A method and system for real-time monitoring of oil inlet pressure of a servo pump group

Abstract

The present application relates to the field of data processing technology, and in particular to a method and system for real-time monitoring of the oil inlet pressure of a servo pump group. The method comprises the following steps: obtaining servo pump operating data, the operating data including the real-time pressure signal and vibration signal of the oil inlet; segmenting the real-time pressure signal and vibration signal to form multiple data windows; constructing a bandwidth adjustment coefficient for each data window based on the frequency of the vibration signal of each data window, obtaining the optimal bandwidth of each data window based on the bandwidth adjustment coefficient, filtering the real-time pressure signal using a band-stop filter based on the optimal bandwidth corresponding to each data window; constructing a temperature compensation curve; and correcting the filtered real-time pressure signal based on the temperature compensation curve to obtain the optimal pressure signal. The present application has the effect of improving the stability of denoising the real-time pressure signal of the servo pump group.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method and system for real-time monitoring of the oil inlet pressure of a servo pump group. Background Art

[0002] A servo pump is a component in a hydraulic system that provides power. It controls the movement of actuators, such as hydraulic cylinders and lifts, by supplying oil. The servo pump primarily controls pressure via the pressure differential between its inlet and outlet ports. The stability of the inlet pressure is directly related to the stable operation of the entire hydraulic system. Therefore, monitoring the inlet pressure of the servo pump is crucial during hydraulic system operation. Inlet pressure is typically acquired using a pressure sensor. Common sensors include piezoresistive sensors, piezoelectric sensors, and strain gauge sensors. These sensors inevitably generate some noise when acquiring real-time pressure signals. Therefore, to facilitate subsequent analysis and processing of the real-time pressure signals, filtering and noise reduction are necessary after acquisition. Due to the frequency difference between noise and normal signals, conventional filtering methods, such as band-stop filters and notch filters, are often used to reduce noise in the raw, collected real-time pressure signals.

[0003] However, the frequency and intensity of the noise generated by the servo pump varies under different loads and oil flow rates. When the servo pump's operating state or operating environment undergoes significant changes, the frequency and intensity of the noise also change, causing the denoising effect of related technologies that filter and denoise real-time pressure signals to degrade. In summary, traditional filtering and denoising methods are not stable enough to cope with the complex operating environment and diverse operating conditions of the servo pump. Summary of the Invention

[0004] In order to solve the problem of unstable denoising effect of the filtering noise reduction method in the related art, the present application provides a real-time monitoring method and system for the oil inlet pressure of a servo pump group.

[0005] In a first aspect, the present application provides a method for real-time monitoring of the oil inlet pressure of a servo pump group, which adopts the following technical solution:

[0006] A method for real-time monitoring of the oil inlet pressure of a servo pump group comprises the following steps: acquiring servo pump operation data, the operation data including a real-time pressure signal and a vibration signal of the oil inlet; segmenting the real-time pressure signal and the vibration signal to form a plurality of data windows; constructing a bandwidth adjustment coefficient for each data window based on the frequency of the vibration signal in each data window, obtaining an optimal bandwidth for each data window based on the bandwidth adjustment coefficient, filtering the real-time pressure signal using a band-stop filter based on the optimal bandwidth corresponding to each data window; constructing a temperature compensation curve, and correcting the filtered real-time pressure signal based on the temperature compensation curve to obtain an optimal pressure signal;

[0007] Among them, the step of constructing the bandwidth adjustment coefficient of each data window includes: obtaining the center frequency of the vibration signal in each data window, setting the adjacent frequency area based on the center frequency, and obtaining the average value of the corresponding amplitude in the adjacent frequency area; using the difference between the center frequency and the amplitude average of the adjacent frequency area as the first control factor, using the difference between the center frequency and the amplitude average corresponding to all frequencies in the window as the second control factor, and using the ratio of the first control factor to the second control factor as the bandwidth adjustment coefficient.

[0008] The beneficial effects are as follows: operating data of the servo pump group to be monitored is obtained and divided into multiple data windows. The optimal bandwidth of each data window is determined based on the vibration signal in each data window, and the real-time pressure signal is filtered based on the optimal bandwidth. Different data windows in the monitored signal are filtered using different bandwidths. This allows the bandwidth to be automatically adjusted in a timely manner when the working environment or operating status of the servo pump group changes, ensuring the filtering effect and improving the accuracy of subsequent servo pump group pressure monitoring.

[0009] In the process of calculating the bandwidth adjustment coefficient, the first control factor is the difference between the center frequency and the mean amplitude of the adjacent frequency area. This part reflects the difference in amplitude between the center frequency and the local area in the data window. The smaller the local amplitude difference, the greater the harmonic intensity carried by the vibration noise, and a large bandwidth should be used for filtering. The second control factor is the difference between the center frequency and the mean amplitude corresponding to all frequencies in the window; it reflects the difference in amplitude between the center frequency and all frequencies in the data window, and reflects the global error between the center frequency and the data window. The first control factor and the second control factor cooperate to adjust the bandwidth adjustment coefficient, and then adaptively adjust the bandwidth. Compared with the method of filtering with a fixed bandwidth in traditional technology, the filtering effect is not affected by the working environment and working status of the servo pump group, and the filtering effect is more stable.

[0010] Optionally, the step of obtaining the optimal bandwidth of each data window based on the bandwidth adjustment coefficient includes: taking the product of a preset initial bandwidth and the bandwidth adjustment coefficient as the adjustment value, and taking the sum of the initial bandwidth and the adjustment value as the optimal bandwidth.

[0011] The beneficial effects are: the adjustment value is calculated based on the preset initial bandwidth, the adjustment value changes based on the dynamic change of the bandwidth adjustment coefficient, and the optimal bandwidth is adaptively adjusted.

[0012] Optionally, the steps of constructing a temperature compensation curve and correcting the filtered real-time pressure signal based on the temperature compensation curve to obtain an optimal pressure signal include: obtaining the historical temperature signal of the servo pump group and dividing it into temperature windows; processing the historical temperature signals in the temperature windows to obtain a temperature change sequence reflecting data changes; classifying the temperature windows to construct a trend set; for each trend set, constructing a polynomial based on the pressure error between the historical pressure signal and the ideal pressure in each trend set, the historical temperature signal and the temperature change sequence, and fitting the temperature compensation curve of each trend set; correcting the filtered real-time pressure signal based on the temperature compensation curve to obtain the optimal pressure signal.

[0013] The beneficial effects are as follows: the historical temperature signals during the historical operation of the servo pump group are obtained, the historical temperature signals are segmented, and multiple temperature windows are obtained. The historical temperature signals in the temperature windows are processed to obtain a temperature change sequence reflecting the temperature change. After the temperature windows of the historical temperature signals are classified, the change trends of the multiple historical temperature signals corresponding to each trend set are similar, and the temperature values ​​are close. It can also be understood that each trend set corresponds to a temperature change. A polynomial fitting temperature compensation curve is constructed based on the corresponding historical temperature signals, pressure errors, and temperature change sequences in the trend set. Subsequently, different temperature compensation curves can be selected to compensate the real-time pressure signal according to the actual temperature change conditions, thereby further improving the accuracy of real-time pressure signal acquisition.

[0014] Optionally, for each temperature window, a first-order difference sequence of a historical temperature signal in the temperature window is obtained, and the first-order difference sequence of the historical temperature signal is used as a temperature change sequence.

[0015] The beneficial effect is that the first-order difference operation of the historical temperature signal can reflect the temperature changes at adjacent moments, and then the first-order difference sequence of the historical temperature signal can represent the change trend of the historical temperature signal in the temperature window.

[0016] Optionally, the step of classifying the temperature windows to construct a trend set includes: clustering the historical temperature signal using K-means to form a plurality of clusters, and taking each cluster as a trend set.

[0017] The beneficial effect is that the temperature windows of historical temperature signals are clustered using a clustering method, so that historical temperature signals with similar change trends are classified into the same cluster cluster to form a trend set. Each trend set represents a temperature change trend.

[0018] Optionally, the steps of constructing a temperature compensation curve and correcting the filtered real-time pressure signal based on the temperature compensation curve to obtain an optimal pressure signal include: obtaining the stationary points of the historical temperature signals of the servo pump group operation, and taking the data between two adjacent stationary points as a changing state interval; processing the historical temperature signals in the temperature window to obtain a temperature change sequence reflecting the data changes; constructing a polynomial based on the pressure error between the corresponding historical pressure signal and the ideal pressure signal in the changing state interval, the temperature change sequence and the historical temperature signal, and fitting the temperature compensation curve; and correcting the filtered real-time pressure signal based on the temperature compensation curve to obtain the optimal pressure signal.

[0019] The beneficial effect is that a stagnation point, also known as a stable point, is the point where a function stops increasing or decreasing. Therefore, the distance between two adjacent stagnation points represents a monotonic change. The area between the two stagnation points forms a state change interval, and each state change interval corresponds to a monotonic temperature change trend. Based on the data in the state change interval, the temperature compensation interval within each state change interval is fitted and constructed.

[0020] Optionally, the step of compensating the real-time temperature signal based on the temperature compensation curve to obtain the optimal pressure signal includes: obtaining the real-time temperature signal of the servo pump group, dividing the real-time temperature signal into a real-time status interval of equal length to the temperature window, matching the temperature compensation curve based on the Euclidean distance between the real-time status interval and the temperature window in the trend set, and obtaining the pressure correction value based on the matched temperature compensation curve; and taking the sum of the real-time pressure signal and the pressure correction value at the corresponding moment as the optimal pressure signal.

[0021] The beneficial effects are: real-time temperature data from the servo pump group to be monitored is collected, the real-time state space is divided, and an appropriate temperature compensation curve is matched based on the Euclidean distance between the temperature data in the real-time state space and the historical temperature signals in the trend set. The real-time collected data includes both temperature signals and real-time pressure signals at the same moment. The temperature data at a certain moment is substituted into the temperature compensation curve to obtain the pressure correction value at that moment. This value is then added to the real-time pressure signal at the same moment to obtain the optimal pressure signal.

[0022] Optionally, the step of compensating the real-time temperature signal based on the temperature compensation curve to obtain the optimal pressure signal includes: obtaining the real-time temperature signal of the servo pump group, dividing the real-time temperature signal based on the stagnation point in the real-time temperature signal to form a real-time state space, matching the temperature compensation curve based on the DTW distance between the temperature data in the real-time state interval and the corresponding temperature data in the changing state interval, and obtaining the pressure correction value based on the matched temperature compensation curve; and taking the sum of the real-time pressure signal and the pressure correction value at the corresponding moment as the optimal pressure signal.

[0023] The beneficial effect is that the DTW distance between the corresponding real-time temperature signal in the real-time state interval and the historical temperature signal in the change state interval represents the similarity between the two, based on which it is possible to determine which temperature compensation curve should be used to compensate the real-time pressure signal.

[0024] Optionally, the step of compensating the real-time pressure signal in the real-time state interval and obtaining the pressure correction value includes: for any moment in the real-time collected temperature signal, substituting the temperature data at that moment into the corresponding function of the temperature compensation curve to obtain the pressure correction value

[0025] In a second aspect, the present application provides a real-time monitoring system for the oil inlet pressure of a servo pump group, which adopts the following technical solution:

[0026] A system for real-time monitoring of the oil inlet pressure of a servo pump group includes a processor and a memory, wherein the memory stores computer program instructions. When the computer program instructions are executed by the processor, a method for real-time monitoring of the oil inlet pressure of a servo pump group is implemented.

[0027] The beneficial effect is: the above-mentioned method for real-time monitoring of the oil inlet pressure of a servo pump group is generated into a computer program and stored in a memory so as to be loaded and executed by a processor, thereby making a system based on the memory and the processor for easy use.

[0028] This application has the following technical effects:

[0029] In this application, the vibration signal and real-time pressure signal of the servo pump group to be monitored are segmented to form a data window. The optimal bandwidth for noise reduction of the real-time pressure signal in each data window is determined based on the vibration signal in the data window. The real-time pressure signal is filtered and denoised with different bandwidths based on different vibration signals to improve the noise reduction effect of the real-time pressure signal. Moreover, the optimal bandwidth is dynamically adjusted during the real-time data acquisition process to adapt to the different operating states and different working environments of the servo pump group, so that the filtering effect of the real-time pressure signal can be stable and efficient, thereby improving the accuracy of subsequent pressure monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 This is a method flow chart of a method for real-time monitoring of the oil inlet pressure of a servo pump group in an embodiment of the present application.

[0031] Figure 2 This is a method for constructing a first temperature compensation curve of a method for real-time monitoring of the oil inlet pressure of a servo pump group in an embodiment of the present application.

[0032] Figure 3 This is a method for constructing the second temperature compensation curve of a real-time monitoring method for the oil inlet pressure of a servo pump group in an embodiment of the present application. DETAILED DESCRIPTION

[0033] The embodiment of the present application discloses a method for real-time monitoring of the oil inlet pressure of a servo pump group. The data to be monitored can also be understood as the operating data of the servo pump group collected in real time. The operating data is segmented to form multiple data windows. For each data window, its bandwidth adjustment coefficient is calculated, and the optimal bandwidth of the data filtering in each data window is calculated based on the bandwidth adjustment coefficient. After filtering, the filtered real-time pressure signal is compensated using a temperature compensation curve constructed based on historical data, so as to accurately obtain pressure information and improve the accuracy of subsequent abnormality detection. In the process of real-time data collection, the bandwidth of the filter of each data window is dynamically adjusted, so as to be able to adapt to the efficient filtering of the servo pump group in different working environments and working states, and improve the accuracy of subsequent abnormality detection.

[0034] Reference Figure 1 A method for real-time monitoring of the oil inlet pressure of a servo pump group includes steps S1 to S4.

[0035] S1: Acquire the servo pump operation data, which includes the real-time pressure signal and vibration signal of the oil inlet, and split the real-time pressure signal and vibration signal to form multiple data windows.

[0036] Various operating data of the servo pump group to be monitored are obtained. In this embodiment, based on the impact of the servo pump group's vibration analysis on the real-time pressure signal, the operating data of the pump group to be monitored obtained in this embodiment includes the real-time oil inlet pressure signal and the oil inlet vibration signal collected by the sensor at the oil inlet. The real-time pressure signal can be collected by a piezoresistive sensor; the oil inlet vibration signal can be collected by a MEMS accelerometer. The collected real-time pressure signal and vibration signal are both stored in the form of a time series. To unify the dimensions, in this embodiment, the collected real-time pressure signal and vibration signal are both standardized (normalized) signals.

[0037] In the process of real-time acquisition of the operating data to be monitored, as the data continues to accumulate, the length of the sequence continues to increase. The operating data is segmented into multiple data windows, each of which includes real-time pressure signals and vibration signals.

[0038] S2: constructing a bandwidth adjustment coefficient for each data window based on the frequency of the vibration signal of each data window, and obtaining an optimal bandwidth for each data window based on the bandwidth adjustment coefficient.

[0039] The center frequency of the vibration signal in each data window is obtained, and the adjacent frequency region is set based on the center frequency.

[0040] For the vibration signal in any data window, the amplitude spectrum is obtained using the Fourier transform method, and the frequency corresponding to the maximum amplitude in the amplitude spectrum is taken as the center frequency of the vibration signal.

[0041] The adjacent frequency region determined based on the center frequency refers to the frequency range close to the center frequency. An exemplary adjacent frequency region can be centered on the center frequency and extend a certain frequency range to both sides. , thereby obtaining the adjacent frequency region. For example, if a center frequency is 20 kHz and the extended frequency range is 3 kHz, then the frequency range of the adjacent frequency region is 17 kHz-23 kHz. In other embodiments, the extended frequency range can be adjusted according to actual conditions.

[0042] Obtain the average value of the corresponding amplitude in the adjacent frequency area; use the difference between the center frequency and the average amplitude of the adjacent frequency area as the first control factor, use the difference between the center frequency and the average amplitude corresponding to all frequencies in the window as the second control factor, and use the ratio of the first control factor to the second control factor as the bandwidth adjustment coefficient.

[0043] Specifically, the calculation process of the bandwidth adjustment coefficient can be expressed as: Where, Indicates the bandwidth adjustment coefficient of the data window, Indicates the amplitude of the center frequency within the data window. Represents the average value of the amplitude in the adjacent frequency region; Represents the average amplitude of all frequencies in the data window.

[0044] The difference between the amplitude of the center frequency and the amplitude in the adjacent frequency area reflects the local amplitude difference of the center frequency relative to the data window. The smaller the local amplitude difference, the greater the harmonic intensity carried by the vibration noise, and a larger bandwidth should be used to suppress the noise. It represents the difference between the amplitude of the center frequency and the amplitude corresponding to all frequencies in the data window, reflecting the global amplitude difference relative to the data window. The smaller the global amplitude difference, the smaller the harmonic intensity carried by the noise, which has less impact on the real-time pressure signal acquisition. A smaller bandwidth should be used to retain the details of the real-time pressure signal.

[0045] S3: Obtain the optimal bandwidth of each data window based on the bandwidth adjustment coefficient.

[0046] The bandwidth adjustment coefficient is proportional to the optimal bandwidth. In this embodiment, the product of the preset initial bandwidth and the bandwidth adjustment coefficient is used as the adjustment value, and the sum of the initial bandwidth and the adjustment value is used as the optimal bandwidth.

[0047] The real-time pressure signal is filtered using a band-stop filter based on the optimal bandwidth corresponding to each data window.

[0048] Each data window corresponding to the operating data to be monitored includes an optimal bandwidth. Based on the optimal bandwidth, a band-stop filter is used to filter and denoise the real-time pressure signal in each window to complete the noise reduction of the real-time pressure signal.

[0049] S4: Construct a temperature compensation curve, and correct the filtered real-time pressure signal based on the temperature compensation curve to obtain an optimal pressure signal.

[0050] Different temperatures require different degrees of compensation for the real-time pressure signal. Therefore, in this embodiment, historical operating data of the servo pump group is obtained for analysis. Here, historical operating data is mainly used to refer to the impact of temperature changes on the pressure measured by the sensor. Therefore, historical operating data can be data from the servo pump group's past operations at different temperatures.

[0051] The ideal pressure signal of the servo pump group is collected in a constant laboratory temperature environment. In this laboratory environment, the ideal pressure signal collected by the sensor is not affected by temperature. The difference between the ideal pressure signal and the data in the historical pressure signal represents the sensor's measurement error. Therefore, the pressure error is obtained by subtracting the data in the same order in the historical pressure signal from the data in the ideal pressure signal. The pressure error reflects the impact of temperature changes on the pressure signal collected by the sensor; multiple pressure errors can form a pressure error sequence. To improve the accuracy of the historical pressure signal acquisition, the historical pressure signal is the signal after filtering through steps S1-S3.

[0052] Reference Figure 2 In one embodiment, the step of constructing the temperature compensation curve includes: step S41 to step S43.

[0053] S41: Obtain historical temperature signals of the servo pump group during operation and divide them into temperature windows, process the historical temperature signals in the temperature windows, and obtain a temperature change sequence reflecting data changes.

[0054] The historical temperature signal and the historical pressure signal are evenly divided to form multiple temperature windows. The historical temperature signal in the temperature window is processed by first-order difference to obtain a temperature change sequence. The data in the temperature change sequence represents the temperature change at adjacent moments.

[0055] S42: Classify the temperature windows to construct a trend set.

[0056] In this example, the K-means clustering method is used to cluster the temperature change sequences corresponding to each temperature window based on Euclidean distance, resulting in multiple clusters, each of which is a trend set. The similarity of the temperature change sequences within each trend set indicates that the temperature data changes in the corresponding temperature windows within each cluster are similar, meaning that each trend set represents a temperature change trend.

[0057] S43: For each trend set, a polynomial is constructed based on the pressure error between the historical pressure signal and the ideal pressure, the historical temperature signal, and the temperature change sequence in each trend set to fit the temperature compensation curve of each trend set.

[0058] A temperature compensation curve is constructed based on the corresponding historical temperature signals, temperature change sequence and pressure error in each cluster.

[0059] Specifically, the polynomial can be expressed as: ; Indicates the The pressure error at the moment, Indicates the number of historical temperature signals Temperature data at the moment, Indicates the temperature change sequence corresponding to The data value at the moment; is the first parameter to be fitted; is the second parameter to be fitted; c is the third parameter to be fitted.

[0060] in the formula Indicates the effect of instantaneous temperature change on pressure error, is the parameter to be fitted, and the least squares method is used to fit the polynomial temperature compensation curve.

[0061] Subsequently, the real-time temperature signal of the servo pump group is obtained, and the real-time temperature signal is divided into a real-time status interval of equal length to the temperature window. The temperature compensation curve is matched based on the Euclidean distance between the real-time status interval and the temperature window in the trend set, and the pressure correction value is obtained based on the matched temperature compensation curve; the sum of the real-time pressure signal and the pressure correction value at the corresponding moment is used as the optimal pressure signal.

[0062] The real-time signal is segmented into multiple real-time status intervals. The Euclidean distance between the data in the real-time status interval and the historical temperature signals in each trend set is calculated. Each trend set corresponds to multiple temperature windows, and each temperature window corresponds to a historical temperature signal. Therefore, the average Euclidean distance between the real-time status area and multiple historical temperature signals is used as the distance between the real-time status interval and the trend set. The trend set with the smallest distance is selected as the optimal matching set.

[0063] The temperature data corresponding to the real-time pressure signal at any moment is substituted into the temperature compensation curve corresponding to the optimal matching set to obtain the pressure correction value. The sum of the real-time pressure signal and the pressure correction value at the corresponding moment is used as the optimal pressure signal. The servo pump group can be subsequently monitored according to the optimal pressure signal to improve the accuracy of monitoring the servo pump group.

[0064] Reference Figure 3 In another embodiment, the step of constructing the temperature compensation curve includes: step S51 - step S52.

[0065] S51: Obtain the stationary points of the historical temperature signal of the servo pump group operation, and use the data between two adjacent stationary points as a change state interval; process the historical temperature signal in the temperature window to obtain a temperature change sequence reflecting the data change.

[0066] The stationary point refers to the point where the historical temperature signal changes to zero, which can be obtained with the help of the first-order difference sequence of the historical temperature signal. This method is a conventional technical means in this field and will not be described in detail here.

[0067] The historical temperature signals corresponding to two adjacent stationary points represent a monotonic temperature change trend, so the historical temperature signals between the two stationary points are defined as a state change interval.

[0068] S52: Constructing a polynomial based on the pressure error between the corresponding historical pressure signal and the ideal pressure signal in the change state interval, the temperature change sequence, and the historical temperature signal, and fitting a temperature compensation curve.

[0069] The steps of constructing a polynomial fitting temperature compensation curve here are the same as those in S43 and will not be repeated here.

[0070] The filtered real-time pressure signal is corrected based on the temperature compensation curve to obtain the optimal pressure signal.

[0071] The real-time temperature signal of the servo pump group is obtained, and the real-time temperature signal is segmented based on the stagnation point in the real-time temperature signal to form a real-time state space. The temperature compensation curve is matched based on the DTW distance between the temperature data in the real-time state interval and the corresponding temperature data in the change state interval. The pressure correction value is obtained based on the matched temperature compensation curve.

[0072] Similar to obtaining stationary points in historical temperature signals, stationary points are obtained for real-time historical temperature signals. The interval between two stationary points in the real-time historical temperature signal constitutes the real-time state interval. The DTW distance between the real-time state interval and the historical state interval is calculated, and the historical state interval with the smallest DTW distance is selected as the optimal matching interval. The real-time temperature information is substituted into the temperature compensation curve corresponding to the optimal matching interval to obtain the pressure correction value. The sum of the real-time pressure signal and the correction value at the corresponding moment is used as the optimal pressure signal.

[0073] The pressure of the servo pump group is monitored based on the optimal pressure signal, thereby improving the accuracy of the final monitoring of the servo pump group.

[0074] An embodiment of the present application also discloses a real-time monitoring system for the oil inlet pressure of a servo pump group, including a processor and a memory, wherein the memory stores computer program instructions. When the computer program instructions are executed by the processor, a real-time monitoring method for the oil inlet pressure of a servo pump group according to the present application is implemented.

[0075] The above system also includes other components well known to those skilled in the art, such as a communication bus and a communication interface. The configuration and functions of these components are known in the art and will not be described in detail here.

[0076] The above are all preferred embodiments of the present application, and are not intended to limit the scope of protection of the present application. Therefore, any equivalent changes made based on the structure, shape, and principle of the present application should be included in the scope of protection of the present application.

Claims

1. A method for real-time monitoring of the oil inlet pressure of a servo pump group, characterized in that: The method comprises the following steps: obtaining servo pump operation data, the operation data including a real-time pressure signal and a vibration signal of an oil inlet; dividing the real-time pressure signal and the vibration signal to form a plurality of data windows; constructing a bandwidth adjustment coefficient for each data window based on the frequency of the vibration signal of each data window, obtaining an optimal bandwidth for each data window based on the bandwidth adjustment coefficient, filtering the real-time pressure signal using a band-stop filter based on the optimal bandwidth corresponding to each data window; constructing a temperature compensation curve, and correcting the filtered real-time pressure signal based on the temperature compensation curve to obtain an optimal pressure signal; The step of constructing the bandwidth adjustment coefficient of each data window includes: obtaining the center frequency of the vibration signal in each data window, setting an adjacent frequency region based on the center frequency, and obtaining the average value of the corresponding amplitude in the adjacent frequency region; using the difference between the center frequency and the average amplitude of the adjacent frequency region as a first control factor, using the difference between the center frequency and the average amplitude corresponding to all frequencies in the window as a second control factor, and using the ratio of the first control factor to the second control factor as the bandwidth adjustment coefficient; The step of obtaining the optimal bandwidth of each data window based on the bandwidth adjustment coefficient includes: taking the product of the preset initial bandwidth and the bandwidth adjustment coefficient as the adjustment value, and taking the sum of the initial bandwidth and the adjustment value as the optimal bandwidth.

2. The method for real-time monitoring of the oil inlet pressure of a servo pump group according to claim 1 is characterized in that: The steps of constructing a temperature compensation curve and correcting the filtered real-time pressure signal based on the temperature compensation curve to obtain an optimal pressure signal include: obtaining the historical temperature signal of the servo pump group and dividing it into temperature windows; processing the historical temperature signals in the temperature windows to obtain a temperature change sequence reflecting data changes; classifying the temperature windows to construct a trend set; for each trend set, constructing a polynomial based on the pressure error between the historical pressure signal and the ideal pressure in each trend set, the historical temperature signal and the temperature change sequence, and fitting the temperature compensation curve of each trend set; correcting the filtered real-time pressure signal based on the temperature compensation curve to obtain the optimal pressure signal.

3. The method for real-time monitoring of the oil inlet pressure of a servo pump group according to claim 2, characterized in that: For each temperature window, a first-order difference sequence of the historical temperature signal in the temperature window is obtained, and the first-order difference sequence of the historical temperature signal is used as a temperature change sequence.

4. The method for real-time monitoring of the oil inlet pressure of a servo pump group according to claim 2, characterized in that: The steps of classifying the temperature windows and constructing the trend set include: clustering the historical temperature signals using K-means to form multiple clusters, and taking each cluster as a trend set.

5. The method for real-time monitoring of the oil inlet pressure of a servo pump group according to claim 1, characterized in that: The steps of constructing a temperature compensation curve and correcting the filtered real-time pressure signal based on the temperature compensation curve to obtain an optimal pressure signal include: obtaining the stationary points of the historical temperature signal of the servo pump group operation, and taking the data between two adjacent stationary points as a change state interval; processing the historical temperature signal in the temperature window to obtain a temperature change sequence reflecting the data change; constructing a polynomial based on the pressure error between the corresponding historical pressure signal and the ideal pressure signal in the change state interval, the temperature change sequence and the historical temperature signal, and fitting the temperature compensation curve; and correcting the filtered real-time pressure signal based on the temperature compensation curve to obtain the optimal pressure signal.

6. The method for real-time monitoring of the oil inlet pressure of a servo pump group according to claim 2, characterized in that: The steps of compensating the real-time temperature signal based on the temperature compensation curve to obtain the optimal pressure signal include: obtaining the real-time temperature signal of the servo pump group, dividing the real-time temperature signal into a real-time status interval of equal length to the temperature window; matching the temperature compensation curve based on the Euclidean distance between the real-time status interval and the temperature window in the trend set, and obtaining a pressure correction value based on the matched temperature compensation curve; and taking the sum of the real-time pressure signal and the pressure correction value at the corresponding moment as the optimal pressure signal.

7. The method for real-time monitoring of the oil inlet pressure of a servo pump group according to claim 5, characterized in that: The steps of compensating the real-time temperature signal based on the temperature compensation curve to obtain the optimal pressure signal include: obtaining the real-time temperature signal of the servo pump group, segmenting the real-time temperature signal based on the stagnation point in the real-time temperature signal to form a real-time state space, matching the temperature compensation curve based on the DTW distance between the temperature data in the real-time state interval and the corresponding temperature data in the change state interval, obtaining a pressure correction value based on the matched temperature compensation curve; and taking the sum of the real-time pressure signal and the pressure correction value at the corresponding moment as the optimal pressure signal.

8. The method for real-time monitoring of the oil inlet pressure of a servo pump group according to claim 7, characterized in that: The step of compensating the real-time pressure signal in the real-time state interval and obtaining the pressure correction value includes: for any moment in the real-time collected temperature signal, substituting the temperature data at that moment into the corresponding function of the temperature compensation curve to obtain the pressure correction value.

9. A real-time monitoring system for the oil inlet pressure of a servo pump group, characterized in that: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a method for real-time monitoring of the oil inlet pressure of a servo pump group according to any one of claims 1 to 8 is implemented.

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