Hydraulic control method, system and equipment for quickly adjusting levelness of machine tool and medium

Abstract

The invention relates to a hydraulic control method, system and equipment for quickly adjusting a machine tool to be horizontal and a medium, and the method comprises the following steps: acquiring real-time attitude data of the machine tool, and performing fusion processing on the real-time attitude data through a data fusion technology to obtain three-dimensional space attitude information of the machine tool; calculating a posture deviation value between the three-dimensional space posture information of the machine tool and a preset horizontal state, generating a corresponding machine tool compensation instruction based on the deviation value, and controlling the output pressure of the hydraulic cylinder according to the machine tool compensation instruction; the output pressure of the hydraulic cylinder is monitored in real time, the output pressure is fed back, and a machine tool compensation instruction is dynamically optimized according to output pressure data; the machine tool change state in the adjusting process is obtained in real time, under the condition that the working condition of the machine tool change state changes, the strategy of the machine tool compensation instruction is corrected according to the machine tool change state, and the hydraulic cylinder control parameters are adjusted in a self-adaptive mode. The effect of improving the horizontal adjustment efficiency of the machine tool is achieved.

Description

Technical Field

[0001] The present application relates to the technical field of hydraulic control, and in particular to a hydraulic control method, system, equipment and medium for quickly adjusting the level of a machine tool. Background Art

[0002] At present, as the manufacturing industry's requirements for machine tool accuracy and stability continue to increase, traditional machine tool horizontal adjustment methods still mainly rely on manual or relatively simple automated adjustment systems. These methods often have problems such as slow response and low accuracy during operation, and are difficult to make quick adjustments when encountering changes in working conditions or external interference.

[0003] The above-mentioned existing technical solutions have the following defects: the existing machine tool horizontal adjustment technology has a slow response speed and low precision, and cannot effectively cope with changes in dynamic working conditions, resulting in low machine tool adjustment efficiency, so there is room for improvement. Summary of the invention

[0004] In order to improve the efficiency of adjusting the level of a machine tool, the present application provides a hydraulic control method, system, equipment and medium for quickly adjusting the level of a machine tool.

[0005] The above-mentioned invention objective of the present application is achieved through the following technical solutions: A hydraulic control method for quickly adjusting the level of a machine tool, the hydraulic control method for quickly adjusting the level of a machine tool comprising: Acquire real-time posture data of the machine tool, and fuse the real-time posture data through data fusion technology to obtain three-dimensional spatial posture information of the machine tool; Calculate the posture deviation between the three-dimensional spatial posture information of the machine tool and a preset horizontal state, generate a corresponding machine tool compensation instruction based on the deviation, and control the output pressure of the hydraulic cylinder according to the machine tool compensation instruction; Monitor the output pressure of the hydraulic cylinder in real time, feed back the output pressure, and dynamically optimize the machine tool compensation instruction according to the output pressure data; The machine tool change state during the adjustment process is acquired in real time. When the machine tool change state shows a working condition change, the strategy of the machine tool compensation instruction is corrected according to the machine tool change state, and the hydraulic cylinder control parameters are adaptively adjusted.

[0006] By adopting the above technical solution, by acquiring the real-time posture data of the machine tool and fusing the real-time posture data through data fusion technology, the three-dimensional spatial posture information of the machine tool can be accurately acquired, so that the adjustment of the machine tool posture can be carried out in real time and accurately, avoiding errors caused by inaccurate posture data; by calculating the posture deviation between the three-dimensional spatial posture information of the machine tool and the preset horizontal state and generating compensation instructions, the posture deviation of the machine tool can be quickly determined, thereby ensuring that the compensation instructions can accurately and effectively correct the posture of the machine tool, avoiding deviations of the machine tool due to improper adjustment; by real-time monitoring of the output pressure of the hydraulic cylinder and dynamically optimizing the machine tool compensation instructions according to the output pressure data, it can be ensured that the output pressure of the hydraulic cylinder matches the compensation instructions, thereby improving the accuracy of hydraulic control and ensuring the stability of the machine tool in dynamic adjustment; by real-time acquisition of the machine tool change state during the adjustment process, and correcting the compensation instruction strategy according to the working condition changes and adaptively adjusting the hydraulic cylinder control parameters according to the working condition changes, it can respond to the working condition changes of the machine tool in a timely manner, avoiding the instability caused by the working condition changes during the adjustment of the machine tool, thereby improving the adaptability and stability of the machine tool adjustment process.

[0007] In one example, the present application may be further configured as follows: the real-time posture data is fused and processed by data fusion technology to obtain the three-dimensional spatial posture information of the machine tool, specifically including: According to the preset sensor collection weight value, the real-time posture data is preliminarily integrated using the Kalman filter algorithm to obtain a preliminary posture estimation value; The preliminary posture estimation value is optimized, and the optimized preliminary posture estimation value is compared with the real-time posture data, and the corresponding posture estimation value is gradually recursively updated and optimized to generate the three-dimensional space posture information.

[0008] By adopting the above technical solution, by collecting weight values ​​according to preset sensors and using the Kalman filter algorithm to preliminarily fuse the real-time posture data, the noise and inaccuracy in the sensor data can be effectively removed, thereby improving the accuracy of posture estimation; by optimizing the preliminary posture estimation value and comparing the optimized preliminary posture estimation value with the real-time posture data, the gradual optimization of the posture estimation can be achieved, so that the adjustment of the machine tool posture can be more precise, and the adjustment efficiency and accuracy can be improved.

[0009] In one example, the present application may be further configured as follows: optimizing the preliminary posture estimation value, comparing the optimized preliminary posture estimation value with the real-time posture data, gradually recursively updating and optimizing the corresponding posture estimation value, and generating the three-dimensional space posture information, specifically including: Performing a preliminary prediction of the real-time posture data at each time step by using a Kalman filter algorithm, and predicting the posture estimation value at the next time step based on the posture estimation value at the previous time step; The real-time posture data is compared with the posture estimation value to obtain corresponding posture difference data, and the current posture estimation value is adjusted based on the posture difference data. The posture estimation value is gradually improved through a recursive optimization process to finally obtain the three-dimensional space posture information.

[0010] By adopting the above technical solution, the real-time attitude data is preliminarily predicted at each time step through the Kalman filter algorithm, and the attitude estimation value at the next time step is predicted based on the attitude estimation value at the previous time step. This can realize dynamic prediction of the machine tool attitude, so as to make adjustment decisions in advance and avoid unnecessary attitude errors caused by delayed adjustments. By comparing the real-time attitude data with the attitude estimation value and adjusting the current attitude estimation value based on the attitude difference data, recursive optimization of the attitude estimation value can be realized, so as to ensure that the accuracy of each adjustment is gradually improved, reduce error accumulation, and further improve the accuracy and response speed of machine tool adjustment.

[0011] In one example, the present application may be further configured as follows: the calculation of the posture deviation between the three-dimensional spatial posture information of the machine tool and the preset horizontal state, and the generation of a corresponding machine tool compensation instruction based on the deviation, specifically including: Calculating the posture deviation of the three-dimensional spatial posture information in each axis to obtain the compensation requirement of each axis; The compensation requirement is input into a multi-dimensional error model, and the machine tool compensation instruction is generated according to an output result of the multi-dimensional error model.

[0012] By adopting the above technical solution, by calculating the posture deviation of the three-dimensional spatial posture information in each axis, the compensation requirement of each axis can be accurately determined, thereby ensuring that the compensation instruction of the hydraulic cylinder can accurately control the posture adjustment of the machine tool and avoid improper adjustment; by inputting the compensation demand into the multi-dimensional error model and generating the compensation instruction according to the output result of the multi-dimensional error model, the deviation of each axis can be accurately reflected, ensuring the rationality and effectiveness of the compensation instruction, thereby realizing the precise adjustment of the machine tool in all directions and improving the adjustment effect and stability.

[0013] In one example, the present application may be further configured as follows: the construction of the multi-dimensional error model specifically includes: Acquire historical machine tool operation data and corresponding posture deviation data, and classify the historical machine tool operation data according to the posture deviation data to obtain a training data set; The preset machine learning algorithm is iteratively trained according to the training data set, and the corresponding model parameters are optimized using a gradient descent algorithm to obtain the multi-dimensional error model.

[0014] By adopting the above technical solution, by acquiring historical machine tool operation data and corresponding posture deviation data, and classifying the historical machine tool operation data according to the posture deviation data, it is possible to provide accurate and diverse training data for training the multi-dimensional error model, thereby improving the generalization ability of the model; by iteratively training the preset machine learning algorithm according to the training data set, and using the gradient descent algorithm to optimize the corresponding model parameters, it is possible to continuously improve the prediction accuracy of the multi-dimensional error model, thereby improving the accuracy and real-time performance of the compensation instruction generation, and ensuring that the dynamic adjustment of the machine tool posture is more accurate.

[0015] In one example, the present application may be further configured as follows: the real-time acquisition of the machine tool change state during the adjustment process, when the machine tool change state has a working condition change, the strategy of correcting the machine tool compensation instruction according to the machine tool change state, and adaptively adjusting the hydraulic cylinder control parameters, specifically including: Collecting the adjustment posture data, vibration data and external environment data of the machine tool in real time as the change state of the machine tool, and comparing the change state of the machine tool with the preset normal working condition parameters to determine whether the machine tool has an abnormal situation; When the machine tool state changes abnormally, an adaptive algorithm is used to analyze the relationship between the machine tool state change and the working condition change, and the hydraulic cylinder control parameters are dynamically adjusted according to the analysis results.

[0016] By adopting the above technical solution, by real-time collection of the machine tool's adjustment posture data, vibration data and external environment data as the machine tool's change state, it is possible to fully understand the machine tool's operating state and provide accurate reference data for subsequent adjustments; by comparing the machine tool's change state with the preset normal operating condition parameters, it is possible to quickly identify whether the machine tool is in a normal working state, thereby avoiding inappropriate adjustments under abnormal conditions and improving the safety of the adjustment process; by using an adaptive algorithm to analyze the relationship between the machine tool's change state and the working condition change, and dynamically adjusting the hydraulic cylinder control parameters according to the analysis results, it is possible to achieve a rapid response to the machine tool state change, thereby ensuring that the machine tool can adaptively adjust when the working condition changes, avoiding adjustment errors caused by changes in the working condition, and improving the adaptability and stability of the machine tool adjustment.

[0017] The second object of the invention is achieved by the following technical solutions: A hydraulic control system for quickly adjusting the level of a machine tool, the hydraulic control system for quickly adjusting the level of a machine tool comprising: The posture data acquisition module is used to acquire the real-time posture data of the machine tool, and fuse the real-time posture data through data fusion technology to obtain the three-dimensional spatial posture information of the machine tool; A posture deviation calculation module, used to calculate the posture deviation between the three-dimensional spatial posture information of the machine tool and a preset horizontal state, generate a corresponding machine tool compensation instruction based on the deviation, and control the output pressure of the hydraulic cylinder according to the machine tool compensation instruction; A hydraulic cylinder feedback monitoring module, used for real-time monitoring of the output pressure of the hydraulic cylinder, feeding back the output pressure, and dynamically optimizing the machine tool compensation instruction according to the output pressure data; The working condition change correction module is used to obtain the machine tool change state during the adjustment process in real time. When the machine tool change state has a working condition change, the strategy of the machine tool compensation instruction is corrected according to the machine tool change state, and the hydraulic cylinder control parameters are adaptively adjusted.

[0018] By adopting the above technical solution, by acquiring the real-time posture data of the machine tool and fusing the real-time posture data through data fusion technology, the three-dimensional spatial posture information of the machine tool can be accurately acquired, so that the adjustment of the machine tool posture can be carried out in real time and accurately, avoiding errors caused by inaccurate posture data; by calculating the posture deviation between the three-dimensional spatial posture information of the machine tool and the preset horizontal state and generating compensation instructions, the posture deviation of the machine tool can be quickly determined, thereby ensuring that the compensation instructions can accurately and effectively correct the posture of the machine tool, avoiding deviations of the machine tool due to improper adjustment; by real-time monitoring of the output pressure of the hydraulic cylinder and dynamically optimizing the machine tool compensation instructions according to the output pressure data, it can be ensured that the output pressure of the hydraulic cylinder matches the compensation instructions, thereby improving the accuracy of hydraulic control and ensuring the stability of the machine tool in dynamic adjustment; by real-time acquisition of the machine tool change state during the adjustment process, and correcting the compensation instruction strategy according to the working condition changes and adaptively adjusting the hydraulic cylinder control parameters according to the working condition changes, it can respond to the working condition changes of the machine tool in a timely manner, avoiding the instability caused by the working condition changes during the adjustment of the machine tool, thereby improving the adaptability and stability of the machine tool adjustment process.

[0019] The third objective of the present application is achieved through the following technical solutions: A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the hydraulic control method for rapidly adjusting the level of a machine tool are implemented.

[0020] The fourth objective of the present application is achieved through the following technical solutions: A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the hydraulic control method for rapidly adjusting the level of a machine tool.

[0021] In summary, this application includes the following beneficial technical effects: 1. By acquiring the real-time posture data of the machine tool and fusing the real-time posture data through data fusion technology, the three-dimensional spatial posture information of the machine tool can be accurately acquired, so that the posture adjustment of the machine tool can be carried out in real time and accurately, avoiding errors caused by inaccurate posture data; by calculating the posture deviation between the three-dimensional spatial posture information of the machine tool and the preset horizontal state and generating compensation instructions, the posture deviation of the machine tool can be quickly determined, thereby ensuring that the compensation instructions can accurately and effectively correct the posture of the machine tool and avoid deviations of the machine tool due to improper adjustment; by real-time monitoring of the output pressure of the hydraulic cylinder and dynamically optimizing the machine tool compensation instructions according to the output pressure data, it can ensure that the output pressure of the hydraulic cylinder matches the compensation instructions, thereby improving the accuracy of hydraulic control and ensuring the stability of the machine tool in dynamic adjustment; by real-time acquisition of the machine tool change state during the adjustment process, and correcting the compensation instruction strategy according to the working condition changes and adaptively adjusting the hydraulic cylinder control parameters according to the working condition changes, it can respond to the working condition changes of the machine tool in a timely manner, avoiding the instability caused by the working condition changes during the adjustment of the machine tool, thereby improving the adaptability and stability of the machine tool adjustment process; 2. By collecting weight values ​​according to preset sensors and using the Kalman filter algorithm to perform preliminary fusion of real-time attitude data, the noise and inaccuracy in the sensor data can be effectively removed, thereby improving the accuracy of attitude estimation; by optimizing the preliminary attitude estimation value and comparing the optimized preliminary attitude estimation value with the real-time attitude data, the attitude estimation can be gradually optimized, so that the adjustment of the machine tool attitude can be more precise, and the adjustment efficiency and accuracy can be improved; 3. The Kalman filter algorithm is used to make a preliminary prediction of the real-time attitude data at each time step, and the attitude estimation value of the next time step is predicted based on the attitude estimation value of the previous time step. This can realize the dynamic prediction of the machine tool attitude, so as to make adjustment decisions in advance and avoid unnecessary attitude errors caused by delayed adjustments. By comparing the real-time attitude data with the attitude estimation value and adjusting the current attitude estimation value based on the attitude difference data, the recursive optimization of the attitude estimation value can be realized, thereby ensuring that the accuracy of each adjustment is gradually improved, reducing error accumulation, and further improving the accuracy and response speed of machine tool adjustment. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 is a flow chart of a hydraulic control method for quickly adjusting the level of a machine tool in an embodiment of the present application; Figure 2This is a flowchart for implementing step S10 in a hydraulic control method for rapidly adjusting the level of a machine tool in an embodiment of the present application; Figure 3 This is a flowchart for implementing step S12 in a hydraulic control method for rapidly adjusting the level of a machine tool in an embodiment of the present application; Figure 4 This is a flowchart for implementing step S21 in a hydraulic control method for rapidly adjusting the level of a machine tool in an embodiment of the present application; Figure 5 It is a flowchart for realizing the construction of a multi-dimensional error model in a hydraulic control method for rapidly adjusting the level of a machine tool in one embodiment of the present application; Figure 6 This is a flowchart for implementing step S40 in a hydraulic control method for rapidly adjusting the level of a machine tool in an embodiment of the present application; Figure 7 This is a principle block diagram of a hydraulic control system for quickly adjusting the level of a machine tool in one embodiment of the present application; Figure 8 It is a schematic diagram of a device in an embodiment of the present application. DETAILED DESCRIPTION

[0023] The present application is further described in detail below in conjunction with the accompanying drawings.

[0024] In one embodiment, if Figure 1 As shown, the present application discloses a hydraulic control method for quickly adjusting the level of a machine tool, which specifically includes the following steps: S10: Acquire the real-time posture data of the machine tool, and fuse the real-time posture data through data fusion technology to obtain the three-dimensional spatial posture information of the machine tool.

[0025] Specifically, when obtaining the real-time posture data of the machine tool, sensors such as accelerometers, gyroscopes and tilt sensors are deployed in various key parts of the machine tool. These sensors will collect data such as the acceleration, angular velocity and tilt angle of the machine tool on the X, Y and Z axes in real time. During the data collection process, lidar and other positioning devices can be used to provide high-precision position data. The data of these sensors are then fused and processed through the Kalman filter algorithm. Different weights are given to each data point according to the measurement accuracy and stability of the sensor. Finally, the three-dimensional spatial posture information of the machine tool is merged, including the tilt angle, rotation angle and position change information of the machine tool in each axis.

[0026] S20: Calculate the posture deviation between the three-dimensional spatial posture information of the machine tool and the preset horizontal state, generate a corresponding machine tool compensation instruction based on the deviation, and control the output pressure of the hydraulic cylinder according to the machine tool compensation instruction.

[0027] Specifically, by comparing the current three-dimensional posture information with the horizontal state of the target, the posture deviation of each axis, namely X, Y, and Z, is calculated. The deviation represents the angle difference and displacement between the current posture and the ideal horizontal state. Then, the deviation is processed using a mathematical model to generate a compensation instruction for the machine tool. The compensation instruction will guide the hydraulic cylinder to adjust the posture of the machine tool, convert the compensation instruction into a hydraulic cylinder control signal, and adjust the output pressure of the hydraulic cylinder. The hydraulic cylinder accurately adjusts the posture of the machine tool according to the change of the control signal to ensure that the machine tool can quickly return to the target horizontal state.

[0028] S30: monitor the output pressure of the hydraulic cylinder in real time, provide feedback on the output pressure, and dynamically optimize the machine tool compensation instructions based on the output pressure data.

[0029] Specifically, the output pressure data of the hydraulic cylinder is continuously collected by the pressure sensor installed on the hydraulic cylinder. These data will be transmitted to the control system. The control system compares the real-time pressure data with the preset target pressure and calculates the pressure error value. These error values ​​reflect the gap between the current output of the hydraulic cylinder and the target output. The feedback pressure error is processed by the PID control algorithm. The control system dynamically adjusts the compensation instruction according to the error value, optimizes the control signal of the hydraulic cylinder, and adjusts the output pressure of the hydraulic cylinder to ensure that the posture adjustment of the machine tool can be completed efficiently and accurately.

[0030] S40: acquiring the machine tool change state during the adjustment process in real time, and in the case that the machine tool change state causes a working condition change, correcting the strategy of the machine tool compensation instruction according to the machine tool change state, and adaptively adjusting the hydraulic cylinder control parameters.

[0031] Specifically, by installing sensors in various parts of the machine tool, such as temperature sensors, vibration sensors and stress sensors, the environmental data and structural data of the machine tool during the adjustment process are collected in real time. These data reflect the working environment and internal state of the machine tool. The data will be transmitted to the control system. The system will compare the collected real-time data with the preset working condition standards to analyze whether the machine tool is within the normal working condition range. When the working condition changes or the machine tool changes abnormally, the control system will correct the compensation instruction strategy and adjust the hydraulic cylinder control parameters such as gain, response time, etc. through adaptive algorithms to ensure that the adjustment response of the hydraulic cylinder can quickly adapt to the new working condition.

[0032] In one embodiment, if Figure 2 As shown, in step S10, the real-time posture data is fused by data fusion technology to obtain the three-dimensional spatial posture information of the machine tool, which specifically includes: S11: According to the preset sensor collection weight values, the real-time posture data is preliminarily integrated using the Kalman filter algorithm to obtain a preliminary posture estimation value.

[0033] Specifically, different weights are assigned to each sensor according to the preset acquisition accuracy and measurement error of each sensor. The larger the weight of the sensor, the greater the influence of its data in the fusion process. The Kalman filter algorithm is used to perform weighted fusion on the real-time data obtained from different sensors. The Kalman filter algorithm first makes a preliminary prediction based on the attitude estimation value of the previous moment, and then makes corrections based on the difference between the measured value and the predicted value of each sensor. Finally, a preliminary attitude estimation value is obtained to provide a basis for subsequent precise attitude calculation.

[0034] S12: Optimize the preliminary posture estimation value, and compare the optimized preliminary posture estimation value with the real-time posture data, gradually recursively update and optimize the corresponding posture estimation value, and generate three-dimensional space posture information.

[0035] Specifically, the prediction step of the Kalman filter algorithm is used to predict the posture change of the machine tool, and then the prediction result is compared with the posture data collected in real time. The difference is calculated and the current posture estimation value is corrected based on the difference value. In the recursive process, the difference between the posture estimation value and the actual posture data is gradually reduced through multiple iterations of optimization, and finally accurate three-dimensional spatial posture information is obtained to ensure that the posture data of the machine tool at every moment can reflect its true state.

[0036] In one embodiment, if Figure 3 As shown, in step S12, the preliminary posture estimation value is optimized, and the optimized preliminary posture estimation value is compared with the real-time posture data, and the corresponding posture estimation value is gradually recursively updated and optimized to generate three-dimensional space posture information, which specifically includes: S121: Perform a preliminary prediction of the real-time posture data at each time step through the Kalman filter algorithm, and predict the posture estimation value of the next time step based on the posture estimation value of the previous time step.

[0037] Specifically, at each time step, the Kalman filter algorithm predicts the posture at the next moment based on the posture estimate at the previous moment and the dynamic model of the machine tool. The prediction step calculates the posture estimate at the current moment based on the data at the previous moment. This estimate will provide a preliminary reference for the subsequent update steps. According to the state transfer matrix and input control quantity of the model, the Kalman filter algorithm estimates the changing trend of the posture data based on the previous data, and uses this as the basis for the next update.

[0038] S122: Compare the real-time posture data with the posture estimation value to obtain corresponding posture difference data, and adjust the current posture estimation value based on the posture difference data, gradually improve the posture estimation value through a recursive optimization process, and finally obtain the three-dimensional space posture information.

[0039] Specifically, the attitude data collected in real time is compared with the attitude estimation value predicted by the Kalman filter algorithm, and the difference in attitude data is calculated. The difference data represents the deviation between the real-time measurement value and the predicted value. Then, the current attitude estimation value is adjusted according to this difference. During the recursive optimization process, the Kalman filter algorithm will continuously adjust the weights according to the difference data, correct the prediction results, and iteratively optimize the attitude estimation value. Through continuous prediction and update steps, accurate three-dimensional spatial attitude information is finally obtained.

[0040] In one embodiment, if Figure 4 As shown, in step S20, the posture deviation between the three-dimensional spatial posture information of the machine tool and the preset horizontal state is calculated, and a corresponding machine tool compensation instruction is generated based on the deviation, which specifically includes: S21: Calculate the posture deviation of the three-dimensional spatial posture information in each axis to obtain the compensation requirement of each axis.

[0041] Specifically, based on the three-dimensional spatial posture information, the posture deviation of the machine tool in the X-axis, Y-axis and Z-axis is calculated through a mathematical model. The posture deviation refers to the difference between the current tilt angle, pitch angle and roll angle of the machine tool and the preset target horizontal state. The obtained deviation represents the degree of adjustment required, thereby determining the compensation requirements for each axis and providing a basis for the generation of compensation instructions.

[0042] S22: Input the compensation demand into the multi-dimensional error model, and generate a machine tool compensation instruction according to the output result of the multi-dimensional error model.

[0043] Specifically, the calculated compensation requirements are input into the trained multi-dimensional error model. The error model further optimizes the compensation requirements by analyzing the deviation of each axis and its changing pattern under different working conditions. The model automatically adjusts the compensation amount of each axis by analyzing historical data and actual operation feedback, and generates machine tool compensation instructions. The instructions control the pressure output by the hydraulic cylinder to ensure that the machine tool posture reaches the target horizontal state.

[0044] In one embodiment, if Figure 5 As shown, in step S22, i.e., the construction of a multi-dimensional error model, specifically includes: S221: Acquire historical machine tool operation data and corresponding posture deviation data, and classify the historical machine tool operation data according to the posture deviation data to obtain a training data set.

[0045] Specifically, the machine tool operation data is extracted from the historical database, the real-time posture deviation data corresponding to each operation is obtained, the data is classified and cleaned, the machine tool operation data under different working conditions are grouped, and the classification criteria are determined according to the deviation data of each group to obtain a training data set. The training data set contains the historical records of machine tool operations under different working conditions, providing diverse training samples for the machine learning algorithm.

[0046] S222: Iteratively train the preset machine learning algorithm according to the training data set, and use the gradient descent algorithm to optimize the corresponding model parameters to obtain a multi-dimensional error model.

[0047] Specifically, the training data set is input into the preset machine learning algorithm. The algorithm uses the gradient descent algorithm to minimize the error of model parameters through continuous iterative training and gradually optimizes the predictive ability of the error model. During the training process, the algorithm adjusts the parameters according to the back propagation of each error to optimize the prediction effect of the model. After multiple iterative processes, an optimized multi-dimensional error model is finally obtained. This model can accurately predict the posture deviation of the machine tool under different working conditions, thereby guiding the generation of compensation instructions.

[0048] In one embodiment, if Figure 6 As shown, in step S40, the machine tool change state during the adjustment process is obtained in real time. When the machine tool change state changes, the strategy of the machine tool compensation instruction is corrected according to the machine tool change state, and the hydraulic cylinder control parameters are adaptively adjusted, which specifically includes: S41: collecting the adjustment posture data, vibration data and external environment data of the machine tool in real time as the machine tool change state, and comparing the machine tool change state with the preset normal working condition parameters to determine whether the machine tool has an abnormal condition.

[0049] Specifically, by deploying sensors such as vibration sensors, temperature sensors, stress sensors, etc. in different parts of the machine tool, the working status of the machine tool and changes in the external environment are recorded in real time. The sensor data will be continuously uploaded and compared with the preset normal operating parameters. The normal operating parameters include temperature range, vibration amplitude, pressure range, etc. The control unit determines whether there are abnormal changes in the machine tool by comparing the real-time data with the operating standards. If the data exceeds the set normal range, it can be identified as an abnormal situation, triggering subsequent corrections and adjustments.

[0050] S42: When the machine tool state changes abnormally, an adaptive algorithm is used to analyze the relationship between the machine tool state change and the working condition change, and the hydraulic cylinder control parameters are dynamically adjusted according to the analysis results.

[0051] Specifically, when it is monitored that the change state of the machine tool exceeds the normal range and an abnormal situation occurs, an adaptive algorithm is used to analyze the relationship between the change state of the machine tool and the change in working conditions. The algorithm uses real-time posture data, vibration data and environmental data combined with historical data for learning and modeling, and analyzes the trend of machine tool changes and working condition fluctuations. Based on the analysis results, the control parameters of the hydraulic cylinder, such as gain, response time, pressure output, etc., are dynamically adjusted to ensure that the hydraulic cylinder can be accurately adjusted according to the new working condition changes and restore the stable horizontal state of the machine tool.

[0052] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0053] In one embodiment, a hydraulic control system for rapidly adjusting the level of a machine tool is provided, and the hydraulic control system for rapidly adjusting the level of a machine tool corresponds one-to-one to the hydraulic control method for rapidly adjusting the level of a machine tool in the above embodiment. Figure 7 As shown, the hydraulic control system for quickly adjusting the level of the machine tool includes a posture data acquisition module, a posture deviation calculation module, a hydraulic cylinder feedback monitoring module, and a working condition change correction module. The detailed description of each functional module is as follows: The posture data acquisition module is used to obtain the real-time posture data of the machine tool, and fuse the real-time posture data through data fusion technology to obtain the three-dimensional spatial posture information of the machine tool; An attitude deviation calculation module is used to calculate the attitude deviation between the three-dimensional spatial attitude information of the machine tool and the preset horizontal state, generate a corresponding machine tool compensation instruction based on the deviation, and control the output pressure of the hydraulic cylinder according to the machine tool compensation instruction; The hydraulic cylinder feedback monitoring module is used to monitor the output pressure of the hydraulic cylinder in real time, provide feedback on the output pressure, and dynamically optimize the machine tool compensation instructions based on the output pressure data; The working condition change correction module is used to obtain the machine tool change state during the adjustment process in real time. When the machine tool change state shows a working condition change, the strategy of the machine tool compensation instruction is corrected according to the machine tool change state, and the hydraulic cylinder control parameters are adaptively adjusted.

[0054] Optionally, the posture data acquisition module specifically includes: The data fusion submodule is used to collect weight values ​​according to preset sensors and use the Kalman filter algorithm to perform preliminary fusion on the real-time attitude data to obtain a preliminary attitude estimation value; The posture estimation optimization submodule is used to optimize the preliminary posture estimation value, compare the optimized preliminary posture estimation value with the real-time posture data, gradually recursively update and optimize the corresponding posture estimation value, and generate three-dimensional space posture information.

[0055] Optionally, the posture estimation optimization submodule specifically includes: A posture prediction unit is used to make a preliminary prediction of the real-time posture data at each time step through the Kalman filter algorithm, and predict the posture estimation value of the next time step based on the posture estimation value of the previous time step; The posture adjustment unit is used to compare the real-time posture data with the posture estimation value to obtain the corresponding posture difference data, and adjust the current posture estimation value based on the posture difference data, gradually improve the posture estimation value through a recursive optimization process, and finally obtain the three-dimensional space posture information.

[0056] Optionally, the posture deviation calculation module specifically includes: The compensation requirement calculation submodule is used to calculate the attitude deviation of the three-dimensional space attitude information in each axis and obtain the compensation requirement of each axis; The compensation instruction generation submodule is used to input the compensation requirements into the multi-dimensional error model and generate machine tool compensation instructions according to the output results of the multi-dimensional error model.

[0057] Optional, construction of a multi-dimensional error model, including: An error model building module is used to obtain historical machine tool operation data and corresponding posture deviation data, and classify the historical machine tool operation data according to the posture deviation data to obtain a training data set; The model training module is used to iteratively train the preset machine learning algorithm according to the training data set, and use the gradient descent algorithm to optimize the corresponding model parameters to obtain a multi-dimensional error model.

[0058] Optionally, the operating condition change correction module specifically includes: The working condition monitoring submodule is used to collect the machine tool's adjustment posture data, vibration data and external environment data in real time as the machine tool's change state, and compare the machine tool's change state with the preset normal working condition parameters to determine whether the machine tool has any abnormal conditions; The adaptive control submodule is used to use an adaptive algorithm to analyze the relationship between the machine tool change state and the working condition change when the machine tool change state is abnormal, and dynamically adjust the hydraulic cylinder control parameters according to the analysis results.

[0059] The specific definition of the hydraulic control system for rapidly adjusting the level of a machine tool can be found in the definition of the hydraulic control method for rapidly adjusting the level of a machine tool in the above text, and will not be repeated here. Each module in the above-mentioned hydraulic control system for rapidly adjusting the level of a machine tool can be implemented in whole or in part by software, hardware, and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each of the above modules.

[0060] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 8 As shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a hydraulic control method for quickly adjusting the level of a machine tool is implemented.

[0061] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program: Acquire the real-time posture data of the machine tool, fuse the real-time posture data through data fusion technology, and obtain the three-dimensional spatial posture information of the machine tool; Calculate the posture deviation between the three-dimensional spatial posture information of the machine tool and the preset horizontal state, generate a corresponding machine tool compensation instruction based on the deviation, and control the output pressure of the hydraulic cylinder according to the machine tool compensation instruction; Monitor the output pressure of the hydraulic cylinder in real time, provide feedback on the output pressure, and dynamically optimize the machine tool compensation instructions based on the output pressure data; The machine tool change state during the adjustment process is acquired in real time. When the working condition changes due to the machine tool change state, the strategy of machine tool compensation instruction is corrected according to the machine tool change state, and the hydraulic cylinder control parameters are adaptively adjusted.

[0062] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented: Acquire the real-time posture data of the machine tool, fuse the real-time posture data through data fusion technology, and obtain the three-dimensional spatial posture information of the machine tool; Calculate the posture deviation between the three-dimensional spatial posture information of the machine tool and the preset horizontal state, generate a corresponding machine tool compensation instruction based on the deviation, and control the output pressure of the hydraulic cylinder according to the machine tool compensation instruction; Monitor the output pressure of the hydraulic cylinder in real time, provide feedback on the output pressure, and dynamically optimize the machine tool compensation instructions based on the output pressure data; The machine tool change state during the adjustment process is acquired in real time. When the working condition changes due to the machine tool change state, the strategy of machine tool compensation instruction is corrected according to the machine tool change state, and the hydraulic cylinder control parameters are adaptively adjusted.

[0063] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0064] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.

[0065] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A hydraulic control method for quickly adjusting the level of a machine tool, characterized in that: The hydraulic control method for quickly adjusting the level of a machine tool comprises: Acquire real-time posture data of the machine tool, and fuse the real-time posture data through data fusion technology to obtain three-dimensional spatial posture information of the machine tool; Calculate the posture deviation between the three-dimensional spatial posture information of the machine tool and a preset horizontal state, generate a corresponding machine tool compensation instruction based on the deviation, and control the output pressure of the hydraulic cylinder according to the machine tool compensation instruction; Monitor the output pressure of the hydraulic cylinder in real time, feed back the output pressure, and dynamically optimize the machine tool compensation instruction according to the output pressure data; The machine tool change state during the adjustment process is acquired in real time. When the machine tool change state shows a working condition change, the strategy of the machine tool compensation instruction is corrected according to the machine tool change state, and the hydraulic cylinder control parameters are adaptively adjusted.

2. The hydraulic control method for rapidly adjusting the level of a machine tool according to claim 1, characterized in that: The real-time posture data is fused and processed by data fusion technology to obtain the three-dimensional spatial posture information of the machine tool, specifically including: According to the preset sensor collection weight value, the real-time posture data is preliminarily integrated using the Kalman filter algorithm to obtain a preliminary posture estimation value; The preliminary posture estimation value is optimized, and the optimized preliminary posture estimation value is compared with the real-time posture data, and the corresponding posture estimation value is gradually recursively updated and optimized to generate the three-dimensional space posture information.

3. The hydraulic control method for rapidly adjusting the level of a machine tool according to claim 2, characterized in that: The step of optimizing the preliminary posture estimation value, comparing the optimized preliminary posture estimation value with the real-time posture data, and gradually recursively updating and optimizing the corresponding posture estimation value to generate the three-dimensional space posture information specifically includes: Performing a preliminary prediction of the real-time posture data at each time step by using a Kalman filter algorithm, and predicting the posture estimation value at the next time step based on the posture estimation value at the previous time step; The real-time posture data is compared with the posture estimation value to obtain corresponding posture difference data, and the current posture estimation value is adjusted based on the posture difference data. The posture estimation value is gradually improved through a recursive optimization process to finally obtain the three-dimensional space posture information.

4. The hydraulic control method for rapidly adjusting the level of a machine tool according to claim 1, characterized in that: The method further comprises: calculating a posture deviation amount between the three-dimensional spatial posture information of the machine tool and a preset horizontal state, and generating a corresponding machine tool compensation instruction based on the deviation amount, specifically comprising: Calculating the posture deviation of the three-dimensional spatial posture information in each axis to obtain the compensation requirement of each axis; The compensation requirement is input into a multi-dimensional error model, and the machine tool compensation instruction is generated according to an output result of the multi-dimensional error model.

5. The hydraulic control method for rapidly adjusting the level of a machine tool according to claim 4, characterized in that: The construction of the multi-dimensional error model specifically includes: Acquire historical machine tool operation data and corresponding posture deviation data, and classify the historical machine tool operation data according to the posture deviation data to obtain a training data set; The preset machine learning algorithm is iteratively trained according to the training data set, and the corresponding model parameters are optimized using a gradient descent algorithm to obtain the multi-dimensional error model.

6. The hydraulic control method for rapidly adjusting the level of a machine tool according to claim 1, characterized in that: The real-time acquisition of the machine tool change state during the adjustment process, when the machine tool change state has a working condition change, the strategy of correcting the machine tool compensation instruction according to the machine tool change state, and adaptively adjusting the hydraulic cylinder control parameters specifically includes: Collecting the adjustment posture data, vibration data and external environment data of the machine tool in real time as the change state of the machine tool, and comparing the change state of the machine tool with the preset normal working condition parameters to determine whether the machine tool has an abnormal situation; When the machine tool state changes abnormally, an adaptive algorithm is used to analyze the relationship between the machine tool state change and the working condition change, and the hydraulic cylinder control parameters are dynamically adjusted according to the analysis results.

7. A hydraulic control system for quickly adjusting the level of a machine tool, characterized in that: The hydraulic control system for quickly adjusting the level of the machine tool comprises: The posture data acquisition module is used to acquire the real-time posture data of the machine tool, and fuse the real-time posture data through data fusion technology to obtain the three-dimensional spatial posture information of the machine tool; A posture deviation calculation module, used to calculate the posture deviation between the three-dimensional spatial posture information of the machine tool and a preset horizontal state, generate a corresponding machine tool compensation instruction based on the deviation, and control the output pressure of the hydraulic cylinder according to the machine tool compensation instruction; A hydraulic cylinder feedback monitoring module, used for real-time monitoring of the output pressure of the hydraulic cylinder, feeding back the output pressure, and dynamically optimizing the machine tool compensation instruction according to the output pressure data; The working condition change correction module is used to obtain the machine tool change state during the adjustment process in real time. When the machine tool change state has a working condition change, the strategy of the machine tool compensation instruction is corrected according to the machine tool change state, and the hydraulic cylinder control parameters are adaptively adjusted.

8. The hydraulic control system for rapidly adjusting the level of a machine tool according to claim 7, characterized in that: The posture data acquisition module specifically includes: A data fusion submodule is used to collect weight values ​​according to preset sensors and use a Kalman filter algorithm to perform preliminary fusion on the real-time posture data to obtain a preliminary posture estimation value; The posture estimation optimization submodule is used to optimize the preliminary posture estimation value, compare the optimized preliminary posture estimation value with the real-time posture data, gradually recursively update and optimize the corresponding posture estimation value, and generate the three-dimensional space posture information.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the hydraulic control method for quickly adjusting the level of a machine tool as described in any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the hydraulic control method for rapidly adjusting the level of a machine tool as claimed in any one of claims 1 to 6 are implemented.

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