Method and device for determining signal characteristic frequency band, electronic equipment and medium
Through frequency analysis and difference calculation of CO2 leakage signal, the optimal characteristic frequency band of CO2 leakage signal is identified, which solves the problem of misjudgment and misjudgment of CO2 leakage signal detection in the prior art, and improves transportation safety.
Patent Information
- Application Number
- CN202411168070.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-08-23
- Publication Date
- 2025-01-07
AI Technical Summary
The prior art is difficult to accurately identify the characteristic frequency bands of CO2 leak signals and non-leak signals, resulting in misjudgment and misjudgment problems in leakage detection during CO2 sea transportation.
By obtaining the time domain monitoring signal of CO2 leakage, performing frequency analysis, dividing it into a series of frequency bands of the same length, calculating the difference between the event power spectrum value and the reference power spectrum value on each frequency band, and selecting the maximum difference corresponding to the maximum difference value The frequency band of the frequency band is used as the optimal characteristic band and is filtered according to the preset filter threshold.
It improves the accuracy of identification of CO2 leaked signals, reduces misjudgment and misjudgment, and ensures the safe operation of offshore CCUS projects.
Smart Images
Figure CN119268937A_ABST
Abstract
Description
Technical Field
[0001] The present disclosure relates to the technical field of signal processing, and in particular to a method, device, electronic device and medium for determining a characteristic frequency band of a signal. Background Art
[0002] Against the backdrop of increasingly severe global climate change, reducing carbon dioxide (CO2) emissions has become a common challenge faced by all countries. my country is actively exploring effective emission reduction technologies, among which offshore CO2 storage technology plays a key role. Although the onshore storage potential in my country's eastern and coastal areas is limited, offshore areas have huge storage potential, providing an important solution for these areas.
[0003] Marine CO2 transportation mainly adopts two modes: ship and pipeline. At present, although large-scale CO2 ship transportation is still in the experimental stage, pipeline transportation technology is relatively mature, with more than 40 years of construction and operation experience. Pipeline transportation has become the preferred CO2 transportation mode for offshore CCUS projects due to its advantages such as strong continuity, high safety, large transportation volume and low operating cost.
[0004] However, as the service life of pipelines increases, pipeline leakage accidents are difficult to completely avoid due to mechanical damage, corrosion, earthquakes, material defects, and improper operation, which poses a serious threat to the successful implementation of CCUS projects. To this end, it is particularly important to develop a method that can accurately detect CO2 leakage. By utilizing the photoacoustic characteristics of CO2 underwater leakage and arranging photoacoustic detectors along the monitored pipeline, CO2 underwater leakage can be effectively detected. Power spectrum analysis is an important tool to reveal the frequency characteristics of signals. By comparing the power spectra of leakage signals and non-leakage signals, the difference between the two can be clearly distinguished. However, how to accurately select the frequency bands that can represent the characteristics of leakage signals and non-leakage signals is still a technical problem that needs to be solved urgently. Summary of the invention
[0005] In view of the above-mentioned shortcomings of the prior art, the purpose of the present disclosure is to provide a method, device, electronic device and medium for determining a signal characteristic frequency band to solve the problems in the related art.
[0006] The first aspect of the present disclosure provides a method for determining a characteristic frequency band of a signal, comprising: obtaining a time domain monitoring signal of a CO2 leak; performing frequency analysis on the time domain monitoring signal to obtain event power spectrum values of the time domain monitoring signal on different frequency components; dividing the frequency components into a series of frequency bands with the same length, and respectively calculating the difference between the event power spectrum value on each frequency band and a preset reference power spectrum value; comparing all the calculated differences, selecting the maximum difference, and determining the frequency band corresponding to the maximum difference as the optimal characteristic frequency band of the time domain monitoring signal.
[0007] In an embodiment of the first aspect, the frequency analysis of the time domain monitoring signal to obtain the event power spectrum values of the time domain monitoring signal at different frequency components includes: setting the parameters required for the Welch method; and performing power spectrum estimation analysis on the time domain monitoring signal using the determined parameters to obtain the event power spectrum values of the time domain monitoring signal at different frequency components.
[0008] In an embodiment of the first aspect, the parameters required for the Welch method include one or more combinations of window type, window length, and overlap rate.
[0009] In an embodiment of the first aspect, the frequency component is divided into a series of frequency bands with the same length, and the difference between the event power spectrum value and the preset reference power spectrum value on each frequency band is calculated respectively, including: plotting the event power spectrum value and the reference power spectrum value of the time domain monitoring signal in the same logarithmic coordinate system; setting the initial parameters of the sliding window; moving the sliding window along a specific direction in the logarithmic coordinate system, and calculating the difference between the area enclosed by the event power spectrum value of the time domain monitoring signal in each sliding window and the area enclosed by the reference power spectrum value.
[0010] In an embodiment of the first aspect, the initial parameters of the sliding window include one or more combinations of a width of the sliding window, a starting position, an end position, and a movement mode of the sliding window.
[0011] In an embodiment of the first aspect, after determining the optimal characteristic frequency band of the time domain monitoring signal, it includes: setting a filtering threshold; judging whether the difference corresponding to the characteristic frequency band exceeds the filtering threshold; if not, resetting the initial parameters of the sliding window; and determining a new characteristic frequency band of the time domain monitoring signal based on the newly set sliding window.
[0012] In an embodiment of the first aspect, the event power spectrum values and the reference power spectrum values of the time domain monitoring signal are plotted in the same logarithmic coordinate system, including: setting the frequency starting point of the X-axis, the frequency end point of the X-axis, and the unit of the Y-axis of the logarithmic coordinate system; based on a preset clarity standard, evaluating the display effect of the event power spectrum values and the reference power spectrum values in the logarithmic coordinate system; if the display effect does not meet the preset standard, readjusting the parameters required for the Welch method.
[0013] The second aspect of the present disclosure discloses a device for determining a characteristic frequency band of a signal, comprising: an acquisition module for acquiring a time domain monitoring signal of a CO2 leak; an analysis module for performing frequency analysis on the time domain monitoring signal to obtain event power spectrum values of the time domain monitoring signal on different frequency components; a calculation module for dividing the frequency components into a series of frequency bands with the same length, and respectively calculating the difference between the event power spectrum value on each frequency band and a preset reference power spectrum value; a determination module for comparing all calculated differences, selecting the maximum difference, and determining the frequency band corresponding to the maximum difference as the optimal characteristic frequency band of the time domain monitoring signal.
[0014] The third aspect of the present disclosure discloses an electronic device, comprising: a processor and a memory; wherein the memory is used to store a computer program; the processor is used to execute the computer program stored in the memory, so that the electronic device executes the method for determining the signal characteristic frequency band as described in any one of the first aspects.
[0015] A fourth aspect of the present disclosure discloses a computer-readable storage medium having a computer program stored thereon, which, when executed by an electronic device, implements the method for determining a signal characteristic frequency band as described in any one of the first aspects.
[0016] As described above, the method, device, electronic device and medium for determining the characteristic frequency band of the signal disclosed in the present invention can obtain the power spectrum estimation data of the monitoring signal on different frequency components through preprocessing and power spectrum analysis of the time domain monitoring signal, which helps to more accurately identify the frequency characteristics of CO2 leakage. In addition, the signal power spectrum estimation data and the reference power spectrum data are plotted in logarithmic coordinates, making the frequency characteristics more intuitive and easy to analyze. And the optimal geometric index is judged according to the preset filtering threshold, and the true optimal characteristic frequency band is further screened out to avoid the occurrence of misjudgment and missed judgment. BRIEF DESCRIPTION OF THE DRAWINGS
[0017] Figure 1 A schematic diagram showing a flow chart of a method for determining a signal characteristic frequency band in an embodiment of the present disclosure.
[0018] Figure 2 A schematic diagram of a logarithmic coordinate system in an embodiment of the present disclosure is shown.
[0019] Figure 3 A module schematic diagram showing a device for determining a signal characteristic frequency band in an embodiment of the present disclosure.
[0020] Figure 4 A schematic diagram of the circuit structure of an electronic device in an embodiment of the present disclosure is shown. DETAILED DESCRIPTION
[0021] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.
[0022] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and thus the drawings only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.
[0023] The technical solutions in the embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings in the embodiments of the present invention.
[0024] like Figure 1 As shown, a flow chart of a method for determining a signal characteristic frequency band in an embodiment of the first aspect of the present disclosure is shown, including steps S11-S14.
[0025] Step S11: Acquire the time domain monitoring signal of CO2 leakage.
[0026] Specifically, obtaining the time domain monitoring signal of CO2 leakage is the key to ensure the safe operation of offshore CCUS projects (carbon dioxide capture, utilization and storage). Since pipeline transportation is the current preferred mode of CO2 transportation, the monitoring system needs to pay special attention to CO2 leakage that may occur during pipeline transportation. In the marine environment, CO2 underwater leakage can produce a photoacoustic effect, that is, when CO2 gas leaks from the pipeline into the water, it will produce sound due to the sudden change in air pressure, thereby generating a detectable photoacoustic signal. This characteristic makes it possible to use photoacoustic detectors to monitor CO2 leaks.
[0027] In order to effectively detect underwater CO2 leaks, appropriate monitoring points should be selected first. The monitoring points should be arranged along the monitored pipeline so that all potential leakage areas can be covered. Specialized photoacoustic detectors are then installed at each monitoring point. These devices can detect the photoacoustic signals generated by CO2 leaks and convert them into electrical signals. The photoacoustic detectors are then connected to a data collection system that can record the data of the photoacoustic signals and transmit them to the central processing unit in real time. Before starting monitoring, the photoacoustic detector is calibrated to ensure its accuracy, which usually involves using a known concentration of CO2 gas to test the reaction of the device. Once set up, the photoacoustic detector will continuously monitor and record the photoacoustic signals generated by the CO2 leak. These data will then be used for frequency analysis and characteristic frequency band searches to identify any abnormal leakage events.
[0028] Step S12: performing frequency analysis on the time domain monitoring signal to obtain event power spectrum values of the time domain monitoring signal at different frequency components.
[0029] In some embodiments, the step S12 includes steps S121-S122.
[0030] Step S121: Setting the parameters required for the Welch method.
[0031] Specifically, the Welch method is a non-parametric method for estimating the power spectral density of a signal, which is particularly suitable for frequency analysis of short-time signals. It is based on Fourier transform and uses a piecewise average method to reduce the influence of random noise, thereby improving the accuracy of spectrum estimation.
[0032] First, the parameter settings of the Welch method need to be determined based on the characteristics of the signal to be analyzed and the expected analysis results. These parameters include the type of window function, window length, and overlap rate.
[0033] Choice of window function: The choice of window function is crucial to reduce spectral leakage and sidelobe levels. For example, the Hanning window can reduce spectral leakage, while the Blackman window can provide a smoother spectrum. The choice of window function depends on the characteristics of the signal and the analysis requirements.
[0034] Determination of window length: According to the Nyquist criterion, the window length should be at least half of the signal period to avoid frequency aliasing. Longer windows can provide better frequency resolution, but may reduce the signal-to-noise ratio. Generally, setting the window length to several times the signal period is a reasonable choice.
[0035] Overlap rate setting: In order to improve frequency resolution and reduce spectrum leakage, there will be a certain overlap between adjacent windows. The overlap rate refers to the ratio of the overlapping part between adjacent windows to the entire window length. A higher overlap rate can improve frequency resolution, but may reduce the signal-to-noise ratio. Generally, setting the overlap rate between 50% and 80% is a compromise choice.
[0036] Step S122: Use the determined parameters to perform power spectrum estimation analysis on the time domain monitoring signal to obtain event power spectrum values of the time domain monitoring signal at different frequency components.
[0037] Specifically, first, the entire time domain monitoring signal is divided into a series of equal-length sub-segments. The length of each sub-segment is determined by the window length, which is based on the window length parameter determined previously. For example, if the selected window length is 512 sample points, then the time domain monitoring signal will be divided into sub-segments containing 512 sample points. In order to reduce spectral leakage and maintain sufficient frequency resolution, there will be a certain overlap between adjacent sub-segments. The length of this overlapping part depends on the set overlap rate. For example, if the overlap rate is set to 50%, each sub-segment overlaps with the previous sub-segment by 256 sample points.
[0038] Apply a Fast Fourier Transform (FFT) to each segmented sub-segment to convert the time domain signal into a frequency domain representation. FFT is an efficient algorithm for calculating the Discrete Fourier Transform (DFT), which converts a time series into its corresponding frequency components. Performing FFT on each sub-segment gives the complex amplitude of the sub-segment at each frequency. Adding the FFT results of all sub-segments gives the total frequency domain representation. Since each sub-segment contains a portion of the original signal, superposition can increase the statistical properties of the signal, making the frequency domain representation smoother and reducing the impact of random noise.
[0039] Calculate the squared magnitude of the total frequency domain representation to get an estimate of the power spectral density. This is because the power spectral density is the distribution of the signal energy in the frequency dimension, and the energy of the signal can be represented by the square of its magnitude. Therefore, by summing the squares of the magnitudes of all frequency components, we can get an estimate of the power spectral density of the entire signal.
[0040] Step S13: Divide the frequency component into a series of frequency bands with the same length, and calculate the difference between the event power spectrum value and the preset reference power spectrum value in each frequency band.
[0041] It should be noted that the difference is a geometric index, which reflects the difference between the event signal and the reference signal, and can be used to distinguish the CO2 leakage signal from other signals.
[0042] In some implementations, step S13 includes steps S131-S133.
[0043] Step S131: plotting the event power spectrum value and the reference power spectrum value of the time domain monitoring signal in the same logarithmic coordinate system.
[0044] In order to achieve a better display effect, step S131 further includes: setting the frequency starting point of the X-axis, the frequency ending point of the X-axis, and the unit of the Y-axis of the logarithmic coordinate system. Based on a preset clarity standard, the display effect of the event power spectrum value and the reference power spectrum value in the logarithmic coordinate system is evaluated. If the display effect does not meet the preset standard, the parameters required by the Welch method are readjusted.
[0045] Specifically, first, it is necessary to determine the parameter settings of the logarithmic coordinate system, including the frequency starting point and frequency end point of the X-axis, and the unit of the Y-axis. These parameters will affect the display effect of the graph and the accuracy of the analysis. For example, the frequency starting point may be set to 0Hz, and the frequency end point may be set to the highest frequency component of the signal. The unit of the Y-axis is usually a logarithmic scale, such as decibel (dB) or power ratio. According to step S122, the event power spectrum value of the time domain monitoring signal has been obtained. Next, these values are plotted in a logarithmic coordinate system. This usually means converting the amplitude of the power spectrum value to a logarithmic scale to facilitate the observation of the energy distribution of different frequency components. In a logarithmic coordinate system, the logarithmic value of the power spectrum value is proportional to the frequency, which can better show the difference between low-energy and high-energy areas. The benchmark power spectrum value is usually the expected power distribution based on a theoretical model or known conditions. In practical applications, these values may come from the specification sheet of the equipment or previous empirical data. Similarly, these benchmark power spectrum values are plotted in a logarithmic coordinate system for comparison with actual measurement results.
[0046] Based on the preset clarity standard, the display effect of the event power spectrum value and the reference power spectrum value in the logarithmic coordinate system is evaluated. The clarity standard may include the readability of the graphics, the clarity of the lines, and the recognition of the characteristic frequency components. If it is found that the display effect does not meet the preset standard, it is necessary to reconsider the selection of parameters such as the window function type, window length, overlap rate, etc.
[0047] According to the evaluation results, if the display effect does not meet the preset standard, it is necessary to readjust the parameters required by the Welch method. This may involve changing the type of window function, adjusting the window length, or changing the overlap rate. After adjusting the parameters, repeat steps S121 and S122 until a satisfactory display effect is obtained.
[0048] Step S132: setting initial parameters of the sliding window.
[0049] In some implementations, the initial parameters of the sliding window include one or more combinations of the width of the sliding window, the starting position, the ending position, and the movement mode of the sliding window.
[0050] Specifically, when performing difference calculation, it is necessary to determine the starting and ending points of a window (called a "sliding window") sliding on the frequency axis and the width of the window. These parameters are crucial for capturing the frequency characteristics of the signal. Among them,
[0051] The width of the sliding window determines how many frequencies are involved in the calculation. A wide window provides better stability, but may reduce the signal-to-noise ratio. Conversely, a narrow window improves the signal-to-noise ratio but reduces stability. The starting position of the sliding window defines the starting point of the window on the frequency axis. Usually, it depends on the distribution of the characteristic signal and the noise signal. The end position of the sliding window defines the end point of the window on the frequency axis. This also depends on the distribution of the characteristic signal and the noise signal to ensure that the entire characteristic signal interval is covered. The sliding window can move with a fixed step size or a variable step size, that is, the speed and distance of the sliding window movement are dynamically adjusted according to the characteristics of the signal.
[0052] Step S133: Move the sliding window along a specific direction in a logarithmic coordinate system, and calculate the difference between the area enclosed by the event power spectrum values of the time domain monitoring signal in each sliding window and the area enclosed by the reference power spectrum values, that is, the geometric index.
[0053] like Figure 2 As shown, a schematic diagram of the movement of the sliding window in a logarithmic coordinate system in an example is shown. Specifically, the sliding window is moved from left to right (or other specified directions). The geometric area enclosed by the event signal and the reference signal within each sliding window boundary is calculated. This area can be obtained by calculating the square of the amplitude of the signal in the frequency domain, and then converting it to a logarithmic scale to draw the power spectrum. The power spectrum value calculated for each sliding window is compared with the preset reference power spectrum value, and the difference between the two is calculated. This difference reflects the change of the signal relative to the reference within the frequency range, and can be used to identify abnormal frequency components or interference in the signal. Repeat the above steps until the sliding window covers the frequency range of the entire power spectrum.
[0054] In addition, in order to accurately capture the spectral characteristics of the signal and minimize spectral leakage, we can continuously adjust the position and width of the sliding window to obtain detailed power spectrum information in each frequency band. This process often requires multiple iterations to optimize the analysis results.
[0055] Furthermore, in order to eliminate the influence of the difference value caused by different sliding window lengths and make the result more intuitive and easier to compare, the calculated difference value may need to be normalized, that is, divided by the sliding window length.
[0056] Step S14: compare all calculated differences, select the maximum difference, and determine the frequency band corresponding to the maximum difference as the optimal characteristic frequency band of the time domain monitoring signal.
[0057] Specifically, the difference calculated in step S13 reflects the difference between the event power spectrum value and the reference power spectrum value, which can be used to identify the characteristics of the CO2 leakage signal. All calculated differences are arranged to find the best one. This can be determined by comparing the size, shape, frequency distribution and other characteristics of the difference. The best one is usually the value with the largest and most significant difference because it best represents the characteristics of the CO2 leakage signal. After determining the optimal difference, find the corresponding frequency band. This frequency band contains the most significant features of the CO2 leakage signal, so it can be used as a key frequency band for identifying and locating CO2 leakage.
[0058] In some embodiments, after determining the optimal characteristic frequency band of the time domain monitoring signal, it includes: setting a filtering threshold; determining whether the difference corresponding to the characteristic frequency band exceeds the filtering threshold; if not, resetting the initial parameters of the sliding window; and determining a new characteristic frequency band of the time domain monitoring signal based on the newly set sliding window.
[0059] Specifically, after determining the optimal characteristic frequency band, in order to further improve the accuracy and reliability of detection, a filtering threshold can be set. This threshold is determined according to a preset standard or experience and is used to distinguish normal signals from abnormal signals. Compare the difference corresponding to the optimal characteristic frequency band with the filtering threshold. If the difference exceeds the filtering threshold, this may indicate that there is an abnormal signal, that is, a CO2 leakage signal. If the difference does not exceed the filtering threshold, this may indicate that the signal is normal and no leakage occurs. If the difference does not exceed the filtering threshold, it may be due to improper initial parameter settings of the sliding window, resulting in failure to accurately identify the optimal characteristic frequency band. In this case, it is necessary to reset the initial parameters of the sliding window, including the width of the sliding window, the starting position, the end position, the movement mode of the sliding window, etc. Use the newly set sliding window parameters to re-perform frequency analysis to determine the new optimal characteristic frequency band. This may require repeating the previous steps, including signal preprocessing, power spectrum analysis, sliding window parameter setting, difference calculation, etc.
[0060] By this method, the accuracy and reliability of detection can be further improved. If the new optimal characteristic frequency band still fails to pass the judgment of the filtering threshold, the above steps can be repeated until an optimal characteristic frequency band that meets the conditions is found.
[0061] In order to better illustrate the above implementation, a specific example is listed below.
[0062] Suppose there is a sensor that monitors CO2 leaks. It records a piece of signal data and then analyzes this data to find out whether a CO2 leak has occurred and determine the location and characteristics of the leak.
[0063] First, a 10-second signal was selected from the data recorded by the sensor as the analysis object. The Hamming window was selected as the window function, the window length was set to 512 sample points, and the overlap rate was set to 80%. The power spectrum of this signal was calculated using the Welch method to obtain the energy distribution diagram at different frequencies. The obtained power spectrum data was plotted on a logarithmic coordinate so that both high-frequency and low-frequency components can be clearly displayed. If the graphic display is not clear enough or contains misleading information, adjust the parameters and recalculate the power spectrum.
[0064] Then, a complete analysis is performed using a 5-second signal, and a power spectrum diagram of a logarithmic coordinate system is obtained. A sliding window of 10 to 100 Hz is used, and the event signal and the reference signal in the sliding window are integrated starting from 10 Hz. The geometric area enclosed by the two is calculated as an indicator for distinguishing signal characteristics. The calculated difference is normalized. Repeat the above steps to obtain a series of normalized differences.
[0065] Compare all normalized geometric indicators to find the indicator that best represents the signal characteristics of these 5 seconds. According to the set threshold (for example, a specific geometric area value), determine which indicators are valid. If the threshold condition is met, the optimal indicator and its corresponding frequency band are output as the optimal search result of the leakage signal characteristic frequency band. If the condition is not met, reset the sliding window parameters and continue to search for the optimal characteristic frequency band.
[0066] It should be particularly noted that the process or method represented by the flowchart of the above embodiment of the present disclosure can be understood as representing a module, fragment or part of a code including one or more sets of executable instructions configured to implement the steps of a specific logical function or process. And the scope of the preferred embodiment of the present disclosure includes other implementations, in which the functions may not be performed in the order shown or discussed, including performing the functions in a substantially simultaneous manner or in a reverse order according to the functions involved.
[0067] like Figure 3 As shown, the signal characteristic frequency band determination device 30 of the present disclosure is shown. It should be noted that the principle and technical implementation of the signal characteristic frequency band determination device can refer to the signal characteristic frequency band determination method embodiment in the previous embodiment (for example Figure 1 ), so it will not be repeated in this embodiment.
[0068] Specifically, the multilingual text recognition device 30 includes: an acquisition module 31, an analysis module 32, a calculation module 33, and a determination module 34, wherein:
[0069] The acquisition module 31 is used to acquire the time domain monitoring signal of CO2 leakage.
[0070] The analysis module 32 is used to perform frequency analysis on the time domain monitoring signal to obtain event power spectrum values of the time domain monitoring signal at different frequency components.
[0071] The calculation module 33 is used to divide the frequency component into a series of frequency bands with the same length, and respectively calculate the difference between the event power spectrum value and the preset reference power spectrum value in each frequency band.
[0072] The determination module 34 is used to compare all the calculated differences, select the maximum difference, and determine the frequency band corresponding to the maximum difference as the optimal characteristic frequency band of the time domain monitoring signal.
[0073] It should be noted that Figure 3 Each functional module in the embodiment can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a program instruction product. The program instruction product includes one or a group of program instructions. When the program instruction is loaded and executed on a computer, the process or function according to the present disclosure is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable devices. The program instruction can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium.
[0074] and, Figure 3 The device disclosed in the embodiment can be implemented by other module division methods. The device embodiments shown above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as a group of modules or modules can be combined or dynamically added to another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or modules can be electrical or other forms.
[0075] in addition, Figure 3Each functional module and submodule in the embodiment can be dynamically in a processing component, or each module can exist physically separately, or two or more modules can be dynamically in one component. The above-mentioned dynamic components can be implemented in the form of hardware or in the form of software functional modules. If the above-mentioned dynamic components are implemented in the form of software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. The storage medium can be a read-only memory, a disk or an optical disk, etc.
[0076] like Figure 4 As shown, a schematic diagram of the structure of an electronic device in an embodiment of the present disclosure is shown.
[0077] The electronic device can execute the following steps by running the computer program instructions: Figure 1 The method in any one of the items. Exemplarily, the electronic device may be a server group / server, a desktop computer, a laptop computer, etc., or a server / server group in the cloud that communicates remotely with a local terminal, a distributed computing node system, etc.
[0078] The electronic device 40 includes a bus 41, a processor 42, and a memory 43. The processor 42 and the memory 43 can communicate with each other through the bus 41. The memory 43 may store program instructions. The processor 42 implements the method steps in the previous embodiment by running the program instructions in the memory 43, such as Figure 1 Any method.
[0079] The bus 41 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, although only one thick line is used in the figure, it does not mean that there is only one bus or one type of bus.
[0080] In some embodiments, the processor 42 may be implemented as a central processing unit (CPU), a microprocessing unit (MCU), a system on chip (System On Chip), or a field programmable logic array (FPGA). The memory 43 may include a volatile memory (Volatile Memory) for temporary storage of data when running a program, such as a random access memory (Random Access Memory, RAM).
[0081] The memory 43 may also include a non-volatile memory (non-volatile memory) for data storage, such as a read-only memory (ROM), a flash memory, a hard disk drive (HDD) or a solid-state disk (SSD).
[0082] In some embodiments, the electronic device 40 may further include a communicator 44. The communicator 44 is used to communicate with the outside. In a specific example, the communicator 44 may include one or a group of wired and / or wireless communication circuit modules. For example, the communicator 44 may include one or more of a wired network card, a USB module, a serial interface module, etc. The wireless communication protocols followed by the wireless communication module include: for example, near field communication (NFC) technology, infrared (IR) technology, global system for mobile communications (GSM), general packet radio service (GPRS), code division multiple access (CDMA), wideband code division multiple access (WCDMA), time division code division multiple access (TD-SCDMA), long term evolution (LTE), Bluetooth (BT), global navigation satellite system (GNSS), etc. One or more.
[0083] In an embodiment of the present disclosure, a computer-readable storage medium may be provided, wherein program instructions are stored, and the program instructions are executed by running, for example Figure 1 A method for determining a signal characteristic frequency band in an embodiment.
[0084] That is, the method steps in the above embodiments are implemented as software or computer code that can be stored in a recording medium (such as a CD ROM, RAM, floppy disk, hard disk or magneto-optical disk), or are implemented as computer code originally stored in a remote recording medium or a non-temporary machine-readable medium downloaded via a network and to be stored in a local recording medium, so that the method represented herein can be stored in such software processing on a recording medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware (such as an ASIC or FPGA).
[0085] In summary, the method, device, equipment and medium for determining the characteristic frequency band of the signal disclosed in the present invention can obtain the power spectrum estimation data of the monitoring signal on different frequency components through the preprocessing and power spectrum analysis of the time domain monitoring signal, which is helpful to more accurately identify the frequency characteristics of CO2 leakage. In addition, the signal power spectrum estimation data and the reference power spectrum data are plotted in logarithmic coordinates, making the frequency characteristics more intuitive and easy to analyze. And the optimal geometric index is judged according to the preset filtering threshold, and the true optimal characteristic frequency band is further screened out to avoid the occurrence of misjudgment and missed judgment.
[0086] The above embodiments are merely illustrative of the principles and effects of the present disclosure, and are not intended to limit the present disclosure. Anyone familiar with the technology may modify or change the above embodiments without violating the spirit and scope of the present disclosure. Therefore, all equivalent modifications or changes made by a person of ordinary skill in the art without departing from the spirit and technical ideas disclosed in the present disclosure shall still be covered by the claims of the present disclosure.
Claims
1. A method for determining a signal characteristic frequency band, characterized in that: include: Obtain time domain monitoring signal of CO2 leakage; Performing frequency analysis on the time domain monitoring signal to obtain event power spectrum values of the time domain monitoring signal at different frequency components; Dividing the frequency component into a series of frequency bands with the same length, and respectively calculating the difference between the event power spectrum value and the preset reference power spectrum value on each frequency band; All calculated differences are compared, a maximum difference is selected, and a frequency band corresponding to the maximum difference is determined as an optimal characteristic frequency band of the time domain monitoring signal.
2. The method for determining a signal characteristic frequency band according to claim 1, characterized in that: The performing frequency analysis on the time domain monitoring signal to obtain event power spectrum values of the time domain monitoring signal at different frequency components includes: Set the parameters required for the Welch method; The power spectrum estimation analysis is performed on the time domain monitoring signal using the determined parameters to obtain event power spectrum values of the time domain monitoring signal at different frequency components.
3. The method for determining the signal characteristic frequency band according to claim 2, characterized in that: The parameters required for the Welch method include one or more combinations of window type, window length, and overlap rate.
4. The method for determining a signal characteristic frequency band according to claim 1, characterized in that: The step of dividing the frequency components into a series of frequency bands having the same length and respectively calculating the difference between the event power spectrum value and the preset reference power spectrum value on each frequency band comprises: Plotting the event power spectrum value and the reference power spectrum value of the time domain monitoring signal in the same logarithmic coordinate system; Set the initial parameters of the sliding window; The sliding window is moved along a specific direction in a logarithmic coordinate system, and the difference between the area enclosed by the event power spectrum values of the time domain monitoring signal in each sliding window and the area enclosed by the reference power spectrum values is calculated.
5. The method for determining the signal characteristic frequency band according to claim 4, characterized in that: The initial parameters of the sliding window include one or more combinations of the width of the sliding window, the starting position, the ending position, and the moving mode of the sliding window.
6. The method for determining a signal characteristic frequency band according to claim 4, characterized in that: After determining the optimal characteristic frequency band of the time domain monitoring signal, the method includes: Set filtering thresholds; Determine whether the difference corresponding to the characteristic frequency band exceeds the filtering threshold; if not, reset the initial parameters of the sliding window; A new characteristic frequency band of the time domain monitoring signal is determined based on the newly set sliding window.
7. The method for determining a signal characteristic frequency band according to claim 4, characterized in that: The step of plotting the event power spectrum value and the reference power spectrum value of the time domain monitoring signal in the same logarithmic coordinate system includes: Set the frequency starting point of the X-axis, the frequency end point of the X-axis and the unit of the Y-axis of the logarithmic coordinate system; Based on a preset clarity standard, evaluating the display effects of the event power spectrum value and the reference power spectrum value in the logarithmic coordinate system; If the displayed effect does not meet the preset standard, the parameters required by the Welch method are readjusted.
8. A device for determining a characteristic frequency band of a signal, characterized in that: include: An acquisition module, used to acquire a time domain monitoring signal of CO2 leakage; An analysis module, used for performing frequency analysis on the time domain monitoring signal to obtain event power spectrum values of the time domain monitoring signal at different frequency components; A calculation module, used for dividing the frequency components into a series of frequency bands with the same length, and respectively calculating the difference between the event power spectrum value and the preset reference power spectrum value on each frequency band; The determination module is used to compare all the calculated differences, select the maximum difference, and determine the frequency band corresponding to the maximum difference as the optimal characteristic frequency band of the time domain monitoring signal.
9. An electronic device, characterized in that: The electronic device comprises: Processor and memory; Wherein, the memory is used to store computer programs; The processor is used to execute the computer program stored in the memory so that the electronic device executes the method for determining the signal characteristic frequency band according to any one of claims 1 to 7.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by an electronic device, the method for determining the signal characteristic frequency band according to any one of claims 1 to 7 is implemented.