Multi-working-condition load matching method and system of hydraulic motor for oil pump

Abstract

The invention discloses a multi-working-condition load matching method and system for a hydraulic motor for an oil pump, and the method comprises the steps: collecting multi-source operation data of the hydraulic motor for the oil pump under different working conditions, and the multi-source operation data comprises pressure, flow and rotating speed signals; performing signal decomposition on the multi-source operation data to obtain a plurality of working condition component signals; extracting time-frequency domain characteristics of each working condition component signal, and calculating an optimal decomposition scale based on extreme point time difference and energy distribution balance; dividing dynamic load sections according to the optimal decomposition scale, and analyzing the main parameter matching degree and the working condition credibility of each load section; and generating a self-adaptive adjustment coefficient based on the main parameter matching degree and the working condition credibility, and dynamically adjusting output parameters of the hydraulic motor according to the self-adaptive adjustment coefficient to realize multi-working-condition load matching. According to the method, the credibility weight factor is calculated through the multi-dimensional parameters, the working condition credibility is accurately quantified, a reliable basis is provided for self-adaptive adjustment of the hydraulic motor for the oil pump, and the multi-working-condition operation performance is improved.

Description

Technical Field

[0001] The present invention relates to the field of automatic control technology, and in particular to a multi-working-condition load matching method and system for a hydraulic motor for an oil pump. Background Art

[0002] In industrial production, hydraulic motors for oil pumps are core components of hydraulic systems. Their stable and efficient operation under multiple load conditions plays a key role in the performance of the entire system. With the development of industrial automation and intelligence, the operating conditions faced by hydraulic systems are becoming increasingly complex and diverse, and the requirements for the accuracy and reliability of load matching of hydraulic motors under different operating conditions are increasing. However, existing technologies have obvious shortcomings when dealing with multi-operating load matching of hydraulic motors for oil pumps. On the one hand, the analysis methods for operating condition characteristics are not comprehensive and in-depth enough, making it difficult to accurately grasp the operating status of each load segment. On the other hand, there is a lack of scientific and effective methods to quantitatively evaluate the reliability of operating conditions and the matching degree of the main parameters of load segments. As a result, hydraulic motors are prone to problems such as irrational energy utilization, reduced operating efficiency, and increased equipment wear during multi-operating conditions. These problems seriously affect the stability and service life of the system and cannot meet the modern industry's demand for high performance and high reliability of hydraulic systems. Innovation and improvement are urgently needed to improve the comprehensive performance of hydraulic motors for oil pumps under multiple operating conditions. Summary of the Invention

[0003] In order to solve at least one of the above-mentioned technical problems, the present invention provides a multi-operating-condition load matching method and system for a hydraulic motor for an oil pump.

[0004] In a first aspect, the present invention provides a multi-operating load matching method for a hydraulic motor for an oil pump, the method comprising:

[0005] Collecting multi-source operating data of the hydraulic motor for the oil pump under different working conditions, wherein the multi-source operating data includes pressure, flow and speed signals;

[0006] performing signal decomposition on the multi-source operating data to obtain a plurality of operating condition component signals;

[0007] Extract the time-frequency domain characteristics of each working condition component signal, and calculate the optimal decomposition scale based on the time difference of extreme points and the balance of energy distribution;

[0008] Divide the dynamic load segment according to the optimal decomposition scale, and analyze the matching degree of main parameters and the reliability of working conditions of each load segment;

[0009] An adaptive adjustment coefficient is generated based on the main parameter matching degree and the working condition credibility, and the output parameters of the hydraulic motor are dynamically adjusted according to the adaptive adjustment coefficient to achieve multi-working condition load matching.

[0010] Preferably, the extracting time-frequency domain features of each operating condition component signal includes:

[0011] Perform Fourier transform on each working condition component signal to obtain a spectrum diagram, identify the spectrum minimum point to divide the frequency bandwidth;

[0012] The standard deviation of the energy proportion of each frequency bandwidth is calculated, and the energy distribution balance index is obtained by combining the time domain energy root mean square value of the component signal.

[0013] Preferably, the calculating of the optimal decomposition scale includes:

[0014] Obtain the time resolution sequence of the extreme point intervals of each component signal under each working condition;

[0015] Construct the correlation coefficient matrix between component signal sequence number and time resolution, and calculate the expected value of multi-scale components by combining the energy distribution balance index;

[0016] The number of extreme intervals corresponding to the minimum expected value of the component is selected as the optimal decomposition scale.

[0017] Preferably, analyzing the matching degree of main parameters of each load segment includes:

[0018] Perform fast Fourier transform on each dynamic load segment to extract the main frequency components;

[0019] The absolute value of the deviation between the main frequency of each load segment and the global main frequency is calculated as the main parameter matching index.

[0020] Preferably, the calculation of the operating condition credibility includes:

[0021] Statistical analysis of the distribution information entropy of pressure and flow parameters in each load segment;

[0022] The credibility weight factor is generated by combining the instantaneous frequency characteristics and the parameter mean, and its expression is expressed as:

[0023] ,

[0024] in, represents the credibility weight factor, is the number of parameter distribution intervals, is the distribution information entropy, is the mean pressure, is the instantaneous frequency.

[0025] Preferably, the dynamic adjustment of the hydraulic motor output parameters includes:

[0026] Adjust the proportional valve opening and motor speed according to the adaptive adjustment coefficient;

[0027] When the adaptive adjustment coefficient is lower than the threshold, wide-window PID control is adopted; when it is higher than the threshold, narrow-window fuzzy control is switched to.

[0028] In a second aspect, the present invention further provides a multi-operating-condition load matching system for a hydraulic motor for an oil pump, the system comprising:

[0029] A data acquisition module is used to collect multi-source operating data of the hydraulic motor for the oil pump under different working conditions, wherein the multi-source operating data includes pressure, flow and speed signals;

[0030] a signal decomposition module, configured to perform signal decomposition on the multi-source operating data to obtain a plurality of operating condition component signals;

[0031] Feature extraction and scale optimization module, used to extract the time-frequency domain features of each working condition component signal and calculate the optimal decomposition scale based on the time difference of extreme points and the balance of energy distribution;

[0032] A load segment division and analysis module is used to divide the dynamic load segments according to the optimal decomposition scale and analyze the matching degree of main parameters and the reliability of working conditions of each load segment;

[0033] The adaptive adjustment control module is used to generate an adaptive adjustment coefficient based on the matching degree of the main parameters and the working condition credibility, and dynamically adjust the output parameters of the hydraulic motor according to the adaptive adjustment coefficient to achieve multi-working condition load matching.

[0034] In a third aspect, the present invention also provides an electronic device comprising a processor and a memory, wherein the memory is used to store computer program code, and the computer program code comprises computer instructions. When the processor executes the computer instructions, the electronic device executes the method as described in the first aspect above and any possible implementation thereof.

[0035] In a fourth aspect, the present invention also provides a computer-readable storage medium, in which a computer program is stored. The computer program includes program instructions, and when the program instructions are executed by a processor of an electronic device, the processor executes the method as described in the first aspect above and any possible implementation method thereof.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] 1) This invention discretizes pressure and flow parameters and calculates the distribution information entropy. It then combines instantaneous frequency characteristics with the mean pressure value to construct a credibility weight factor. This accurately quantifies the credibility of working conditions from multiple dimensions, providing a reliable basis for the hydraulic motor's adaptive adjustment of output parameters. This effectively improves the hydraulic motor's operating stability and accuracy under multiple working conditions, reduces energy loss, extends equipment service life, and enhances the overall performance of the hydraulic system.

[0038] 2) The present invention obtains a spectrum diagram by performing Fourier transform on each working condition component signal and accurately identifies the spectrum minimum point to divide the frequency bandwidth. It can clearly show the characteristics of the signal in different frequency ranges, calculate the standard deviation of the energy proportion of each frequency bandwidth, and obtain the energy distribution balance index by combining the time domain energy root mean square value, so that the description of the signal energy distribution is more comprehensive and accurate, which is conducive to in-depth analysis of the time and frequency domain characteristics of the working condition component signal, and provides a more accurate and reliable data basis for the subsequent calculation of the optimal decomposition scale, analysis of the main parameter matching degree of the load segment, and the working condition credibility, thereby improving the accuracy and reliability of the multi-working condition load matching method of the entire hydraulic motor for the oil pump, and ensuring that the motor can achieve efficient and stable operation under various complex working conditions.

[0039] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the background technology, the drawings required for use in the embodiments of the present invention or the background technology will be described below.

[0041] The accompanying drawings herein are incorporated into and constitute a part of the specification. These drawings illustrate embodiments consistent with the present disclosure and, together with the specification, are used to explain the technical solutions of the present disclosure.

[0042] Figure 1 A schematic flow chart of a multi-operating-condition load matching method for a hydraulic motor for an oil pump provided in an embodiment of the present invention;

[0043] Figure 2 A schematic structural diagram of a multi-operating-condition load matching system for a hydraulic motor for an oil pump provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0044] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0045] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present invention. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute a separate or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0046] When dealing with multi-operating load matching for hydraulic motors for oil pumps, existing technologies do not fully analyze the operating characteristics and lack effective quantitative evaluation methods, resulting in problems such as irrational energy utilization, low operating efficiency, and increased equipment wear, and are unable to meet the needs of modern industry.

[0047] See also Figure 1 , Figure 1 The present invention provides a flow chart of a multi-condition load matching method for a hydraulic motor for an oil pump. Figure 1 As shown, the method includes:

[0048] S100, collecting multi-source operating data of a hydraulic motor for an oil pump under different working conditions, wherein the multi-source operating data includes pressure, flow, and speed signals;

[0049] In this embodiment, a high-precision pressure sensor is installed on the side of the oil pump's output pipeline near the motor. This sensor can sense changes in the hydraulic oil pressure in the pipeline in real time and convert the pressure into a proportional electrical signal. For example, a pressure sensor based on the piezoresistive effect has an output voltage that increases linearly with increasing pressure. An electromagnetic flow sensor is installed in the main channel of the hydraulic circuit. This sensor detects the induced electromotive force generated by the flow of a conductive liquid in a magnetic field to obtain flow information and converts the flow rate into an electrical signal for output. To collect the motor's speed signal, a photoelectric speed sensor is installed at the end of the motor's rotating shaft. By measuring the frequency at which light is blocked by the light shield on the shaft, the sensor calculates the motor's real-time speed and generates a corresponding speed signal. The electrical signals output by the pressure sensor, flow sensor, and speed sensor enter the signal conditioning module. Within this module, the signals are amplified to enhance their strength for subsequent processing. A filtering circuit removes high-frequency noise and low-frequency drift, such as a second-order Butterworth filter with an appropriate cutoff frequency to preserve the valid signal frequency band. The conditioned signal is converted into digital form by the data acquisition card, which converts the analog electrical signal into a digital signal and transmits it to the central processor of the control system through the data bus, completing the collection process of multi-source operation data.

[0050] S200, performing signal decomposition on the multi-source operating data to obtain a plurality of operating condition component signals;

[0051] When performing signal decomposition on the collected multi-source operating data, a wavelet decomposition algorithm is used to process the pressure, flow, and speed signals separately. First, based on the frequency characteristics and operating condition variations of the multi-source operating data, the db4 wavelet is selected as the decomposition basis function. This wavelet has excellent time-frequency localization capabilities and is adaptable to the non-stationary characteristics of hydraulic motor operating signals. The number of decomposition layers is determined to be five, based on an analysis of the typical operating frequency range of hydraulic motors. This ensures that the decomposed operating condition component signals cover the frequency range of different operating conditions, from low-frequency stable operating conditions to high-frequency transient shock conditions. Taking the pressure signal as an example, it is input into the wavelet decomposition algorithm. The first layer of decomposition is performed, yielding a high-frequency detail signal and a low-frequency approximate signal. The high-frequency detail signal contains rapidly varying components of the pressure signal, such as pressure fluctuations during sudden load changes. The low-frequency approximate signal reflects the overall pressure trend and slow changes during stable operation. The low-frequency approximate signal is then decomposed into a second layer, and this process continues until the fifth layer of decomposition is completed, ultimately yielding five high-frequency operating condition component signals and one low-frequency operating condition component signal. The same method is applied to the decomposition of the flow and speed signals, resulting in multiple corresponding operating condition component signals. Each operating condition component signal corresponds to a different frequency band, characterizing the operating characteristics of the hydraulic motor under different operating conditions and providing clear signal components for subsequent time-frequency domain feature extraction.

[0052] S300, extracting the time-frequency domain features of the component signals of each working condition, and calculating the optimal decomposition scale based on the time difference of the extreme points and the balance of energy distribution;

[0053] To extract the time-frequency domain features of each operating condition component signal, a Fourier transform is performed on each component signal to convert the time domain signal into the frequency domain, generating a spectrogram. Within the spectrogram, a peak detection algorithm is used to identify spectral minima. These minima divide the spectrum into different frequency bandwidths. For example, in the spectrogram of a flow condition component signal, minima are detected at 50 Hz and 150 Hz, thus dividing the spectrum into three frequency bandwidths: 0-50 Hz, 50-150 Hz, and above 150 Hz. The energy fraction within each frequency bandwidth is calculated—that is, the ratio of the signal energy within that bandwidth to the total signal energy. The standard deviation of these energy fractions is then calculated, reflecting the uniformity of energy distribution within the different bandwidths. The time-domain RMS energy of the component signal is also calculated, representing the signal's energy in the time domain. The energy fraction standard deviation and the time-domain RMS energy value are combined to generate an energy distribution balance index, which measures the energy balance of the operating condition component signal in the time-frequency domain. When calculating the optimal decomposition scale, the extreme points of each component signal under each operating condition—that is, the maximum and minimum points in the signal—are first obtained. The time intervals between adjacent extreme points are calculated to obtain the time resolution sequence of the extreme point intervals. A correlation coefficient matrix between the component signal sequence number and the time resolution is then constructed. The elements in this matrix represent the strength of the correlation in time resolution between different component signals. Combined with the previously obtained energy distribution balance index, the expected values ​​of the multi-scale components are calculated through matrix operations and mathematical fitting. This expected value comprehensively considers the signal's time resolution and energy distribution characteristics. The number of extreme value intervals with the smallest expected value for the multi-scale components is selected as the optimal decomposition scale. This scale achieves the best resolution balance in signal decomposition across time and frequency, ensuring more accurate subsequent load segmentation.

[0054] S400, dividing the dynamic load segments according to the optimal decomposition scale, and analyzing the matching degree of main parameters and the reliability of working conditions of each load segment;

[0055] To analyze the matching of the primary parameters of each load segment, a fast Fourier transform (FFT) is performed on the signal of each dynamic load segment to obtain its frequency domain characteristics. The dominant frequency component with the largest energy contribution is extracted from the frequency domain signal. This dominant frequency reflects the primary vibration or frequency variation of the load segment. For example, if the energy contribution at 100 Hz for a load segment reaches 70% after FFT, 100 Hz is determined to be the dominant frequency of that load segment. The absolute value of the deviation between this dominant frequency and the global dominant frequency throughout operation is calculated. The global dominant frequency is the primary operating frequency determined by statistical analysis of all collected signals. This absolute value of the deviation serves as the primary parameter matching indicator. A smaller value indicates a closer match between the primary frequency of the load segment and the global dominant frequency, and more stable load operation. To calculate the operating condition credibility, the pressure and flow parameters within each load segment are divided into pre-set intervals. The number of parameter occurrences within each interval is counted, and the distribution information entropy is calculated. The distribution information entropy reflects the degree of disorder in the parameter distribution. A larger entropy value indicates a more dispersed parameter distribution and a less stable operating condition. At the same time, the instantaneous frequency characteristics are obtained by time-frequency analysis of the signal. The instantaneous frequency represents the frequency value of the signal at a certain moment, reflecting the real-time changes in the working conditions. The mean value of the pressure parameter is calculated, which represents the average level of pressure in the load section. Using the formula Generate a credibility weight factor, where K is the number of pre-set parameter distribution intervals and E is the distribution information entropy. is the mean pressure, and f is the instantaneous frequency. This weight factor comprehensively considers the uniformity of parameter distribution, pressure level, and frequency variation. A larger weight factor indicates a higher reliability of the working condition.

[0056] S500 , generating an adaptive adjustment coefficient based on the main parameter matching degree and the working condition credibility, and dynamically adjusting the output parameters of the hydraulic motor according to the adaptive adjustment coefficient to achieve multi-working condition load matching.

[0057] When adjusting the hydraulic motor's output parameters, an adaptive adjustment coefficient is generated using a specific mathematical model based on the matching degree of the primary parameters and the reliability of the operating conditions in each load range. The adjustment coefficient represents the degree of match and reliability between the current operating conditions and the ideal operating conditions. Once the adaptive adjustment coefficient is generated, it is input as a control signal into the actuator to adjust the proportional valve opening and motor speed. The proportional valve is a key component in the hydraulic system. By varying its opening, the flow and pressure of the hydraulic oil are adjusted, thereby varying the motor load. Adjusting the motor speed directly affects the motor's output power and torque.

[0058] Different control strategies are selected according to the size of the adaptive adjustment coefficient. When the adaptive adjustment coefficient is lower than the preset threshold, it means that the current working condition is relatively stable and the load changes are small, and wide-window PID control is adopted. The PID control algorithm adjusts the output parameters through a linear combination of the three links of proportion, integration, and differentiation. The wide window setting can improve the stability of the system and reduce fluctuations in the control process. When the adaptive adjustment coefficient is higher than the threshold, it indicates that the working condition changes more drastically and the load fluctuates greatly. At this time, it switches to narrow-window fuzzy control. Fuzzy control is based on fuzzy logic theory and converts fuzzy quantities such as the input adaptive adjustment coefficient into precise control signals. The narrow window setting can improve the sensitivity and response speed of the control, and can quickly adapt to changes in working conditions and achieve precise matching of multi-working loads.

[0059] In this embodiment, the pressure and flow parameters are discretized and the distribution information entropy is calculated. The instantaneous frequency characteristics and the pressure mean are combined to construct a credibility weight factor. The credibility of the working conditions is accurately quantified from multiple dimensions, providing a reliable basis for the hydraulic motor to adaptively adjust the output parameters, effectively improving its stability and accuracy in multiple working conditions, reducing energy loss, extending the service life of the equipment and enhancing the overall performance of the hydraulic system.

[0060] Preferably, the extracting time-frequency domain features of each operating condition component signal includes:

[0061] Perform Fourier transform on each working condition component signal to obtain a spectrum diagram, identify the spectrum minimum point to divide the frequency bandwidth;

[0062] The standard deviation of the energy proportion of each frequency bandwidth is calculated, and the energy distribution balance index is obtained by combining the time domain energy root mean square value of the component signal.

[0063] For each operating component signal, a Fourier transform is used to convert it from the time domain to the frequency domain, generating a spectrum plot. Within the spectrum plot, a peak detection algorithm is used to precisely identify spectral minima. These minima are located using an algorithm based on local extrema search. For a specific operating component signal, this algorithm detects spectral minima at specific frequency locations, such as 50 Hz and 150 Hz. Based on these minima, the spectrum is divided into different frequency bandwidths, such as 0-50 Hz, 50-150 Hz, and above 150 Hz. The energy contribution within each frequency bandwidth is calculated. The energy within each frequency bandwidth is calculated and compared with the total energy of the entire signal to obtain the energy contribution. The standard deviation of these energy contributions is also determined. For example, a small standard deviation for a group of energy contributions indicates a relatively uniform energy distribution within that bandwidth; conversely, a large standard deviation indicates a relatively dispersed energy distribution. The time-domain root mean square (RMS) energy of the component signals is then calculated. The time-domain RMS energy value is calculated based on the signal's amplitude information in the time domain and represents the signal's energy in the time domain. The energy distribution balance index is derived by combining the energy proportion standard deviation and the time-domain RMS energy value. This index comprehensively and accurately measures the energy balance of the operating condition component signals in the time-frequency domain. For example, when the energy distribution balance index is within a certain range, it indicates that the signal's energy distribution in the time-frequency domain is relatively reasonable, facilitating subsequent analysis and processing of signal characteristics.

[0064] In this embodiment, by performing Fourier transform on each working condition component signal to obtain a spectrum diagram, and accurately identifying the spectrum minimum point to divide the frequency bandwidth, the characteristics of the signal in different frequency ranges can be clearly displayed, the standard deviation of the energy proportion of each frequency bandwidth is calculated, and the energy distribution balance index is obtained by combining the time domain energy root mean square value, so that the description of the signal energy distribution is more comprehensive and accurate, which is conducive to in-depth analysis of the time and frequency domain characteristics of the working condition component signal, and provides a more accurate and reliable data basis for the subsequent calculation of the optimal decomposition scale, analysis of the main parameter matching degree of the load segment, and working condition credibility, thereby improving the accuracy and reliability of the multi-working condition load matching method of the entire hydraulic motor for the oil pump, and ensuring that the motor can achieve efficient and stable operation under various complex working conditions.

[0065] Preferably, the calculating of the optimal decomposition scale includes:

[0066] Obtain the time resolution sequence of the extreme point intervals of each component signal under each working condition;

[0067] Construct the correlation coefficient matrix between component signal sequence number and time resolution, and calculate the expected value of multi-scale components by combining the energy distribution balance index;

[0068] The number of extreme intervals corresponding to the minimum expected value of the component is selected as the optimal decomposition scale.

[0069] When obtaining a time-resolution series of extreme point intervals for each component signal, a smoothing filter algorithm is used for preprocessing to eliminate noise interference and enhance the signal's true characteristics. Common smoothing filters include moving average filtering, which smooths the signal by calculating the average value within a specified window. After preprocessing, a derivative-based extreme point detection method is used to identify the signal's extreme points. Specifically, the first-order derivative of the signal is calculated. When the derivative changes from positive to negative, the corresponding point is a maximum point; when the derivative changes from negative to positive, the corresponding point is a minimum point. By accurately recording the timestamps of these extreme points, the time intervals between adjacent extreme points are calculated. To ensure accurate time resolution, high-precision clock synchronization technology is used to keep the clocks of all data acquisition devices consistent. These calculated time intervals are arranged in the order of the component signals to form a time-resolution series of extreme point intervals. This series reflects the temporal characteristics of each component signal and provides basic data for subsequent analysis.

[0070] When constructing the correlation coefficient matrix between component signal numbers and temporal resolution, each operating condition component signal is first assigned a unique serial number. Using statistical analysis methods, the correlation coefficient between the serial number of each operating condition component signal and the corresponding temporal resolution is calculated. The Pearson correlation coefficient is a commonly used correlation coefficient calculation method, which measures the degree of linear correlation between two variables. All calculated correlation coefficients are arranged in a matrix format to form a correlation coefficient matrix. This matrix intuitively demonstrates the strength of the correlation between the serial number and temporal resolution of each operating condition component signal. The energy distribution balance index is a key parameter obtained during the time-frequency domain feature extraction of each operating condition component signal. It reflects the uniformity of the signal's energy distribution within different frequency bandwidths. Combined with this index, a weighted summation method is used to calculate the expected value of the multi-scale component. Specifically, different weights are assigned to each element in the correlation coefficient matrix and the energy distribution balance index. A weighted summation operation is then performed to obtain the expected value of the multi-scale component. This expected value comprehensively considers the signal's temporal correlation and energy distribution characteristics, providing a more comprehensive reflection of the signal's characteristics at different scales.

[0071] Sort the calculated expected values ​​of the multi-scale components and find the minimum. The extreme interval number corresponding to this minimum value is particularly significant, indicating that at this extreme interval number, the signal decomposition achieves the best balance of resolution between time and frequency. By selecting this extreme interval number as the optimal decomposition scale, subsequent load segmentation can more accurately reflect changes in actual operating conditions.

[0072] This embodiment, by obtaining the time-resolution sequence of extreme point intervals for each component signal under each operating condition, can deeply explore the signal's temporal variation patterns, providing precise time information for subsequent analysis. A correlation coefficient matrix between the component signal sequence number and the temporal resolution is constructed, and the multi-scale component expected values ​​are calculated in conjunction with an energy distribution balance index. This comprehensively considers the signal's temporal correlation and energy distribution characteristics, resulting in a more comprehensive and accurate signal analysis. Selecting the number of extreme point intervals corresponding to the minimum component expected value as the optimal decomposition scale finds the optimal balance among numerous possible decomposition scales, ensuring that the signal decomposition achieves optimal resolution in both time and frequency.

[0073] Preferably, analyzing the matching degree of main parameters of each load segment includes:

[0074] Perform fast Fourier transform on each dynamic load segment to extract the main frequency components;

[0075] The absolute value of the deviation between the main frequency of each load segment and the global main frequency is calculated as the main parameter matching index.

[0076] All collected dynamic load segment signals are processed through a fast Fourier transform and the main frequency component is extracted to obtain the main frequency of each load segment. Different weights are assigned to each load segment based on its importance or representativeness. For example, factors such as the duration and energy of the load segment can be used as a basis for weighting. Load segments with longer duration or higher energy are assigned higher weights, while those with lower energy are assigned lower weights. A weighted average method is used to calculate the global main frequency. This method multiplies the main frequency of each load segment by its corresponding weight, then sums the results and divides them by the sum of all weights. After obtaining the main frequency of each load segment and the global main frequency, the absolute value of the deviation between them is calculated. The specific calculation method is to subtract the global main frequency from the main frequency of each load segment and then take the absolute value of the difference. This absolute value of the deviation serves as an indicator of the main parameter matching degree and can intuitively reflect the degree of deviation of the main frequency of each load segment from the global main frequency. The smaller the index value, the closer the main frequency of the load segment is to the global main frequency, the more stable the load operation is, and the higher the matching degree with the overall working condition is; conversely, the larger the index value, the more the main frequency of the load segment deviates from the global main frequency, the load operation may have unstable factors, and the matching degree with the overall working condition is low.

[0077] In this embodiment, the main frequency component is extracted by performing fast Fourier transform on the dynamic load segment signal, and the absolute value of its deviation from the global main frequency is calculated as the main parameter matching index. This can accurately quantify the frequency matching degree between each load segment and the overall working condition, providing an intuitive and reliable basis for subsequent adaptive adjustment, so that the hydraulic motor can dynamically adjust the output parameters according to the matching status of different load segments, effectively improving the operating stability and efficiency under multiple working conditions, reducing energy loss and extending the service life of the equipment.

[0078] Preferably, the calculation of the operating condition credibility includes:

[0079] Statistical analysis of the distribution information entropy of pressure and flow parameters in each load segment;

[0080] The credibility weight factor is generated by combining the instantaneous frequency characteristics and the parameter mean, and its expression is expressed as:

[0081] ,

[0082] in, represents the credibility weight factor, is the number of parameter distribution intervals, is the distribution information entropy, is the mean pressure, is the instantaneous frequency.

[0083] K represents the discretization accuracy of pressure and flow parameters in the load section, by dividing the continuous parameter value into K equal widths, for example, the pressure parameter interval is divided into ,in is the current load section pressure extreme value, and the flow parameter interval is divided into ,in It is the flow extreme value of the current load segment.

[0084] Discretize the pressure and flow parameters in each load segment and divide the pressure parameters into intervals Divide ( is the current load section pressure extreme value), flow parameters are as follows Divide ( is the current load segment flow extreme value). By counting the frequency of parameters in each interval, using the information entropy formula Calculate the information entropy of the distribution ( is the frequency of occurrence of parameters in each interval). Combined with the instantaneous frequency characteristic f and the pressure mean Generate the credibility weight factor, whose expression is , where K is the number of parameter distribution intervals, which represents the discretization processing accuracy of pressure and flow parameters in the load section. This is achieved by dividing the continuous parameter values ​​into K equal-width intervals to ensure the standardization and consistency of the discretization processing.

[0085] In this embodiment, the uncertainty characteristics of the parameter distribution are accurately characterized by statistically analyzing the distribution information entropy of the pressure and flow parameters in the load segment. At the same time, the credibility weight factor is generated by combining the discretization processing accuracy, pressure mean and instantaneous frequency f. The credibility of the working condition is quantified from multiple dimensions, integrating the parameter distribution characteristics, discretization accuracy, pressure mean and frequency characteristics, making the assessment of the working condition credibility more scientific and accurate, and being able to effectively identify the reliability of the load segment working condition, providing a more accurate basis for subsequent adaptive adjustment, thereby improving the stability and adaptability of the hydraulic motor for the oil pump under multiple working conditions, reducing energy loss or equipment abnormality caused by misjudgment of the working condition, and enhancing the overall performance of the system.

[0086] Preferably, the dynamic adjustment of the hydraulic motor output parameters includes:

[0087] Adjust the proportional valve opening and motor speed according to the adaptive adjustment coefficient;

[0088] When the adaptive adjustment coefficient is lower than the threshold, wide-window PID control is adopted; when it is higher than the threshold, narrow-window fuzzy control is switched to.

[0089] In summary, the method provided in this embodiment can at least achieve the following effects:

[0090] 1) This invention discretizes pressure and flow parameters and calculates the distribution information entropy. It then combines instantaneous frequency characteristics with the mean pressure value to construct a credibility weight factor. This accurately quantifies the credibility of working conditions from multiple dimensions, providing a reliable basis for the hydraulic motor's adaptive adjustment of output parameters. This effectively improves the hydraulic motor's operating stability and accuracy under multiple working conditions, reduces energy loss, extends equipment service life, and enhances the overall performance of the hydraulic system.

[0091] 2) The present invention obtains a spectrum diagram by performing Fourier transform on each working condition component signal and accurately identifies the spectrum minimum point to divide the frequency bandwidth. It can clearly show the characteristics of the signal in different frequency ranges, calculate the standard deviation of the energy proportion of each frequency bandwidth, and obtain the energy distribution balance index by combining the time domain energy root mean square value, so that the description of the signal energy distribution is more comprehensive and accurate, which is conducive to in-depth analysis of the time and frequency domain characteristics of the working condition component signal, and provides a more accurate and reliable data basis for the subsequent calculation of the optimal decomposition scale, analysis of the main parameter matching degree of the load segment, and the working condition credibility, thereby improving the accuracy and reliability of the multi-working condition load matching method of the entire hydraulic motor for the oil pump, and ensuring that the motor can achieve efficient and stable operation under various complex working conditions.

[0092] See also Figure 2 In one embodiment, a multi-operating-condition load matching system for a hydraulic motor for an oil pump is further provided, the system comprising:

[0093] The data acquisition module 100 is used to collect multi-source operating data of the hydraulic motor for the oil pump under different working conditions, wherein the multi-source operating data includes pressure, flow and speed signals;

[0094] A signal decomposition module 200 is used to perform signal decomposition on the multi-source operating data to obtain multiple operating condition component signals;

[0095] The feature extraction and scale optimization module 300 is used to extract the time-frequency domain features of each working condition component signal and calculate the optimal decomposition scale based on the time difference of the extreme points and the energy distribution balance;

[0096] The load segment division and analysis module 400 is used to divide the dynamic load segment according to the optimal decomposition scale and analyze the matching degree of the main parameters and the reliability of the working condition of each load segment;

[0097] The adaptive adjustment control module 500 is used to generate an adaptive adjustment coefficient based on the matching degree of the main parameters and the working condition credibility, and dynamically adjust the output parameters of the hydraulic motor according to the adaptive adjustment coefficient to achieve multi-working condition load matching.

[0098] It can be understood that the functions or modules included in the system provided in this embodiment can be used to execute the method described in the above method embodiment. Its specific implementation can refer to the description of the above method embodiment. For the sake of brevity, it will not be repeated here.

[0099] The present invention also provides an electronic device, including a processor and a memory, wherein the memory is used to store computer program code, and the computer program code includes computer instructions. When the processor executes the computer instructions, the electronic device executes a method as described in any one of the possible implementation modes.

[0100] The present invention also provides a computer-readable storage medium, in which a computer program is stored. The computer program includes program instructions. When the program instructions are executed by a processor of an electronic device, the processor executes a method as described in any one of the possible implementation methods described above.

[0101] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0102] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here. Those skilled in the art will also clearly understand that the descriptions of the various embodiments of the present invention have different focuses. For the convenience and brevity of description, the same or similar parts may not be repeated in different embodiments. Therefore, for parts not described or not described in detail in a certain embodiment, reference can be made to the descriptions of other embodiments.

Claims

1. A multi-operating load matching method for a hydraulic motor for an oil pump, characterized in that: The method comprises: Collecting multi-source operating data of the hydraulic motor for the oil pump under different working conditions, wherein the multi-source operating data includes pressure, flow and speed signals; performing signal decomposition on the multi-source operating data to obtain a plurality of operating condition component signals; Extract the time-frequency domain characteristics of each working condition component signal, and calculate the optimal decomposition scale based on the time difference of extreme points and the balance of energy distribution; Divide the dynamic load segment according to the optimal decomposition scale, and analyze the matching degree of main parameters and the reliability of working conditions of each load segment; An adaptive adjustment coefficient is generated based on the main parameter matching degree and the working condition credibility, and the output parameters of the hydraulic motor are dynamically adjusted according to the adaptive adjustment coefficient to achieve multi-working condition load matching.

2. The multi-operating load matching method for a hydraulic motor for an oil pump according to claim 1, characterized in that: The extracting of time-frequency domain features of each operating condition component signal includes: Perform Fourier transform on each working condition component signal to obtain a spectrum diagram, identify the spectrum minimum point to divide the frequency bandwidth; The standard deviation of the energy proportion of each frequency bandwidth is calculated, and the energy distribution balance index is obtained by combining the time domain energy root mean square value of the component signal.

3. The multi-operating load matching method for a hydraulic motor for an oil pump according to claim 1, characterized in that: The calculating of the optimal decomposition scale includes: Obtain the time resolution sequence of the extreme point intervals of each component signal under each working condition; Construct the correlation coefficient matrix between component signal sequence number and time resolution, and calculate the expected value of multi-scale components by combining the energy distribution balance index; The number of extreme intervals corresponding to the minimum expected value of the component is selected as the optimal decomposition scale.

4. The multi-operating load matching method for a hydraulic motor for an oil pump according to claim 1, characterized in that: The analysis of the matching degree of the main parameters of each load segment includes: Perform fast Fourier transform on each dynamic load segment to extract the main frequency components; The absolute value of the deviation between the main frequency of each load segment and the global main frequency is calculated as the main parameter matching index.

5. The multi-operating load matching method for a hydraulic motor for an oil pump according to claim 1, characterized in that: The calculation of the working condition credibility includes: Statistical analysis of the distribution information entropy of pressure and flow parameters in each load segment; The credibility weight factor is generated by combining the instantaneous frequency characteristics and the parameter mean, and its expression is expressed as: ， in, represents the credibility weight factor, is the number of parameter distribution intervals, is the distribution information entropy, is the mean pressure, is the instantaneous frequency.

6. The multi-operating load matching method for a hydraulic motor for an oil pump according to claim 1, characterized in that: The dynamic adjustment of the hydraulic motor output parameters includes: Adjust the proportional valve opening and motor speed according to the adaptive adjustment coefficient; When the adaptive adjustment coefficient is lower than the threshold, wide-window PID control is adopted; when it is higher than the threshold, narrow-window fuzzy control is switched to.

7. A multi-operating load matching system for a hydraulic motor for an oil pump, characterized in that: The system comprises: A data acquisition module is used to collect multi-source operating data of the hydraulic motor for the oil pump under different working conditions, wherein the multi-source operating data includes pressure, flow and speed signals; a signal decomposition module, configured to perform signal decomposition on the multi-source operating data to obtain a plurality of operating condition component signals; Feature extraction and scale optimization module, used to extract the time-frequency domain features of each working condition component signal and calculate the optimal decomposition scale based on the time difference of extreme points and the balance of energy distribution; A load segment division and analysis module is used to divide the dynamic load segments according to the optimal decomposition scale and analyze the matching degree of main parameters and the reliability of working conditions of each load segment; The adaptive adjustment control module is used to generate an adaptive adjustment coefficient based on the matching degree of the main parameters and the working condition credibility, and dynamically adjust the output parameters of the hydraulic motor according to the adaptive adjustment coefficient to achieve multi-working condition load matching.

8. An electronic device, characterized in that: include: A processor and a memory, the memory being used to store computer program code, the computer program code including computer instructions, and when the processor executes the computer instructions, the electronic device executes the multi-working condition load matching method for a hydraulic motor for an oil pump as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which includes program instructions. When the program instructions are executed by a processor of an electronic device, the processor executes the multi-working condition load matching method for a hydraulic motor for an oil pump according to any one of claims 1 to 6.

Images