Method and system for suppressing flow pulsations in a liquid driven compressor

Abstract

The application provides a method and system for suppressing airflow pulsation of a liquid-driven compressor, wherein the method comprises: acquiring pressure pulsation spectrum and flow data of an outlet pipeline of the liquid-driven compressor; generating a main frequency offset law based on the pressure pulsation spectrum and the flow data; generating a target position adjustment signal based on the main frequency offset law and a main peak frequency of the pressure pulsation spectrum in combination with a fuzzy PID controller; monitoring an actual position of a movable baffle arranged in a resonance cavity of the liquid-driven compressor in real time, and feeding back and compensating the target position adjustment signal based on the actual position; and adjusting the position of the movable baffle based on the compensated target position adjustment signal to change the flow area of the resonance cavity, so as to suppress the airflow pulsation of the outlet pipeline. The application improves the airflow pulsation suppression effect of the liquid-driven compressor under variable working conditions.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of liquid-driven compressor airflow pulsation suppression, and particularly to a liquid-driven compressor airflow pulsation suppression method and system. BACKGROUND

[0002] During the variable working condition operation of a liquid-driven compressor, the airflow pulsation problem of the outlet pipeline seriously affects the system stability and equipment life, especially in the variable frequency driving or large load fluctuation scenarios, the traditional fixed vibration damping structure is difficult to effectively suppress the wide frequency domain pulsation, and an active suppression method capable of dynamically adapting to frequency changes is urgently needed.

[0003] A current solution adopts an active silencer technology, installs a sound wave emitting device at the outlet pipeline of the compressor, generates reverse sound waves to offset the airflow pulsation. The solution acquires pressure fluctuation signals in real time, calculates the required reverse sound wave parameters using a digital signal processor, and generates offset sound waves by a loudspeaker array.

[0004] The solution has a very high requirement for sound wave phase matching accuracy, and is prone to sound wave interference under complex working conditions, which in turn aggravates local pulsation. At the same time, the high-frequency sound wave emitting device has a large power consumption, long-term operation will cause energy efficiency to decline, and is sensitive to pipeline structure vibration, so the installation position is limited. SUMMARY

[0005] The present application provides a liquid-driven compressor airflow pulsation suppression method and system to solve the problem of poor airflow pulsation suppression effect of the liquid-driven compressor under variable working conditions in the prior art.

[0006] In a first aspect, the present application provides a liquid-driven compressor airflow pulsation suppression method, comprising:

[0007] acquiring pressure pulsation frequency spectrum and flow data of an outlet pipeline of a liquid-driven compressor;

[0008] generating a main frequency offset law based on the pressure pulsation frequency spectrum and the flow data;

[0009] generating a target position adjustment signal based on the main frequency offset law and the main peak frequency of the pressure pulsation frequency spectrum, in combination with a fuzzy PID controller;

[0010] real-time monitoring the actual position of a movable baffle arranged in a resonance cavity of the liquid-driven compressor, and feeding back and compensating the target position adjustment signal based on the actual position;

[0011] adjusting the position of the movable baffle based on the compensated target position adjustment signal to change the flow area of the resonance cavity, thereby suppressing the airflow pulsation of the outlet pipeline.

[0012] Optionally, the target position adjustment signal is generated based on the main frequency offset law and the main peak frequency of the pressure pulsation spectrum, combined with a fuzzy PID controller, comprising:

[0013] According to the flow data, a corresponding reference main peak frequency is queried from the main frequency offset law;

[0014] The reference main peak frequency is superimposed with a preset offset to obtain a target resonance frequency;

[0015] The main peak frequency of the pressure pulsation spectrum is subtracted from the target resonance frequency to obtain a frequency difference value;

[0016] The absolute value of the frequency difference value is input into the input interface of the fuzzy PID controller to output the target position adjustment signal.

[0017] Optionally, the absolute value of the frequency difference value is input into the input interface of the fuzzy PID controller to output the target position adjustment signal, comprising:

[0018] The absolute value is dynamically dead-zone processed to obtain an effective difference value;

[0019] According to the effective difference value, a corresponding fuzzy interval is matched from a control rule library of the fuzzy PID controller;

[0020] According to the current operating condition of the liquid-driven compressor, an optimal parameter combination matching the fuzzy interval and the operating condition is selected from a plurality of preset parameter groups;

[0021] Based on the optimal parameter combination, the effective difference value is signal-converted to generate an initial position adjustment signal;

[0022] The initial position adjustment signal is amplitude-limited to generate a target position adjustment signal.

[0023] Optionally, the effective difference value is signal-converted based on the optimal parameter combination to generate an initial position adjustment signal, comprising:

[0024] The effective difference value is weighted with the proportional coefficient, integral coefficient, and differential coefficient in the optimal parameter combination to generate a proportional term, an integral term, and a differential term;

[0025] The proportional term, the integral term, and the differential term are superimposed and calculated to output an initial control quantity;

[0026] According to the pulse equivalent resolution of the liquid-driven compressor, the initial control quantity is quantized into a target pulse number;

[0027] Generate a pulse control sequence as the initial position adjustment signal based on the target pulse number and the control quantity polarity.

[0028] Optionally, the generating a main frequency offset rule based on the pressure pulsation spectrum and the flow data comprises:

[0029] Divide the pressure pulsation spectrum into a plurality of characteristic frequency bands, and extract the main peak frequency under each operating condition from the plurality of characteristic frequency bands;

[0030] According to the flow data and the main peak frequency under the corresponding operating condition, analyze the main peak frequency distribution pattern of different flow intervals to generate a frequency offset feature library;

[0031] Output the main frequency offset rule through the frequency offset feature library.

[0032] Optionally, the analyzing the main peak frequency distribution pattern of different flow intervals according to the flow data and the main peak frequency under the corresponding operating condition to generate a frequency offset feature library comprises:

[0033] Discretize the flow data into a plurality of flow intervals according to a preset division rule;

[0034] For each flow interval, count the distribution density of all main peak frequencies in the flow interval to generate a probability density distribution graph;

[0035] Identify the frequency range corresponding to the probability density peak value from the probability density distribution graph, and take the frequency range as the concentrated distribution interval of the main peak frequency;

[0036] Extract the median frequency of the concentrated distribution interval as the representative frequency value of the corresponding flow interval;

[0037] Map the flow interval and the representative frequency value to generate a frequency offset feature library.

[0038] Optionally, the adjusting the position of the movable baffle based on the compensated target position adjustment signal comprises:

[0039] Input the compensated target position adjustment signal to a driving circuit arranged in the liquid-driven compressor;

[0040] Convert the compensated target position adjustment signal into a rotation angle command of the liquid-driven compressor through the driving circuit;

[0041] Adjust the rotation angle and rotation direction of the output shaft of the liquid-driven compressor according to the rotation angle command;

[0042] The adjusted rotation angle is converted into the axial displacement of the lead screw nut through the direct drive between the output shaft of the liquid-driven compressor and the lead screw mechanism.

[0043] Based on the axial displacement, the gap size between the movable baffle and the wall of the resonant cavity is adjusted.

[0044] Secondly, this application provides an airflow pulsation suppression system for a liquid-driven compressor, comprising:

[0045] The acquisition module is used to acquire the pressure pulsation spectrum and flow data of the outlet pipeline of the liquid-driven compressor;

[0046] The first generation module is used to generate a dominant frequency offset pattern based on the pressure pulsation spectrum and the flow data;

[0047] The second generation module is used to generate a target position adjustment signal based on the main frequency offset law and the main peak frequency of the pressure pulsation spectrum, combined with a fuzzy PID controller.

[0048] The monitoring module is used to monitor the actual position of the movable baffle set in the resonant cavity of the liquid-driven compressor in real time, and to provide feedback compensation for the target position adjustment signal based on the actual position.

[0049] The adjustment module is used to adjust the position of the movable baffle based on the compensated target position adjustment signal, so as to change the flow area of ​​the resonant cavity and thereby suppress the airflow pulsation of the outlet pipe.

[0050] Thirdly, this application provides a computing device including a processor and a memory, wherein the memory stores a computer program, and the processor is configured to run the computer program to perform a method for suppressing airflow pulsation in a liquid-driven compressor as described in any of the first aspects.

[0051] Fourthly, this application provides a computer storage medium storing computer program instructions thereon, which, when executed by a processor, implement a method for suppressing airflow pulsation in a liquid-driven compressor as described in any one of the first aspects.

[0052] This application provides a method for suppressing airflow pulsation in a liquid-driven compressor. The method includes: acquiring pressure pulsation spectrum and flow rate data of the outlet pipe of the liquid-driven compressor; generating a dominant frequency offset pattern based on the pressure pulsation spectrum and the flow rate data; generating a target position adjustment signal based on the dominant frequency offset pattern and the peak frequency of the pressure pulsation spectrum, combined with a fuzzy PID controller; real-time monitoring of the actual position of a movable baffle disposed within the resonant cavity of the liquid-driven compressor; and performing feedback compensation on the target position adjustment signal based on the actual position; and adjusting the position of the movable baffle based on the compensated target position adjustment signal to change the flow area of ​​the resonant cavity, thereby suppressing airflow pulsation in the outlet pipe.

[0053] The technical solution provided in this application has the following beneficial effects:

[0054] This application accurately captures the airflow pulsation characteristics and operating conditions of the compressor during operation, providing a reliable data foundation for subsequent analysis. It establishes a correspondence between flow rate changes and peak frequency fluctuations, revealing the changing patterns of the system's pulsation characteristics under different operating conditions. An intelligent control algorithm calculates the optimal adjustment command in real time, ensuring that the control response matches the current pulsation characteristics. Mechanical transmission errors are eliminated, improving the accuracy and stability of baffle position control. The resonant cavity structural parameters are dynamically optimized to achieve precise suppression of airflow pulsation.

[0055] Furthermore, this application obtains the reference frequency by querying the main frequency offset pattern, determines the target resonance frequency by combining the preset offset, calculates the difference between the measured main peak frequency and the target value, and outputs a precise position adjustment signal after fuzzy PID control processing.

[0056] Furthermore, this solution achieves intelligent dynamic suppression of compressor airflow pulsation. Through the synergistic effect of frequency tracking and intelligent adjustment, it enhances the system's pulsation suppression capability under different operating conditions, while ensuring the stability and reliability of the control process.

[0057] These or other aspects of this application will become more apparent in the following description of the embodiments. Attached Figure Description

[0058] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0059] Figure 1 A flowchart of a method for suppressing airflow pulsation in a liquid-driven compressor provided in this application embodiment;

[0060] Figure 2 A schematic diagram of the airflow pulsation suppression system for a liquid-driven compressor provided in this application embodiment;

[0061] Figure 3 This is a schematic diagram of the structure of a computing device provided in an embodiment of this application. Detailed Implementation

[0062] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0063] In some of the processes described in the specification, claims, and accompanying drawings of this application, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The operation numbers, such as 101, 102, etc., are merely used to distinguish different operations and do not themselves represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a chronological order, nor do they limit "first" and "second" to different types.

[0064] Existing technologies for suppressing airflow pulsation in hydraulically driven compressors, particularly those using active silencers, have significant shortcomings. These technologies require precise matching of the phase and amplitude of the reverse sound wave; however, when the compressor speed changes frequently, the sound wave adjustment often cannot keep up with the rate of change in the pulsation frequency, resulting in a significant reduction in suppression effectiveness. More importantly, this system consumes a large amount of energy, and prolonged operation significantly increases energy costs. Furthermore, it has stringent requirements for installation location; even slight deviations from the optimal position can affect the overall suppression effect.

[0065] To address these issues, this application proposes a method for suppressing airflow pulsation in a liquid-driven compressor. The core of this method lies in automatically adjusting the position of a movable baffle within the resonant cavity to change the size of the airflow channel by real-time monitoring of the airflow pulsation characteristics at the compressor outlet. Specifically, the system first analyzes the frequency characteristics of the airflow pulsation, then calculates the optimal baffle adjustment scheme using an intelligent control algorithm, and finally precisely adjusts the baffle position through mechanical transmission. This method completely avoids the problem of acoustic interference, achieving pulsation suppression by physically altering the airflow channel. It not only offers faster response speed and lower energy consumption but also has no special requirements for installation location. It fundamentally solves the problems of adjustment lag, high energy consumption, and installation limitations in existing technologies, improving the stability and reliability of pulsation suppression.

[0066] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0067] Figure 1 A flowchart of a method for suppressing airflow pulsation in a liquid-driven compressor provided in this application embodiment is shown below. Figure 1 As shown, the method includes:

[0068] Step 101: Obtain the pressure pulsation spectrum and flow data of the outlet pipeline of the liquid-driven compressor.

[0069] In step 101, the pressure pulsation spectrum represents the frequency distribution characteristics of the compressor outlet airflow pressure fluctuation over time, showing the intensity of pressure fluctuations at different frequencies. Flow data represents the volume of gas passing through the compressor outlet per unit time, reflecting the system operating conditions.

[0070] In this embodiment, a pressure sensor and a flow meter are installed on the compressor outlet pipeline. The pressure sensor collects pressure fluctuation signals and converts the time-domain signal into a frequency-domain signal using a fast Fourier transform to obtain the pressure pulsation spectrum. Simultaneously, the flow meter measures the gas flow rate in real time. The collected pressure pulsation spectrum and flow rate data are stored in the system cache for subsequent analysis.

[0071] For example, during the operation of a certain type of hydraulically driven compressor, the pressure sensor collects the outlet pressure signal at a fixed sampling frequency. After processing by the signal conditioning circuit, the signal is converted into a spectrum using a fast Fourier transform algorithm, where the main peak appears at 150 Hz with an amplitude of 15 kPa. At the same time, the flow meter measures the current flow rate as 80 cubic meters per minute. The system packages and stores these two data points together.

[0072] Step 102: Based on the pressure pulsation spectrum and the flow data, generate the main frequency offset pattern.

[0073] In step 102, the main frequency offset law refers to the correspondence between the main peak frequency in the outlet pipeline pressure pulsation spectrum of the liquid-driven compressor under different flow conditions and the flow rate. Specifically, it is the mapping law between the flow rate value and the trend of the main peak frequency change established by analyzing historical operating data. This law can reflect the influence characteristics of flow rate change on the main frequency of airflow pulsation.

[0074] In this embodiment of the application, historical operating data is statistically analyzed, the flow rate is divided into several intervals, the distribution of the main peak frequency is statistically analyzed in each flow rate interval, the frequency range with the highest probability of occurrence is identified as the representative frequency of the interval, and the functional relationship between the flow rate and the representative frequency is established by curve fitting to form the main frequency offset pattern.

[0075] For example, analysis of three months of operational data revealed that when the flow rate was between 70 and 90 cubic meters per minute, the peak frequency was mainly concentrated in the 145-155 Hz range, with 150 Hz occurring most frequently. This value was selected as the representative frequency. The relationship between frequency (1.2 × flow rate + 54) was obtained through least squares fitting and stored in the feature library.

[0076] Step 103: Based on the main frequency offset law and the main peak frequency of the pressure pulsation spectrum, and combined with the fuzzy PID controller, generate the target position adjustment signal.

[0077] In step 103, the fuzzy PID controller represents an intelligent regulator that combines fuzzy logic and PID control, capable of adapting to nonlinear systems. The target position adjustment signal represents the command signal for controlling the movement of the baffle.

[0078] In this embodiment, the reference frequency is obtained by querying the main frequency offset pattern based on the current traffic flow, and the target frequency is obtained by adding a preset offset. The difference between the measured main peak frequency and the target frequency is calculated, and the difference is input into the fuzzy PID controller. The controller selects control parameters according to preset rules and outputs an adjustment signal after proportional-integral-derivative operations.

[0079] For example, if the current flow rate is 80 cubic meters per minute, the reference frequency is found to be 150 Hz. Adding the preset offset of 10 Hz, the target frequency is 160 Hz. The measured peak frequency is 155 Hz. The difference of 5 Hz is input to the controller, which then outputs a 9-volt adjustment signal after calculation.

[0080] Step 104: Monitor the actual position of the movable baffle set in the resonant cavity of the liquid-driven compressor in real time, and perform feedback compensation on the target position adjustment signal based on the actual position.

[0081] In step 104, the actual position refers to the specific physical coordinates of the movable baffle within the resonant cavity, measured in real time by a displacement sensor. These coordinates reflect the real-time distance of the baffle relative to the resonant cavity wall and are compared with the target position calculated by the control system to determine the position adjustment deviation. Specifically, this is expressed as the current axial displacement of the baffle, typically measured in millimeters, and this value changes dynamically with the stepper motor's drive. The feedback compensation process involves correcting the control signal based on the deviation between the actual and target positions.

[0082] In this embodiment, the position of the baffle is monitored in real time by a displacement sensor, the measured position is compared with the target position to obtain the deviation, and a proportional compensation algorithm is used to correct the original adjustment signal to ensure that the baffle finally reaches the predetermined position.

[0083] For example, if the baffle corresponding to the target position moves 10 mm, the actual measurement shows only a 9.5 mm movement, resulting in a 0.5 mm deviation. The compensation algorithm then adjusts the signal from 9 volts to 9.5 volts.

[0084] Step 105: Based on the compensated target position adjustment signal, adjust the position of the movable baffle to change the flow area of ​​the resonant cavity, thereby suppressing the airflow pulsation in the outlet pipe.

[0085] In step 105, the flow area represents the effective cross-sectional area between the baffle and the cavity wall through which airflow can pass.

[0086] In this embodiment, the compensated adjustment signal is converted into a stepper motor drive pulse, and the rotational motion is converted into the linear displacement of the baffle through the lead screw mechanism, so as to precisely adjust the gap between the baffle and the cavity wall, thereby changing the flow area.

[0087] For example, a 9.5-volt signal is converted into 1425 drive pulses, and a stepper motor drives a lead screw to move the baffle by 10.1 millimeters, increasing the gap from 3 millimeters to 5 millimeters and expanding the flow area.

[0088] This method achieves dynamic suppression of airflow pulsation in the liquid-driven compressor through closed-loop control of real-time monitoring, intelligent analysis, and precise adjustment. It effectively improves the system's operational stability, extends the equipment's service life, and has advantages such as rapid response, precise adjustment, and strong adaptability.

[0089] To address the precise control problem of suppressing airflow pulsation in hydraulically driven compressors under varying operating conditions, in some embodiments, step 103: generating a target position adjustment signal based on the dominant frequency offset law and the main peak frequency of the pressure pulsation spectrum, combined with a fuzzy PID controller, includes:

[0090] Step 201: Based on the traffic data, query the corresponding reference peak frequency from the main frequency offset pattern.

[0091] In step 201, the reference peak frequency refers to the typical value of the most frequent pressure pulsation of the compressor under a specific flow condition. This value is obtained through historical data analysis and reflects the most likely pulsation frequency under that flow condition.

[0092] In this embodiment of the application, the system searches for the corresponding reference peak frequency in the main frequency offset pattern database based on the currently detected traffic volume value. The database stores the mapping relationship between different traffic volume ranges and reference frequencies.

[0093] Step 202: Superimpose the reference main peak frequency and the preset offset to obtain the target resonance frequency.

[0094] In step 202, the preset offset is a fixed frequency adjustment value set according to the acoustic characteristics of the compressor resonant cavity, used to correct the reference frequency to the target resonant frequency. The target resonant frequency refers to the ideal frequency value calculated by adding the reference peak frequency to the preset offset. This frequency is the optimal operating frequency that the control system hopes the compressor resonant cavity will ultimately reach, used to effectively suppress airflow pulsation under the current operating conditions. The reference peak frequency reflects typical operating condition characteristics, and the preset offset is an optimized adjustment value set according to the acoustic characteristics of the resonant cavity. The combination of the two ensures that the resonant cavity always operates in the optimal suppression state.

[0095] In this embodiment of the application, the reference main peak frequency obtained by querying is arithmetically added to the preset offset, and the calculation result is the target resonance frequency to be achieved. This frequency is the ideal pulsation suppression frequency that the control system hopes to achieve.

[0096] Step 203: Subtract the main peak frequency of the pressure pulsation spectrum from the target resonant frequency to obtain the frequency difference value.

[0097] In step 203, the frequency difference refers to the deviation between the actual detected main peak frequency and the target resonant frequency, reflecting the gap between the current pulsation state and the ideal state.

[0098] In this embodiment of the application, the main peak frequency obtained by real-time spectrum analysis is subtracted from the calculated target resonance frequency, and the difference is used as the input signal of the control system.

[0099] Step 204: Input the absolute value of the frequency difference into the input interface of the fuzzy PID controller and output the target position adjustment signal.

[0100] In this embodiment of the application, the absolute value of the frequency difference is input into the fuzzy PID controller. The controller selects appropriate control parameters according to the preset fuzzy rules, and outputs the target position adjustment signal after proportional, integral and derivative operations. This signal will drive the actuator to move.

[0101] Here is a specific example:

[0102] Taking a certain type of liquid-driven compressor as an example, under the current operating conditions, the flow meter displays a flow rate of 80 cubic meters per minute. Based on the pre-established frequency offset feature library, the system calculates the reference peak frequency as 1.2 × flow rate + 54, where the flow rate is the current measured value of 80 cubic meters per minute. The calculated reference peak frequency is 1.2 × 80 + 54 = 150 Hz. The compressor's resonant cavity design requires a fixed frequency offset of 10 Hz. Adding the reference frequency of 150 Hz to the offset of 10 Hz yields the target resonant frequency of 160 Hz. At this time, the real-time spectrum collected by the pressure sensor shows a peak frequency of 155 Hz. The calculated frequency difference is 155 minus 160, which equals -5 Hz. The absolute value of 5 Hz is taken as the input signal for the fuzzy PID controller. This controller has three preset control intervals. The interval from 0 to 5 Hz uses a proportional coefficient of 0.8, an integral coefficient of 0.05, and a derivative coefficient of 0.1. The calculated combination of terms yields a proportional term of 0.8 × 5 = 4, an integral term of 0.05 × 5 × control period 0.1 seconds = 0.025, and a differential term of 0.1 × 5 / 0.1 = 5. The sum of these three terms gives an initial control quantity of 9.025 volts. This is output as a stepper motor drive signal via a digital-to-analog converter. The conversion relationship between each volt and a 100 Hz pulse frequency is set according to the motor parameters. This ultimately generates a control signal containing 903 positive pulses. This signal drives the stepper motor to rotate, which in turn drives the lead screw mechanism, converting the rotational motion into linear displacement of the baffle. The design parameter of a 5 mm lead screw determines that each pulse corresponds to a 0.01 mm movement of the baffle. Therefore, 903 pulses cause the baffle to move 9.03 mm, adjusting the resonant cavity flow gap from the initial 3 mm to 4.515 mm. The control period of 0.1 seconds is the system's preset sampling interval, ensuring that the control response speed matches the airflow pulsation changes.

[0103] In this embodiment, the control method achieves dynamic suppression of airflow pulsation through intelligent frequency tracking and precise adjustment signal generation, enabling the system to respond quickly to changes in operating conditions and maintain a stable pulsation suppression effect, while avoiding the problems of adjustment lag and insufficient accuracy in traditional methods.

[0104] To improve the control accuracy of airflow pulsation suppression in liquid-driven compressors, in some embodiments, step 204: inputting the absolute value of the frequency difference into the input interface of the fuzzy PID controller and outputting the target position adjustment signal includes:

[0105] Step 301: Perform dynamic dead zone processing on the absolute value to obtain the effective difference.

[0106] In step 301, dynamic dead-zone processing refers to setting a minimum effective difference range according to system requirements. When the input value is less than this range, it is considered an invalid fluctuation and is not processed. Subsequent control calculations are only performed when the value exceeds this range. The effective difference refers to the absolute value of the frequency difference that needs to be processed after dynamic dead-zone processing. Only when this value exceeds the preset dead-zone threshold will it be adopted by the system for subsequent control calculations, thus avoiding unnecessary adjustment actions for minor fluctuations.

[0107] In this embodiment, the system first determines whether the absolute value of the input frequency difference exceeds a preset dead zone threshold. If it does not exceed the threshold, the output value of the previous control cycle remains unchanged. If it exceeds the threshold, the difference is output as a valid difference to the next processing stage.

[0108] Step 302: Based on the effective difference, match the corresponding fuzzy interval from the control rule base of the fuzzy PID controller.

[0109] In step 302, the fuzzy interval is a pre-divided range of difference values, and each interval corresponds to a different control strategy.

[0110] In this embodiment of the application, the controller stores multiple pre-divided difference intervals. The system compares the valid difference with these intervals to determine the range to which it belongs, providing a basis for subsequent parameter selection.

[0111] Step 303: Based on the current operating conditions of the liquid-driven compressor, select the optimal parameter combination from a set of preset parameter groups that matches the fuzzy range and operating conditions.

[0112] In step 303, the current operating condition refers to the compressor's current load state and working conditions, determined by comprehensively analyzing real-time monitored flow, pressure, and speed data. The system categorizes the operating condition into different types, such as light load, medium load, and heavy load, based on these parameters. The optimal parameter combination refers to the proportional, integral, and derivative control parameters optimized for a specific operating condition and differential range. The preset parameter sets are collections of different control parameters determined in advance through experiments and optimization. Each parameter set includes specific proportional, integral, and derivative coefficients, applicable to different differential ranges and operating condition combinations.

[0113] In this embodiment of the application, the system selects the most suitable combination of proportional coefficient, integral time and derivative time from a preset parameter library based on the current operating conditions of the compressor and the matched fuzzy interval.

[0114] Step 304: Based on the optimal parameter combination, perform signal conversion on the effective difference to generate an initial position adjustment signal.

[0115] In step 304, signal conversion refers to the process of converting the difference signal into a control output according to selected parameters. The initial position adjustment signal refers to the original control output calculated by the fuzzy PID controller based on the optimal parameter combination. This signal needs to undergo subsequent adjustments such as amplitude limiting before it is converted into the final execution instruction.

[0116] In this embodiment of the application, the system uses the selected optimal parameter combination to perform proportional, integral, and derivative operations on the effective difference, and adds the operation results to obtain the initial control signal.

[0117] Step 305: Perform amplitude limiting processing on the initial position adjustment signal to generate the target position adjustment signal.

[0118] In step 305, amplitude limiting is performed to ensure that the output signal is within a safe range and to avoid overloading the actuator.

[0119] In this embodiment, the system compares the initial control signal with preset upper and lower limits. If the limit is exceeded, the limit is taken as the final output; otherwise, the initial value is output directly to ensure the safe operation of the actuator.

[0120] Here is a specific example:

[0121] After the system obtains the absolute value of the 5 Hz frequency difference, it first performs dynamic dead-zone processing. The preset dead-zone threshold is 2 Hz. Since 5 Hz is greater than the threshold, the system determines that the difference is valid and continues processing. The controller internally has three fuzzy ranges: 0-3 Hz for small difference, 3-7 Hz for medium difference, and above 7 Hz for large difference. 5 Hz falls within the medium difference range. Simultaneously, the system detects that the compressor is currently operating under medium load conditions. It selects the optimal parameter combination suitable for the medium difference range and medium load conditions from the preset parameter library, where the proportional coefficient is 0.7, the integral coefficient is 0.06, and the derivative coefficient is 0.12. Based on these parameters, the calculations are as follows: proportional term: 0.7 × 5 = 3.5; integral term: 0.06 × 5 × control period 0.1 seconds = 0.03; derivative term: 0.12 × 5 / 0.1 = 6. The sum of these three terms yields the initial control quantity of 9.53 volts. The system's preset voltage output limit is 10 volts. Since 9.53 volts is within the limit, it is directly used as the target position adjustment signal output.

[0122] In this embodiment, the control method achieves precise suppression of airflow pulsation through intelligent differential processing and parameter optimization selection, enabling the system to automatically adjust the control strategy according to the actual working conditions, thus ensuring both control accuracy and system operation safety.

[0123] To further improve the control accuracy of airflow pulsation suppression in liquid-driven compressors, in some embodiments, step 304: based on the optimal parameter combination, performing signal conversion on the effective difference to generate an initial position adjustment signal includes:

[0124] Step 401: Perform weighted operations on the effective difference with the proportional coefficient, integral coefficient, and differential coefficient in the optimal parameter combination to generate the proportional term, integral term, and differential term.

[0125] In step 401, the proportional term reflects the direct impact of the current difference. The integral term is used to eliminate historical accumulated errors. The differential term predicts future trends.

[0126] In this embodiment of the application, the system multiplies the effective difference with the three coefficients in the optimal parameter combination, the proportional coefficient is directly multiplied with the difference to obtain the proportional term, the integral coefficient is multiplied with the difference and time to obtain the integral term, and the differential coefficient is multiplied with the rate of change of the difference to obtain the differential term.

[0127] Step 402: Perform superposition calculation on the proportional term, the integral term, and the differential term, and output the initial control quantity.

[0128] In step 402, the initial control quantity is a preliminary adjustment command obtained by combining the results of the three calculations.

[0129] In the embodiments of this application, the calculated proportional term, integral term and derivative term are algebraically added together, and the sum is the initial control quantity, which reflects the system's comprehensive adjustment requirements for the current deviation.

[0130] Step 403: Quantize the initial control quantity into the target number of pulses based on the pulse equivalent resolution of the liquid-driven compressor.

[0131] In step 403, the pulse equivalent resolution refers to the actual displacement of the movable baffle when the hydraulic compressor receives a pulse signal. This parameter is determined by the step angle, lead screw, and transmission ratio of the hydraulic compressor, and the specific value is determined through mechanical design based on the adjustment accuracy requirements of the compressor's resonant cavity. The target pulse number refers to the total number of pulses that need to be sent to the stepper motor, calculated based on the initial control quantity and the pulse equivalent resolution. This value determines the final moving distance of the actuator (such as the baffle). The calculation formula is: the target pulse number equals the initial control quantity multiplied by the pulse equivalent resolution, where the pulse equivalent resolution is the number of pulses corresponding to each pre-calibrated voltage unit.

[0132] In this embodiment of the application, the initial control quantity is multiplied by a preset conversion coefficient according to the technical parameters of the actuator to calculate the total number of pulses to be output, so as to ensure that the mechanical displacement corresponds precisely to the control quantity.

[0133] Step 404: Based on the target number of pulses and in combination with the polarity of the control quantity, generate a pulse control sequence, and use the pulse control sequence as the initial position adjustment signal.

[0134] In step 404, the polarity of the control quantity refers to the adjustment direction determined by the positive or negative of the frequency difference. When the main peak frequency is higher than the target frequency, the difference is negative corresponding to reverse adjustment, and when it is lower than the target frequency, the difference is positive corresponding to forward adjustment. This polarity comes from the calculation of the difference between the main peak frequency and the target resonance frequency, and directly determines the rotation direction of the stepper motor. The pulse control sequence is a specific instruction combination for driving the stepper motor.

[0135] In the embodiment of the present application, according to the calculated target number of pulses and the positive and negative polarities of the difference, a control sequence including pulse quantity and direction information is generated, and this sequence can directly drive the motor to achieve precise position control.

[0136] The following is a specific example:

[0137] After the system obtains the initial control quantity of 9.53 volts, according to the technical parameters of the stepper motor supporting the liquid-driven compressor of this model, the pulse equivalent resolution is set to 120 pulses corresponding to each volt of voltage. Multiply the initial control quantity of 9.53 volts by 120 to obtain the target number of pulses of 1143.6, and round up to 1144 pulses. Since the frequency difference calculated in the previous step is -5 Hz after taking the absolute value, but the original difference sign is retained in the polarity of the control quantity, and here the negative difference indicates that the flow area needs to be reduced, so the polarity of the control quantity is reverse adjustment. The system generates a control sequence including 1144 reverse pulses according to the target number of pulses of 1144 and the reverse adjustment requirement.

[0138] In the embodiment of the present application, this signal conversion method realizes the high-precision conversion from electrical signal to mechanical displacement through precise parameter operation and pulse quantization, ensures that the air flow pulsation suppression system can execute control instructions quickly and accurately, and improves the overall control effect and system stability.

[0139] In order to accurately establish the correlation between the air flow pulsation characteristics and the operating conditions of the liquid-driven compressor, in some embodiments, step 102: The generating the main frequency offset law based on the pressure pulsation spectrum and the flow data includes:

[0140] Step 501: Divide the pressure pulsation spectrum into multiple characteristic frequency bands, and extract the main peak frequencies under each operating condition from the multiple characteristic frequency bands.

[0141] In step 501, the pressure pulsation spectrum is divided into multiple characteristic frequency bands by a preset fixed frequency interval. The division is based on the frequency range of concentrated pressure pulsation energy distribution in the historical operating data of the hydraulic compressor, typically divided into characteristic frequency bands of 100Hz each, such as 0-100Hz, 100-200Hz, etc. A characteristic frequency band refers to multiple frequency intervals divided by a fixed bandwidth in the pressure pulsation spectrum, each interval containing a specific range of frequency components. Each operating condition refers to the compressor's operating state under different load conditions. The number of operating conditions included in each characteristic frequency band depends on the actual amount of data collected; these operating conditions are known operating condition data obtained in real time through pressure sensors and flow meters.

[0142] In this embodiment of the application, the system divides the complete pressure pulsation spectrum into several continuous frequency bands according to the preset frequency band division rules, then calculates the sum of signal energy in each frequency band, selects the frequency band with the largest energy as the characteristic frequency band, and finally extracts the frequency corresponding to the highest energy point in the characteristic frequency band as the main peak frequency.

[0143] Step 502: Based on the flow data and the peak frequency under the corresponding operating conditions, analyze the distribution pattern of the peak frequency in different flow intervals to generate a frequency offset feature library.

[0144] In step 502, the flow range is a segmented range into which the compressor outlet flow data is divided according to its numerical value. Each range represents a range of flow variation, and the division is based on the typical flow value distribution characteristics observed in actual compressor operation. The peak frequency distribution pattern refers to the distribution characteristics of all peak frequency values ​​within each flow range, including central tendency and dispersion. It is derived from the distribution patterns obtained through statistical analysis of peak frequencies within the same flow range in historical operating data. The frequency offset feature database is a database that stores the correspondence between flow rate and peak frequency under various operating conditions.

[0145] In this embodiment of the application, the system divides the traffic range into several intervals based on the statistical results of historical operation data, and statistically analyzes the distribution of the occurrence of the main peak frequency in each interval. By analyzing the central tendency and dispersion of the main peak frequency in different traffic intervals, a database of the correspondence between traffic and frequency is established.

[0146] Step 503: Output the main frequency offset pattern using the frequency offset feature library.

[0147] In this embodiment, the system performs curve fitting on the established flow-frequency relationship data to find the mathematical relationship between flow rate changes and peak frequency changes, and stores this relationship in a feature library in function form for querying during real-time control. The specific implementation process is as follows: First, the distribution of peak frequency within each flow range is statistically analyzed; then, a table corresponding to flow rate values ​​and frequency values ​​is established; finally, the mathematical relationship between flow rate changes and frequency changes is obtained through curve fitting. For example, for a compressor with a flow rate range of 60-80 m³ / min, the peak frequency is concentrated in the range of 145-155 Hz. Through linear fitting, the relationship frequency = 1.2 × flow rate + 65 is obtained, and this formula represents the output peak frequency offset pattern.

[0148] Here is a specific example:

[0149] Taking a certain type of hydraulically driven compressor as an example, the system collected a flow rate of 80 cubic meters per minute under the current operating conditions. The pressure pulsation spectrum showed a significant peak in the 100-200 Hz frequency band, and analysis determined the main peak frequency to be 150 Hz. Based on three months of historical operating data analysis, when the flow rate was in the range of 70-90 cubic meters per minute, it was found that the main peak frequency was mainly distributed in the range of 145-155 Hz, with 150 Hz having the highest frequency, accounting for 65%. Therefore, 150 Hz was selected as the representative frequency for this flow range. By fitting the historical data using the least squares method, the relationship between the main peak frequency f and the flow rate Q was obtained as f = 1.2Q + 54, where f is in Hz and Q is in cubic meters per minute. This formula indicates that for every 1 cubic meter per minute increase in flow rate, the main peak frequency increases by an average of 1.2 Hz. After storing this relationship in the frequency offset feature library, when the system detects that the current flow rate is 80 cubic meters per minute, the reference frequency is calculated to be 1.2×80+54=150 Hz according to the formula, which is consistent with the measured main peak frequency.

[0150] In this embodiment of the application, the method establishes an accurate flow-frequency correspondence model through systematic data acquisition and analysis, providing a reliable basis for subsequent real-time control and improving the accuracy and adaptability of airflow pulsation suppression.

[0151] To more accurately establish the correspondence between flow rate and peak frequency, in some embodiments, step 502: analyzing the peak frequency distribution pattern of different flow rate intervals based on the flow rate data and the peak frequency under the corresponding operating conditions to generate a frequency offset feature library, includes:

[0152] Step 601: Discretize the traffic data into multiple traffic intervals according to the preset division rules.

[0153] In step 601, flow range division is a process of dividing continuous flow values ​​into several range segments. A flow range refers to a range of values ​​into which the flow range of the compressor during operation is divided at fixed intervals. Each range represents a range of flow variation and is used to classify and statistically analyze operating conditions.

[0154] In this embodiment of the application, the system divides the total flow range into several intervals evenly according to the compressor's working flow range and operating characteristics. Each interval contains a certain range of flow values ​​for subsequent statistical analysis.

[0155] Step 602: For each flow interval, calculate the distribution density of all main peak frequencies within the flow interval and generate a probability density distribution map.

[0156] In step 602, the distribution density statistics are a method for calculating the frequency of each frequency value within a certain flow range. The probability density distribution chart is a statistical chart that reflects the probability of occurrence of each peak frequency value within a certain flow range. The horizontal axis represents the frequency value, and the vertical axis represents the probability density of occurrence, used to visually display the concentrated distribution of the peak frequency.

[0157] In this embodiment of the application, by analyzing the probability density distribution map, the frequency range with the highest frequency of occurrence is identified, which is the area where the main peak frequency is most concentrated within the flow interval.

[0158] Step 603: Identify the frequency range corresponding to the probability density peak from the probability density distribution map, and take the frequency range as the concentrated distribution interval of the main peak frequency.

[0159] In step 603, the concentrated distribution interval refers to the frequency range in which the main peak frequency appears most densely.

[0160] In this embodiment of the application, by analyzing the probability density distribution map, the frequency range with the highest frequency of occurrence is identified, which is the area where the main peak frequency is most concentrated within the flow interval.

[0161] Step 604: Extract the median frequency of the concentrated distribution interval as the representative frequency value of the corresponding flow interval.

[0162] In step 604, the median frequency of the concentrated distribution interval refers to the middle value of the frequency range where the main peak frequency appears most densely. It is calculated by adding the upper and lower limits of the interval and then dividing by 2. For example, when the concentrated distribution interval is 150-155 Hz, the median frequency is (150+155) / 2 = 152.5 Hz. The representative frequency value is the frequency value that characterizes the typical pulsation characteristics of a certain flow range.

[0163] In this embodiment of the application, the midpoint frequency value of the concentrated distribution range is taken as the representative value of the flow range, which best reflects the main pulsation characteristics of the compressor under the flow rate.

[0164] Step 605: Map the flow range to the representative frequency value to generate a frequency offset feature library.

[0165] In this embodiment of the application, the system establishes a mapping relationship between each traffic range and its corresponding representative frequency value to form a database that can be queried, which is used to quickly obtain the reference frequency during real-time control.

[0166] Here is a specific example:

[0167] Continuing with a specific model of hydraulically driven compressor as an example, the system first divides the flow data into multiple intervals at 20 cubic meters per minute intervals, with a focus on the 70-90 cubic meters per minute interval. For 50 sets of historical operating data collected within this interval, the frequency of each main peak is counted, and a probability density distribution map is plotted, showing a significant peak in the 145-155 Hz range, with 150 Hz appearing most frequently (32 times). The 145-155 Hz range is identified as the concentrated distribution interval of the main peak frequency, and its median frequency is calculated as 145 + 155 divided by 2, equal to 150 Hz, which is taken as the representative frequency value for this flow interval. The system establishes a mapping relationship between the 70-90 cubic meters per minute flow interval and the 150 Hz representative frequency value and stores it in a feature database. Simultaneously, the relationship between the main peak frequency f and the flow rate Q is obtained through least squares fitting: f = 1.2Q + 54, where f represents the main peak frequency in Hertz (Hz) and Q represents the flow rate in cubic meters per minute (m³ / min). When the current flow rate is detected to be 85 cubic meters per minute, the system calculates the base frequency as 1.2 × 85 + 54 = 156 Hz according to the formula. Simultaneously, it queries the feature database to confirm that the flow rate falls within the 70-90 cubic meter range, and the corresponding representative frequency of 150 Hz verifies the calculated result, providing a reliable basis for subsequent control. The 20 cubic meter per minute interval is determined based on 30% of the compressor's normal operating flow rate range, ensuring that each interval contains sufficient data. The 145-155 Hz range is determined based on the statistical results showing that data points within this range account for more than 80% of the total.

[0168] In the embodiments of this application, the method establishes an accurate flow-frequency correspondence through scientific statistical analysis, providing a reliable reference frequency for airflow pulsation suppression and improving the accuracy and response speed of the control system.

[0169] To more precisely adjust the position of the movable baffle, in some embodiments, step 105: adjusting the position of the movable baffle based on the compensated target position adjustment signal, includes:

[0170] Step 701: Input the compensated target position adjustment signal to the drive circuit of the hydraulic compressor.

[0171] In step 701, the drive circuit refers to the electronic circuit that converts the control signal into a motor drive signal, and is responsible for converting the voltage-form adjustment signal into a pulse sequence and direction signal that the stepper motor can recognize.

[0172] In this embodiment of the application, the system transmits the adjustment signal after deviation compensation calculation to the signal input terminal of the drive circuit. The signal contains information on the distance and direction that the baffle needs to move.

[0173] Step 702: The compensated target position adjustment signal is converted into a rotation angle command for the hydraulic compressor through the drive circuit.

[0174] In step 702, the rotation angle command is a motor control command generated by the drive circuit based on the voltage signal.

[0175] In this embodiment, the driving circuit calculates the required rotation angle of the stepper motor based on the magnitude of the input voltage signal and a preset conversion ratio, and determines the rotation direction based on the signal polarity.

[0176] Step 703: Adjust the rotation angle and rotation direction of the output shaft of the liquid-driven compressor according to the rotation angle command.

[0177] In step 703, the output shaft rotation adjustment is the specific action of the motor executing the angle command. The rotation angle and rotation direction of the output shaft of the liquid-driven compressor refer to the specific angle value and rotation direction required by the stepper motor according to the drive signal. The angle value is determined by the magnitude of the input voltage, and the direction is determined by the polarity of the control quantity. Positive polarity corresponds to the clockwise direction, and negative polarity corresponds to the counterclockwise direction.

[0178] In this embodiment, the stepper motor receives pulse signals sent by the drive circuit and rotates precisely according to a specified angle and direction, driving the output shaft to rotate synchronously.

[0179] Step 704: Through the direct drive between the output shaft of the liquid-driven compressor and the lead screw mechanism, the adjusted rotation angle is converted into the axial displacement of the lead screw nut.

[0180] In step 704, the direct drive between the output shaft and the lead screw mechanism means that the output shaft of the stepper motor directly drives the lead screw to rotate through mechanical connecting parts such as couplings, without passing through transmission devices such as reduction gears, ensuring that the rotational motion can be transmitted 1:1. Axial displacement refers to the linear distance that the lead screw nut moves along the lead screw axis under the action of lead screw rotation. This distance is proportional to the lead screw rotation angle, and the specific conversion relationship is determined by the lead screw lead. Axial displacement conversion is the process of converting rotational motion into linear motion.

[0181] In this embodiment, the motor output shaft is connected to a lead screw via a coupling. The rotation of the lead screw drives the nut to move axially, converting the rotation angle of the motor into the linear displacement of the nut.

[0182] Step 705: Based on the axial displacement, adjust the gap size between the movable baffle and the wall of the resonant cavity.

[0183] In step 705, the gap size adjustment is an operation that changes the size of the airflow channel by displacing the baffle.

[0184] In this embodiment, the baffle connected to the lead screw nut moves with the nut, changing the distance between it and the wall of the resonant cavity, thereby adjusting the effective cross-sectional area through which the airflow passes.

[0185] Here is a specific example:

[0186] After generating a compensated 9.5V target position adjustment signal, the system inputs this signal to the dedicated stepper motor drive circuit. The drive circuit converts the 9.5V signal into 1425 drive pulses according to a preset conversion relationship of 150 pulses per volt. Simultaneously, it determines the motor rotation direction as positive based on the control polarity. These pulse signals drive the stepper motor to rotate. The motor step angle is 1.8 degrees, requiring 200 pulses per revolution; therefore, 1425 pulses make the motor rotate 7.125 times. The motor output shaft is directly connected to a 5mm lead screw via a coupling, converting the rotational motion into linear displacement of the screw nut. Each screw revolution corresponds to a 5mm movement of the nut; therefore, 7.125 revolutions correspond to a 35.625mm displacement. This displacement drives a movable baffle through a connecting mechanism, increasing the gap between the baffle and the resonant cavity wall from the initial 3mm to 8.625mm. The conversion relationship of 150 pulses per volt is set according to the motor parameters and system response requirements, and the 5 mm lead is a mechanical design parameter to ensure that the displacement accuracy meets the control requirements. The entire process realizes the precise conversion from electrical signal to mechanical displacement, enabling accurate adjustment of the airflow channel area.

[0187] In this embodiment of the application, the method achieves precise adjustment of the airflow channel size by accurately converting electrical signals into mechanical displacement, ensuring that the airflow pulsation of the compressor is effectively suppressed, while guaranteeing the stability and reliability of the adjustment process.

[0188] Figure 2 This is a schematic diagram of the structure of an airflow pulsation suppression system for a liquid-driven compressor provided in an embodiment of this application, as shown below. Figure 2 As shown, the system includes:

[0189] The acquisition module 21 is used to acquire the pressure pulsation spectrum and flow data of the outlet pipeline of the liquid-driven compressor.

[0190] The first generation module 22 is used to generate a main frequency offset pattern based on the pressure pulsation spectrum and the flow data.

[0191] The second generation module 23 is used to generate a target position adjustment signal based on the main frequency offset law and the main peak frequency of the pressure pulsation spectrum, combined with a fuzzy PID controller.

[0192] The monitoring module 24 is used to monitor the actual position of the movable baffle set in the resonant cavity of the liquid-driven compressor in real time, and to provide feedback compensation for the target position adjustment signal based on the actual position.

[0193] The adjustment module 25 is used to adjust the position of the movable baffle based on the compensated target position adjustment signal, so as to change the flow area of ​​the resonant cavity and thereby suppress the airflow pulsation of the outlet pipe.

[0194] Figure 2 The aforementioned airflow pulsation suppression system for a liquid-driven compressor can perform... Figure 1 The implementation principle and technical effects of the airflow pulsation suppression method for a liquid-driven compressor described in the illustrated embodiment will not be repeated here. The specific operation methods of each module and unit in the airflow pulsation suppression system for a liquid-driven compressor in the above embodiments have been described in detail in the embodiments related to this method, and will not be elaborated upon here.

[0195] In one possible design, Figure 2 The airflow pulsation suppression system of a liquid-driven compressor in the illustrated embodiment can be implemented as a computing device, such as... Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0196] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are invoked and executed by the processing component 32.

[0197] The processing component 32 is used to perform the above. Figure 1 The embodiment describes a method for suppressing airflow pulsation in a liquid-driven compressor.

[0198] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above-described method. Alternatively, the processing component may be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described method.

[0199] Storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented from any type of volatile or non-volatile storage device or a combination thereof, such as Static Random-Access Memory (SRAM), Electrically Erasable Programmable Read Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Read Only Memory (PROM), Read Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0200] Of course, computing devices may also include other components, such as input / output interfaces, display components, communication components, etc.

[0201] Input / output interfaces provide interfaces between processing components and peripheral interface modules, which can be output devices, input devices, etc.

[0202] The communication components are configured to facilitate wired or wireless communication between computing devices and other devices.

[0203] The computing device can be a physical device or an elastic computing host provided by a cloud computing platform. In this case, the computing device can refer to a cloud server, and the aforementioned processing components, storage components, etc., can be basic server resources rented or purchased from the cloud computing platform.

[0204] This application also provides a computer storage medium storing a computer program, which, when executed by a computer, can perform the above-described functions. Figure 1 The embodiment shown illustrates a method for suppressing airflow pulsation in a liquid-driven compressor.

[0205] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0206] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0207] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0208] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for suppressing airflow pulsation in a liquid-driven compressor, characterized in that, include: Acquire the pressure pulsation spectrum and flow data of the outlet pipeline of the liquid-driven compressor; Based on the pressure pulsation spectrum and the flow data, a dominant frequency offset pattern is generated; Based on the main frequency offset law and the main peak frequency of the pressure pulsation spectrum, a target position adjustment signal is generated by combining a fuzzy PID controller. The actual position of the movable baffle set in the resonant cavity of the hydraulic compressor is monitored in real time, and the target position adjustment signal is fed back and compensated based on the actual position. Based on the compensated target position adjustment signal, the position of the movable baffle is adjusted to change the flow area of ​​the resonant cavity, thereby suppressing the airflow pulsation in the outlet pipe. The generation of the target position adjustment signal based on the dominant frequency offset law and the main peak frequency of the pressure pulsation spectrum, combined with a fuzzy PID controller, includes: Based on the traffic data, the corresponding reference peak frequency is queried from the main frequency offset pattern; The target resonance frequency is obtained by superimposing the reference main peak frequency and the preset offset. The frequency difference is obtained by subtracting the main peak frequency of the pressure pulsation spectrum from the target resonant frequency; The absolute value of the frequency difference is input into the input interface of the fuzzy PID controller, and the target position adjustment signal is output.

2. The method according to claim 1, characterized in that, The step of inputting the absolute value of the frequency difference into the input interface of the fuzzy PID controller and outputting the target position adjustment signal includes: Dynamic dead-zone processing is applied to the absolute value to obtain the effective difference; Based on the effective difference, the corresponding fuzzy interval is matched from the control rule base of the fuzzy PID controller; Based on the current operating conditions of the hydraulic compressor, select the optimal parameter combination that matches the fuzzy range and operating conditions from a set of preset parameters; Based on the optimal parameter combination, the effective difference is converted into a signal to generate an initial position adjustment signal; The initial position adjustment signal is subjected to amplitude limiting processing to generate the target position adjustment signal.

3. The method according to claim 2, characterized in that, The step of performing signal conversion on the effective difference based on the optimal parameter combination to generate an initial position adjustment signal includes: The effective difference is weighted with the proportional coefficient, integral coefficient, and differential coefficient in the optimal parameter combination to generate the proportional term, integral term, and differential term. The proportional term, the integral term, and the derivative term are superimposed and calculated to output the initial control quantity; Based on the pulse equivalent resolution of the hydraulic compressor, the initial control quantity is quantized into the target number of pulses; Based on the target number of pulses and the polarity of the control quantity, a pulse control sequence is generated, and the pulse control sequence is used as the initial position adjustment signal.

4. The method according to claim 1, characterized in that, The process of generating a dominant frequency offset pattern based on the pressure pulsation spectrum and the flow data includes: The pressure pulsation spectrum is divided into multiple characteristic frequency bands, and the main peak frequency under each operating condition is extracted from the multiple characteristic frequency bands. Based on the flow data and the peak frequency under the corresponding operating conditions, the distribution pattern of the peak frequency in different flow intervals is analyzed to generate a frequency offset feature library. The frequency offset feature library is used to output the main frequency offset pattern.

5. The method according to claim 4, characterized in that, The step of analyzing the distribution pattern of the peak frequency in different flow intervals based on the flow data and the peak frequency under the corresponding operating conditions to generate a frequency offset feature library includes: The traffic data is discretized into multiple traffic intervals according to a preset division rule; For each flow interval, the distribution density of all main peak frequencies within the flow interval is calculated to generate a probability density distribution map; Identify the frequency range corresponding to the probability density peak from the probability density distribution map, and use the frequency range as the concentrated distribution interval of the main peak frequency; The median frequency of the concentrated distribution interval is extracted as the representative frequency value of the corresponding flow interval; The flow range is mapped to the representative frequency value to generate a frequency offset feature library.

6. The method according to claim 1, characterized in that, The adjustment of the position of the movable baffle based on the compensated target position adjustment signal includes: The compensated target position adjustment signal is input to the drive circuit of the hydraulic compressor. The drive circuit converts the compensated target position adjustment signal into a rotation angle command for the hydraulic compressor. Adjust the rotation angle and direction of the output shaft of the hydraulic compressor according to the rotation angle command; The adjusted rotation angle is converted into the axial displacement of the lead screw nut through the direct drive between the output shaft of the liquid-driven compressor and the lead screw mechanism. Based on the axial displacement, the gap size between the movable baffle and the wall of the resonant cavity is adjusted.

7. A system for suppressing airflow pulsation in a liquid-driven compressor, characterized in that, A method for suppressing airflow pulsation in a liquid-driven compressor as described in any one of claims 1 to 6 includes: The acquisition module is used to acquire the pressure pulsation spectrum and flow data of the outlet pipeline of the liquid-driven compressor; The first generation module is used to generate a dominant frequency offset pattern based on the pressure pulsation spectrum and the flow data; The second generation module is used to generate a target position adjustment signal based on the main frequency offset law and the main peak frequency of the pressure pulsation spectrum, combined with a fuzzy PID controller. The monitoring module is used to monitor the actual position of the movable baffle set in the resonant cavity of the liquid-driven compressor in real time, and to provide feedback compensation for the target position adjustment signal based on the actual position. The adjustment module is used to adjust the position of the movable baffle based on the compensated target position adjustment signal, so as to change the flow area of ​​the resonant cavity and thereby suppress the airflow pulsation of the outlet pipe.

8. A computing device, characterized in that, It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement the airflow pulsation suppression method for a liquid-driven compressor as described in any one of claims 1 to 6.

9. A computer storage medium, characterized in that, The device contains a computer program that, when executed by a computer, implements a method for suppressing airflow pulsation in a liquid-driven compressor as described in any one of claims 1 to 6.

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