (approximately isothermal).

[0095] When the pressure sensor detects that the accumulator pressure reaches the preset first critical value, it is considered that the stored energy has reached the upper limit allowed by the accumulator. At this time, switch to the bypass overflow state, make the backflow liquid pass through the overflow valve and lead out, prevent overpressure damage to the accumulator. In the subsequent next machining cycle (such as the start of the next main cylinder working stroke), the stored hydraulic energy is preferentially released from the accumulator, and the bypass circuit control logic is used to assist the driving of the main oil cylinder to run, reducing the load of the hydraulic pump, thereby achieving the effect of energy saving and consumption reduction.

[0096] Further, step S400 includes:

[0097] Step S410: constructing a four-dimensional state space based on the pressure data of the accumulator and the real-time working condition information of the oil cylinder.

[0098] Step S420: calculating a multi-dimensional correlation coefficient according to the four-dimensional state space, performing correlation analysis according to the multi-dimensional correlation coefficient, and extracting a plurality of strongly correlated dimension pairs.

[0099] Step S430: performing principal component analysis on the plurality of strongly correlated dimension pairs, performing orthogonal mapping according to the analysis result, and constructing a pressure-working condition coupling matrix.

[0100] Step S440: performing dynamic programming based on the pressure-working condition coupling matrix, solving the energy control distribution ratio of the machine tool oil cylinder, and generating the cooperative control parameter according to the energy control distribution ratio.

[0101] Specifically, a four-dimensional state space is constructed according to the pressure data of the accumulator and the real-time working condition information of the oil cylinder, which is used to describe the running dynamics of the oil cylinder, including accumulator pressure data (MPa), piston speed (mm / s), oil cylinder load pressure (MPa), and unit displacement (mm). The pressure data of the accumulator and the real-time working condition information of the oil cylinder at each time point form a four-dimensional point, which forms a sample point in the four-dimensional state space.

[0102] The Pearson correlation coefficient of each dimension data in the state space is calculated, a multi-dimensional correlation coefficient matrix is obtained, and all multi-dimensional correlation coefficients corresponding to the dimension pairs in the multi-dimensional correlation coefficient matrix that are greater than a preset threshold value are extracted as strongly correlated dimension pairs, for example: (accumulator pressure, load pressure) (piston speed, unit displacement).

[0103] The strongly correlated dimension pairs are reduced in dimension using principal component analysis (PCA), the first two principal components are extracted, and orthogonal mapping is performed to form a two-dimensional coupling matrix, i.e., a pressure-working condition coupling matrix, which reflects the "working condition-pressure" coupling mode under different state combinations.

[0104] An energy consumption cost function is introduced based on the pressure-working condition coupling matrix: Wherein, J is the total energy consumption cost, representing the total cost or energy consumption of the system during the entire working cycle (time step t = 1 to T), t is the time step representing a discrete time point, for example, sampling once every 10 ms, a total of T sampling points. Q1(t) is the oil supply flow of the hydraulic pump at time t, representing the instantaneous flow of the hydraulic pump alone to the cylinder, the pump flow is usually controlled by a servo motor or a variable displacement mechanism, and the energy consumption is roughly proportional to the square of the flow, so the square term is used to represent its consumption cost. Q2(t) is the oil supply flow of the accumulator at time t, representing the hydraulic energy released from the accumulator converted into flow, and the square term is also used to represent the consumption cost of the accumulator. Alpha is the hydraulic pump flow cost weight, the larger the value, the more the cylinder tends to reduce the pump energy supply and preferentially use the accumulator to reduce the power consumption. Beta is the accumulator flow cost weight, which is less than alpha, indicating that the accumulator energy supply is low cost. The dynamic programming (DP) algorithm is used to optimally allocate the energy supply ratio of the hydraulic pump and the accumulator in each cycle to obtain the energy control allocation ratio, and the collaborative control parameters are generated accordingly.

[0105] In summary, the energy-saving driving method for machine tools provided by the embodiments of the present application has the following beneficial effects:

[0106] 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.

[0107] 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:

[0108] 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.

[0109] 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.

[0110] An energy recovery module 30 is configured to start an energy recovery mode to convert brake kinetic energy into hydraulic energy and store the hydraulic energy into an accumulator when the machine tool oil cylinder is in an oil cylinder return brake stage, and the accumulator contains pressure data of the machine tool oil cylinder.

[0111] A dynamic control analysis module 40 is configured to perform dynamic control analysis on the pressure data of the accumulator and real-time working condition information of the oil cylinder to generate a cooperative control parameter, and to drive the machine tool oil cylinder through the cooperative control parameter to form a closed-loop energy-saving driving strategy.

[0112] Further, the driving demand prediction module 10 is further configured to perform the following steps:

[0113] The load pressure of the machine tool oil cylinder is collected in real time through an oil cylinder front-end pressure sensor and an oil cylinder rear-end pressure sensor to obtain bidirectional pressure values; the piston rod in the machine tool oil cylinder is subjected to telescopic displacement sensing to obtain real-time displacement data and movement speed data; the bidirectional pressure values and the real-time displacement data are aligned according to time sequences to construct a pressure-displacement phase relationship matrix; the machine tool oil cylinder is calculated by traversing the pressure-displacement phase relationship matrix to obtain an instantaneous output power of the oil cylinder; a pressure gradient value is determined based on gradient analysis of the bidirectional pressure values, and the movement direction is calibrated based on the real-time displacement data according to the movement speed data; power waveform information is identified based on the instantaneous output power of the oil cylinder, and the pressure gradient value and the movement direction are integrated according to the power waveform information to determine the dynamic load parameter set.

[0114] Further, the driving demand prediction module 10 is further configured to perform the following steps:

[0115] The pressure gradient change rate is obtained by recording changes according to the power waveform information based on the pressure gradient value; the machining process data of the machine tool oil cylinder is called to match the pressure gradient change rate combined with the machining process data of the machine tool oil cylinder to determine machining load characteristic information; when the pressure gradient change rate exceeds a preset threshold, a high-frequency sampling mode is activated, and the pressure of the machine tool oil cylinder is sampled according to the machining load characteristic information through the high-frequency sampling mode to obtain pressure pulsation spectral features; the pressure pulsation spectral features are identified to extract a load mutation type to determine the oil cylinder driving demand information according to a machining cycle.

[0116] Further, the energy-saving control analysis module 20 is further configured to perform the following steps:

[0117] Based on the oil cylinder driving demand information, a target driving force is determined, a control parameter constraint condition set is determined by control limit analysis according to the target driving force, multi-objective optimization calculation is performed on the machine tool oil cylinder according to the control parameter constraint condition set, a hydraulic pump displacement solution set is generated, a proportional valve opening degree feasible region is set, the hydraulic pump displacement solution set is screened according to the proportional valve opening degree feasible region, a displacement adjustment curve is constructed, and a valve port opening degree sequence is formulated based on the displacement adjustment curve; and the valve port opening degree sequence is added to the energy-saving control instruction.

[0118] Further, the energy-saving control analysis module 20 of the embodiment of the present application is further used to perform the following steps:

[0119] The energy-saving control instruction is executed to synchronously record the machine tool oil cylinder, a plurality of real-time control data is obtained, the plurality of real-time control data includes pump outlet real-time pressure data, valve core real-time displacement data, and oil cylinder real-time acceleration data; the pump outlet real-time pressure data, the valve core real-time displacement data, and the oil cylinder real-time acceleration data are integrated according to the running time sequence identification time stamp of the machine tool oil cylinder to obtain an oil cylinder real-time working condition log; real-time working condition feedback parameter set is generated by comparing and analyzing the oil cylinder real-time working condition log and the oil cylinder driving demand information; and the oil cylinder real-time working condition information is obtained by performing dynamic compensation on the oil cylinder real-time working condition log according to the real-time working condition feedback parameter set.

[0120] Further, the energy recovery module 30 of the embodiment of the present application is further used to perform the following steps:

[0121] Based on the oil cylinder real-time acceleration data, the piston displacement speed of the machine tool oil cylinder is calculated to obtain a speed drop rate; the front-end pressure value of the oil cylinder and the rear-end pressure value of the oil cylinder are subtracted based on the bidirectional pressure value to generate a front-rear cavity pressure difference, and the front-rear cavity pressure difference includes pressure difference gradient data, wherein the pressure difference gradient data can be positive gradient data or negative gradient data; and when the speed drop rate exceeds a preset critical threshold value and the pressure difference gradient data is negative gradient data, it is determined that the machine tool oil cylinder is in an oil cylinder return braking stage.

[0122] Further, the energy recovery module 30 of the embodiment of the present application is further used to perform the following steps:

[0123] When the machine tool oil cylinder is in the oil cylinder return brake stage, the energy recovery mode is started, the main oil supply pipeline electromagnetic valve is closed and switched to the energy recovery circuit through the energy recovery mode, the rod cavity hydraulic oil is injected into the accumulator through the one-way valve group, the pressure data of the accumulator is collected in real time through the pressure sensor in the accumulator, the energy storage capacity of the accumulator is calculated according to the pressure data, and when the pressure data reaches a first critical value, the bypass overflow state is switched to, and the hydraulic energy stored in the accumulator is preferentially used to drive the machine tool oil cylinder in the initial stage of the next working stroke.

[0124] Further, the dynamic control analysis module 40 is further used to execute the following steps:

[0125] Based on the pressure data of the accumulator and the real-time working condition information of the oil cylinder, a four-dimensional state space is constructed, a multi-dimensional correlation coefficient is calculated according to the four-dimensional state space, correlation analysis is performed according to the multi-dimensional correlation coefficient, and a plurality of strong correlation dimension pairs are extracted. Principal component analysis is performed on the plurality of strong correlation dimension pairs, orthogonal mapping is performed according to the analysis result, a pressure-working condition coupling matrix is constructed, dynamic programming is performed based on the pressure-working condition coupling matrix, an energy control distribution ratio of the machine tool oil cylinder is solved, and the cooperative control parameter is generated according to the energy control distribution ratio.

[0126] Through the foregoing detailed description of the energy-saving driving method for a machine tool, those skilled in the art can clearly understand the energy-saving driving system for a machine tool in the embodiment. For the system disclosed in embodiment two, since it corresponds to the method disclosed in embodiment one, it has corresponding functional modules and beneficial effects. For related parts, refer to the method part description.

[0127] In embodiment three, based on the same inventive concept as the energy-saving driving method for a machine tool in the foregoing embodiment one, the present application further provides an electronic device, which comprises at least one processor and a memory connected in communication with 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 execute the steps of the method disclosed in embodiment one.

[0128] As Figure 3As shown, the bus architecture is represented with a bus 300, which can include any number of interconnected buses and bridges, the bus 300 connects various circuits including one or more processors represented by processor 302 and memory represented by memory 304. The bus 300 can also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and thus, will not be further described herein. A bus interface 305 provides an interface between the bus 300 and the receiver 301 and transmitter 303. The receiver 301 and transmitter 303 can be the same device, i.e., a transceiver, providing a means for communicating with various other apparatus over a transmission medium. The processor 302 is responsible for managing the bus 300 and general processing, while the memory 304 can be used for storing data used by the processor 302 in executing operations.

[0129] The above description of disclosed embodiments provides enabling teaching to a person skilled in the art to implement or use the present application. Numerous modifications to these embodiments will be apparent to those skilled in the art, and general principles defined herein can be applied to other embodiments without departing from the spirit or scope of the application. Therefore, the present application is not intended 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. Energy-saving drive method for machine tools, characterized in that, The method comprises: Real-time monitoring of the load pressure and movement direction of the machine tool oil cylinder, generating a dynamic load parameter set to drive prediction of the machine tool oil cylinder, and determining oil cylinder drive demand information; Real-time acquisition of the load pressure of the machine tool oil cylinder through the oil cylinder front end pressure sensor and the oil cylinder rear end pressure sensor to obtain bidirectional pressure values; The piston rod in the machine tool oil cylinder is subjected to telescopic displacement sensing to obtain real-time displacement data and movement speed data; Aligning the bidirectional pressure values and the real-time displacement data according to time sequences to construct a pressure-displacement phase relationship matrix; Iterating through the pressure-displacement phase relationship matrix to perform operation calculation on the machine tool oil cylinder to obtain oil cylinder instantaneous output power; Based on the bidirectional pressure values, gradient analysis is performed to determine pressure gradient values, and based on the real-time displacement data, the movement direction is calibrated according to the movement speed data; Based on the oil cylinder instantaneous output power, power waveform information is identified, and the power waveform information is integrated according to the pressure gradient values and the movement direction to determine the dynamic load parameter set; Based on the pressure gradient values, change records are made according to the power waveform information to obtain a pressure gradient change rate; Machine tool oil cylinder processing process data is called, and the pressure gradient change rate is matched with the machine tool oil cylinder processing process data to determine processing load characteristic information; When the pressure gradient change rate exceeds a preset threshold, a high-frequency sampling mode is activated, and the machine tool oil cylinder is pressure-sampled according to the processing load characteristic information through the high-frequency sampling mode to obtain pressure pulsation spectral features; Based on the pressure pulsation spectral features, identification is performed, load mutation types are extracted, and drive prediction is performed according to the processing cycle to determine the oil cylinder drive demand information; Control analysis is performed according to the oil cylinder drive demand information to generate energy-saving control instructions, and the energy-saving control instructions are executed for synchronous recording to obtain oil cylinder real-time working condition information; When the machine tool oil cylinder is in the oil cylinder return braking stage, an energy recovery mode is started to convert braking kinetic energy into hydraulic energy and store it in an accumulator, and the accumulator contains pressure data of the machine tool oil cylinder; According to the dynamic control analysis of the pressure data of the accumulator and the oil cylinder real-time working condition information, cooperative control parameters are generated, and the machine tool oil cylinder is driven for energy saving through the cooperative control parameters to form a closed-loop energy-saving driving strategy; Based on the pressure data of the accumulator and the oil cylinder real-time working condition information, a four-dimensional state space is constructed; According to the four-dimensional state space, multi-dimensional correlation coefficients are calculated, correlation analysis is performed according to the multi-dimensional correlation coefficients, and multiple strongly correlated dimension pairs are extracted; Principal component analysis is performed using the multiple strongly correlated dimension pairs, and according to the analysis results, an orthogonal mapping is performed to construct a pressure-working condition coupling matrix; Based on the pressure-working condition coupling matrix, dynamic programming is performed to solve the energy control distribution ratio of the machine tool oil cylinder, and the cooperative control parameters are generated according to the energy control distribution ratio.

2. The energy-saving drive method for a machine tool according to Claim 1, wherein The method comprises: Based on the oil cylinder driving demand information, a target driving force is determined, control parameter constraint conditions are determined by control limit analysis according to the target driving force, and a hydraulic pump displacement solution set is generated by multi-objective optimization calculation of the machine tool oil cylinder according to the control parameter constraint conditions; A proportional valve opening degree feasible region is set, the hydraulic pump displacement solution set is screened according to the proportional valve opening degree feasible region, a displacement adjustment curve is constructed, and a valve port opening degree sequence is formulated based on the displacement adjustment curve; The valve port opening degree sequence is added to the energy-saving control instruction. The energy-saving control instruction is executed for synchronous recording, and oil cylinder real-time working condition information is obtained, the method comprising:

3. The energy-saving drive method for a machine tool according to Claim 1, wherein The energy-saving control instruction is executed for synchronous recording of the machine tool oil cylinder, and a plurality of real-time control data is obtained, the plurality of real-time control data including pump outlet real-time pressure data, valve core real-time displacement data, and oil cylinder real-time acceleration data; The pump outlet real-time pressure data, the valve core real-time displacement data, and the oil cylinder real-time acceleration data are integrated according to the running time sequence identification time stamp of the machine tool oil cylinder to obtain an oil cylinder real-time working condition log; Based on the oil cylinder real-time working condition log and the oil cylinder driving demand information, a real-time working condition feedback parameter set is generated; According to the real-time working condition feedback parameter set, the oil cylinder real-time working condition log is dynamically compensated to obtain the oil cylinder real-time working condition information. A process for determining whether the machine tool oil cylinder is in the oil cylinder return braking stage, the method comprising:

4. The energy-saving drive method for a machine tool according to Claim 3, characterized in that, Based on the oil cylinder real-time acceleration data, the piston displacement speed of the machine tool oil cylinder is calculated to obtain a speed drop rate; Based on the bidirectional pressure value, the oil cylinder front end pressure value and the oil cylinder rear end pressure value are subtracted to generate a front and rear cavity pressure difference, the front and rear cavity pressure difference including pressure difference gradient data, wherein the pressure difference gradient data can be positive gradient data or negative gradient data; When the speed drop rate exceeds the preset critical threshold and the pressure difference gradient data is negative gradient data, it is determined that the machine tool oil cylinder is in the oil cylinder return braking stage. When the machine tool oil cylinder is in the oil cylinder return braking stage, an energy recovery mode is started to convert braking kinetic energy into hydraulic energy and store it in an accumulator, the accumulator including pressure data of the machine tool oil cylinder, the method comprising:

5. The energy-saving drive method for machine tools according to Claim 1, wherein When the machine tool oil cylinder is in the oil cylinder return braking stage, the energy recovery mode is started, the energy recovery mode closes the main oil supply pipeline electromagnetic valve and switches to the energy recovery circuit, so that the rod cavity hydraulic oil is 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 in the accumulator, and the energy storage capacity of the accumulator is calculated according to the pressure data; When the pressure data reaches a first critical value, the bypass overflow state is switched to, and the hydraulic energy stored in the accumulator is preferentially used to drive the machine tool oil cylinder at the beginning of the next working stroke. The system is used to execute the energy-saving driving method for machine tools according to any one of claims 1-5, comprising:

6. Energy-saving drive system for machine tools, characterized in that, A driving demand prediction module is used to monitor the load pressure and movement direction of the machine tool oil cylinder in real time, generate a dynamic load parameter set to predict the driving of the machine tool oil cylinder, and determine the oil cylinder driving demand information; ​ An energy-saving control analysis module is configured to perform control analysis according to the oil cylinder driving demand information, generate an energy-saving control instruction, execute the energy-saving control instruction to perform synchronous recording, and obtain real-time working condition information of the oil cylinder; An energy recovery module is configured to start an energy recovery mode to convert braking kinetic energy into hydraulic energy and store the hydraulic energy into an accumulator when the machine tool oil cylinder is in an oil cylinder return braking stage, and the accumulator contains pressure data of the machine tool oil cylinder; A dynamic control analysis module is configured to perform dynamic control analysis according to the pressure data of the accumulator and the real-time working condition information of the oil cylinder, generate a cooperative control parameter, and perform energy-saving driving on the machine tool oil cylinder through the cooperative control parameter to form a closed-loop energy-saving driving strategy. 7.An electronic device comprising a memory and a processor, the memory storing a computer program, wherein, The processor executes the computer program to implement the steps of the energy-saving driving method for a machine tool according to any one of claims 1-5.

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