Wavelet analysis method and system suitable for counting winding form of river channel

Abstract

The invention discloses a wavelet analysis method and system suitable for counting the winding form of a river channel, and relates to the technical field of river channel form analysis, and the method comprises the steps: obtaining the longitudinal section elevation data of a target river reach, preprocessing the obtained elevation data, obtaining a continuous signal, carrying out the multi-scale analysis of the preprocessed signal through wavelet transformation, and obtaining the winding form of the river channel. And obtaining a wavelet analysis result, extracting river channel winding characteristic parameters according to the wavelet analysis result, evaluating river channel stability based on the obtained characteristic parameters, calculating a river reach risk value, setting a risk value threshold value, and generating a risk evaluation report. According to the method, the accuracy and the efficiency of riverway winding form statistics are improved, a comprehensive, dynamic and scientific solution is provided for riverway stability assessment and risk management, and the beneficial effects of improving the riverway management level and preventing disasters are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of river channel morphology analysis, and in particular to a wavelet analysis method and system suitable for statistically analyzing the meandering morphology of river channels. Background Art

[0002] In the fields of fluvial geomorphology and hydraulic engineering, the statistics and analysis of river channel meandering morphology have always been an important research topic. Traditional methods mainly rely on topographic mapping and simple statistical analysis. Although these methods can provide macroscopic morphological information of river channels, they have obvious deficiencies in revealing the spatio-temporal variation characteristics of river channels and stability assessment. In recent years, with the development of signal processing technology, wavelet analysis, as a new tool, has begun to be applied to the analysis of river channel morphology. It can effectively extract multi-scale features of river channel morphology and provide a new perspective for the stability assessment of river channels.

[0003] However, there are still some limitations in the existing wavelet analysis methods when applied to the statistics of river channel meandering morphology. First, when traditional methods process longitudinal section elevation data, they often neglect the preprocessing of data, resulting in the analysis results being affected by measurement noise and reducing the accuracy of the analysis. Second, the selection of wavelet functions and parameter optimization in the existing technology are relatively empirical, lacking customized analysis for specific river channel characteristics, which limits the application effect of wavelet analysis in river channel morphology recognition. In addition, when the existing technology conducts river channel stability assessment, it often lacks comprehensive consideration of the spatio-temporal dynamic changes of river channels, resulting in inaccurate risk assessment results and being difficult to guide actual river channel management and maintenance work. Summary of the Invention

[0004] In view of the above existing problems, the present invention provides a wavelet analysis method and system suitable for statistically analyzing the meandering morphology of river channels, so as to solve the problems in the existing technology that there is a lack of customized analysis for specific river channel characteristics, the risk assessment results are not accurate enough, and it is difficult to guide actual river channel management and maintenance work.

[0005] [[ID=e19]]To solve the above technical problems, a wavelet analysis method suitable for statistically analyzing the meandering morphology of river channels is proposed, including,

[0006] obtaining the longitudinal section elevation data of the target river reach, preprocessing the obtained elevation data to obtain a continuous signal; performing multi-scale analysis on the preprocessed signal by using wavelet transform to obtain wavelet analysis results, and extracting river channel meandering feature parameters according to the wavelet analysis results; evaluating the river channel stability based on the obtained feature parameters, calculating the reach risk value, setting a risk value threshold, and generating a risk assessment report.

[0007] As a preferred embodiment of the wavelet analysis method for statistically analyzing the meandering morphology of a river channel according to the present invention, wherein: the longitudinal section elevation data includes setting measurement sections along the river channel extension direction, collecting the elevation values of each section point, and recording the spatial position information of each measurement point.

[0008] As a preferred embodiment of the wavelet analysis method for statistically analyzing the meandering morphology of a river channel according to the present invention, wherein: the preprocessing of the obtained elevation data includes interpolating the discrete elevation data, eliminating measurement noise, and normalizing the signal.

[0009] As a preferred embodiment of the wavelet analysis method for statistically analyzing the meandering morphology of a river channel according to the present invention, wherein: the multi-scale analysis includes selecting a mother wavelet function, dynamically adjusting the scale parameter and the translation parameter, and correcting the frequency parameter according to the time step of the actual research;

[0010] Selecting the mother wavelet function includes matching the wavelet basis function according to the data characteristics, optimizing the frequency parameter of the wavelet function through trial calculation, and limiting the value range of the frequency parameter within a preset interval.

[0011] As a preferred embodiment of the wavelet analysis method for statistically analyzing the meandering morphology of a river channel according to the present invention, wherein: obtaining the wavelet analysis result includes generating a time-frequency energy spectrum diagram and extracting the spatio-temporal distribution parameters of the river bend morphology based on the energy spectrum diagram.

[0012] The wavelet analysis formula is expressed as:

[0013]

[0014] Wherein, WT(a,τ) is the wavelet transform result, f(t) is the elevation data signal after preprocessing, t is the time variable, ψ * is the complex conjugate of the mother wavelet function, ψ(t) is the mother wavelet function, a is the scale parameter, b is the translation parameter, i is the imaginary unit, and ω0 is the frequency parameter;

[0015] Optimizing the frequency parameter of the wavelet function includes setting the frequency parameter to 1 / 3600 Hz when the time step is in hours, dynamically adjusting the analysis window width according to the river channel evolution rate, triggering the frequency parameter recalculation process when it is monitored that the time step change exceeds 15%, and re-estimating the optimal frequency parameter value using the sliding window method, updating the calculation parameters and maintaining the compatibility of historical data.

[0016] As a preferred embodiment of the wavelet analysis method for statistically analyzing the meandering morphology of a river channel according to the present invention, wherein: extracting the river channel meandering feature parameters includes calculating the squared modulus of the wavelet coefficients at each scale, setting a dynamic energy threshold to identify the energy concentration region, and dividing the distribution characteristics of stable and unstable river bends through a clustering algorithm.

[0017] The formula for calculating the modulus square of wavelet coefficients is as follows:

[0018] E(a, b) = |W(a, b)| 2 = [Re(W(a, b))] 2 + [Im(W(a, b))] 2

[0019]

[0020] Wherein, E(a, b) is the energy density at scale a and position b, W(a, b) is the complex wavelet transform coefficient, Re is the operation of taking the real part, Im is the operation of taking the imaginary part, a is the scale parameter, b is the translation parameter, S(a) is the average energy at scale a, and N is the number of spatial position points;

[0021] The clustering algorithm includes constructing a feature space, improving DBSCAN clustering, and spatio-temporal correlation analysis; constructing the feature space includes, for each identified energy concentration region, determining the central position coordinates, which are represented by the mileage distance along the river course, and obtaining the characteristic wavelength through conversion according to the scale parameter of wavelet analysis, and obtaining the spatial range characteristics of the energy concentration region by calculating the area of the region enclosed by the energy isoline; based on the time series data, calculating the energy change rate between adjacent time points, and calculating the moving distance of the central position within a unit time to reflect the dynamic change characteristics of the meander position;

[0022] Improving DBSCAN clustering includes calculating the average value of all characteristic wavelengths in the research reach, determining the neighborhood search radius for clustering analysis, and adaptively setting the minimum number of clustering points according to the total number of feature vectors; according to the preset threshold standard, dividing the clustering results into three stability levels, a stable meander needs to meet the criteria of both energy change and position drift, an unstable meander will trigger a determination for any change in either index, and the remaining meanders are determined as transitional meanders;

[0023] Spatio-temporal correlation analysis includes, in continuous time series data, through the criteria of position proximity and wavelength similarity, associating and matching the meander characteristics at different times, establishing cross-time trajectory identifiers, setting a matching tolerance, filling in the data missing periods caused by monitoring interruptions using the smooth interpolation method, and integrating the meander characteristic data at all time points to construct a structured spatio-temporal trajectory matrix.

[0024] As a preferred embodiment of the wavelet analysis method applicable to statistically analyzing the meandering morphology of a river course according to the present invention, wherein: the evaluation of river course stability includes establishing a relationship sequence between the meander length and time, determining the existence of a bank collapse risk when the meander length exceeds the preset threshold range, constructing a risk probability model based on historical bank collapse event data, inputting the real-time analysis results into the risk probability model, and generating a risk level evaluation report;

[0025] The establishment of the relationship sequence between the length of the river bend and time includes timestamp calibration of the river bend feature data at each monitoring time point, establishing a unified river channel mileage coordinate system, extracting the centerline length, curvature radius, and sinuosity index of each river bend, and calculating the parameter change rate between adjacent time points;

[0026] Constructing the risk probability model includes calculating the single-factor risk index, obtaining the composite risk value using the single-factor risk index, setting the composite risk threshold, and generating a risk level assessment report; the single-factor risk index includes the length mutation factor, curvature change factor, and energy fluctuation factor, and the formulas are expressed as:

[0027] F1=(L s -L0) / L0×100

[0028] F2 = |1 / R s -1 / R0|×R0

[0029] F3 = σ(E) / μ(E)

[0030] where, L s is the current centerline length, L0 is the centerline reference length, F1 is the length mutation factor, F2 is the curvature change factor, R s is the current curvature radius, R0 is the average curvature radius during the historical stable period, σ(E) is the standard deviation of the energy in the most recent 5 monitors, μ(E) is the energy mean, and F3 is the energy fluctuation factor;

[0031] The composite risk value is expressed as:

[0032] P = αF1+βF2+γF3

[0033] where, α is the weight coefficient of the length mutation factor, β is the weight factor of the curvature change factor, γ is the weight coefficient of the energy fluctuation factor, P is the composite risk value, F1 is the length mutation factor, F2 is the curvature change factor, and F3 is the energy fluctuation factor;

[0034] When 0≤P<0.3, it is determined as low risk and routine detection is carried out. When 0.3≤P<0.6, it is determined as medium risk and the inspection frequency is increased. When P≥0.6, it is determined as high risk and an emergency response is required.

[0035] Another object of the present invention is to provide a wavelet analysis system suitable for statistically analyzing the meandering morphology of river channels. The present invention effectively extracts the meandering characteristic parameters of river channels and realizes a comprehensive evaluation of the river channel stability. The system of the present invention acquires the longitudinal section elevation data of the target river reach and preprocesses it, uses wavelet transform technology to perform multi-scale analysis on continuous signals, constructs a risk probability model by establishing a relationship sequence between the meander length and time, calculates the composite risk value, provides a risk level assessment report for river channel managers, and guides river channel risk prevention and control and emergency response measures to ensure the safety of the river channel and the stability of the ecological environment.

[0036] As a preferred embodiment of a wavelet analysis system suitable for statistically analyzing the meandering morphology of river channels according to the present invention, it is characterized in that it includes a data acquisition and preprocessing module, a wavelet feature analysis module, a stability clustering module, and a risk assessment module.

[0037] The data acquisition and preprocessing module is used to acquire the longitudinal section elevation data of the target river reach, establish a river channel data set with unified spatio-temporal reference, and preprocess the collected data.

[0038] The wavelet feature analysis module includes a wavelet basis selection unit, a multi-scale decomposition unit, and a parameter optimization unit, and is used to extract the multi-scale time-frequency features of the river channel morphology, quantify the dynamic change law of the meandering degree, convert the spatial elevation signal into an analyzable time-frequency energy spectrum, establish a mapping relationship between the river channel morphology and the wavelet features, and perform feature parameterization expression.

[0039] The stability clustering module includes a feature space construction unit, a density clustering unit, and a quality assessment unit, and is used to identify the stable state and abnormal state of river channel evolution, establish a stability classification system, and evaluate the overall regional stability.

[0040] The risk assessment module is used to quantify the risk of river channel instability, integrate multi-source information for comprehensive judgment, and generate prevention and control suggestions.

[0041] A computer device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of a method for wavelet analysis suitable for statistically analyzing the meandering morphology of river channels.

[0042] A computer-readable storage medium stores a computer program thereon, and when the computer program is executed by a processor, it implements the steps of a method for wavelet analysis suitable for statistically analyzing the meandering morphology of river channels.

[0043] Advantages of the present invention: By obtaining and preprocessing the longitudinal section elevation data of the target river section, the present invention realizes the precision and continuity of the data, reduces measurement noise, improves the quality of signal processing, and provides a high-quality basic signal for wavelet transform; uses wavelet transform to perform multi-scale analysis on the signal, carefully decomposes the meandering form of the river channel, and dynamically adjusts parameters to optimize the matching between the wavelet function and the river channel data, revealing the complexity and dynamic changes of the river channel form, and enhancing the ability to extract characteristic parameters; generates a time-frequency energy spectrum diagram and extracts spatio-temporal distribution parameters, providing a quantitative description of the changes in the river channel form, and improving the scientificity and practicality of stability assessment; effectively identifies the stability characteristics of the river bend through calculating the sum of the squares of the wavelet coefficient modulus and the clustering algorithm, providing detailed information for managers; realizes the dynamic assessment of the river channel stability risk by establishing a relationship sequence between the river bend length and time and constructing a risk probability model, timely discovers potential bank collapse risks, provides decision-making support for river channel risk management, and ensures the safety of the river channel and the stability of the ecological environment. Description of the Drawings

[0044] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for description in the embodiments. Obviously, the following-described drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0045] Figure 1 It is the overall flowchart of a wavelet analysis method applicable to statistically analyzing the meandering form of a river channel provided by an embodiment of the present invention.

[0046] Figure 2 It is the schematic diagram of the bank height intercepted by the longitudinal section of a wavelet analysis method applicable to statistically analyzing the meandering form of a river channel provided by an embodiment of the present invention.

[0047] Figure 3 It is the schematic diagram of the wavelet analysis result of a wavelet analysis method applicable to statistically analyzing the meandering form of a river channel provided by an embodiment of the present invention.

[0048] Figure 4 It is the system scheme flowchart of a wavelet analysis system applicable to statistically analyzing the meandering form of a river channel provided by an embodiment of the present invention. Detailed Embodiments

[0049] To make the above objects, features, and advantages of the present invention more apparent and understandable, the following provides a detailed description of the specific embodiments of the present invention in conjunction with the accompanying drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0050] In the following description, many specific details are set forth to facilitate a thorough understanding of the present invention. However, the present invention may be implemented in other ways different from those described herein. Those skilled in the art can make similar generalizations without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0051] Secondly, the so-called "one embodiment" or "embodiment" herein refers to specific features, structures, or characteristics that may be included in at least one implementation manner of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it an embodiment that is mutually exclusive with other embodiments alone or selectively.

[0052] The present invention is described in detail in conjunction with schematic diagrams. When detailing the embodiments of the present invention, for ease of explanation, the cross-sectional views showing the device structure will be enlarged locally in a non-general proportion, and the schematic diagrams are only examples and should not limit the scope of protection of the present invention herein. In addition, in actual production, three-dimensional spatial dimensions of length, width, and depth should be included.

[0053] At the same time, in the description of the present invention, it should be noted that the orientation or positional relationships indicated by terms such as "upper, lower, inner, and outer" are based on the orientation or positional relationships shown in the accompanying drawings. This is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present invention. In addition, the terms "first, second, or third" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance.

[0054] Unless otherwise clearly defined and limited in the present invention, the terms "installed, connected, and coupled" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can also be a mechanical connection, an electrical connection, or a direct connection, and can also be indirectly connected through an intermediate medium, or it can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0055] Example 1, referring to Figures 1-3 , which is the first embodiment of the present invention. This embodiment provides a wavelet analysis method applicable to statistically analyzing the meandering morphology of a river channel, including:

[0056] S1: Obtain the longitudinal section elevation data of the target river reach, preprocess the obtained elevation data to obtain a continuous signal.

[0057] The longitudinal section elevation data includes setting measurement sections along the river channel extension direction, collecting the elevation values of each section point, and recording the spatial position information of each measurement point.

[0058] The preprocessing of the obtained elevation data includes interpolating the discrete elevation data, eliminating measurement noise, and normalizing the signal.

[0059] S2: Perform multi-scale analysis on the preprocessed signal using wavelet transform to obtain the wavelet analysis result, and extract the river channel meandering characteristic parameters according to the wavelet analysis result.

[0060] Furthermore, the multi-scale analysis includes selecting a mother wavelet function, dynamically adjusting the scale parameter and translation parameter, and correcting the frequency parameter according to the time step of the actual research;

[0061] Selecting a mother wavelet function includes matching the wavelet basis function according to the data characteristics, optimizing the frequency parameter of the wavelet function through trial calculation, and limiting the value range of the frequency parameter within a preset interval.

[0062] The obtaining of the wavelet analysis result includes generating a time-frequency energy spectrum diagram and extracting the spatio-temporal distribution parameters of the river bend morphology based on the energy spectrum diagram;

[0063] The wavelet analysis formula is expressed as:

[0064]

[0065] where WT(a,τ) is the wavelet transform result, f(t) is the preprocessed elevation data signal, t is the time variable, ψ * is the complex conjugate of the mother wavelet function, ψ(t) is the mother wavelet function, a is the scale parameter, b is the translation parameter, i is the imaginary unit, and ω0 is the frequency parameter;

[0066] The optimization of the frequency parameter of the wavelet function includes setting the frequency parameter to 1 / 3600 Hz when the time step is in hours, dynamically adjusting the analysis window width according to the river channel evolution rate, triggering the frequency parameter recalculation process when it is monitored that the time step change exceeds 15%, and re-estimating the optimal frequency parameter value using the sliding window method, updating the calculation parameters and maintaining the historical data compatibility.

[0067] Even further, the extraction of the river channel meandering characteristic parameters includes calculating the squared modulus of the wavelet coefficients at each scale, setting a dynamic energy threshold to identify the energy concentration region, and dividing the distribution characteristics of stable and unstable river bends through a clustering algorithm;

[0068] The formula for calculating the modulus square of wavelet coefficients is as follows:

[0069] E(a,b) = |W(a,b)| 2 = [Re(W(a,b))] 2 + [Im(W(a,b))] 2

[0070]

[0071] Where E(a,b) is the energy density at scale a and position b, W(a,b) is the complex wavelet transform coefficient, Re is the operation of taking the real part, Im is the operation of taking the imaginary part, a is the scale parameter, b is the translation parameter, S(a) is the average energy at scale a, and N is the number of spatial position points;

[0072] The clustering algorithm includes constructing a feature space, improving DBSCAN clustering, and spatio-temporal correlation analysis; constructing the feature space includes, for each identified energy concentration region, determining the central position coordinates, which are represented by the mileage distance along the river course, and obtaining the characteristic wavelength through conversion according to the scale parameter of wavelet analysis, and obtaining the spatial range characteristics of the energy concentration region by calculating the area of the region enclosed by the energy contour line; based on time series data, calculating the energy change rate between adjacent time points, and calculating the moving distance of the central position within a unit time to reflect the dynamic change characteristics of the meander position;

[0073] Improving DBSCAN clustering includes calculating the average value of all characteristic wavelengths in the study reach, determining the neighborhood search radius for clustering analysis, and adaptively setting the minimum number of clustering points according to the total number of feature vectors; according to the preset threshold criteria, dividing the clustering results into three stability levels, a stable meander needs to meet the criteria of both energy change and position drift, an unstable meander will trigger a determination for any change in either index, and the remaining meanders are determined to be transitional meanders;

[0074] Spatio-temporal correlation analysis includes, in continuous time series data, through the criteria of position proximity and wavelength similarity, associating and matching the meander characteristics at different times, establishing cross-time trajectory identifiers, and setting a matching tolerance. For the data missing periods caused by monitoring interruptions, the smooth interpolation method is used for filling, and the meander characteristic data at all time points are integrated to construct a structured spatio-temporal trajectory matrix.

[0075] S3: Based on the obtained characteristic parameters, evaluate the river channel stability, calculate the reach risk value, set the risk value threshold, and generate a risk assessment report.

[0076] Further, the evaluation of river channel stability includes establishing a relationship sequence between the length of the river bend and time. When the length of the river bend exceeds the preset threshold range, it is determined that there is a risk of bank collapse. A risk probability model is constructed based on historical bank collapse event data, and the real-time analysis results are input into the risk probability model to generate a risk level assessment report;

[0077] The establishment of the relationship sequence between the length of the river bend and time includes calibrating the time stamps of the river bend characteristic data at each monitoring time point, establishing a unified river channel mileage coordinate system, extracting the center line length, curvature radius, and sinuosity index of each river bend, and calculating the parameter change rate between adjacent time points;

[0078] Constructing the risk probability model includes calculating the single-factor risk index, obtaining the composite risk value using the single-factor risk index, setting the composite risk threshold, and generating a risk level assessment report; the single-factor risk index includes the length mutation factor, curvature change factor, and energy fluctuation factor, and is expressed by the formula:

[0079] F1 = (L s - L0) / L0 × 100

[0080] F2 = |1 / R s - 1 / R0| × R0

[0081] F3 = σ(E) / μ(E)

[0082] where L s is the current center line length, L0 is the center line reference length, F1 is the length mutation factor, F2 is the curvature change factor, R s is the current curvature radius, R0 is the average curvature radius during the historical stable period, σ(E) is the standard deviation of the energy in the last 5 monitors, μ(E) is the energy mean, and F3 is the energy fluctuation factor;

[0083] The composite risk value is expressed as:

[0084] P = αF1 + βF2 + γF3

[0085] where α is the weight coefficient of the length mutation factor, β is the weight factor of the curvature change factor, γ is the weight coefficient of the energy fluctuation factor, P is the composite risk value, F1 is the length mutation factor, F2 is the curvature change factor, and F3 is the energy fluctuation factor;

[0086] When 0 ≤ P < 0.3, it is determined as a low risk and routine detection is carried out. When 0.3 ≤ P < 0.6, it is determined as a medium risk and patrols are strengthened. When P ≥ 0.6, it is determined as a high risk and an emergency response is required.

[0087] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

[0088] Example 2, referring to Figure 3 , which is an embodiment of the present invention, provides a wavelet analysis method applicable to the statistical analysis of river channel meandering morphology. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through experiments.

[0089] The present invention is applied to the situation where the flood duration and intensity are both moderate. The flood cycle time is about 40 hours, and 50 groups of cycles are carried out. The river channel meandering slowly moves downstream with the flood evolution. The lengths of all river bends are basically equal at 580m. When moving, the height will change, but the length remains unchanged; Figure 3 In [reference], the blue color represents the main changes in the bank slope morphology obtained after wavelet analysis; within 0 to the 500th hour, no regular meandering has formed inside the river channel, so the lengths and elevations obtained by statistics are unstable. Between the 500th hour and the 2000th hour, after the meandering becomes stable, it can be seen that the blue line segment in the figure represents the length of a river bend as a stable 580m.

[0090] Example 3, referring to Figure 4 , which is the second embodiment of the present invention. This embodiment provides a wavelet analysis system applicable to the statistical analysis of river channel meandering morphology, including a data acquisition and preprocessing module, a wavelet feature analysis module, a stability clustering module, and a risk assessment module.

[0091] The data acquisition and preprocessing module is used to collect the longitudinal section elevation data of the target river section, establish a river channel data set with unified spatio-temporal reference, and preprocess the collected data.

[0092] The wavelet feature analysis module includes a wavelet basis selection unit, a multi-scale decomposition unit, and a parameter optimization unit, and is used to extract the multi-scale time-frequency features of the river channel morphology, quantify the dynamic change law of the meandering degree, convert the spatial elevation signal into an analyzable time-frequency energy spectrum, establish the mapping relationship between the river channel morphology and the wavelet features, and perform feature parameterization expression.

[0093] The stability clustering module includes a feature space construction unit, a density clustering unit, and a quality assessment unit, and is used to identify the stable state and abnormal state of the river channel evolution, establish a stability classification system, and evaluate the overall stability of the region.

[0094] The risk assessment module is used to quantify the risk of river channel instability, integrate multi-source information for comprehensive judgment, and generate prevention and control suggestions.

[0095] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

[0096] Embodiment 4, the fourth embodiment of the present invention, which is different from the previous three embodiments in that:

[0097] If the above functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the essence of the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0098] The logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in combination with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device.

[0099] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection (electronic device) having one or more wirings, a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable media can even be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or other appropriate processing as necessary, and then stored in a computer memory.

[0100] It should be understood that the various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, the multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), and the like.

Claims

1. A wavelet analysis method applicable to statistically analyzing the meandering form of a river channel, characterized in that: including Obtain the longitudinal section elevation data of the target river section, preprocess the obtained elevation data to obtain a continuous signal; Perform multi-scale analysis on the preprocessed signal using wavelet transform to obtain the wavelet analysis result, and extract the river channel meandering characteristic parameters according to the wavelet analysis result; Evaluate the river channel stability based on the obtained characteristic parameters, calculate the river section risk value, set the risk value threshold, and generate a risk assessment report.

2. The wavelet analysis method applicable to the statistics of river channel meandering patterns according to claim 1, characterized in that: The longitudinal section elevation data includes setting measurement sections along the river channel extension direction, collecting the elevation values of each section point, and recording the spatial position information of each measurement point.

3. A wavelet analysis method applicable to the statistics of river channel meandering morphology according to claim 2, characterized in that: The preprocessing of the obtained elevation data includes interpolating the discrete elevation data, eliminating measurement noise, and normalizing the signal.

4. A wavelet analysis method applicable to the statistical analysis of river channel meandering morphology as described in claim 3, characterized in that: The multi-scale analysis includes selecting a mother wavelet function, dynamically adjusting the scale parameter and translation parameter, and correcting the frequency parameter according to the time step of the actual study; Selecting the mother wavelet function includes matching the wavelet basis function according to the data characteristics, optimizing the frequency parameter of the wavelet function through trial calculation, and limiting the value range of the frequency parameter within a preset interval.

5. The wavelet analysis method applicable to the statistical analysis of the meandering form of a river channel according to claim 4, wherein: The obtaining of the wavelet analysis result includes generating a time-frequency energy spectrum diagram and extracting the spatio-temporal distribution parameters of the river bend morphology based on the energy spectrum diagram; The wavelet analysis formula is expressed as: Among them, WT(a,τ) is the result of wavelet transform, f(t) is the preprocessed elevation data signal, t is the time variable, ψ * is the complex conjugate of the mother wavelet function, ψ(t) is the mother wavelet function, a is the scale parameter, b is the translation parameter, i is the imaginary unit, and ω0 is the frequency parameter; The optimization of the frequency parameter of the wavelet function includes setting the frequency parameter to 1 / 3600 Hz when the time step is in hours, dynamically adjusting the analysis window width according to the river channel evolution rate, triggering the frequency parameter recalculation process when it is monitored that the time step change exceeds 15%, and re-estimating the optimal frequency parameter value using the sliding window method, updating the calculation parameters and maintaining the historical data compatibility.

6. The wavelet analysis method applicable to statistically analyzing the meandering shape of a river channel according to claim 5, characterized in that: The extraction of the river channel meandering characteristic parameters includes calculating the modulus square of the wavelet coefficient at each scale, setting a dynamic energy threshold to identify the energy concentration area, and dividing the distribution characteristics of stable and unstable river bends through a clustering algorithm; The formula for calculating the modulus square of the wavelet coefficient is: E(a,b) = |W(a,b)| 2 = [Re(W(a,b))] 2 + [Im(W(a,b))] 2 where E(a,b) is the energy density at position b of scale a, W(a,b) is the complex wavelet transform coefficient, Re is the real part operation, Im is the imaginary part operation, a is the scale parameter, b is the translation parameter, S(a) is the average energy of scale a, and N is the number of spatial position points; The clustering algorithm includes constructing a feature space, improving DBSCAN clustering and spatio-temporal correlation analysis; constructing the feature space includes determining the central position coordinates for each identified energy concentration area, representing the coordinates in terms of the mileage distance along the river channel, and obtaining the characteristic wavelength through conversion according to the scale parameter of the wavelet analysis, and obtaining the spatial range characteristics of the energy concentration area by calculating the area enclosed by the energy contour line; based on the time series data, calculating the energy change rate between adjacent time points, and calculating the moving distance of the central position within a unit time to reflect the dynamic change characteristics of the river bend position; The improved DBSCAN clustering includes calculating the average value of all characteristic wavelengths of the research river section, determining the neighborhood search radius for clustering analysis, adaptively setting the minimum number of cluster points according to the total number of feature vectors; dividing the clustering results into three stability levels according to the preset threshold criteria. A stable river bend needs to meet the criteria of both energy change and position drift simultaneously. An unstable river bend will trigger a determination for any change in either index, and the remaining river bends are determined as transitional river bends; The spatio-temporal correlation analysis includes, in continuous time-series data, associating and matching the bend features at different times through the criteria of position proximity and wavelength similarity, establishing cross-time trajectory identifiers, setting a matching tolerance, filling in the data missing periods caused by monitoring interruptions using the smooth interpolation method, and integrating the bend feature data at all time points to construct a structured spatio-temporal trajectory matrix.

7. A wavelet analysis method applicable to statistical analysis of river channel meandering patterns as claimed in claim 6, characterized in that: The evaluation of river channel stability includes establishing a relationship sequence between the bend length and time. When the bend length exceeds the preset threshold range, it is determined that there is a risk of bank collapse, and a risk probability model is constructed based on historical bank collapse event data. The real-time analysis results are input into the risk probability model to generate a risk level assessment report; The establishment of the relationship sequence between the bend length and time includes calibrating the time stamps of the bend feature data at each monitoring time point, establishing a unified river channel mileage coordinate system, extracting the center line length, curvature radius, and meandering index of each bend, and calculating the parameter change rates at adjacent time points; Constructing the risk probability model includes calculating the single-factor risk index and obtaining the composite risk value using the single-factor risk index, setting the composite risk threshold, and generating a risk level assessment report; the single-factor risk index includes the length mutation factor, curvature change factor, and energy fluctuation factor, and the formula is expressed as: F1 = (L s - L0) / L0 × 100 F2 = |1 / R s - 1 / R0| × R0 F3 = σ(E) / μ(E) Among them, L s is the current centerline length, L0 is the reference centerline length, F1 is the length mutation factor, F2 is the curvature change factor, R s is the current radius of curvature, R0 is the average radius of curvature during the historical stable period, σ(E) is the standard deviation of the energy of the most recent 5 monitoring times, μ(E) is the energy mean, and F3 is the energy fluctuation factor; The composite risk value is expressed as: P = αF1 + βF2 + γF3 where α is the weight coefficient of the length mutation factor, β is the weight factor of the curvature change factor, γ is the weight coefficient of the energy fluctuation factor, P is the composite risk value, F1 is the length mutation factor, F2 is the curvature change factor, and F3 is the energy fluctuation factor; When 0 ≤ P < 0.3, it is determined as a low risk and routine detection is carried out. When 0.3 ≤ P < 0.6, it is determined as a medium risk and the inspection frequency is increased. When P ≥ 0.6, it is determined as a high risk and an emergency response is required.

8. A system adopting a wavelet analysis method for statistically analyzing the meandering form of a river channel as described in any one of claims 1 to 7, characterized in that: It includes a data acquisition and preprocessing module, a wavelet feature analysis module, a stability clustering module, and a risk assessment module; The data acquisition and preprocessing module is used to collect the longitudinal section elevation data of the target river section, establish a river channel data set with unified spatio-temporal reference, and preprocess the collected data; The wavelet feature analysis module includes a wavelet basis selection unit, a multi-scale decomposition unit, and a parameter optimization unit, which is used to extract the multi-scale time-frequency features of the river channel morphology, quantify the dynamic change law of meandering degree, convert the spatial elevation signal into an analyzable time-frequency energy spectrum, establish the mapping relationship between the river channel morphology and wavelet features, and perform feature parameterization expression; The stability clustering module includes a feature space construction unit, a density clustering unit, and a quality assessment unit, which are used to identify the stable and abnormal states of river channel evolution, establish a stability classification system, and evaluate the overall regional stability; The risk assessment module is used to quantify the risk of river channel instability, and integrate multi-source information for comprehensive judgment to generate prevention and control suggestions.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of a wavelet analysis method applicable to statistical river channel meandering forms described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of a wavelet analysis method applicable to statistical river channel meandering forms described in any one of claims 1 to 7.

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