Wavelet denoising parameter determination method and system in OTDR (Optical Time Domain Reflectometer) system
By introducing adaptive factors negatively related to the number of iterations into the intelligent optimization algorithm, the problem that the algorithm is prone to fall into local optimal solutions is solved, and the effective determination of wavelet denoising parameters is realized, which improves the signal denoising effect and timeliness of the OTDR system.
Patent Information
- Application Number
- CN202510539218.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-27
- Publication Date
- 2025-06-10
AI Technical Summary
In the prior art, intelligent optimization algorithms are prone to fall into local optimal solutions and are difficult to effectively determine the wavelet denoising parameters, making it difficult to directly apply the OTDR system when facing different characteristic signals.
The adaptive factors negatively correlated with the number of iterations are introduced, and the basic step size and step size correction coefficients of the exploration stage of the intelligent optimization algorithm are adjusted, so that the algorithm can expand the optimization range in the early stage of iteration, avoid local optimal solutions, narrow the optimization range in the later stage, and maintain convergence performance.
The optimization ability of the intelligent optimization algorithm is improved, and it avoids prematurely falling into the local optimal solution, ensuring the effective determination of wavelet denoising parameters, and improving the signal denoising effect and timeliness of the OTDR system.
Smart Images
Figure CN120128256A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of optical signal detection, and particularly to a method and system for determining wavelet denoising parameters in an OTDR (Optical Time Domain Reflectometer) system. Background Art
[0002] With the rapid development of the communication field, OTN (Optical Transport Network) has been widely used. As an important tool for maintaining the state of optical fibers, OTDR can detect the state of optical fibers by analyzing the recovered backscattered optical signals, providing support for the monitoring and maintenance of OTN. Especially in the monitoring of ultra-long optical cables, coherent OTDR has become the main equipment for submarine cable monitoring due to its advantages of long-distance measurement and high sensitivity. In order to improve the performance of OTDR equipment, it is necessary to effectively suppress noise to accurately measure signals.
[0003] In engineering, the commonly used method for processing noisy signals is cumulative averaging. Theoretically, the signal-to-noise ratio gain after n times of accumulation is the square root of n. In practice, in an OTDR system, to obtain an ideal signal-to-noise ratio, 30,000 times of accumulation are required, and it takes about 15 minutes to obtain an OTDR signal curve, which is difficult to meet the needs of a new OTDR fiber sensing system that requires real-time monitoring and early warning.
[0004] Wavelet denoising is an effective signal processing method that transcends the limitations of traditional filtering techniques. Especially the wavelet threshold denoising method, due to its simple principle, powerful noise filtering ability, and low computing requirements, can theoretically effectively improve the timeliness of signal noise processing and meet the needs of new OTDR equipment, and has received wide attention. The wavelet threshold denoising method first selects a suitable wavelet basis function and decomposition level, decomposes the noisy signal by wavelet, and obtains a series of low-frequency wavelet coefficients and high-frequency wavelet coefficients. Generally speaking, the useful signals are mainly distributed in the low-frequency wavelet coefficients, while the noise signals are mainly distributed in the high-frequency wavelet coefficients. During the denoising process, a threshold is generated by estimating the noise intensity, and the noise-related components in the high-frequency wavelet coefficients are processed using the threshold function, thereby increasing the proportion of useful signals. Finally, the processed high-frequency coefficients and low-frequency coefficients are reconstructed to obtain the signal after noise removal. However, the wavelet threshold denoising method is restricted by the selection of a large number of parameters such as wavelet basis functions and threshold functions. Different parameter combinations are suitable for noisy signals with different frequency characteristics, and it is difficult to directly apply when facing signals with different characteristics. Manually adjusting parameters requires a large number of experiments, and the cost is extremely high.
[0005] Intelligent optimization algorithm is a kind of computational method that simulates biological evolution or group intelligent behavior in nature. It is widely used due to its global optimization characteristics. It is often used to solve complex optimization problems such as path planning and production scheduling. In recent years, it has also been used for neural network parameter optimization. Similarly, it is also applicable to parameter optimization problems in wavelet threshold denoising.
[0006] Take the fox optimization algorithm in the intelligent optimization algorithm as an example. This algorithm is inspired by the hunting behavior of the red fox in nature and is divided into two stages: exploration and exploitation. The exploration stage is also called the random optimization stage, which is a common stage in the intelligent optimization algorithm represented by the fox optimization algorithm, and the exploitation stage is a stage unique to the fox optimization algorithm. After completing this iteration and obtaining the best position, the exploration stage is randomly selected to perform random optimization around the best position, or the exploitation stage is selected to change the position according to the unique hunting path of the red fox. The "position" information is decoded to correspond to different settings of different wavelet denoising parameters, and the quality is calculated and judged by the judgment index of signal denoising, usually the signal-to-noise ratio or mean square error. This type of intelligent optimization algorithm represented by the fox optimization algorithm uses the current best position, that is, the position with the best fitness, as the basis in the random optimization stage, and uses the following formula (1) to perform a random search strategy around the current best position.
[0007] X (l+1) =BestX l *rand(1,a)*C (1)
[0008] Among them, X (l+1) Indicates the position at the beginning of the next iteration; BestX l represents the best position after the completion of this round of iteration; l represents the number of iterations; rand(1,a) represents the basic step size, which is a random matrix with 1 row and a columns; C represents the compensation correction coefficient of the algorithm, which is used to ensure that the random search can effectively find the optimal solution. C, like rand(1,a), is also random. Based on the randomness, the positions of multiple foxes at the beginning of the next round of iteration can be calculated according to the best position obtained in the previous round of iteration.
[0009] Intelligent optimization algorithms can solve such parameter optimization problems, but even the latest and most competitive fox algorithm still has the problem of insufficient randomness and is prone to falling into local optimal solutions, so there is still room for further improvement. Summary of the invention
[0010] The present application provides a method and system for determining wavelet denoising parameters in an OTDR system, which can solve the technical problem in the prior art that the intelligent optimization algorithm is prone to fall into a local optimal solution.
[0011] First aspect, an embodiment of the present application provides a method for determining wavelet denoising parameters in an OTDR system, the method comprising:
[0012] Set an adaptive factor negatively correlated with the number of iterations;
[0013] In the exploration stage of the intelligent optimization algorithm, after each round of iteration, multiply the base step size, step size correction coefficient, adaptive factor, and the best position obtained at the end of this round of iteration to obtain the initial position at the start of the next round of iteration;
[0014] Based on the intelligent optimization algorithm, use the best position obtained after multiple rounds of iteration as the wavelet denoising parameter.
[0015] Combined with the first aspect, in an implementation manner, the maximum value of the adaptive factor is greater than 1, and the minimum value is less than 1.
[0016] Combined with the first aspect, in an implementation manner, the adaptive factor is calculated using the following formula:
[0017]
[0018] where F dynamic represents the adaptive factor; F max represents a custom maximum dynamic factor; F min represents a custom minimum dynamic factor; l represents the current iteration number; Max_iter represents the custom maximum iteration number.
[0019] Combined with the first aspect, in an implementation manner, the maximum dynamic factor is greater than 1, and the minimum dynamic factor is less than 1.
[0020] Combined with the first aspect, in an implementation manner, the best position is obtained using the following steps:
[0021] After configuring multiple sets of denoising parameters of the wavelet noise filtering method with the multiple initial positions at the start of each round of iteration, perform wavelet noise filtering on the reflected signals received by the OTDR system based on each set of denoising parameters, compare the denoised signals with the signals before denoising, obtain the signal-to-noise ratio or root mean square error, and use the initial position corresponding to the set of denoising parameters with the maximum signal-to-noise ratio or the minimum root mean square error as the best position at the end of this round of iteration.
[0022] Second aspect, an embodiment of the present application provides a system for determining wavelet denoising parameters in an OTDR system, the system comprising:
[0023] A custom module for setting an adaptive factor negatively correlated with the number of iterations;
[0024] A processing module, which is used to multiply the basic step size, the step size correction coefficient, the adaptive factor, and the best position obtained at the end of each iteration during the exploration stage of the intelligent optimization algorithm to obtain the initial position at the start of the next iteration; based on the intelligent optimization algorithm, the best position obtained after multiple iterations is used as the wavelet denoising parameter.
[0025] Combined with the second aspect, in an implementation, the maximum value of the adaptive factor is greater than 1, and the minimum value is less than 1.
[0026] Combined with the second aspect, in an implementation, the custom module calculates the adaptive factor using the following formula:
[0027]
[0028] where F dynamic represents the adaptive factor; F max represents the custom maximum dynamic factor; F min represents the custom minimum dynamic factor; l represents the current iteration number; Max_iter represents the custom maximum iteration number.
[0029] Combined with the second aspect, in an implementation, the maximum dynamic factor is greater than 1, and the minimum dynamic factor is less than 1.
[0030] Combined with the second aspect, in an implementation, after the processing module configures multiple sets of denoising parameters of the wavelet filtering method with the multiple initial positions at the start of each iteration, it performs wavelet denoising on the reflection signals received by the OTDR system based on each set of denoising parameters, compares the denoised signal with the signal before denoising to obtain the signal-to-noise ratio or the root mean square error, and uses the initial position corresponding to the set of denoising parameters with the maximum signal-to-noise ratio or the minimum root mean square error as the best position at the end of this iteration.
[0031] The beneficial effects brought by the technical solutions provided in the embodiments of the present application include:
[0032] An adaptive factor negatively correlated with the iteration number is introduced in the random optimization stage of the intelligent optimization algorithm, enabling the algorithm to expand the optimization range in the initial stage to avoid premature convergence to local optimal solutions, and narrowing the optimization range in the later stage to maintain the original convergence performance. BRIEF DESCRIPTION OF THE DRAWINGS
[0033] Figure 1 It is a schematic flowchart of an embodiment for determining the wavelet denoising parameter in the OTDR system of the present application;
[0034] Figure 2 It is a schematic diagram of the attenuation curve of the test optical fiber before denoising in a specific embodiment of the present application;
[0035] Figure 3 Schematic diagram of the attenuation curve after denoising the test optical fiber in a specific embodiment of the present application;
[0036] Figure 4 Schematic diagram of the functional modules of an embodiment of a system for determining wavelet denoising parameters in an OTDR system of the present application. Detailed implementation manners
[0037] In order to enable those skilled in the art to better understand the solution of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.
[0038] To make the objectives, technical solutions and advantages of the present application clearer, the embodiments of the present application will be further described in detail below in conjunction with the accompanying drawings.
[0039] In a first aspect, an embodiment of the present application provides a method for determining wavelet denoising parameters in an OTDR system.
[0040] In one embodiment, referring to Figure 1 , Figure 1 is a schematic flowchart of the first embodiment of the method for determining wavelet denoising parameters in the OTDR system of the present application. As Figure 1 shown, the method for determining wavelet denoising parameters in the OTDR system includes:
[0041] Step S1: Set an adaptive factor negatively correlated with the number of iterations;
[0042] Step S2: In the exploration stage of the intelligent optimization algorithm, after each round of iteration, multiply the basic step size, step size correction coefficient, adaptive factor, and the best position obtained at the end of this round of iteration to obtain the initial position at the start of the next round of iteration; Based on the intelligent optimization algorithm, use the best position obtained after multiple rounds of iteration as the wavelet denoising parameters.
[0043] In this embodiment, in the OTDR system, the working state of the laser is controlled by the controller module. The laser starts to work and emits detection light, which is modulated with an external electrical pulse into an optical pulse signal and enters the fiber coupler. The fiber coupler is responsible for injecting the detection optical pulse into the fiber under test and receiving the reflected light returned from the fiber. The detector module is used to receive the reflected light of the fiber under test output by the fiber coupler. First, it performs photoelectric conversion to convert the reflected light signal into an electrical signal for signal processing, and then performs analog-to-digital conversion on the electrical signal. The controller module extracts the digital signal and outputs it to the signal processing module for denoising processing, and the denoised signal can be displayed.
[0044] Aiming at the signal processing problems encountered in the current research and development of OTDR systems in engineering, the wavelet denoising method is introduced into the signal processing unit of the system to improve the signal denoising effect and timeliness. Aiming at the problem that wavelet denoising is restricted by parameter configuration and is difficult to be directly applied, the configuration of each parameter of wavelet denoising is encoded and substituted into the intelligent optimization algorithm for iterative processing and continuous optimization. Finally, when the iteration ends, the parameter configuration information most suitable for the current OTDR signal can be obtained, thus accelerating the determination of wavelet denoising parameters and enabling its successful application.
[0045] Among them, in order to further improve the performance of the OTDR system and solve the long-term problem that the intelligent optimization algorithm is prone to falling into local optimal solutions, the current intelligent optimization algorithm is adaptively improved. An adaptive factor that changes with the number of iterations is introduced in the random search stage, and the search step size shows a decreasing trend as the number of iterations increases. Therefore, the algorithm has a larger search step size in the initial stage of iteration, improving the optimization ability, and reduces the search step size in the later stage of iteration to ensure the convergence of the algorithm.
[0046] Furthermore, in one embodiment, the maximum value of the above-mentioned adaptive factor is greater than 1, and the minimum value is less than 1.
[0047] In this embodiment, by setting the maximum value of the adaptive factor in the initial stage of iteration to be greater than 1, the initial search step size of the improved intelligent optimization algorithm is greater than that of the original intelligent optimization algorithm. By setting the minimum value of the adaptive factor in the final stage of iteration to be less than 1, the final search step size of the improved intelligent optimization algorithm is less than that of the original intelligent optimization algorithm. The improved initial step size is significantly greater than the initial step size before improvement, and the improved final step size is significantly less than the final step size before improvement, making the improved intelligent optimization algorithm have the characteristics of stronger initial optimization ability and stronger final convergence.
[0048] Furthermore, in one embodiment, the above-mentioned adaptive factor is calculated by the following formula (2):
[0049]
[0050] Where F dynamicdenotes the adaptive factor; F max denotes the custom maximum dynamic factor; F min denotes the custom minimum dynamic factor; l denotes the current iteration number; Max_iter denotes the custom maximum iteration number.
[0051] Furthermore, in one embodiment, the above-mentioned maximum dynamic factor is greater than 1, and the above-mentioned minimum dynamic factor is less than 1.
[0052] In this embodiment, after repeated experiments, when F max and F min are respectively set to 1.5 and 0.5, relatively excellent results can be obtained, which combines better random optimization effect and the original convergence performance.
[0053] Furthermore, in one embodiment, the above-mentioned optimal position is obtained by the following steps:
[0054] After configuring multiple sets of denoising parameters of the wavelet denoising method with multiple initial positions at the beginning of each round of iteration, based on each set of denoising parameters, the reflected signals received by the OTDR system are subjected to wavelet denoising respectively. The denoised signal is compared with the signal before denoising to obtain the signal-to-noise ratio or root mean square error. The initial position corresponding to the set of denoising parameters with the maximum signal-to-noise ratio or the minimum root mean square error is used as the optimal position at the end of this round of iteration.
[0055] In this embodiment, most intelligent optimization algorithms simulate natural processes and are widely used due to their global optimization characteristics. They are often used to solve path planning and production scheduling problems, and recently have also been used for neural network parameter optimization. Similarly, they are also applicable to the parameter optimization problem in wavelet denoising.
[0056] Taking the typical fox algorithm in the intelligent optimization algorithm as an example, in the fox algorithm, first, the coding information is mapped to different parameter selections in wavelet denoising through coding, and the signal-to-noise ratio or mean square error is used as the fitness, that is, the optimization objective. In each round of iteration, the position coding of N red foxes is processed by the algorithm. The obtained new position information is decoded and applied to wavelet denoising to calculate the fitness. The position corresponding to the best fitness is used as the optimal position, and then the next round of iteration processing is carried out.
[0057] In a specific embodiment, the optimization method of the original fox algorithm includes: First, a random optimization strategy based on the current best position. Second, a position update strategy based on the hunting behavior of foxes. The improvement of the present invention aims at the first point, multiplying the original iteration formula by an adaptive factor, referring to the above formula (2). This adaptive factor can ensure that the algorithm has a larger search step size in the initial stage of iteration, improving the optimization ability, and reducing the search step size in the later stage of iteration to ensure the convergence of the algorithm.
[0058] In the signal processing unit of the OTDR system, when using the fox algorithm to accelerate parameter determination, encoding operation is first performed. The position information of n foxes is randomly set, and the position information of each fox is a matrix of 1 row and a columns, corresponding to a parameter configurations of wavelet threshold denoising, including wavelet basis functions, threshold functions, etc. The value range of the random numbers is the value range of each parameter.
[0059] Taking the maximum improvement of the signal-to-noise ratio as the optimization goal, iterative calculation of the algorithm is carried out. In each round of iteration, the position information of n foxes is decoded, and the obtained parameter configuration scheme after decoding is substituted into wavelet denoising. The difference between the signal-to-noise ratio of the denoised signal and the original OTDR signal is calculated, or the root mean square error is calculated. The position information corresponding to the scheme with the largest signal-to-noise ratio difference or the smallest root mean square error is the best position of this round of iteration. Then the next round of iteration begins. According to the principle of the algorithm, random optimization is carried out based on the current best position using the above formula (2), or the position is updated based on the principle of fox hunting to obtain the position information of n foxes in the next round.
[0060] When the algorithm reaches the set maximum number of iterations, the iteration stops, and the finally obtained best position information is output, that is, the wavelet threshold denoising parameter scheme most suitable for the OTDR signal.
[0061] In order to verify the beneficial effects of the method proposed in this patent, a simulation experiment was designed. The traditional genetic algorithm, the original fox algorithm, and the adaptive fox algorithm proposed in this patent were used to determine the parameters of wavelet denoising respectively. The improvement effects of the signal-to-noise ratio and the root mean square error are shown in Table 1. The results show that the method proposed in this patent can effectively realize the application of wavelet threshold denoising. The obtained parameter configuration scheme ensures the largest improvement in the signal-to-noise ratio after wavelet threshold denoising and the largest reduction in the root mean square error. The time consumption is significantly reduced compared with the traditional algorithm. Applying it to the OTDR system can effectively solve the timeliness problem brought by the commonly used cumulative averaging method.
[0062] Table 1 Comparison of results of different algorithms for accelerating the determination of wavelet denoising parameters
[0063]
[0064] As Figure 2 shown is the schematic diagram of the attenuation curve of the test optical fiber before denoising, Figure 3 shown is the schematic diagram of the attenuation curve of the test optical fiber after denoising. It can also be seen from the figure that the denoising effect of the wavelet denoising function after optimization using the improved intelligent optimization algorithm has been significantly improved.
[0065] In summary, the combined use of wavelet denoising and intelligent optimization algorithms has relevant patents and papers published, but the algorithms used are relatively old and lack innovation. This patent first applies the new fox optimization algorithm to wavelet denoising and makes innovations at the algorithm level.
[0066] As a new intelligent optimization algorithm proposed in 2022, the fox optimization algorithm has relatively few related studies. Only in 2023, some scholars improved the constant parameters in the algorithm to adaptive parameters, but there is a lack of theoretical logic. Based on the original algorithm, the present invention introduces a new adaptive parameter in the random search stage, effectively improving the optimization ability of the algorithm and making the improved algorithm a more competitive intelligent optimization algorithm at present.
[0067] In the random optimization part of the original fox optimization algorithm, an adaptive factor that changes with the number of iterations is introduced, and the adaptive fox algorithm is proposed, which enhances the random search ability and avoids premature convergence to local optimal solutions. After verification, the improved algorithm has stronger optimization ability than the original algorithm. This algorithm can not only be applied to wavelet denoising, but also to any optimization problems that other intelligent optimization algorithms are good at.
[0068] Combining the advanced adaptive fox optimization algorithm with wavelet denoising to process the OTDR signal enables wavelet denoising that requires numerous parameter settings to be effectively applied in practice and achieves ideal results when facing signals with different characteristics, improving the practicality and effectiveness of the wavelet denoising method.
[0069] In a second aspect, the embodiments of the present application also provide a system for determining wavelet denoising parameters in an OTDR system.
[0070] In one embodiment, referring to Figure 4 , Figure 4 is a schematic diagram of the functional modules of an embodiment of the system for determining wavelet denoising parameters in the OTDR system of the present application. As shown in Figure 4 , the system for determining wavelet denoising parameters in the OTDR system includes:
[0071] Custom module 1, which is used to set an adaptive factor negatively correlated with the number of iterations;
[0072] Processing module 2, which is used to multiply the basic step size, step size correction coefficient, adaptive factor, and the best position obtained at the end of the current iteration after each round of iteration in the exploration stage of the intelligent optimization algorithm to obtain the initial position at the start of the next round of iteration; based on the intelligent optimization algorithm, the best position obtained after multiple rounds of iteration is used as the wavelet denoising parameter.
[0073] In this embodiment, in the OTDR system, the working state of the laser is controlled by the controller module. The laser starts to work and emits detection light, which is modulated with an external electrical pulse into an optical pulse signal and enters the optical fiber coupler. The optical fiber coupler is responsible for injecting the detection optical pulse into the fiber under test and receiving the reflected light returned from the fiber. The detector module is used to receive the reflected light of the fiber under test output by the optical fiber coupler. First, it performs photoelectric conversion to convert the reflected light signal into an electrical signal for signal processing, and then performs analog-to-digital conversion on the electrical signal. The controller module extracts the digital signal and outputs it to the signal processing module 2 for denoising processing, and the denoised signal can be displayed.
[0074] In view of the signal processing problems encountered in the current research and development of OTDR systems in engineering, the wavelet denoising method is introduced into the signal processing unit of the system to improve the signal denoising effect and timeliness. Aiming at the problem that wavelet denoising is restricted by parameter configuration and is difficult to be directly applied, the configuration of each parameter of wavelet denoising is encoded and substituted into the intelligent optimization algorithm for iterative processing and continuous optimization. Finally, when the iteration ends, the parameter configuration information most suitable for the current OTDR signal can be obtained, thus accelerating the determination of wavelet denoising parameters and enabling its successful application.
[0075] Among them, in order to further improve the performance of the OTDR system and solve the long-term problem that the intelligent optimization algorithm is prone to falling into local optimal solutions, the current intelligent optimization algorithm is adaptively improved. An adaptive factor that changes with the number of iterations is introduced in the random search stage, and the search step size shows a decreasing trend as the number of iterations increases. Thus, the algorithm has a larger search step size in the initial stage of iteration to improve the optimization ability, and reduces the search step size in the later stage of iteration to ensure the convergence of the algorithm.
[0076] Furthermore, in one embodiment, the above-mentioned custom module 1 calculates the adaptive factor using the following formula (2):
[0077]
[0078] Where, F dynamic represents the adaptive factor; F max represents the custom maximum dynamic factor; F min represents the custom minimum dynamic factor; l represents the current number of iterations; Max_iter represents the custom maximum number of iterations.
[0079] Furthermore, in one embodiment, the maximum value of the above-mentioned adaptive factor is greater than 1, and the minimum value is less than 1.
[0080] In this embodiment, by setting the maximum value of the adaptive factor at the initial stage of iteration to be greater than 1, the initial search step of the improved intelligent optimization algorithm is made larger than that of the original intelligent optimization algorithm. By setting the minimum value of the adaptive factor at the end stage of iteration to be less than 1, the final search step of the improved intelligent optimization algorithm is made smaller than that of the original intelligent optimization algorithm. The initial step of the improved algorithm is significantly larger than that before improvement, and the final step of the improved algorithm is significantly smaller than that before improvement, making the improved intelligent optimization algorithm have the characteristics of stronger initial optimization ability and stronger final convergence ability.
[0081] Further, in one embodiment, the above maximum dynamic factor is greater than 1, and the above minimum dynamic factor is less than 1.
[0082] In this embodiment, after repeated experiments, setting F max and F min to 1.5 and 0.5 respectively can obtain relatively excellent results, combining better random optimization effect and the original convergence performance.
[0083] Further, in one embodiment, after the above processing module 2 configures multiple sets of denoising parameters of the wavelet denoising method with multiple initial positions at the beginning of each round of iteration, based on each set of denoising parameters, the reflected signals received by the OTDR system are subjected to wavelet denoising respectively. The denoised signal is compared with the signal before denoising to obtain the signal-to-noise ratio or the root mean square error, and the initial position corresponding to the set of denoising parameters with the maximum signal-to-noise ratio or the minimum root mean square error is used as the best position at the end of this round of iteration.
[0084] In this embodiment, most intelligent optimization algorithms simulate natural processes and are widely used because of their global optimization characteristics. They are often used to solve path planning and production scheduling problems, and recently they have also been used for neural network parameter optimization. Similarly, they are also applicable to the parameter optimization problem in wavelet denoising.
[0085] Taking the typical fox algorithm in the intelligent optimization algorithm as an example, in the fox algorithm, first, the coding information is mapped to different parameter selections in wavelet denoising through coding, and the signal-to-noise ratio or the mean square error is used as the fitness, that is, the optimization goal. In each round of iteration, the position coding of N red foxes is processed by the algorithm. The obtained new position information is decoded and applied to wavelet denoising to calculate the fitness. The position corresponding to the best fitness is used as the best position, and then the next round of iteration processing is carried out.
[0086] Among them, the function implementation of each module in the system for determining wavelet denoising parameters in the above OTDR system corresponds to each step in the embodiment of the method for determining wavelet denoising parameters in the above OTDR system, and its function and implementation process will not be elaborated here one by one.
[0087] In practical applications, the optimization algorithm can be combined with the wavelet threshold denoising method, which can configure appropriate parameters for signals with different characteristics to ensure the effectiveness and practicality of wavelet denoising.
[0088] In practical applications, it is used in conjunction with the cumulative average denoising method with fewer times to effectively save time and achieve the best denoising effect.
[0089] An adaptive parameter is introduced into the fox optimization algorithm, which improves the global optimization ability on the basis of the new intelligent optimization algorithm. It has passed the test function set and significance test, making the algorithm highly competitive at present and achieving good results in different application scenarios.
[0090] It should be noted that the serial numbers of the embodiments of the present application above are only for description and do not represent the advantages or disadvantages of the embodiments.
[0091] In the description of the embodiments of the present application, the terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above drawings are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products or devices. The descriptions of the terms "first", "second", "third", etc. are used to distinguish different objects, etc., and do not represent a sequence, nor do they limit that "first", "second", and "third" are different types.
[0092] In the description of the embodiments of the present application, terms such as "exemplary", "for example" or "for illustration" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as "exemplary", "for example" or "for illustration" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of terms such as "exemplary", "for example" or "for illustration" is intended to present relevant concepts in a specific manner.
[0093] In the description of the embodiments of the present application, unless otherwise specified, " / " means "or". For example, A / B can mean A or B; "and / or" in the text is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of the present application, "a plurality" means two or more than two.
[0094] In some processes described in the embodiments of the present application, there are multiple operations or steps that appear in a specific order. However, it should be understood that these operations or steps may not be executed in the order in which they appear in the embodiments of the present application or may be executed in parallel. The serial numbers of the operations are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed in sequence or in parallel, and these operations or steps may be combined.
[0095] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-described embodiment methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above and includes several instructions for causing a terminal device to execute the methods described in the various embodiments of the present application.
[0096] The above are only the preferred embodiments of the present application, and do not limit the patent scope of the present application. Any equivalent structural or equivalent process transformation made by using the contents of the specification and drawings of the present application, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present application.
Claims
1. A method for determining wavelet denoising parameters in an OTDR system, characterized in that: The method comprises: Set an adaptive factor that is negatively correlated with the number of iterations; In the exploration phase of the intelligent optimization algorithm, after each round of iteration, the basic step size, step size correction coefficient, adaptive factor, and the best position obtained at the end of this round of iteration are multiplied to obtain the initial position at the beginning of the next round of iteration; Based on the intelligent optimization algorithm, the best position obtained after multiple rounds of iterations is used as the wavelet denoising parameter.
2. The method for determining wavelet denoising parameters in an OTDR system according to claim 1, wherein: The maximum value of the adaptive factor is greater than 1, and the minimum value is less than 1.
3. The method for determining wavelet denoising parameters in the OTDR system according to claim 1, characterized in that: The adaptive factor is calculated using the following formula: Among them, F dynamic represents the adaptive factor; F max Indicates the maximum dynamic factor of the user-defined function; F min Indicates the customized minimum dynamic factor; l indicates the current number of iterations; Max_iter indicates the customized maximum number of iterations.
4. The method for determining wavelet denoising parameters in an OTDR system according to claim 3, wherein: The maximum dynamic factor is greater than 1, and the minimum dynamic factor is less than 1.
5. The method for determining wavelet denoising parameters in an OTDR system according to claim 1, wherein: The optimal position is obtained by the following steps: After configuring multiple sets of noise filtering parameters of the wavelet noise filtering method at multiple initial positions at the beginning of each round of iteration, wavelet noise is filtered out for the reflected signal received by the OTDR system based on each set of noise filtering parameters, and the denoised signal is compared with the signal before denoising to obtain the signal-to-noise ratio or root mean square error. The initial position corresponding to the set of noise filtering parameters with the maximum signal-to-noise ratio or the minimum root mean square error is taken as the optimal position at the end of this round of iteration.
6. A system for determining wavelet denoising parameters in an OTDR system, characterized in that: The system comprises: A custom module, which is used to set an adaptive factor that is negatively correlated with the number of iterations; The processing module is used to multiply the basic step size, step size correction coefficient, adaptive factor, and the best position obtained at the end of each round of iteration in the exploration stage of the intelligent optimization algorithm to obtain the initial position at the beginning of the next round of iteration; based on the intelligent optimization algorithm, the best position obtained after multiple rounds of iteration is used as the wavelet denoising parameter.
7. The system for determining wavelet denoising parameters in an OTDR system according to claim 6, characterized in that: The maximum value of the adaptive factor is greater than 1, and the minimum value is less than 1.
8. The system for determining wavelet denoising parameters in an OTDR system according to claim 6, characterized in that: The custom module uses the following formula to calculate the adaptive factor: Among them, F dynamoc represents the adaptive factor; F max Indicates the maximum dynamic factor of the user-defined function; F mon Indicates the customized minimum dynamic factor; l indicates the current number of iterations; Max_iter indicates the customized maximum number of iterations.
9. The system for determining wavelet denoising parameters in an OTDR system according to claim 8, characterized in that: The maximum dynamic factor is greater than 1, and the minimum dynamic factor is less than 1.
10. The system for determining wavelet denoising parameters in an OTDR system according to claim 6, characterized in that: After the processing module configures multiple sets of noise filtering parameters of the wavelet noise filtering method at multiple initial positions at the beginning of each round of iteration, the reflected signal received by the OTDR system is subjected to wavelet noise filtering based on each set of noise filtering parameters, and the denoised signal is compared with the signal before denoising to obtain a signal-to-noise ratio or a root mean square error. The initial position corresponding to the set of noise filtering parameters with the maximum signal-to-noise ratio or the minimum root mean square error is used as the optimal position at the end of this round of iteration.