Flow velocity data multi-scale period rule extraction method and device based on improved Morlet wavelet and readable storage medium thereof

Abstract

The invention provides a flow velocity data multi-scale period rule extraction method and device based on an improved Morlet wavelet and a readable storage medium thereof, and aims to solve the problems of insufficient time domain positioning, unbalanced high and low frequency resolution and noise interference in non-stationary flow velocity data in a traditional method. The method is realized through the following steps: carrying out five-point Gaussian interpolation completion and normalization preprocessing on original flow velocity data; a Morlet wavelet function with the center frequency changing adaptively along with the scale is constructed, and parameters are dynamically optimized based on a period identification contrast index by using an improved whale optimization algorithm; carrying out wavelet transformation on the preprocessed data and calculating a power spectrum; and screening and extracting a key period through an energy spectrum density local maximum value and a threshold value. According to the method, the period recognition precision is remarkably improved, the signal-to-noise ratio is improved, accurate and stable extraction of multi-scale period characteristics is achieved, and the method is suitable for non-stationary signal analysis scenes such as hydrological flow velocity state monitoring.

Description

Technical Field

[0001] The present invention relates to the technical field of signal processing and hydrological information analysis, and particularly to a method, device and readable storage medium for extracting multi-scale periodic laws of flow velocity data based on an improved Morlet wavelet. Background Art

[0002] In natural environments such as rivers and lakes, flow velocity data is affected by multiple factors such as tides and winds, showing non-stationary and multi-scale periodic fluctuation characteristics. Traditional analysis methods such as the fast Fourier transform (FFT) lack time-domain localization ability and are difficult to identify non-stationary periodic changes; in the standard continuous wavelet transform (CWT), the Morlet wavelet uses a fixed center frequency and cannot balance the resolution of high-frequency and low-frequency periods; moreover, flow velocity signals often contain high noise, short-period perturbations and long-period trends, and traditional methods are prone to losing weak periodic features or misidentifying the main periodic frequency band. There is an urgent need for an adaptive and multi-scale periodic extraction method to improve detection accuracy, stability and time-frequency resolution ability. Summary of the Invention

[0003] Embodiments of the present invention provide a method, device and readable storage medium for extracting multi-scale periodic laws of flow velocity data based on an improved Morlet wavelet, aiming at the problems existing in the current technology, such as insufficient time-domain localization, imbalance between high and low frequency resolutions, and susceptibility to noise interference resulting in loss of weak periods or misidentification of main periods in the multi-scale period analysis of non-stationary flow velocity data.

[0004] The core technology of the present invention mainly constructs an improved Morlet wavelet function with a center frequency that adaptively changes with the scale, and dynamically optimizes the wavelet parameters based on the period recognition contrast index and the improved whale optimization algorithm to achieve high-precision extraction of multi-scale periodic features of flow velocity data.

[0005] In the first aspect, the present invention provides a method for extracting multi-scale periodic laws of flow velocity data based on an improved Morlet wavelet, and the method includes the following steps: Preprocess the original flow velocity data to generate a continuous time series signal; Construct a Morlet wavelet function with a center frequency that adaptively changes with the scale, and dynamically determine the wavelet function parameters based on the period recognition contrast index through an optimization algorithm; Perform wavelet transform on the preprocessed flow velocity data and calculate the wavelet power spectrum; Identify the period peak based on the wavelet power spectrum and extract the key period factor; Wherein, the period recognition contrast index is the ratio of the average power of the target period interval to the average power of the non-target period interval.

[0006] Further, the preprocessing includes: completing the missing data by using the 5-point Gaussian interpolation method. The 5-point Gaussian interpolation constructs a regression model based on the 4-frame historical data at adjacent times and calculates the estimated value at the missing time.

[0007] Further, the expression of the Morlet wavelet function with the center frequency adaptively changing with the scale is:

[0008] where the center frequency has a linear relationship with the scale factor a: ; is the base frequency, is the adaptive coefficient.

[0009] Further, the optimization algorithm is an improved whale optimization algorithm, and the optimal adaptive coefficient is determined by maximizing the contrast index .

[0010] Further, the improved whale optimization algorithm includes: initializing the population of whale individuals, and each individual corresponds to an adaptive coefficient ; calculating the individual fitness, that is, the contrast index, and recording the optimal individual; updating the individual position through the mechanism of surrounding the prey or the mechanism of approaching in a spiral; iterating until the convergence condition is satisfied, and outputting the optimal parameters, that is, the optimal adaptive coefficient ; where the convergence factor b of the convergence condition is dynamically adjusted according to the cosine function with the number of iterations.

[0011] Further, the scale set of the wavelet transform grows exponentially, and the calculation formula is:

[0012] where is the minimum scale, is the logarithmic step size, j is the scale series number, and J is the upper limit of the scale series number.

[0013] Further, the period peak recognition step includes: calculating the energy spectral density of each scale, screening the local maximum values and combining with the threshold to exclude noise, and extracting the key periods.

[0014] In the second aspect, the present invention provides a device for extracting multi-scale periodic laws of flow velocity data based on an improved Morlet wavelet, including: a preprocessing module, configured to preprocess the original flow velocity data to generate a continuous time series signal; An adaptive Morlet wavelet construction module for constructing a Morlet wavelet function with a center frequency that adaptively changes with the scale; A parameter optimization module for dynamically determining wavelet function parameters based on the period recognition contrast index through an optimization algorithm; An improved wavelet transform calculation module for performing wavelet transform on the preprocessed flow velocity data and calculating the wavelet power spectrum; A period component extraction and feature analysis module for identifying period peaks based on the wavelet power spectrum and extracting key period factors.

[0015] In a third aspect, the present invention provides an electronic device, including a memory and a processor. A computer program is stored in the memory, and the processor is configured to run the computer program to execute the above-mentioned method for extracting multi-scale periodic laws of flow velocity data based on the improved Morlet wavelet.

[0016] In a fourth aspect, the present invention provides a readable storage medium. A computer program is stored in the readable storage medium, and the computer program includes program codes for controlling a process to execute the process. The process includes the method for extracting multi-scale periodic laws of flow velocity data based on the improved Morlet wavelet as described above.

[0017] The main contributions and innovations of the present invention are as follows: 1. Dynamically balance multi-scale resolution: By associating the Morlet wavelet center frequency with the scale factor, the problem of "poor low-frequency frequency resolution and poor high-frequency time resolution" caused by the traditional fixed center frequency is solved, enabling good time-frequency analysis capabilities at different scales.

[0018] 2. Adaptive parameter optimization improves recognition accuracy: Based on the period recognition contrast index and the improved whale optimization algorithm, the parameter \(\alpha\) is dynamically optimized, significantly improving the energy discrimination between the target period and the non-target period. The tidal period recognition error is reduced from 1.6% of the traditional method to 0.8%, and the daily period error is reduced from 5.1% to 0.9%.

[0019] 3. Enhance anti-noise ability: Through energy spectral density threshold screening and adaptive parameter optimization, the signal-to-noise ratio (SNR) is increased from 9.06 dB of the traditional method to 9.54 dB, effectively suppressing the interference of high noise and short-period perturbations on weak period features and avoiding misidentification of the main period.

[0020] 4. Improve analysis stability and interpretability: Through quantitative analysis of the multi-scale time-frequency spectrum and the energy concentration scale, accurate and stable extraction of the periodic laws of flow velocity data is achieved, which is applicable to multi-scale periodic analysis scenarios of non-stationary signals such as hydrological monitoring.

[0021] Details of one or more embodiments of the present invention are set forth in the following drawings and description, so that other features, objects, and advantages of the present invention will become more concise and understandable. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The drawings described herein are used to provide a further understanding of the present invention and form a part of the present invention. The illustrative embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings: Figure 1 is a flowchart of a method for extracting multi-scale periodic laws of flow velocity data based on an improved Morlet wavelet according to an embodiment of the present invention; Figure 2 is a graph of flow velocity data of a certain river according to an embodiment of the present invention; Figure 3 is an analysis result graph of a traditional Morlet wavelet; Figure 4 is an analysis result graph using the method of the present invention according to an embodiment of the present invention; Figure 5 is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0023] Here, the exemplary embodiments will be described in detail, and the examples are shown in the drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with one or more embodiments of this specification. On the contrary, they are merely examples of devices and methods that are consistent with some aspects of one or more embodiments of this specification as detailed in the appended claims.

[0024] It should be noted that: in other embodiments, the steps of the corresponding methods are not necessarily executed in the order shown and described in this specification. In some other embodiments, the steps included in the method may be more or fewer than those described in this specification. In addition, a single step described in this specification may be decomposed into multiple steps for description in other embodiments; and multiple steps described in this specification may also be combined into a single step for description in other embodiments.

[0025] In the monitoring of the flow velocity of natural water bodies, traditional methods (such as FFT and standard Morlet wavelet transform) have significant defects: 1. The Fourier transform (FFT) cannot capture the time-varying periodic characteristics of non-stationary signals; 2. The standard Morlet wavelet uses a fixed center frequency, resulting in an imbalance between the time resolution of the high-frequency band (small scale) and the frequency resolution of the low-frequency band (large scale); 3. The flow velocity data contains noise, short-period disturbances, and long-period trends. Traditional methods are prone to losing weak periods or misidentifying the main period (for example, the measured error reaches 5.1%).

[0026] Based on this, the present invention solves the problems existing in the prior art by dynamically adjusting the central frequency of the Morlet wavelet.

[0027] Embodiment 1 The present invention aims to propose a method for extracting multi-scale periodic laws of flow velocity data based on an improved Morlet wavelet. By dynamically adjusting the central frequency of the Morlet wavelet and combining an improved whale optimization algorithm based on period contrast, the adaptive high-precision extraction of multi-scale periodic features is realized.

[0028] Specifically, the embodiment of the present invention provides a method for extracting multi-scale periodic laws of flow velocity data based on an improved Morlet wavelet. Specifically, referring to Figure 1 , the method includes the following steps: Step 1. Data preprocessing For the flow velocity data obtained by the original Doppler current meter, according to the sampling moments of their respective data, 5-point Gaussian interpolation and normalization processing are performed to obtain a continuous flow velocity time series signal.

[0029] In this embodiment, for data such as water flow velocity that changes relatively slowly, the data correlation between adjacent moments is greater, while the data correlation between moments with a large difference is smaller. Therefore, the 5-point Gaussian interpolation method is used to complete the missing data. Specifically: First, for the flow velocity data of a part of the complete time series, a Gaussian function regression model is established:

[0030] where is the parameter coefficient (or weight coefficient) obtained by fitting the historical complete data, which reflects the contribution of data at different moments to the intermediate value, is the measured value (missing value) estimated at the intermediate moment, are the first 4 frames of data before and after the intermediate data (adjacent to the t moment). After establishing the Gaussian function regression model, substitute the first 4 frames of data before and after the missing data to calculate the missing measured data.

[0031] Essentially, it uses the characteristics of strong time correlation and slow change of the flow velocity data to predict the missing value through the weighted linear combination of the adjacent 4 frames of data, rather than simple equal-weight interpolation (such as linear interpolation), so as to more accurately retain the data trend.

[0032] It is worth mentioning that "Gaussian" here does not refer to the traditional Gaussian function (exponential decay form), but emphasizes the weighted strategy based on data correlation - in flow rate data, the correlation between adjacent time moments (such as t - 1, t + 1) is usually stronger than that between more distant time moments (such as t - 2, t + 2), similar to the characteristic of "higher weight for neighboring points" in the Gaussian distribution.

[0033] Step Two: Construction of Adaptive Morlet Wavelet Construct the Morlet wavelet function, and its mathematical expression is:

[0034] where represents the moment, represents the set central frequency of the wavelet, is the imaginary unit, making the wavelet function have a complex form, which can analyze both the amplitude and phase information of the signal simultaneously. is the complex exponential modulation part, which is essentially a sine wave oscillation, determining the central frequency of the wavelet function and used to extract the frequency characteristics in the signal. is the Gaussian window function part, in the shape of a bell curve, used for time - domain localization of the complex exponential signal, restricting the range of action of the wavelet function on the time axis and ensuring the locality of time - frequency analysis. The overall effect: the product of the complex exponential and the Gaussian window makes the Morlet wavelet a time - frequency localized "band - pass filter", which can sensitively capture the changes in the time domain of the signal components near a specific frequency.

[0035] where , is the base frequency, is the adjustable scale factor, is the scale factor. The original Morlet wavelet uses a fixed central frequency, which will lead to unbalanced resolution at different scales. Therefore, in this invention, the scale factor is considered in the calculation of the central frequency, so that when the low frequency ( is larger), a higher frequency resolution is maintained to avoid blurring; when the high frequency ( is smaller), a good time resolution is obtained.

[0036] From the above analysis, it can be seen that the value of the scale factor is crucial for the calculation of the central frequency. Therefore, an adaptive Morlet wavelet parameter optimization method based on period recognition contrast is proposed. This method uses the contrast between the target period energy and the non - target period energy as the optimization index to adaptively adjust the central frequency control parameter in the wavelet function, thereby enhancing the ability to identify key period components in the flow rate data.

[0037] Specifically, the central frequency of the adaptive Morlet wavelet is set in the form of a function that varies with the scale:

[0038] where is the base central frequency, which determines the initial frequency of the minimum scale (highest frequency); a is the scale parameter, is an adjustable adaptive coefficient that controls the rate of change of the central frequency with the scale.

[0039] In the classical Morlet wavelet transform, a fixed central frequency is adopted. For high-frequency (small-scale) information, the time resolution is good, but the frequency resolution is poor; for low-frequency (large-scale) information, the frequency resolution is good, but the time resolution is poor. The fixed central frequency cannot balance the resolutions of the two frequency bands, and the performance deteriorates when analyzing multi-scale mixed signals. Therefore, the scale factor a is introduced to achieve dynamic balance and solve the resolution imbalance problem of the classical Morlet wavelet at large and small scales.

[0040] For each candidate value, continuous wavelet transform is performed and the power spectrum is calculated, and then the average power in the target period interval and the average power in the non-target period interval are statistically calculated to construct a period recognition contrast index:

[0041] where ϵ is a small positive number to prevent the denominator from being zero. By maximizing this contrast index, the optimal value ( ) can be automatically selected to achieve adaptive adjustment of the wavelet transform parameters, so that the periodic components appear as more concentrated high-energy peaks in the power spectrum, improving the accuracy and resolution ability of period extraction.

[0042] Preferably, the optimization algorithm is an improved whale optimization algorithm. To solve the optimal parameter , the improved whale optimization algorithm is used for global optimization, and the specific steps are as follows: 2-1) Initialize the population of whale individuals, and each individual position corresponds to a parameter ; 2-2) Calculate the individual fitness, that is, the index function ; Here the formula is expressed as follows:

[0043] where is the average power within the target period range when the parameter value is . is the average power of the non-target period range when the parameter value is . ϵ is a small positive number to prevent the denominator from being zero. Thus, when takes the maximum value, it can ensure that when the parameter is , it can ensure the best ability to extract key period parameters.

[0044] 2 - 3) Compare in the population and record the optimal position , and its corresponding parameter is the current optimal parameter.

[0045] 2 - 4) Update the individual positions, including two mechanisms: Each individual calculates its own update parameter random number . When , use the mechanism of surrounding the prey for update; otherwise, use the mechanism of spiral approach for update.

[0046] Among them, the mechanism of surrounding the prey:

[0047] Among them , the convergence factor decreases linearly with time. The absolute value |A| of A determines the step size for the individual to approach the optimal solution. |A| > 1 indicates that the individual position update range expands, enhancing the global exploration ability. |A| < 1 indicates that the individual shrinks near the optimal solution, focusing on local exploitation; is a random vector, introducing randomness to avoid premature convergence. C adjusts the distance weight between the current individual and the optimal solution, enabling the individual to adaptively adjust the degree of dependence on the optimal solution during update; is the individual position at the current iteration number t (corresponding to the candidate solution of the parameter α to be optimized); is the global optimal individual position in the current iteration (the current optimal parameter ); is the individual position at the next iteration number t + 1 (the updated parameter candidate solution); A and C are coefficient vectors, controlling the "exploration" and "exploitation" modes of the search behavior. This formula belongs to the core iteration rule of the Whale Optimization Algorithm (WOA), used to simulate the behavior of humpback whales "surrounding the prey". WOA searches for the optimal solution by iteratively updating the positions of the population individuals (whales). The present invention adopts an improved whale optimization algorithm, improving the optimization efficiency by dynamically adjusting parameters, and is used to solve the optimal parameters of the adaptive Morlet wavelet .

[0048] Among them, the spiral approach mechanism:

[0049] where is a constant that determines the shape of the spiral (e.g., a standard logarithmic spiral when c = 1; controls the spiral shape, and the diversity of the spiral path is achieved through randomness; represents the exponential function term that controls the contraction or expansion rate of the spiral; represents the spiral radius after exponential scaling of the distance between the current individual and the optimal solution; is the cosine function term that controls the rotation direction and position of the spiral in polar coordinates through a random angle . This formula is the core rule in the Whale Optimization Algorithm (WOA) that simulates the behavior of humpback whales "spiraling upward to approach prey" and, together with the "encircling prey mechanism", constitutes two modes of individual position update. In the present invention, this mechanism is used to optimize the adaptive coefficient α of the Morlet wavelet, and by combining global search and local exploitation, the maximum value of the cycle recognition contrast index is improved.

[0050] When c > 0, e cl increases exponentially with l increasing, and the spiral radius expands (exploration mode); when c < 0, e cl exponentially decays, and the spiral radius contracts (exploitation mode).

[0051] Preferably, of the encircling prey mechanism is a random number within [0,1], is the convergence factor, and it satisfies . To further increase the diversity of population individuals and enhance the local optimization ability, the present invention also proposes an improved iterative calculation method for the convergence factor as follows:

[0052] where is distributed as the maximum and minimum values; (0 ≤ t ≤ T) is the current iteration number; T is the maximum iteration number. For example: parameters , , , iteration number , to avoid excessive consumption of computing resources.

[0053] In this way, by non-linearly dynamically adjusting b, the oscillatory characteristics of the cosine function are simulated, the position diversity of population individuals during the iteration process is increased, and at the same time, the local search accuracy is enhanced in the later stage, improving the parameter optimization efficiency.

[0054] (2 - 5) Iteratively update the above steps until the maximum number of iterations or the convergence condition is met.

[0055] Through the combination of the spiral approaching mechanism and the prey surrounding mechanism, the improved whale optimization algorithm of the present invention achieves: 1. Higher parameter optimization accuracy: As shown by the subsequent experimental data, the tidal period recognition error is reduced from 1.6% of the traditional method to 0.8%, and the daily period error is reduced from 5.1% to 0.9%. 2. Stronger robustness: The signal - to - noise ratio (SNR) is increased from 9.06 dB to 9.54 dB, effectively suppressing the interference of noise on weak periodic features. 3. Wider applicability: It can automatically adapt to the period analysis of flow velocity data in different hydrological scenarios (such as the influence of tides and rainfall), without the need for manual parameter adjustment.

[0056] This mechanism is the key innovation point of the present invention's deep integration of heuristic algorithms and signal processing technologies, providing an efficient optimization tool for multi - scale feature extraction of non - stationary time - series data.

[0057] Step 3: Improved wavelet transform calculation Perform wavelet transform on the pre - processed flow velocity data. Given the flow velocity data , time interval , wavelet scale set , the wavelet transform is defined as:

[0058] where the input signal: is the pre - processed continuous flow velocity time series (missing values have been filled by 5 - point Gaussian interpolation and normalized).

[0059] The wavelet function: is the complex conjugate of the Morlet wavelet, used for time - frequency localization correlation operations with the input signal. The introduction of the complex conjugate makes the wavelet coefficients contain phase information, which can be used to analyze the phase changes of periodic components.

[0060] The scale parameter s: Controls the "stretching degree" of the wavelet function. The larger s is, the wider the wavelet function is in the time domain, corresponding to analyzing low - frequency (long - period) signals; the smaller s is, the narrower the wavelet function is, corresponding to analyzing high - frequency (short - period) signals. is the normalization factor to ensure energy conservation at different scales.

[0061] The time parameter : Represents the translation position of the wavelet function on the time axis, used to locate the occurrence time of periodic components in the time domain.

[0062] Thus, the wavelet transform calculates the wavelet coefficients by matching the flow velocity signal with Morlet wavelets of different scales and positions. The modulus value of the coefficient reflects the energy intensity of the signal at scale s and time . The larger the modulus value, the more significant the periodic component at that scale.

[0063] Scale and the analysis frequency are inversely related, and the approximate formula is:

[0064] where corresponds to the frequency of scale ; is the central frequency of the wavelet; is the sampling interval. The scale set usually grows exponentially to obtain an equally distributed frequency resolution.

[0065] Thus, by adjusting the scale s, the full cycle range of the flow velocity data can be systematically scanned to ensure that no periodic components of any scale (such as daily cycle, tidal cycle, etc.) are missed.

[0066]

[0067] where is the minimum scale (corresponding to the maximum frequency); is the logarithmic step size of the scale interval, which takes the value of 0.1 in this embodiment. is the upper limit of the scale series (determined by the maximum scale ). The scale grows exponentially to make the frequency resolution evenly distributed on the logarithmic scale.

[0068] Preferably, to more precisely illustrate the value logic, taking the M2 tidal cycle with a sampling interval of 300 seconds as an example. Let the central frequency , then:

[0069]

[0070] The obtained scale means that when the wavelet scale is set to about 142, the Morlet wavelet transform is most sensitive to the 12.42-hour periodic component. Therefore, when dealing with actual unknown periodic data, can be set, and is used to construct scales, and are selected in turn and then calculations are performed.

[0071] Based on , calculate the wavelet power spectrum:

[0072] where the larger the value of the power spectrum , the more concentrated the energy of the flow velocity data at this scale (period) and time point, corresponding to a significant periodic component. By visualizing as a two-dimensional image (the abscissa is time, the ordinate is the period, and the darker the color, the higher the energy), the time distribution and intensity change of the periodic component can be intuitively identified. For example: the tidal period (12.42 hours) appears as a continuous high-energy band at about 12.42 hours on the ordinate in the power spectrum; the diurnal period (24 hours) appears as an energy peak at 24 hours on the ordinate.

[0073] Step 4. Periodic component extraction and feature analysis Based on the energy spectral density, identify the periodic peaks, extract the key periodic factors, and obtain the periodic analysis results.

[0074] At each scale, integrate over time to obtain the scale energy spectral density:

[0075] is the wavelet power spectrum , representing the energy density (energy per unit time) at scale s and time ; is the integral of the power spectrum over the time axis, that is, the total energy at scale s, reflecting the degree of energy concentration corresponding to this scale (period) in the flow velocity data. Furthermore, it can be judged on which scales (periods) the overall energy is concentrated. By finding 's local maximum and further performing threshold screening, obvious periodic information can be extracted.

[0076] Specifically, let be the local maximum condition, then it satisfies: and

[0077] By traversing the values of each , the local maximum of can be obtained.

[0078] Preferably, in order to exclude the interference of weak noise signals, a threshold method can also be used to exclude the periodic values obtained from the noise information analysis, and the final periodic information obtained from the analysis can be obtained.

[0079] If the noise information is not analyzed and excluded by using the threshold method without considering the interference of weak noise signals, multiple false periodic values may occur. Therefore, the expected value of the energy spectral density can be used as the screening threshold. Only when the local maximum is higher than the threshold will it be considered as the effective periodic information extracted. Otherwise, it is considered as the false local maximum information caused by signal interference such as noise and is not effective periodic information.

[0080] For example, taking the data measured from the flow velocity of a certain river as an example for processing and explanation, the model of the Doppler current meter used for sampling is the acoustic Doppler current meter developed by Kaihong Fluid Technology, with the model number 0D6X-T, and the sampling interval is 5 minutes.

[0081] The flow velocity data of a certain river is as Figure 2 shown. After analyzing this data and combining it with the records of the hydrological observation station, it can be obtained that there are 2 groups of flow velocity data periods in this time period, which are 12.42 hours (tidal period) and 24 hours (daily period) respectively.

[0082] Among them, the analysis results of the traditional Morlet wavelet are as Figure 3 shown, and it can be recognized that: Number of periods: 2; Period values (hours): [12.62 25.23]; Period recognition errors are: 1.6%, 5.1%; Signal-to-noise ratio SNR (dB): 9.06; And the analysis results of the method of the present invention are as Figure 4 shown, and it can be recognized that: Number of periods: 2; Recognized period values (hours): [12.52 24.23]; Period recognition errors are: 0.8%, 0.9%; Signal-to-noise ratio SNR (dB): 9.54; Comparing the structures of the traditional Morlet wavelet and the method of the present invention, it can be seen that by using the method of the present invention, the recognition accuracy of periodic data can be significantly improved.

[0083] Embodiment 2 Based on the same concept, the present invention also proposes a device for extracting multi-scale periodic laws of flow velocity data based on an improved Morlet wavelet, including: A preprocessing module for preprocessing the original flow velocity data to generate a continuous time series signal; An adaptive Morlet wavelet construction module for constructing a Morlet wavelet function with a center frequency that adaptively changes with the scale; A parameter optimization module, configured to dynamically determine wavelet function parameters based on the period recognition contrast index through an optimization algorithm; An improved wavelet transform calculation module, configured to perform wavelet transform on the preprocessed flow velocity data and calculate the wavelet power spectrum; A period component extraction and feature analysis module, configured to identify period peaks based on the wavelet power spectrum and extract key period factors.

[0084] Embodiment III This embodiment also provides an electronic device, referring to Figure 5 , including a memory 404 and a processor 402. A computer program is stored in the memory 404, and the processor 402 is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0085] Specifically, the above-mentioned processor 402 may include a central processing unit (CPU), or a specific integrated circuit (Application Specific Integrated Circuit, abbreviated as ASIC), or may be configured as one or more integrated circuits for implementing the embodiments of the present invention.

[0086] Among them, the memory 404 may include a mass memory 404 for data or instructions. By way of example and not limitation, the memory 404 may include a hard disk drive (HDD), a floppy disk drive, a solid state drive (SSD), a flash memory, an optical disc, a magneto-optical disc, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory 404 may include removable or non-removable (or fixed) media. Where appropriate, the memory 404 may be internal or external to the data processing device. In a particular embodiment, the memory 404 is non-volatile memory. In a particular embodiment, the memory 404 includes a read-only memory (ROM) and a random access memory (RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically alterable ROM (EAROM), or a flash memory, or a combination of two or more of these. Where appropriate, the RAM may be a static random access memory (SRAM) or a dynamic random access memory (DRAM), where the DRAM may be a fast page mode dynamic random access memory (FPMDRAM), an extended data output dynamic random access memory (EDODRAM), a synchronous dynamic random access memory (SDRAM), etc.

[0087] The memory 404 can be used to store or cache various data files required for processing and / or communication, as well as possible computer program instructions executed by the processor 402.

[0088] By reading and executing the computer program instructions stored in the memory 404, the processor 402 implements any one of the above-described methods for extracting the multi-scale periodic law of flow velocity data based on the improved Morlet wavelet.

[0089] Optionally, the above electronic device may further include a transmission device 406 and an input / output device 408. Among them, the transmission device 406 is connected to the above processor 402, and the input / output device 408 is connected to the above processor 402.

[0090] The transmission device 406 can be used to receive or send data via a network. Specific examples of the above network may include wired or wireless networks provided by the communication provider of the electronic device. In one example, the transmission device includes a network adapter (Network Interface Controller, abbreviated as NIC), which can be connected to other network devices through a base station and thus communicate with the Internet. In one example, the transmission device 406 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0091] The input / output device 408 is used to input or output information.

[0092] Embodiment IV This embodiment also provides a readable storage medium. The readable storage medium stores a computer program, and the computer program includes program codes for controlling a process to execute the process. The process includes the method for extracting the multi-scale periodic law of flow velocity data based on the improved Morlet wavelet according to Embodiment I.

[0093] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementation manners, and will not be elaborated here.

[0094] Generally, various embodiments can be implemented in hardware or special circuits, software, logic, or any combination thereof. Some aspects of the present invention can be implemented in hardware, while other aspects can be implemented by firmware or software executed by a controller, a microprocessor, or other computing devices, but the present invention is not limited thereto. Although various aspects of the present invention can be shown and described as block diagrams, flowcharts, or using some other graphical representation, it should be understood that, as a non-limiting example, the blocks, devices, systems, technologies, or methods described herein can be implemented in hardware, software, firmware, special circuits or logic, general hardware or a controller or other computing devices, or some combination thereof.

[0095] Embodiments of the present invention may be implemented by computer software, which is executable by a data processor of a mobile device, such as in a processor entity, or by hardware, or by a combination of software and hardware. A computer software or program (also referred to as a program product), including software routines, applets, and / or macros, may be stored in any device-readable data storage medium, and they include program instructions for performing specific tasks. The computer program product may include one or more computer-executable components configured to execute the embodiments when the program runs. The one or more computer-executable components may be at least one software code or a part thereof. Additionally, in this regard, it should be noted that any box in the logical flow, as Figure 1 described, may represent a program step, or interconnected logic circuits, boxes, and functions, or a combination of program steps and logic circuits, boxes, and functions. The software may be stored on physical media such as memory chips or storage blocks implemented within the processor, magnetic media such as hard disks or floppy disks, and optical media such as, for example, DVDs and their data variants, CDs. The physical media are non-transitory media.

[0096] Those skilled in the art should understand that the technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as falling within the scope described in this specification.

[0097] The above embodiments only represent several implementation manners of the present invention, and their descriptions are relatively specific and detailed, but should not be construed as limiting the scope of the present invention. It should be pointed out that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the appended claims.

Claims

1. A method for extracting multi-scale periodic patterns of flow velocity data based on improved Morlet wavelet, characterized in that It includes the following steps: Preprocess the original flow velocity data to generate a continuous time series signal; Construct a Morlet wavelet function with a center frequency adaptively varying with scale, and based on the period recognition contrast index, dynamically determine the wavelet function parameters through an optimization algorithm; Perform wavelet transform on the preprocessed flow velocity data and calculate the wavelet power spectrum; Identify the period peak based on the wavelet power spectrum and extract the key period factor; Among them, the period recognition contrast index is the ratio of the average power in the target period interval to the average power in the non-target period interval.

2. The multi-scale periodic law extraction method for flow velocity data based on the improved Morlet wavelet as claimed in claim 1, wherein The preprocessing includes: using a 5-point Gaussian interpolation method to complete the missing data, and the 5-point Gaussian interpolation constructs a regression model based on 4 frames of historical data at adjacent times to calculate the estimated value at the missing time.

3. A method for extracting multi-scale periodic laws of flow velocity data based on improved Morlet wavelet, as claimed in claim 1, wherein The expression of the Morlet wavelet function with a center frequency adaptively varying with scale is: ； Among them, the center frequency and the scale factor a satisfy a linear relationship: ; is the base frequency, is the adaptive coefficient.

4. The multi-scale periodic law extraction method for flow velocity data based on improved Morlet wavelet according to claim 3, characterized in that The optimization algorithm is an improved whale optimization algorithm, and the optimal adaptive coefficient is determined by maximizing the contrast index .

5. A method for extracting multi-scale periodic patterns of flow velocity data based on improved Morlet wavelet as claimed in claim 4, characterized in that, The improved whale optimization algorithm includes: initializing a population of whale individuals, where each individual corresponds to an adaptive coefficient ; Calculate the individual fitness, that is, the contrast index, and record the optimal individual; Update the individual position through the mechanism of surrounding the prey or the mechanism of spiral approach; Iterate until the convergence condition is met, and output the optimal parameters, that is, the optimal adaptive coefficient ; Among them, the convergence factor b of the convergence condition is dynamically adjusted according to the cosine function with the number of iterations.

6. The multi-scale periodic law extraction method for flow velocity data based on improved Morlet wavelet according to claim 1, characterized in that, The scale set of the wavelet transform increases exponentially, and the calculation formula is: ； Among them, is the minimum scale, is the logarithmic step size, j is the scale series, and J is the upper limit of the scale series.

7. A method for extracting multi-scale periodic patterns of flow velocity data based on improved Morlet wavelet according to any one of claims 1 to 6, characterized in that, The period peak identification step includes: calculating the energy spectral density of each scale, extracting the key period by screening the local maximum and combining the threshold to exclude noise.

8. An apparatus for extracting multi-scale periodic patterns of flow velocity data based on improved Morlet wavelet, characterized in that, It includes: A preprocessing module for preprocessing the original flow velocity data to generate a continuous time series signal; An adaptive Morlet wavelet construction module for constructing a Morlet wavelet function with a center frequency adaptively varying with scale; A parameter optimization module for dynamically determining the wavelet function parameters through an optimization algorithm based on the period recognition contrast index; An improved wavelet transform calculation module for performing wavelet transform on the preprocessed flow velocity data and calculating the wavelet power spectrum; A period component extraction and feature analysis module for identifying the period peak based on the wavelet power spectrum and extracting the key period factor.

9. An electronic device, comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is set to run the computer program to execute the method for extracting the multi-scale period law of flow velocity data based on the improved Morlet wavelet according to any one of claims 1 to 7.

10. A readable storage medium, characterized in that, The readable storage medium stores a computer program, and the computer program includes program codes for controlling a process to execute the process, and the process includes the method for extracting the multi-scale period law of flow velocity data based on the improved Morlet wavelet according to any one of claims 1 to 7.

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