Intelligent torque correction method and system for hydraulic motor

Abstract

The invention relates to an intelligent torque correction method for a hydraulic motor, and the method comprises the steps: obtaining the torque generated by a liquid inlet plunger in a hydraulic part, and calculating the real-time hydraulic output torque of the hydraulic part; an actual torque deviation value is calculated based on the real-time motor output torque and the real-time hydraulic output torque, an actual torque loss rate is calculated through an efficiency loss model based on the actual torque deviation value and the total input power of the hydraulic motor, the lubricating liquid film thickness in the hydraulic part is obtained, and a loss-liquid film data set is constructed; the liquid film deviation factors are input into a preset deviation analysis model, and the deviation analysis model carries out analysis to generate the liquid film deviation factors; and inputting the historical actual torque loss rate and the historical liquid film deviation factor into a preset torque deviation analysis model to analyze and generate a predicted torque loss rate, and calculating a predicted torque deviation value based on the motor output torque and the predicted torque loss rate. The method has the effect of improving the prediction accuracy of the torque of the hydraulic motor.

Description

Technical Field

[0001] The present invention relates to the technical field of hydraulic motors, and in particular to a torque intelligent correction method and system for hydraulic motors. Background Art

[0002] A hydraulic motor is a device that converts hydraulic energy into mechanical energy. It is commonly found in engineering machinery, agricultural machinery, industrial production and other fields. A hydraulic motor consists of a motor part and a hydraulic part (the motor part provides driving force for the hydraulic part, and the output power of the hydraulic motor is provided by the hydraulic part). The torque of the hydraulic motor is a key parameter for measuring the output power and performance of the hydraulic motor. The torque prediction of the hydraulic motor is related to the formulation of production strategies and affects production efficiency.

[0003] The existing torque prediction for hydraulic motors is generally based on the input power of the hydraulic motor and the working power of the motor part to calculate the torque, and the calculation result is used as the torque prediction result. However, in actual work, there is power consumption in the motor part and the hydraulic part, which leads to a difference between the actual output torque of the hydraulic motor and the calculation result, reducing the accuracy of the torque prediction of the hydraulic motor. Summary of the invention

[0004] In order to improve the prediction accuracy of the torque of a hydraulic motor, the present application provides a method and system for intelligently correcting the torque of a hydraulic motor.

[0005] In the first aspect, the above-mentioned invention objective of the present application is achieved through the following technical solutions:

[0006] A method for intelligent torque correction of a hydraulic motor, the method comprising the following steps:

[0007] Obtain the torque generated by each inlet plunger in the hydraulic part and calculate the real-time hydraulic output torque of the hydraulic part;

[0008] The real-time power and the real-time motor output torque of the motor part are obtained, and the actual torque deviation value is calculated based on the real-time motor output torque and the real-time hydraulic output torque, and the actual torque loss rate is calculated through a preset efficiency loss model based on the actual torque deviation value and the total input power of the hydraulic motor.

[0009] Acquire the lubricating film thickness in the hydraulic part, and associate the actual torque loss rate with the lubricating film thickness based on a timestamp to construct a loss-film data set;

[0010] Inputting the loss-liquid film data set into a preset deviation analysis model, wherein the deviation analysis model analyzes the loss-liquid film data set through a machine self-learning algorithm to generate a liquid film deviation factor;

[0011] The actual torque loss rate and the liquid film deviation factor in history are input into a preset torque deviation analysis model for analysis to generate a predicted torque loss rate. A predicted torque deviation value is calculated based on the motor output torque and the predicted torque loss rate. The predicted torque deviation value is used to perform torque correction on the hydraulic motor.

[0012] By adopting the above technical scheme, the actual torque loss rate is calculated to understand the loss of the motor when it is working, which is helpful to evaluate the motor performance and provide a basis for the subsequent hydraulic motor torque correction. The historical data is input into the preset model to analyze and generate the predicted torque loss rate, and then the predicted torque deviation value is calculated based on the motor output torque and the predicted torque loss rate for torque correction of the hydraulic motor. The torque correction through the predicted torque deviation value can correct the torque deviation of the hydraulic motor in advance. Compared with the existing method of using historical hydraulic motor torque as predicted torque, this scheme is predictive, predicts the torque deviation of the hydraulic motor in advance and corrects the torque, thereby improving the prediction accuracy of the hydraulic motor torque, and arranges production based on the corrected hydraulic motor torque, thereby extending equipment life and improving work efficiency. Compared with manually correcting various torque data of the hydraulic motor, this scheme automatically corrects the hydraulic motor according to the detection data, making the torque prediction intelligent.

[0013] In a preferred example, the present application can be further configured as follows: in the step of obtaining the torque generated by each liquid inlet plunger in the hydraulic part and calculating the real-time hydraulic output torque of the hydraulic part, the real-time hydraulic output torque calculation formula is:

[0014] Where ρ i is the center pole diameter of the roller, β is the pressure angle between the roller and the track, v φi is the speed of the plunger.

[0015] By adopting the above technical solution, the real-time hydraulic output torque is calculated using the hydraulic output torque calculation formula, thereby improving the data accuracy in the hydraulic motor torque calculation process and further improving the accuracy of torque measurement.

[0016] In a preferred example, the present application can be further configured as follows: after obtaining the real-time power and the real-time motor output torque of the motor part, and calculating the actual torque deviation value based on the real-time motor output torque and the real-time hydraulic output torque, and calculating the actual torque loss rate based on the actual torque deviation value and the total input power of the hydraulic motor through a preset efficiency loss model, the following steps are included:

[0017] Acquire the hydraulic part speed, and calculate the effective output power of the hydraulic motor based on the real-time hydraulic output torque and the hydraulic part speed;

[0018] Obtaining the effective input power of the load device and comparing it with the effective output of the hydraulic motor to calculate the output transmission efficiency of the hydraulic motor;

[0019] Associating the output transmission efficiency and the actual torque loss rate based on a timestamp to construct a loss-transmission data set;

[0020] The loss-transmission data set is input into the deviation analysis model, and the deviation analysis model analyzes the loss-transmission data set through a machine self-learning algorithm to generate a transmission deviation factor.

[0021] By adopting the above technical scheme, the effective output power of the hydraulic motor is calculated using the real-time hydraulic output torque and speed. Obtaining the effective output power of the hydraulic motor can accurately reflect the actual working state of the motor, and provide important data for the subsequent output transmission efficiency calculation and deviation analysis. The intelligent correction method of the hydraulic motor torque is further improved. By obtaining the speed of the hydraulic part and the effective power of the load equipment, the effective output power and output transmission efficiency of the hydraulic motor are calculated, and they are associated with the actual torque loss rate. The working state and energy conversion efficiency of the hydraulic motor are more comprehensively evaluated to improve the accuracy of the torque deviation analysis, and generate a more reliable transmission deviation factor, providing a more accurate basis for the torque correction of the hydraulic motor. The overall solution improves the working efficiency of the hydraulic motor, reduces energy loss, and extends the service life of the equipment.

[0022] In a preferred example, the present application may be further configured as follows: before the step of associating the output transmission efficiency and the actual torque loss rate based on the timestamp to construct the loss-transmission data set, the step includes:

[0023] Acquire load equipment data of the hydraulic motor, and construct a load profile based on the load equipment data, wherein the load profile includes the type of the load equipment and the connection mode between the load equipment and the hydraulic motor;

[0024] The output transmission efficiency is associated with the corresponding load portrait, and a preset load analysis model is analyzed based on the association between the output transmission efficiency and the corresponding load portrait to generate a load correlation coefficient, which is used to predict the impact of the load equipment on the output transmission efficiency.

[0025] By adopting the above technical solution, by constructing load portraits and predicting load correlation coefficients, the impact of load equipment on transmission efficiency can be analyzed more accurately. Compared with traditional empirical estimation methods, this solution provides more accurate load analysis results, which helps to optimize the design and performance of the system. By understanding the type and connection method of the load equipment, the factors affecting the transmission efficiency can be considered in the system design stage, and the corresponding hydraulic motor torque can be calculated, so as to select the appropriate transmission device and optimize the system structure, improve the overall efficiency and performance of the system, and by predicting the impact of the load equipment on the transmission efficiency, potential efficiency problems can be identified in advance and corresponding optimization measures can be taken.

[0026] In a preferred example, the present application can be further configured as follows: in the step of inputting the loss-liquid film data set into a preset deviation analysis model, the deviation analysis model analyzes the loss-liquid film data set through a machine self-learning algorithm to generate a liquid film deviation factor, including the following steps:

[0027] constructing a loss liquid film correlation curve based on the loss-liquid film data set, and using the loss liquid film correlation curve as a prediction benchmark for the actual torque loss rate;

[0028] The loss liquid film correlation curve is analyzed, and a correlation analysis is performed based on the correlation between the actual torque loss rate and the lubricating liquid film thickness. The liquid film deviation factor is generated according to the analysis result, and the liquid film deviation factor is used to predict the actual torque loss rate corresponding to the lubricating liquid film thickness.

[0029] By adopting the above technical solution and performing correlation analysis on the loss film correlation curve, the deviation factor of the actual torque loss rate corresponding to the lubricating film thickness is obtained, which is used to predict the actual torque loss rate under different lubricating film thicknesses. This step provides the ability to quantitatively predict the effect of lubricating film thickness on the actual torque loss rate, thereby providing a basis for judging the torque based on the lubrication management and performance of the hydraulic motor system.

[0030] In a preferred example, the present application can be further configured as follows: after analyzing the loss liquid film correlation curve, performing correlation analysis based on the correlation relationship between the actual torque loss rate and the lubricating liquid film thickness, and generating the liquid film deviation factor according to the analysis result, the step includes:

[0031] The actual thickness of the lubricating liquid film in the hydraulic part is obtained, and a preset torque correction model generates a first deviation correction torque loss rate based on the liquid film deviation factor and the actual torque loss rate;

[0032] Acquire load device data of the output end of the hydraulic motor, determine a load correlation coefficient according to the load portrait, and calculate the first correction torque loss rate based on the transmission deviation factor and the load correlation coefficient by the torque correction model to generate a second correction torque loss rate;

[0033] Calculating a corrected torque deviation value based on the second deviation-correcting torque loss rate and the total input power of the hydraulic motor;

[0034] The corrected hydraulic portion torque is calculated based on the real-time motor output torque and the corrected torque deviation value, and the corrected hydraulic portion torque is used as the output torque of the hydraulic motor.

[0035] By adopting the above technical scheme, through the calculation and correction of the liquid film deviation factor, load-related coefficient and torque deviation value, the torque measurement has the ability to correct the torque loss rate, the torque loss rate of the hydraulic motor can be corrected more accurately, and the accuracy and reliability of the torque loss rate can be improved. By calculating and correcting the torque of the hydraulic part in real time, the output performance of the hydraulic motor can be optimized during operation, the response speed and efficiency of the system can be improved, and it has real-time performance optimization function. This scheme uses the actual data of the hydraulic motor and the preset model for analysis and correction, and provides a data-driven performance optimization method, which is more accurate and reliable than the traditional empirical estimation method, further improves the data-driven analysis and correction capabilities, and improves the torque measurement correction capability of the hydraulic motor.

[0036] In a preferred example, the present application may be further configured as follows: in the step of analyzing the loss-liquid film data set by the deviation analysis model through a machine self-learning algorithm, the machine self-learning algorithm includes a neural network algorithm and a regression analysis algorithm.

[0037] By adopting the above technical solutions, the neural network algorithm is suitable for processing complex nonlinear relationships and large-scale data, can learn from the data and discover hidden patterns and laws, and has robustness and generalization capabilities. The linear regression algorithm provides better model interpretability, can clearly reveal the degree and direction of the influence of independent variables on dependent variables, has high computational efficiency and is suitable for simple linear relationship modeling. Therefore, in this solution, according to data characteristics, problem complexity and prediction requirements, neural network algorithms and linear regression algorithms are used to obtain better results and improve the effect of data processing.

[0038] In the second aspect, the above invention objective of the present application is achieved through the following technical solutions:

[0039] A torque intelligent correction device for a hydraulic motor, the device comprising:

[0040] A real-time hydraulic output torque calculation unit, used to obtain the torque generated by each liquid inlet plunger in the hydraulic part and calculate the real-time hydraulic output torque of the hydraulic part;

[0041] an actual torque loss rate calculation unit, for obtaining the real-time power of the motor part and the real-time motor output torque, and calculating the actual torque deviation value based on the real-time motor output torque and the real-time hydraulic output torque, and calculating the actual torque loss rate based on the actual torque deviation value and the total input power of the hydraulic motor through a preset efficiency loss model,

[0042] A loss-liquid film data set construction unit is used to obtain the lubricating film thickness in the hydraulic part, and associate the actual torque loss rate with the lubricating film thickness based on a timestamp to construct a loss-liquid film data set;

[0043] A deviation analysis model building unit, used for presetting a deviation analysis model, and inputting the loss-liquid film data set into the pre-set deviation analysis model, wherein the deviation analysis model analyzes the loss-liquid film data set through a machine self-learning algorithm to generate a liquid film deviation factor;

[0044] The hydraulic motor torque correction unit is used to input the historical actual torque loss rate and the historical liquid film deviation factor into a preset torque deviation analysis model to analyze and generate a predicted torque loss rate, and calculate a predicted torque deviation value based on the motor output torque and the predicted torque loss rate. The predicted torque deviation value is used to perform torque correction on the hydraulic motor.

[0045] By adopting the above technical scheme, the actual torque loss rate is calculated to understand the loss of the motor when it is working, which is helpful to evaluate the motor performance and provide a basis for the subsequent hydraulic motor torque correction. The historical data is input into the preset model to analyze and generate the predicted torque loss rate, and then the predicted torque deviation value is calculated based on the motor output torque and the predicted torque loss rate for torque correction of the hydraulic motor. The torque correction through the predicted torque deviation value can correct the torque deviation of the hydraulic motor in advance. Compared with the existing method of using historical hydraulic motor torque as predicted torque, this scheme is predictive, predicts the torque deviation of the hydraulic motor in advance and corrects the torque, thereby improving the prediction accuracy of the hydraulic motor torque, and arranges production based on the corrected hydraulic motor torque, thereby extending equipment life and improving work efficiency. Compared with manually correcting various torque data of the hydraulic motor, this scheme automatically corrects the hydraulic motor according to the detection data, making the torque prediction intelligent.

[0046] On the third aspect, the above-mentioned purpose of the present application is achieved through the following technical solutions:

[0047] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned method for intelligent torque correction of a hydraulic motor when executing the computer program.

[0048] Fourthly, the above-mentioned purpose of the present application is achieved through the following technical solutions:

[0049] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned method for intelligent torque correction of a hydraulic motor are implemented.

[0050] In summary, the present application includes at least one of the following beneficial technical effects:

[0051] 1. By calculating the actual torque loss rate, the loss of the motor during operation can be understood, which is helpful to evaluate the motor performance and provide a basis for the subsequent torque correction of the hydraulic motor. The historical data is input into the preset model to analyze and generate the predicted torque loss rate, and then the predicted torque deviation value is calculated based on the motor output torque and the predicted torque loss rate, which is used to correct the torque of the hydraulic motor. The torque deviation of the hydraulic motor can be corrected in advance by using the predicted torque deviation value for torque correction. Compared with the existing method of using the historical hydraulic motor torque as the predicted torque, this scheme is predictive, predicting the torque deviation of the hydraulic motor in advance and correcting the torque, thereby improving the prediction accuracy of the hydraulic motor torque, and making production arrangements based on the corrected hydraulic motor torque, extending the equipment life and improving work efficiency. Compared with manually correcting various torque data of the hydraulic motor, this scheme automatically corrects the hydraulic motor according to the detection data, making the torque prediction intelligent.

[0052] 2. The effective output power of the hydraulic motor is calculated using the real-time hydraulic output torque and speed. Obtaining the effective output power of the hydraulic motor can accurately reflect the actual working state of the motor, provide important data for the subsequent output transmission efficiency calculation and deviation analysis, and further improve the intelligent correction method of the hydraulic motor torque. By obtaining the speed of the hydraulic part and the effective power of the load equipment, the effective output power and output transmission efficiency of the hydraulic motor are calculated, and they are associated with the actual torque loss rate. The working state and energy conversion efficiency of the hydraulic motor are more comprehensively evaluated to improve the accuracy of the torque deviation analysis and generate a more reliable transmission deviation factor, providing a more accurate basis for the torque correction of the hydraulic motor. The overall solution improves the working efficiency of the hydraulic motor, reduces energy loss, and extends the service life of the equipment;

[0053] 3. By constructing load portraits and predicting load correlation coefficients, the impact of load equipment on transmission efficiency can be analyzed more accurately. Compared with traditional empirical estimation methods, this solution provides more accurate load analysis results, which helps optimize the design and performance of the system. By understanding the type and connection method of the load equipment, the factors affecting transmission efficiency can be considered during the system design stage, and the corresponding hydraulic motor torque can be calculated, so as to select the appropriate transmission device and optimize the system structure, thereby improving the overall efficiency and performance of the system. By predicting the impact of load equipment on transmission efficiency, potential efficiency problems can be identified in advance and corresponding optimization measures can be taken;

[0054] 4. By calculating and correcting the liquid film deviation factor, load-related coefficient and torque deviation value, the torque measurement has the ability to correct the torque loss rate, which can more accurately correct the torque loss rate of the hydraulic motor and improve the accuracy and reliability of the torque loss rate. By calculating and correcting the torque of the hydraulic part in real time, the output performance of the hydraulic motor can be optimized during operation, and the response speed and efficiency of the system can be improved. It has real-time performance optimization function. This solution uses the actual data of the hydraulic motor and the preset model for analysis and correction, and provides a data-driven performance optimization method. Compared with the traditional empirical estimation method, it is more accurate and reliable, further improving the data-driven analysis and correction capabilities, and improving the torque measurement correction capability of the hydraulic motor. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a schematic diagram of the steps of a method for intelligent torque correction of a hydraulic motor in one embodiment of the present application;

[0056] Figure 2 This is a principle block diagram of a torque intelligent correction device for a hydraulic motor in one embodiment of the present application;

[0057] Figure 3 It is a schematic diagram of an electronic device in an embodiment of the present application.

[0058] Figure Number:

[0059] 1. Real-time hydraulic output torque calculation unit; 2. Actual torque loss rate calculation unit; 3. Loss-liquid film data set construction unit; 4. Deviation analysis model construction unit; 5. Hydraulic motor torque correction unit. DETAILED DESCRIPTION

[0060] The present application is further described in detail below in conjunction with the accompanying drawings.

[0061] In one embodiment, if Figure 1 As shown, the present application discloses a torque intelligent correction method for a hydraulic motor, which specifically includes the following steps:

[0062] S10: acquiring the torque generated by each liquid inlet plunger in the hydraulic part, and calculating the real-time hydraulic output torque of the hydraulic part;

[0063] Specifically, there are multiple liquid-inlet plungers in the hydraulic motor. The torque value generated by each plunger is obtained through the sensor, and the real-time hydraulic output torque of the hydraulic part is calculated based on the torque value generated by each plunger, which helps to grasp the working status of the hydraulic system and provide real-time data support for subsequent torque intelligent correction methods.

[0064] S20: acquiring the real-time power and the real-time motor output torque of the motor part, and calculating the actual torque deviation value based on the real-time motor output torque and the real-time hydraulic output torque, and calculating the actual torque loss rate based on the actual torque deviation value and the total input power of the hydraulic motor through a preset efficiency loss model;

[0065] Specifically, the real-time power and real-time output torque of the motor part are obtained through the sensor, and then the actual torque deviation value is calculated according to the real-time motor output torque and real-time hydraulic output torque detected by the sensor, and the actual torque loss rate is calculated according to the formula, which is: ρ 实际损耗 =(T2-T1) / Pin, where ρ 实际损耗 is the actual torque loss rate, T2 is the real-time motor output torque, T1 is the real-time motor output torque, Pin is the total input power of the hydraulic motor. By calculating the actual torque loss rate, we can understand the loss of the motor when it is working, which is helpful to evaluate the motor performance and provide a basis for the subsequent hydraulic motor torque correction.

[0066] S30: Acquire the lubricating film thickness in the hydraulic part, and associate the actual torque loss rate with the lubricating film thickness based on a timestamp to construct a loss-film data set;

[0067] Specifically, the lubricating film thickness in the hydraulic part is obtained and associated with the actual torque loss rate to construct a loss-film data set.

[0068] S40: inputting the loss-liquid film data set into a preset deviation analysis model, wherein the deviation analysis model analyzes the loss-liquid film data set through a machine self-learning algorithm to generate a liquid film deviation factor;

[0069] Specifically, generating a liquid film deviation factor through a machine learning algorithm can more accurately reflect the effect of lubricating film thickness on torque loss in the hydraulic system, providing a basis for subsequent torque correction.

[0070] S50: inputting the historical actual torque loss rate and the historical liquid film deviation factor into a preset torque deviation analysis model to analyze and generate a predicted torque loss rate, and calculating a predicted torque deviation value based on the motor output torque and the predicted torque loss rate, wherein the predicted torque deviation value is used to perform torque correction on the hydraulic motor;

[0071] Specifically, historical data is input into a preset model to analyze and generate a predicted torque loss rate, and then a predicted torque deviation value is calculated based on the motor output torque and the predicted torque loss rate for use in torque correction of the hydraulic motor. Torque correction through the predicted torque deviation value can correct the torque deviation of the hydraulic motor in advance. Compared with the existing method of using historical hydraulic motor torque as predicted torque, this solution is predictive, predicts the torque deviation of the hydraulic motor in advance and performs torque correction, thereby improving the prediction accuracy of the hydraulic motor torque, and arranges production based on the corrected hydraulic motor torque, thereby extending equipment life and improving work efficiency. Compared with manual correction of various torque data of the hydraulic motor, this solution automatically corrects the hydraulic motor according to the detection data, making the torque prediction intelligent.

[0072] It should be noted that, in the embodiment of the present application, the hydraulic motor includes a plunger pair (Piston Pairing) and a roller pair (Roller Pairing). The plunger pair is a common structure in hydraulic motors. In the embodiment of the present application, the plunger pair is composed of multiple plungers and a corresponding number of plunger sleeves. A sealed gap is formed between the plunger and the plunger sleeve. The hydraulic oil applies pressure through this gap to push the plunger to move in the plunger sleeve. The plunger pair can achieve high pressure, high torque and high speed output. The plunger pair is generally suitable for high-power and high-performance hydraulic motor applications; the roller pair is also a common hydraulic motor structure. In the embodiment of the present application, the roller pair is composed of multiple rollers and a corresponding number of roller sleeves. The rollers roll through the teeth of the roller sleeve to form relative motion. The hydraulic oil applies pressure through the gap between the roller and the roller sleeve to push the roller to roll in the roller sleeve.

[0073] The real-time hydraulic output torque calculation formula is: Where ρ i is the center pole diameter of the roller, β is the pressure angle between the roller and the track, v φi The speed of the plunger is calculated by the hydraulic output torque calculation formula to improve the data accuracy in the hydraulic motor torque calculation process and further improve the accuracy of torque measurement.

[0074] Explanation of the speed: In the plunger auxiliary structure, there is usually a swashplate on the plunger sleeve, which is connected to the drive shaft. When the drive shaft rotates, the inclination angle of the swashplate causes the plunger to move up and down in the plunger sleeve. The speed refers to the rate of change of the inclination angle of the swashplate over time. It is further explained that the speed is usually controlled by adjusting the inclination angle of the swashplate to control the output speed and torque of the hydraulic motor. When the inclination angle of the swashplate increases, the reciprocating speed of the plunger will also increase, thereby increasing the output speed and torque of the hydraulic motor; conversely, when the inclination angle of the swashplate decreases, the movement speed and output speed of the plunger will also decrease;

[0075] The formula for lubricating film thickness is H=C×(1+ε×cosθ), where H is the lubricating film thickness, C is the radial clearance, ε is the eccentricity, and ε=e / C, e is the eccentricity, and θ is the angular angle of the roller.

[0076] After the step of obtaining the real-time power and the real-time motor output torque of the motor part, calculating the actual torque deviation value based on the real-time motor output torque and the real-time hydraulic output torque, and calculating the actual torque loss rate based on the actual torque deviation value and the total input power of the hydraulic motor through a preset efficiency loss model, the following steps are included:

[0077] S201: Acquire the speed of the hydraulic part, and calculate the effective output power of the hydraulic motor based on the real-time hydraulic output torque and the speed of the hydraulic part;

[0078] Specifically, the output speed of the hydraulic part is obtained through the sensor, and the effective output power of the hydraulic motor is calculated using the real-time hydraulic output torque and speed. Obtaining the effective output power of the hydraulic motor can accurately reflect the actual working state of the motor and provide important data for subsequent output transmission efficiency calculation and deviation analysis.

[0079] S202: Acquire the effective input power of the load device, and compare it with the effective output of the hydraulic motor to calculate the output transmission efficiency of the hydraulic motor;

[0080] Specifically, in the embodiment of the present application, the load device is driven by a hydraulic motor, that is, the working power of the load device is used as the effective input power of the load device. In the embodiment of the present application, the output transmission efficiency = (effective input power of the load device / effective output power of the hydraulic motor) * 100%.

[0081] S203: Associating the output transmission efficiency and the actual torque loss rate based on the timestamp to construct a loss-transmission data set;

[0082] S204: inputting the loss-transmission data set into the deviation analysis model, wherein the deviation analysis model analyzes the loss-transmission data set through a machine self-learning algorithm to generate a transmission deviation factor;

[0083] Specifically, in the embodiment of the present application, the deviation analysis model is provided with a neural network algorithm and a regression analysis algorithm, and the transmission deviation factor is obtained by analyzing the loss-transmission data set through the deviation analysis model through the neural network algorithm and the regression analysis algorithm. In the embodiment of the present application, the transmission deviation factor is a variable. As time and working conditions change, the deviation analysis model continuously optimizes the transmission deviation factor to reflect the transmission efficiency under the current working state. It should be noted that in other embodiments, the transmission deviation factor can be a constant.

[0084] In the embodiment of the present application, the beneficial effect of steps S201, S202 and S203 compared with the prior art is that the intelligent correction method of hydraulic motor torque is further improved: by obtaining the speed of the hydraulic part and the effective power of the load equipment, the effective output power and output transmission efficiency of the hydraulic motor are calculated, and they are associated with the actual torque loss rate, so as to more comprehensively evaluate the working state and energy conversion efficiency of the hydraulic motor, so as to improve the accuracy of torque deviation analysis, and generate a more reliable transmission deviation factor, so as to provide a more accurate basis for the torque correction of the hydraulic motor. The overall solution improves the working efficiency of the hydraulic motor, reduces energy loss, and extends the service life of the equipment.

[0085] The transmission deviation factor can be applied in the control and optimization of hydraulic systems to improve the performance and energy efficiency of the system. Here are some examples of applying the transmission deviation factor:

[0086] Power deviation correction: By applying the transmission deviation factor, the power deviation output by the hydraulic motor can be corrected. According to the actual transmission deviation factor value, the torque output by the motor is corrected to reduce the power deviation and improve the transmission efficiency of the system.

[0087] Energy management: The transmission deviation factor can be used for energy management and energy efficiency optimization. By understanding the changing trend and value of the transmission deviation factor, the energy conversion process of the hydraulic system can be optimized to maximize the energy utilization efficiency and reduce energy loss.

[0088] Fault warning: The transmission deviation factor can also be used for fault warning and diagnosis. By monitoring the changes in the transmission deviation factor, abnormal conditions and faults in the transmission system can be discovered in time, and maintenance measures can be taken in advance to avoid system failures from causing serious impacts on production and equipment.

[0089] Optimization control strategy: The transmission deviation factor can be used as an input parameter of the optimization control strategy. By incorporating the transmission deviation factor into the control algorithm, real-time adjustment and optimization of the hydraulic system parameters can be achieved to meet the performance requirements under different working conditions and improve the system's response speed and control accuracy.

[0090] Before the step of S203: associating the output transmission efficiency and the actual torque loss rate based on the timestamp to construct a loss-transmission data set, the following steps are included:

[0091] S2031: Acquire load equipment data of the hydraulic motor, and construct a load profile based on the load equipment data, wherein the load profile includes the type of the load equipment and the connection method between the load equipment and the hydraulic motor;

[0092] Specifically, the hydraulic motor is used to drive a hydraulic pump and a hydraulic cylinder, wherein the hydraulic pump is connected to the motor through a transmission device. This step provides detailed information about the load equipment, including type and connection method, which provides a basis for subsequent load analysis and transmission efficiency analysis.

[0093] S2032: Associating the output transmission efficiency with the corresponding load profile, and performing analysis based on the association between the output transmission efficiency and the corresponding load profile by a preset load analysis model to generate a load correlation coefficient, wherein the load correlation coefficient is used to predict the influence of a load device on the output transmission efficiency;

[0094] For example, by analyzing the transmission efficiency of hydraulic motors under different load conditions and associating the type and connection method of the load equipment, the load-related coefficient can be obtained to predict the impact of different load equipment on the transmission efficiency. This provides the ability to quantitatively predict the impact of load equipment on the transmission efficiency and provides a basis for system design and optimization.

[0095] The beneficial effects of steps S2031 and S2032 of this solution compared with the prior art include:

[0096] Accurate load analysis: By constructing load profiles and predicting load correlation coefficients, the impact of load equipment on transmission efficiency can be analyzed more accurately. Compared with traditional empirical estimation methods, this solution provides more accurate load analysis results, which helps optimize system design and performance.

[0097] Efficient system design: By understanding the type and connection method of the load equipment, the factors affecting the transmission efficiency can be taken into account during the system design stage, so as to select the appropriate transmission device and optimize the system structure to improve the overall efficiency and performance of the system.

[0098] Prediction and optimization capabilities: By predicting the impact of load equipment on transmission efficiency, potential efficiency issues can be identified in advance and corresponding optimization measures can be taken. This helps improve system reliability and work efficiency and reduce energy consumption and maintenance costs.

[0099] In summary, this scheme provides more accurate load analysis and transmission efficiency prediction capabilities through the construction and analysis of load portraits and load correlation coefficients, which provides beneficial effects for the torque evaluation of hydraulic motor systems.

[0100] In S40, the loss-liquid film data set is input into a preset deviation analysis model, and the deviation analysis model analyzes the loss-liquid film data set through a machine self-learning algorithm to generate a liquid film deviation factor. This step includes the following steps:

[0101] S41: constructing a loss liquid film correlation curve based on the loss-liquid film data set, and using the loss liquid film correlation curve as a prediction benchmark for the actual torque loss rate;

[0102] S42: analyzing the loss liquid film correlation curve, performing correlation analysis based on the correlation between the actual torque loss rate and the lubricating liquid film thickness, and generating the liquid film deviation factor according to the analysis result, wherein the liquid film deviation factor is used to predict the actual torque loss rate corresponding to the lubricating liquid film thickness;

[0103] Specifically, by performing a correlation analysis on the loss film correlation curve, the deviation factor of the actual torque loss rate corresponding to the lubricating film thickness is obtained, which is used to predict the actual torque loss rate under different lubricating film thicknesses. This step provides the ability to quantitatively predict the effect of lubricating film thickness on the actual torque loss rate, thereby providing a basis for judging the torque based on the lubrication management and performance of the hydraulic motor system.

[0104] After the step of analyzing the loss liquid film correlation curve, performing correlation analysis based on the correlation relationship between the actual torque loss rate and the lubricating liquid film thickness, and generating the liquid film deviation factor according to the analysis result, the following steps are included:

[0105] S421: Acquire the actual thickness of the lubricating liquid film in the hydraulic part, and generate a first deviation correction torque loss rate based on the liquid film deviation factor and the actual torque loss rate by a preset torque correction model;

[0106] Specifically, the actual thickness of the lubricating film in the hydraulic part is measured, and the first correction torque loss rate is calculated by combining the preset model of the liquid film deviation factor and the actual torque loss rate. The formula is: first correction torque loss rate = actual torque loss rate * liquid film deviation factor.

[0107] S422: acquiring load device data of the output end of the hydraulic motor, determining a load correlation coefficient according to the load portrait, and calculating the first correction torque loss rate based on the transmission deviation factor and the load correlation coefficient by the torque correction model to generate a second correction torque loss rate;

[0108] Specifically, the load correlation coefficient is determined according to the load equipment data and load portrait at the output end of the hydraulic motor, and combined with the transmission deviation factor, the second correction torque loss rate can be calculated. This step takes into account the influence of the load equipment on the torque loss rate, thereby improving the accuracy and applicability of the torque loss rate. The second correction torque loss rate = the first correction torque loss rate * transmission deviation factor * load correlation coefficient.

[0109] S423: calculating a corrected torque deviation value based on the second deviation-correcting torque loss rate and the total input power of the hydraulic motor;

[0110] Specifically, the corrected torque deviation value=the second deviation-correcting torque loss rate*the total input power.

[0111] S424: calculating a corrected hydraulic partial torque based on the real-time motor output torque and the corrected torque deviation value, and using the corrected hydraulic partial torque as the output torque of the hydraulic motor;

[0112] Specifically, the corrected hydraulic partial torque is obtained by calculating the real-time motor output torque and the corrected torque deviation value, and the formula is as follows: Corrected hydraulic partial torque = real-time motor output torque + corrected torque deviation value. This step provides a corrected output torque value, making the output torque of the hydraulic motor more accurate and reliable.

[0113] 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.

[0114] In one embodiment, a torque intelligent correction device for a hydraulic motor is provided, and the torque intelligent correction device for a hydraulic motor corresponds one-to-one with the torque intelligent correction method for a hydraulic motor in the above embodiment. Figure 2 As shown, the torque intelligent correction device for a hydraulic motor comprises: a real-time hydraulic output torque calculation unit 1, which is used to obtain the torque generated by each liquid inlet plunger in the hydraulic part and calculate the real-time hydraulic output torque of the hydraulic part;

[0115] The actual torque loss rate calculation unit 2 is used to obtain the real-time power and real-time motor output torque of the motor part, and calculate the actual torque deviation value based on the real-time motor output torque and the real-time hydraulic output torque, and calculate the actual torque loss rate based on the actual torque deviation value and the total input power of the hydraulic motor through a preset efficiency loss model.

[0116] A loss-liquid film data set construction unit 3 is used to obtain the lubricating film thickness in the hydraulic part, and associate the actual torque loss rate with the lubricating film thickness based on a timestamp to construct a loss-liquid film data set;

[0117] The deviation analysis model building unit 4 is used to preset a deviation analysis model and input the loss-liquid film data set into the preset deviation analysis model, wherein the deviation analysis model analyzes the loss-liquid film data set through a machine self-learning algorithm to generate a liquid film deviation factor;

[0118] The hydraulic motor torque correction unit 5 is used to input the historical actual torque loss rate and the historical liquid film deviation factor into a preset torque deviation analysis model to analyze and generate a predicted torque loss rate, and calculate a predicted torque deviation value based on the motor output torque and the predicted torque loss rate. The predicted torque deviation value is used to perform torque correction on the hydraulic motor.

[0119] For the specific definition of the intelligent torque correction device for hydraulic motors, please refer to the definition of the intelligent torque correction method for hydraulic motors mentioned above, which will not be repeated here. Each module in the above-mentioned intelligent torque correction device for hydraulic motors can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the electronic device in the form of hardware, or can be stored in the memory of the electronic device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0120] In one embodiment, an electronic device is provided. The electronic device may be a server, and its internal structure diagram may be as follows: Figure 3 As shown. The electronic device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic 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 database of the electronic device is used to store a database. The network interface of the electronic device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a torque intelligent correction method for a hydraulic motor is implemented.

[0121] In one embodiment, an electronic 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:

[0122] Obtain the torque generated by each inlet plunger in the hydraulic part and calculate the real-time hydraulic output torque of the hydraulic part;

[0123] The real-time power and the real-time motor output torque of the motor part are obtained, and the actual torque deviation value is calculated based on the real-time motor output torque and the real-time hydraulic output torque, and the actual torque loss rate is calculated through a preset efficiency loss model based on the actual torque deviation value and the total input power of the hydraulic motor.

[0124] Acquire the lubricating film thickness in the hydraulic part, and associate the actual torque loss rate with the lubricating film thickness based on a timestamp to construct a loss-film data set;

[0125] Inputting the loss-liquid film data set into a preset deviation analysis model, wherein the deviation analysis model analyzes the loss-liquid film data set through a machine self-learning algorithm to generate a liquid film deviation factor;

[0126] The actual torque loss rate and the liquid film deviation factor in history are input into a preset torque deviation analysis model for analysis to generate a predicted torque loss rate. A predicted torque deviation value is calculated based on the motor output torque and the predicted torque loss rate. The predicted torque deviation value is used to perform torque correction on the hydraulic motor.

[0127] 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:

[0128] Obtain the torque generated by each inlet plunger in the hydraulic part and calculate the real-time hydraulic output torque of the hydraulic part;

[0129] The real-time power and the real-time motor output torque of the motor part are obtained, and the actual torque deviation value is calculated based on the real-time motor output torque and the real-time hydraulic output torque, and the actual torque loss rate is calculated through a preset efficiency loss model based on the actual torque deviation value and the total input power of the hydraulic motor.

[0130] Acquire the lubricating film thickness in the hydraulic part, and associate the actual torque loss rate with the lubricating film thickness based on a timestamp to construct a loss-film data set;

[0131] Inputting the loss-liquid film data set into a preset deviation analysis model, wherein the deviation analysis model analyzes the loss-liquid film data set through a machine self-learning algorithm to generate a liquid film deviation factor;

[0132] The actual torque loss rate and the liquid film deviation factor in history are input into a preset torque deviation analysis model for analysis to generate a predicted torque loss rate. A predicted torque deviation value is calculated based on the motor output torque and the predicted torque loss rate. The predicted torque deviation value is used to perform torque correction on the hydraulic motor.

[0133] 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 can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can 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).

[0134] Those skilled in the art can clearly understand that for the convenience and simplicity 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 device can be divided into different functional units or modules to complete all or part of the functions described above.

[0135] 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 method for intelligent torque correction of a hydraulic motor, characterized in that: The method comprises the following steps: acquiring the torque generated by each liquid-intake plunger in the hydraulic part, and calculating the real-time hydraulic output torque of the hydraulic part; The real-time power and the real-time motor output torque of the motor part are obtained, and the actual torque deviation value is calculated based on the real-time motor output torque and the real-time hydraulic output torque, and the actual torque loss rate is calculated through a preset efficiency loss model based on the actual torque deviation value and the total input power of the hydraulic motor. Acquire the lubricating film thickness in the hydraulic part, and associate the actual torque loss rate with the lubricating film thickness based on a timestamp to construct a loss-film data set; Inputting the loss-liquid film data set into a preset deviation analysis model, wherein the deviation analysis model analyzes the loss-liquid film data set through a machine self-learning algorithm to generate a liquid film deviation factor; The actual torque loss rate and the liquid film deviation factor in history are input into a preset torque deviation analysis model for analysis to generate a predicted torque loss rate. A predicted torque deviation value is calculated based on the motor output torque and the predicted torque loss rate. The predicted torque deviation value is used to perform torque correction on the hydraulic motor.

2. A method for intelligent torque correction of a hydraulic motor according to claim 1, characterized in that: In the step of obtaining the torque generated by each liquid inlet plunger in the hydraulic part and calculating the real-time hydraulic output torque of the hydraulic part, the real-time hydraulic output torque calculation formula is: Where ρ i is the center pole diameter of the roller, β is the pressure angle between the roller and the track, v φi is the speed of the plunger.

3. The method for intelligent torque correction of a hydraulic motor according to claim 2, characterized in that: After obtaining the real-time power and the real-time motor output torque of the motor part, calculating the actual torque deviation value based on the real-time motor output torque and the real-time hydraulic output torque, and calculating the actual torque loss rate based on the actual torque deviation value and the total input power of the hydraulic motor through a preset efficiency loss model, the following steps are included: Acquire the hydraulic part speed, and calculate the effective output power of the hydraulic motor based on the real-time hydraulic output torque and the hydraulic part speed; Obtaining the effective input power of the load device and comparing it with the effective output of the hydraulic motor to calculate the output transmission efficiency of the hydraulic motor; Associating the output transmission efficiency and the actual torque loss rate based on a timestamp to construct a loss-transmission data set; The loss-transmission data set is input into the deviation analysis model, and the deviation analysis model analyzes the loss-transmission data set through a machine self-learning algorithm to generate a transmission deviation factor.

4. The method for intelligent torque correction of a hydraulic motor according to claim 3, characterized in that: Before the step of associating the output transmission efficiency and the actual torque loss rate based on the timestamp to construct a loss-transmission data set, the following steps are included: Acquire load equipment data of the hydraulic motor, and construct a load profile based on the load equipment data, wherein the load profile includes the type of the load equipment and the connection mode between the load equipment and the hydraulic motor; The output transmission efficiency is associated with the corresponding load portrait, and a preset load analysis model is analyzed based on the association between the output transmission efficiency and the corresponding load portrait to generate a load correlation coefficient, which is used to predict the impact of the load equipment on the output transmission efficiency.

5. The method for intelligent torque correction of a hydraulic motor according to claim 4, characterized in that: The step of inputting the loss-liquid film data set into a preset deviation analysis model, wherein the deviation analysis model analyzes the loss-liquid film data set through a machine self-learning algorithm to generate a liquid film deviation factor includes the following steps: constructing a loss liquid film correlation curve based on the loss-liquid film data set, and using the loss liquid film correlation curve as a prediction benchmark for the actual torque loss rate; The loss liquid film correlation curve is analyzed, and a correlation analysis is performed based on the correlation between the actual torque loss rate and the lubricating liquid film thickness. The liquid film deviation factor is generated according to the analysis result, and the liquid film deviation factor is used to predict the actual torque loss rate corresponding to the lubricating liquid film thickness.

6. A method for intelligent torque correction of a hydraulic motor according to claim 5, characterized in that: After analyzing the loss liquid film correlation curve, performing correlation analysis based on the correlation relationship between the actual torque loss rate and the lubricating liquid film thickness, and generating the liquid film deviation factor according to the analysis result, the method includes the following steps: The actual thickness of the lubricating liquid film in the hydraulic part is obtained, and a preset torque correction model generates a first deviation correction torque loss rate based on the liquid film deviation factor and the actual torque loss rate; Acquire load device data of the output end of the hydraulic motor, determine a load correlation coefficient according to the load portrait, and calculate the first correction torque loss rate based on the transmission deviation factor and the load correlation coefficient by the torque correction model to generate a second correction torque loss rate; Calculating a corrected torque deviation value based on the second deviation-correcting torque loss rate and the total input power of the hydraulic motor; The corrected hydraulic portion torque is calculated based on the real-time motor output torque and the corrected torque deviation value, and the corrected hydraulic portion torque is used as the output torque of the hydraulic motor.

7. The method for intelligent torque correction of a hydraulic motor according to claim 1, characterized in that: In the step where the deviation analysis model analyzes the loss-liquid film data set by a machine self-learning algorithm, the machine self-learning algorithm includes a neural network algorithm and a regression analysis algorithm.

8. A torque intelligent correction device for a hydraulic motor, applied to a torque intelligent correction method for a hydraulic motor as claimed in any one of claims 1 to 7, characterized in that: It comprises a real-time hydraulic output torque calculation unit (1) for obtaining the torque generated by each liquid inlet plunger in the hydraulic part and calculating the real-time hydraulic output torque of the hydraulic part; The actual torque loss rate calculation unit (2) is used to obtain the real-time power of the motor part and the real-time motor output torque, and calculate the actual torque deviation value based on the real-time motor output torque and the real-time hydraulic output torque, and calculate the actual torque loss rate based on the actual torque deviation value and the total input power of the hydraulic motor through a preset efficiency loss model. A loss-liquid film data set construction unit (3) is used to obtain the lubricating film thickness in the hydraulic part, and associate the actual torque loss rate with the lubricating film thickness based on the timestamp to construct a loss-liquid film data set; A deviation analysis model building unit (4) is used to pre-set a deviation analysis model and input the loss-liquid film data set into the pre-set deviation analysis model, wherein the deviation analysis model analyzes the loss-liquid film data set through a machine self-learning algorithm to generate a liquid film deviation factor; A hydraulic motor torque correction unit (5) is used to input the historical actual torque loss rate and the historical liquid film deviation factor into a preset torque deviation analysis model to analyze and generate a predicted torque loss rate, and calculate a predicted torque deviation value based on the motor output torque and the predicted torque loss rate. The predicted torque deviation value is used to perform torque correction on the hydraulic motor.

9. An electronic 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 torque intelligent correction method for a hydraulic motor as described in any one of claims 1 to 7 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 a torque intelligent correction method for a hydraulic motor as claimed in any one of claims 1 to 7 are implemented.

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