A pump stroke cycle detection method and detection system

Abstract

The application relates to the field of measurement while drilling, and discloses a pump stroke cycle detection method and a detection system, in particular to a method for detecting a pump stroke cycle by using a pressure sensor. The application does not depend on a pump stroke sensor, but detects a mud pulse signal by using the pressure sensor, accurately detects a pump stroke cycle by using a peak value detection and a spectrum analysis method, corrects an outlier of the detected pump stroke cycle by periodically detecting a signal peak value and combining a mud pump basic frequency obtained through spectrum analysis, and further obtains an accurate pump stroke cycle position. The technical scheme can reduce the cost and improve the stability and accuracy of the system in a harsh drilling environment.

Description

Technical Field

[0001] This application relates to the field of measurement while drilling technology, and to a method and system for detecting pump stroke cycle, particularly a method for detecting pump stroke cycle using a pressure sensor. Background Technology

[0002] In oil exploration and production, drilling is a high-risk activity that requires real-time measurement to monitor drilling conditions. The rapid development of two-way communication technology between the surface and downhole has accelerated the automation of drilling operations. Currently, in the oil exploration and production field, drilling mud is often used as a channel to transmit downhole condition information or surface command information via wireless telemetry. Compared to positive or negative pulse transmission methods using drilling mud, continuous wave signal transmission using drilling fluid has become the most common mud pulse telemetry communication method in the current measurement-while-drilling / logging-while-drilling field due to its high transmission rate and robustness.

[0003] In existing measurement-while-drilling (MWD) technologies, the detection of pump surge signals often relies on pump surge sensors, which not only increases equipment costs but also enhances system complexity. Furthermore, the accuracy and stability of pump surge sensors are often affected by harsh drilling environments. Summary of the Invention

[0004] To address the aforementioned problems in the existing technology, the present invention provides a pump cycle detection method for mud pulse telemetry systems that does not rely on pump pulse sensors, particularly for detecting the pump cycle using mud pulse signals acquired by pressure sensors.

[0005] To achieve the above objectives, the technical solutions adopted in the embodiments of this application are as follows:

[0006] In a first aspect, a pump stroke cycle detection method is provided, the method comprising: performing spectrum analysis and peak detection on the acquired pulse signal respectively to determine the peak information and frequency characteristics of the pulse signal; the pulse signal includes a pressure reading and a timestamp; determining an initial pump stroke cycle based on the peak information and the frequency characteristics, and correcting values ​​that do not meet the distribution requirements based on the distribution characteristics of the initial pump stroke cycle to obtain a target pump stroke cycle.

[0007] In some specific embodiments, the method further includes noise reduction processing of the pulse signal to remove AC interference from the pulse signal.

[0008] In some specific embodiments, the step of performing spectrum analysis on the acquired pulse signals includes: performing a discrete Fourier transform on each pulse signal, statistically analyzing the amplitude distribution of the pulse signals, and obtaining the frequency component with the maximum amplitude as the fundamental frequency for the operation of the mud pump.

[0009] In some specific embodiments, the peak detection of the acquired pulse signal includes: performing bandpass filtering on the noise-reduced pulse signal according to the fundamental frequency, extracting the signal component corresponding to the fundamental frequency, and performing peak detection based on the extracted signal component.

[0010] In some specific embodiments, the peak detection based on the extracted signal components includes: comparing consecutive pressure readings in the signal to identify local maxima, and using the local maxima as the peak point of the pulse signal.

[0011] In some specific embodiments, the distribution characteristics of the initial pump stroke cycle include the average pump stroke cycle and outliers relative to the average pump stroke cycle.

[0012] In some specific implementations, the distribution characteristics based on the initial pump cycle will correct values ​​that do not meet the distribution requirements, including correcting outliers.

[0013] In some specific implementations, the correction of outliers includes deleting, replacing, or interpolating the outliers.

[0014] Secondly, a pump flushing cycle detection system is provided, applicable to a mud pulse telemetry system, comprising: at least one sensor, the sensor being disposed at a location along the mud flow path or at a location within a circumferential range along the mud flow path, for acquiring pump flushing signals during mud flow; and a processor, which receives the pulse signals via a communication link and executes the pump flushing cycle detection method described above for detecting the pump flushing cycle.

[0015] In some specific embodiments, the processor includes: a pulse signal processing module, used to perform peak detection and spectrum analysis on the acquired pulse signal to determine the peak information and frequency characteristics of the pulse signal; and a pump-pump cycle calculation module, used to determine an initial pump-pump cycle based on the peak information and the frequency characteristics, and to correct values ​​that do not meet the distribution requirements based on the distribution characteristics of the initial pump-pump cycle to obtain a target pump-pump cycle.

[0016] The technical solution provided in this application does not rely on a pump pulse sensor, but only uses a pressure sensor to detect mud pulse signals. The pump pulse cycle is accurately detected through peak detection and spectrum analysis. Furthermore, by periodically detecting signal peaks and combining them with the fundamental frequency of the mud pump pulse obtained from spectrum analysis, outlier corrections are applied to the detected pump pulse cycle, thereby obtaining the accurate pump pulse cycle position. This technical solution reduces costs and improves the stability and accuracy of the system in harsh drilling environments. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] The methods, systems, and / or procedures shown in the accompanying drawings will be further described with reference to exemplary embodiments. These exemplary embodiments will be described in detail with reference to the drawings. These exemplary embodiments are non-limiting exemplary embodiments, wherein example figures represent similar mechanisms in the various views of the drawings.

[0019] Figure 1 This is a schematic diagram of the system structure provided in the embodiments of this application;

[0020] Figure 2 This is a schematic flowchart of the pump cycle detection method provided in the embodiments of this application;

[0021] Figure 3 This is a waveform diagram of the signal after noise reduction in the embodiments of this application.

[0022] Figure 4 This is a schematic diagram of the spectrum analysis results in an embodiment of this application.

[0023] Figure 5 The images shown are of the bandpass filtering results and peak detection results in the embodiments of this application.

[0024] Figure 6 This is a schematic diagram of a virtual device structure provided in an embodiment of this application.

[0025] Figure 7 This is a schematic diagram of the terminal device structure provided in the embodiments of this application. Detailed Implementation

[0026] To better understand the above technical solutions, the technical solutions of this application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of this application and the specific features in the embodiments are detailed descriptions of the technical solutions of this application, rather than limitations on the technical solutions of this application. In the absence of conflict, the embodiments of this application and the technical features in the embodiments can be combined with each other.

[0027] In the detailed description below, numerous specific details are illustrated with examples to provide a comprehensive understanding of the relevant guidance. However, it will be apparent to those skilled in the art that this application can be practiced without these details. In other instances, well-known methods, procedures, systems, components, and / or circuits have been described at a relatively high level without detail to avoid unnecessarily obscuring aspects of this application.

[0028] This application uses flowcharts to illustrate the execution process performed by a system according to embodiments of this application. It should be clearly understood that the execution processes in the flowcharts may not be executed sequentially. Instead, these execution processes may be executed in reverse order or simultaneously. Additionally, at least one other execution process may be added to the flowchart. One or more execution processes may be deleted from the flowchart.

[0029] Before providing a further detailed description of the embodiments of the present invention, the nouns and terms involved in the embodiments of the present invention will be explained, and the nouns and terms involved in the embodiments of the present invention shall be interpreted as follows.

[0030] (1) In response to, used to indicate the conditions or states on which the operation is performed depends. When the conditions or states on which the operation is performed are met, one or more operations may be performed in real time or with a set delay. Unless otherwise specified, there is no restriction on the order in which the multiple operations are performed.

[0031] (2) Based on, used to indicate the conditions or states on which the operation is performed depends. When the conditions or states on which it depends are met, one or more operations can be performed in real time or with a set delay. Unless otherwise specified, there is no restriction on the order of execution of the multiple operations.

[0032] High-speed mud continuous wave transmission systems have achieved good applicability in various aspects after years of development. However, due to the complexity of drilling conditions, the extraction and processing of surface mud continuous wave signals is extremely difficult, which is very detrimental to the development of drilling fluid pulse signal transmission technology. Drilling fluid continuous wave signals are affected by mud pump noise, reflection noise, and other random noise during transmission. Furthermore, the continuous wave signal attenuates continuously during transmission, causing the useful signal to be almost completely submerged in noise, resulting in an extremely low signal-to-noise ratio for the signals received by surface sensors. As a major component of noise, mud pump noise has a large amplitude, numerous harmonics, and a wide bandwidth, often overlapping with the useful continuous wave signal. Failure to accurately remove mud pump noise can lead to significant errors in the extraction and identification of the useful signal. Therefore, pump noise removal has always been a challenging aspect of noise reduction in measurement-while-drilling systems.

[0033] Research on mud pump noise removal algorithms is diverse and varied, each with its own characteristics. Smith et al. invented a device that includes a pressure sensor and an adaptive estimator, which can remove or filter pump noise and other noise interference in real time using the adaptive estimator. However, this device is designed for drilling fluid positive pulse transmission technology and has not been verified for continuous wave transmission technology. Kosmala et al. first operated the mud pump without MWD data signals and processed the received pressure signals in the Fourier domain to distribute them to various parts and complete the calibration. Then, under normal operating conditions, they tracked the piston position of each mud pump and subtracted the sum of the mud pressure signals generated by the mud pump based on its piston position from the total received signal to recover the MWD signal. Jarrot et al. used a Bayesian filtering algorithm to remove pump noise in the continuous wave frequency band for QPSK modulated continuous wave signals, thereby achieving signal denoising and filtering.

[0034] Regarding the above technical background, please refer to... Figure 1 This application provides a system 10, applicable to a mud pulse telemetry system, including at least one sensor 11 and a processor 12 communicating with the sensor. The sensor is positioned at a location along the mud flow path or within a circumferential range of the mud flow path to acquire physical information about the mud flow process, i.e., pump pulse signals. This signal is transmitted to the processor via a communication link, where the pulse signal is processed using a processing method configured in the processor to detect the pump pulse cycle.

[0035] Unlike the pump-flush sensors commonly used in existing technologies, this embodiment employs a pressure sensor. Correspondingly, the physical information collected by the pressure sensor, i.e., the pump-flush signal, consists of the pressure reading and corresponding timestamp during the mud flow process. The sensor transmits this pulse signal to the processor via a communication link. The processor processes the received pressure change signal to detect the pump-flush cycle.

[0036] For details regarding the processing methods configured in the processor, please refer to [link / reference needed]. Figure 2 This includes the following steps:

[0037] Step S21. Perform peak detection and spectrum analysis on the pulse signal respectively to determine the peak information and frequency characteristics of the pulse signal.

[0038] For the acquired pulse signal, an appropriate analysis window is selected based on the characteristics of the pulse signal to perform spectral analysis. The selection of the analysis window can be configured in specific practical scenarios, and will not be elaborated upon in this embodiment.

[0039] Specifically, the spectral analysis of pulse signals first involves frequency domain transformation of each pulse signal, converting it into a frequency domain signal. This frequency domain transformation is implemented using Discrete Fourier Transform (DFT). A spectrum diagram is generated from the transformed signal. Based on the amplitude distribution of the spectrum diagram, the frequency component with the maximum amplitude is obtained, which represents the fundamental frequency and harmonic frequencies of the mud pump. These frequencies correspond to the working cycle of the mud pump. The results of the spectral analysis can be found in [reference needed]. Figure 4 .

[0040] The peak detection process first involves bandpass filtering the pulse signal based on the fundamental frequency to extract the signal components corresponding to the fundamental frequency. Then, continuous pressure readings are compared to identify local maxima, which are taken as the peak point of the pulse signal. In this embodiment, to extract mud pump noise from the signal, bandpass filtering is performed on the signal using the fundamental frequency. This frequency selectivity improves signal quality, enhances specific frequency components, reduces interference and noise, and improves the accuracy and efficiency of signal processing. The bandpass filtering result is a filtered pulse signal waveform, which shows the corresponding signal peak. Further details on this result can be found in [reference needed]. Figure 5 The result of bandpass filtering is the waveform structure shown in the figure, while peak detection corresponds to the linear structure.

[0041] It is worth noting that in real-world scenarios, pulse signals can be inaccurate during acquisition due to AC interference. To further improve the accuracy and efficiency of peak detection, AC interference signals can be removed before peak detection. (See also...) Figure 3 The diagram shows the waveform of the pulse signal after denoising. Specific processing methods can be implemented using existing filtering methods such as Kalman filtering, which will not be elaborated upon in this embodiment.

[0042] Step S22. Determine the initial pump stroke cycle based on the peak information and the frequency characteristics, and correct the values ​​that do not meet the distribution requirements based on the distribution characteristics of the initial pump stroke cycle to obtain the target pump stroke cycle.

[0043] In this embodiment, the pump stroke cycle can be initially calculated based on the peak value and fundamental frequency obtained in step S21 to obtain the initial pump stroke cycle. Statistical analysis is then performed on the initially calculated initial pump stroke cycle to determine the corresponding average pump stroke cycle. Outliers that deviate significantly from the average pump stroke cycle are then identified based on the average pump stroke cycle.

[0044] Outliers are corrected to obtain the final target pump cycle. In this embodiment, the correction method can be any of the following: deletion, replacement, or interpolation.

[0045] The pump cycle detection method provided in this application does not rely on a pump pulse sensor. Instead, it detects the mud pulse signal using only a pressure sensor and accurately detects the pump cycle through peak detection and spectrum analysis. This method periodically detects the signal peak value and, combined with the fundamental frequency of the mud pump obtained from spectrum analysis, corrects for outliers in the detected pump cycle, thereby obtaining the accurate pump cycle position.

[0046] See Figure 6 In this embodiment, a virtual device 60 is also provided. This device is disposed within the processor of the pump cycle detection system and is used to execute the processing steps S21-S22. The device includes:

[0047] The pulse signal processing module 61 is used to perform peak detection and spectrum analysis on the acquired pulse signal to determine the peak information and frequency characteristics of the pulse signal.

[0048] The pump stroke cycle calculation module 62 is used to determine the initial pump stroke cycle based on the peak information and the frequency characteristics, and to correct the values ​​that do not meet the distribution requirements based on the distribution characteristics of the initial pump stroke cycle to obtain the target pump stroke cycle.

[0049] See Figure 7 The above methods can also be integrated into the provided terminal device 70. Since the device may vary significantly due to differences in configuration or performance, it may include one or more processors 701 and memories 702. The memories 702 may store one or more application programs or data. The memories 702 can be temporary or persistent storage. The application programs stored in the memories 702 may include one or more modules (not shown in the figures), each module may include a series of computer-executable instructions from the terminal device. Furthermore, the processor 701 may be configured to communicate with the memories 702, and the terminal device may execute the series of computer-executable instructions stored in the memories 702. The terminal device may also include one or more power supplies 703, one or more wired or wireless network interfaces 704, one or more input / output interfaces 705, one or more keyboards 706, etc.

[0050] In one specific embodiment, the terminal device includes a memory and one or more programs, wherein the one or more programs are stored in the memory, and the one or more programs may include one or more modules, and each module may include a series of computer-executable instructions for use in the terminal device, and is configured to be executed by one or more processors. The one or more programs include computer-executable instructions for performing the following:

[0051] The acquired pulse signals are subjected to spectrum analysis and peak detection to determine the peak information and frequency characteristics of the pulse signals; the pulse signals include pressure readings and timestamps.

[0052] The initial pump stroke cycle is determined based on the peak information and the frequency characteristics, and the values ​​that do not meet the distribution requirements are corrected based on the distribution characteristics of the initial pump stroke cycle to obtain the target pump stroke cycle.

[0053] The following is a detailed introduction to each component of the processor:

[0054] In this embodiment, the processor is an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application, such as one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs).

[0055] Alternatively, the processor can perform various functions, such as the aforementioned functions, by running or executing software programs stored in memory and by accessing data stored in memory. Figure 2 The method shown.

[0056] In a specific implementation, as one example, the processor may include one or more microprocessors.

[0057] The memory is used to store the software program that executes the solution of this application, and the execution is controlled by the processor. The specific implementation method can be referred to the above method embodiment, which will not be repeated here.

[0058] Optionally, the memory can be read-only memory (ROM) or other types of static storage devices capable of storing static information and instructions, random access memory (RAM) or other types of dynamic storage devices capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory can be integrated with the processor or exist independently and coupled to the processing unit through the processor's interface circuitry; this application embodiment does not specifically limit this.

[0059] It should be noted that the processor structure shown in this embodiment does not constitute a limitation on the device. The actual device may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0060] Furthermore, the technical effects of the processor can be referred to the technical effects of the methods described in the above-described method embodiments, and will not be repeated here.

[0061] It should be understood that the processor in the embodiments of this application may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0062] It should also be understood that the memory in the embodiments of this application can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0063] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0064] In this application, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or multiple items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0065] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0066] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0067] 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.

[0068] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0069] 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 units can be selected to achieve the purpose of this embodiment according to actual needs.

[0070] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0071] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0072] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for detecting pump stroke cycle, characterized in that, The method includes: The acquired pulse signals are subjected to spectrum analysis and peak detection to determine the peak information and frequency characteristics of the pulse signals; the pulse signals include pressure readings and timestamps. The initial pump stroke cycle is determined based on the peak information and the frequency characteristics, and the values ​​that do not meet the distribution requirements are corrected based on the distribution characteristics of the initial pump stroke cycle to obtain the target pump stroke cycle.

2. The pump stroke cycle detection method according to claim 1, characterized in that, The method further includes noise reduction processing of the pulse signal to remove AC interference from the pulse signal.

3. The pump stroke cycle detection method according to claim 2, characterized in that, The step of performing spectrum analysis on the acquired pulse signals includes: converting each pulse signal into a frequency domain signal, statistically analyzing its amplitude distribution, and obtaining the frequency component with the maximum amplitude as the fundamental frequency for the operation of the mud pump.

4. The pump stroke cycle detection method according to claim 3, characterized in that, The peak detection of the acquired pulse signal includes: performing bandpass filtering on the noise-reduced pulse signal according to the fundamental frequency, extracting the signal component corresponding to the fundamental frequency, and performing peak detection based on the extracted signal component.

5. The pump stroke cycle detection method according to claim 4, characterized in that, The peak detection based on the extracted signal components includes: comparing consecutive pressure readings in the signal to identify local maxima, and using the local maxima as the peak point of the pulse signal.

6. The pump stroke cycle detection method according to claim 5, characterized in that, The distribution characteristics of the initial pump stroke cycle include the average pump stroke cycle and outliers relative to the average pump stroke cycle.

7. The pump stroke cycle detection method according to claim 6, characterized in that, The correction of values ​​that do not meet the distribution requirements based on the distribution characteristics of the initial pump cycle includes: correcting the outliers.

8. The pump stroke cycle detection method according to claim 7, characterized in that, The correction of outliers includes deleting, replacing, or interpolating the outliers.

9. A pump pulse cycle detection system, applicable to mud pulse telemetry systems, characterized in that, include: At least one sensor is provided, which is located at a position along the mud flow path or at a position within a circumferential range along the mud flow path, for acquiring pumping signals during mud flow. The processor receives the pulse signal via a communication link and executes the pump stroke cycle detection method according to any one of claims 1-8 for detecting the pump stroke cycle.

10. The pump stroke cycle detection system according to claim 9, characterized in that, The processor includes: The pulse signal processing module is used to perform peak detection and spectrum analysis on the acquired pulse signal to determine the peak information and frequency characteristics of the pulse signal. The pump stroke cycle calculation module is used to determine the initial pump stroke cycle based on the peak information and the frequency characteristics, and to correct values ​​that do not meet the distribution requirements based on the distribution characteristics of the initial pump stroke cycle to obtain the target pump stroke cycle.

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