Ultrasonic echo signal processing method, system and electronic device

Abstract

The application provides an ultrasonic echo signal processing method and system and electronic equipment, and relates to the technical field of ultrasonic signal processing. The method only optimizes the sampling delay time and attenuation coefficient of the ultrasonic echo signal, reduces the complexity of calculation, introduces a nonlinear iteration process into an artificial bee colony algorithm, and improves the performance of the artificial bee colony algorithm by using a particle swarm optimization algorithm, so that efficient signal reconstruction is realized, and the interference of noise in the ultrasonic echo signal is effectively suppressed.

Description

Technical Field

[0001] The present invention relates to the technical field of ultrasonic signal processing, and in particular to an ultrasonic echo signal processing method, system and electronic equipment. Background Art

[0002] In ultrasonic testing technology, echo signal parameter estimation and denoising are critical steps, directly impacting detection accuracy and real-time performance. Traditional ultrasonic echo signal models typically incorporate multiple parameters (such as amplitude, frequency, and attenuation coefficient), requiring joint estimation using complex optimization algorithms. This results in high computational complexity and slow convergence, making it difficult to meet the demands of real-time scenarios such as industrial nondestructive testing and medical imaging. Furthermore, in high-noise environments, traditional methods lack the ability to suppress noise, significantly increasing parameter estimation errors and further impacting reconstruction accuracy. Summary of the Invention

[0003] In view of this, the object of the present invention is to provide an ultrasonic echo signal processing method, system and electronic equipment, which only optimizes the parameters of the sampling delay time and attenuation coefficient of the ultrasonic echo signal to reduce the complexity of the calculation; and introduces a nonlinear iterative process into the artificial bee colony algorithm, and uses the particle swarm optimization algorithm to improve the performance of the artificial bee colony algorithm, thereby achieving efficient signal reconstruction and effectively suppressing the interference of noise in the ultrasonic echo signal.

[0004] In a first aspect, an embodiment of the present invention provides a method for processing an ultrasonic echo signal, the method comprising:

[0005] A mathematical model corresponding to the ultrasonic echo signal is constructed according to the sampling time, amplitude, frequency, sampling delay time and attenuation coefficient of the ultrasonic echo signal, and the sampling delay time and attenuation coefficient are determined as parameters to be optimized in the mathematical model;

[0006] Based on the mathematical model, the improved artificial bee colony algorithm is used to perform nonlinear selection and iterative processing on the optimization parameters. The particle swarm optimization algorithm is used to accelerate the iterative processing process and obtain the fitness value of the parameters to be optimized in real time.

[0007] When the fitness value meets the preset convergence condition, the iterative processing process is stopped, and the ultrasonic echo signal is reconstructed using the current sampling delay time and attenuation coefficient.

[0008] Optionally, when the sampling time is not less than the sampling delay time, the mathematical model is:

[0009] ;

[0010] in, The sampling time is Ultrasonic echo signal at the time; is an amplitude of the ultrasonic echo signal; is a first attenuation coefficient; is a sampling delay time; is a first frequency of the ultrasonic echo signal.

[0011] Optionally, when the sampling time is less than the sampling delay time, the mathematical model is:

[0012] ;

[0013] wherein, is the ultrasonic echo signal when the sampling time is ; is an amplitude of the ultrasonic echo signal; is a second attenuation coefficient; is a sampling delay time; is a second frequency of the ultrasonic echo signal.

[0014] Optionally, before constructing the mathematical model corresponding to the ultrasonic echo signal according to the sampling time, the amplitude, the frequency, the sampling delay time and the attenuation coefficient of the ultrasonic echo signal, the method further comprises:

[0015] obtaining a data set corresponding to the ultrasonic echo signal, and determining the amplitude based on a maximum value of the signal in the data set;

[0016] calculating and obtaining the frequency based on a wave number between preset adjacent sampling points of the ultrasonic echo signal and a sampling frequency;

[0017] calculating and obtaining the sampling time according to the sampling frequency and a marking value of the sampling point.

[0018] Optionally, the nonlinear selection iteration processing is performed on the to-be-optimized parameters based on the mathematical model and by using the improved artificial bee colony algorithm, comprising:

[0019] generating an initial honey source corresponding to the to-be-optimized parameters in the artificial bee colony algorithm based on the mathematical model, and obtaining a plurality of honey sources corresponding to the to-be-optimized parameters based on the initial honey source;

[0020] calculating a normalized fitness value corresponding to the honey source, and calculating a normalized fitness sum corresponding to all the honey sources by using the normalized fitness value;

[0021] obtaining an iteration number corresponding to the normalized fitness value, and calculating a nonlinear attenuation factor by using the iteration number;

[0022] calculating a probability value of the honey source being selected by using the normalized fitness sum and the nonlinear attenuation factor;

[0023] updating and iterating the to-be-optimized parameters corresponding to the honey source according to the probability value.

[0024] Optionally, the probability value is calculated using the following formula:

[0025] ;

[0026] in, The number of iterations is When selecting The probability value corresponding to each nectar source; For the Normalized fitness value corresponding to each nectar source; is the sum of normalized fitness; The number of iterations is The nonlinear attenuation factor when ;

[0027] Normalized fitness value Calculated by the following formula:

[0028] ;

[0029] in, ; For the The original fitness value corresponding to each nectar source, and are the original signal function value and the reconstructed signal function value corresponding to the parameters to be optimized at the kth iteration respectively; is the sampling time.

[0030] Optionally, a particle swarm optimization algorithm is used to accelerate the iterative process and obtain the fitness value of the parameters to be optimized in real time, including:

[0031] Obtain the initial positions of employed bees, observer bees, and scout bees corresponding to the artificial bee colony algorithm, as well as the fitness values ​​corresponding to the nectar source;

[0032] The particle swarm optimization algorithm is used to adjust the position of the hired bees, and the fitness value is used to determine the search direction of the hired bees;

[0033] When the number of nectar source iterations exceeds the preset threshold, a scout bee is obtained and the particle swarm optimization algorithm is used to calculate the new nectar source corresponding to the scout bee;

[0034] Obtain the new fitness value corresponding to the new nectar source in real time. Stop the iteration when the new fitness value converges and obtain the current new fitness value.

[0035] Optionally, after the step of obtaining the initial positions of employed bees, observer bees, and scout bees corresponding to the artificial bee colony algorithm and the fitness values ​​corresponding to the nectar sources, the method further includes:

[0036] Multiple threads are created based on the search tasks corresponding to employed bees, observer bees and scout bees, so that each thread executes the search task independently.

[0037] In a second aspect, the present invention provides an ultrasonic echo signal processing system, the system comprising:

[0038] A model building processing module is used to build a mathematical model corresponding to the ultrasonic echo signal according to the sampling time, amplitude, frequency, sampling delay time and attenuation coefficient of the ultrasonic echo signal, and determine the sampling delay time and attenuation coefficient as parameters to be optimized in the mathematical model;

[0039] The global optimization processing module is used to perform nonlinear selection and iterative processing of the optimization parameters based on the mathematical model and the improved artificial bee colony algorithm, and to accelerate the iterative processing process by using the particle swarm optimization algorithm and obtain the fitness value of the optimization parameters in real time;

[0040] The signal reconstruction processing module is used to stop the iterative processing process when the fitness value meets the preset convergence condition, and reconstruct the ultrasonic echo signal using the current sampling delay time and attenuation coefficient.

[0041] In a third aspect, an embodiment of the present invention further provides an electronic device comprising a processor and a memory, wherein the memory stores computer-executable instructions that can be executed by the processor, and the processor executes the computer-executable instructions to implement the steps of the ultrasonic echo signal processing method provided in the first aspect.

[0042] Embodiments of the present invention provide an ultrasonic echo signal processing method, system, and electronic device. During the noise reduction and generation process of an ultrasonic echo signal, a mathematical model corresponding to the ultrasonic echo signal is first constructed based on the ultrasonic echo signal's sampling time, amplitude, frequency, sampling delay, and attenuation coefficient. The sampling delay and attenuation coefficient are determined as the parameters to be optimized in the mathematical model. Based on the mathematical model, an improved artificial bee colony algorithm is then used to perform nonlinear iterative selection of the parameters to be optimized. A particle swarm optimization algorithm is used to accelerate the iterative process and obtain the fitness values ​​of the parameters to be optimized in real time. When the fitness values ​​meet a preset convergence condition, the iterative process is terminated, and the ultrasonic echo signal is reconstructed using the current sampling delay and attenuation coefficient. This method optimizes only the sampling delay and attenuation coefficient of the ultrasonic echo signal, reducing computational complexity. A nonlinear iterative process is introduced into the artificial bee colony algorithm, and the particle swarm optimization algorithm is used to improve the performance of the artificial bee colony algorithm, thereby achieving efficient signal reconstruction and effectively suppressing noise interference in the ultrasonic echo signal.

[0043] Other features and advantages of the present application will be set forth in the descriptions that follow, and in part will be apparent from the description, or can be learned by practice of the application. The purposes and other advantages of the application will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.

[0044] In order to make the above objectives, features and advantages of the present application more apparent, the following will describe a preferred embodiment in detail, and the accompanying drawings will be described as follows. BRIEF DESCRIPTION OF DRAWINGS

[0045] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the following will briefly introduce the drawings needed to be used in the specific embodiments or the prior art description. Obviously, the drawings described below are some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor based on these drawings.

[0046] Figure 1 A flow chart of an ultrasonic echo signal processing method provided by an embodiment of the present application;

[0047] Figure 2 A flow chart before step S101 of an ultrasonic echo signal processing method provided by an embodiment of the present application;

[0048] Figure 3 A flow chart of nonlinear selection and iteration processing of the to-be-optimized parameters based on the mathematical model and using the improved artificial bee colony algorithm in step S102 of an ultrasonic echo signal processing method provided by an embodiment of the present application;

[0049] Figure 4 A flow chart of using the particle swarm optimization algorithm to accelerate the calculation of the iteration processing flow and to obtain the fitness value of the to-be-optimized parameters in real time in step S102 of an ultrasonic echo signal processing method provided by an embodiment of the present application;

[0050] Figure 5 An iteration process schematic diagram of an ultrasonic echo signal processing method provided by an embodiment of the present application;

[0051] Figure 6 An effect comparison diagram of an ultrasonic echo signal processing method provided by an embodiment of the present application;

[0052] Figure 7 A structural schematic diagram of an ultrasonic echo signal processing system provided by an embodiment of the present application;

[0053] Figure 8 A structural schematic diagram of an electronic device provided by an embodiment of the present application.

[0054] icon:

[0055] 710-model building processing module; 720-global optimization processing module; 730-signal reconstruction processing module;

[0056] 101 - processor; 102 - memory; 103 - bus; 104 - communication interface. DETAILED DESCRIPTION

[0057] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0058] In ultrasonic testing technology, echo signal parameter estimation and denoising are critical steps, directly impacting detection accuracy and real-time performance. Traditional ultrasonic echo signal models typically incorporate multiple parameters (such as amplitude, frequency, and attenuation coefficient), requiring joint estimation using complex optimization algorithms. This results in high computational complexity and slow convergence, making it difficult to meet the demands of real-time scenarios such as industrial nondestructive testing and medical imaging. Furthermore, in high-noise environments, traditional methods lack the ability to suppress noise, significantly increasing parameter estimation errors and further impacting reconstruction accuracy.

[0059] Existing optimization algorithms (such as genetic algorithms) are inefficient when processing high-dimensional parameter spaces and have difficulty balancing global search and convergence speed. Although GPU parallel computing technology can improve efficiency, its combination with optimization algorithms often has limited acceleration effects due to problems such as thread synchronization and memory access conflicts. Therefore, how to achieve efficient parameter estimation and denoising of ultrasonic echo signals in high-noise environments by simplifying models, optimizing algorithm architectures, and using multi-threaded parallel acceleration technology has become an urgent problem to be solved. Based on this, the present invention implements a method, system, and electronic device for processing ultrasonic echo signals. This method only optimizes the sampling delay time and attenuation coefficient of the ultrasonic echo signal to reduce the complexity of the calculation; and introduces a nonlinear iterative process into the artificial bee colony algorithm, and uses the particle swarm optimization algorithm to improve the performance of the artificial bee colony algorithm, thereby achieving efficient signal reconstruction and effectively suppressing the interference of noise in the ultrasonic echo signal.

[0060] To facilitate understanding of this embodiment, a method for processing ultrasonic echo signals disclosed in an embodiment of the present invention is first described in detail. Figure 1 As shown, the method includes:

[0061] Step S101, a mathematical model corresponding to the ultrasonic echo signal is constructed according to the sampling time, amplitude, frequency, sampling delay time and attenuation coefficient of the ultrasonic echo signal, and the sampling delay time and the attenuation coefficient are determined as the parameters to be optimized of the mathematical model.

[0062] Among the five parameters of the sampling time, amplitude, frequency, sampling delay time and attenuation coefficient involved in the mathematical model, only the sampling delay time and the attenuation coefficient are determined as the parameters to be optimized of the mathematical model, and the remaining parameters are quickly obtained through a specific calculation method, thereby significantly reducing the calculation complexity and improving the model convergence speed.

[0063] Optionally, when the sampling time is not less than the sampling delay time, the mathematical model is:

[0064] ;

[0065] When the sampling time is less than the sampling delay time, the mathematical model is:

[0066] ;

[0067] Wherein, is the ultrasonic echo signal when the sampling time is ; is the amplitude of the ultrasonic echo signal; is the first attenuation coefficient; is the second attenuation coefficient; is the sampling delay time; is the first frequency of the ultrasonic echo signal; is the second frequency of the ultrasonic echo signal.

[0068] In the above mathematical model, the parameters to be optimized are only the sampling delay time , the attenuation coefficient and , and the remaining parameters such as , , can be directly calculated, and the calculation process will be introduced later.

[0069] Step S102, based on the mathematical model and using the improved artificial bee colony algorithm, the parameters to be optimized are processed by nonlinear selection iteration, and the particle swarm optimization algorithm is used to accelerate the calculation of the iteration process and obtain the fitness value of the parameters to be optimized in real time.

[0070] After obtaining the mathematical model, the artificial bee colony algorithm used is , , The global optimization involves a nonlinear decreasing selection strategy, thereby performing nonlinear selection iteration processing on the to-be-optimized parameters. The core steps of the artificial bee colony algorithm mainly include:

[0071] Initialization: randomly generate an initial population of honey sources (solutions).

[0072] Employed bee phase: employed bees search for new honey sources (new solutions) near the current honey source and select better honey sources according to the quality (fitness function).

[0073] Onlooker bee phase: onlooker bees select honey sources and perform further search according to the information shared by employed bees.

[0074] Scout bee phase: if a honey source has not been improved after multiple iterations, a scout bee will be sent out to explore a new honey source.

[0075] Repeat phase: repeat the above steps until the termination condition (such as the number of iterations, solution quality, etc.) is reached.

[0076] This step introduces nonlinear selection iteration processing in the artificial bee colony algorithm, dynamically adjusts the priority of onlooker bees selecting honey sources, realizes the balance between strong global search in the early stage and strong local search in the later stage of the algorithm, accelerates the calculation of the iteration processing flow by introducing the particle swarm optimization algorithm, and obtains the fitness value of the to-be-optimized parameters in real time, which significantly improves the optimization performance of the particle swarm optimization algorithm.

[0077] Step S103, when the fitness value meets the preset convergence condition, stop the iteration processing flow, and reconstruct the ultrasonic echo signal using the current sampling delay time and attenuation coefficient.

[0078] After the specific convergence condition of the fitness value in the iteration processing flow is met, the optimal parameter of the to-be-optimized parameter is obtained, and the ultrasonic echo signal is reconstructed using the sampling delay time and attenuation coefficient in the current to-be-optimized parameter, thereby obtaining the denoised ultrasonic echo signal.

[0079] Optionally, before constructing the mathematical model corresponding to the ultrasonic echo signal according to the sampling time, amplitude, frequency, sampling delay time, and attenuation coefficient of the ultrasonic echo signal, as shown in the method further includes: Figure 2

[0080] Step S201, obtaining a data set corresponding to the ultrasonic echo signal, and determining the amplitude based on the maximum value of the signal in the data set;

[0081] Step S202, calculating and obtaining the frequency based on the wave number between the preset adjacent sampling points of the ultrasonic echo signal and the sampling frequency;

[0082] ​Step S203, the sampling time is calculated according to the sampling frequency and the marking value of the sampling point.

[0083] Amplitude of the ultrasonic echo signal is obtained from the collected ultrasonic signal data set by calculating the maximum value in the data set and determining it as the amplitude of the ultrasonic echo signal .

[0084] Due to the frequency and correspond to the frequency in two scenarios respectively, for the convenience of description, it is described as , the frequency is obtained from the reciprocal of the period T, and T is the number of sampling points multiplied by the reciprocal of the sampling frequency, which is as follows:

[0085] ;

[0086] Wherein, is the abscissa corresponding to the first sampling point, is the abscissa corresponding to the second sampling point, and the difference between the two abscissas is the number of corresponding sampling points. N is the number of waves between the two sampling points, is the reciprocal of the sampling frequency. Assuming =201, =1, and the number of waves N between them is 4, and the sampling frequency is 20MHz, then milliseconds, then through the above formula, T=400 milliseconds can be obtained, and the frequency of the wave , that is:

[0087] .

[0088] The sampling time t is calculated by , wherein i represents the i-th sampling point.

[0089] Optionally, the nonlinear selection iteration processing of the to-be-optimized parameters is performed based on the mathematical model and by using the improved artificial bee colony algorithm, as shown in Figure 3 , including:

[0090] Step S301, generating an initial honey source corresponding to the to-be-optimized parameters in the artificial bee colony algorithm based on the mathematical model, and obtaining a plurality of honey sources corresponding to the to-be-optimized parameters based on the initial honey source;

[0091] Step S302, calculating the normalized fitness value corresponding to the honey source, and calculating the normalized fitness sum corresponding to all honey sources by using the normalized fitness value;

[0092] Step S303, obtaining the number of iterations corresponding to the normalized fitness value, and using the number of iterations to calculate the nonlinear attenuation factor;

[0093] Step S304, calculating the probability value of the nectar source being selected using the normalized fitness sum and the nonlinear attenuation factor;

[0094] Step S305: update and iterate the parameters to be optimized corresponding to the nectar source according to the probability value.

[0095] After obtaining multiple nectar sources, the normalized fitness value of the i-th nectar source is calculated by the following formula: Calculated by the following formula:

[0096] ;

[0097] in, ; For the The original fitness value corresponding to each nectar source, and are the original signal function value and the reconstructed signal function value corresponding to the parameters to be optimized at the kth iteration respectively; is the sampling time; For the The normalized fitness value corresponding to each nectar source.

[0098] Then, the normalized fitness value is used to calculate the sum of the normalized fitness corresponding to all nectar sources, which is calculated using the following formula:

[0099] ;

[0100] in, is the sum of normalized fitness.

[0101] Then the nonlinear attenuation factor is calculated according to the current iteration number i ,in .

[0102] The final probability value is calculated using the following formula:

[0103] ;

[0104] in, The number of iterations is When selecting The probability value corresponding to each nectar source can be obtained through the roulette mechanism and according to Select honey sources for updating to achieve an adaptive balance between the early global search and the later local search of the algorithm.

[0105] Optionally, a particle swarm optimization algorithm is used to accelerate the iterative process and obtain the fitness value of the parameters to be optimized in real time, such as Figure 4 Shown, including:

[0106] Step S401, obtaining the initial positions of employed bees, observer bees, and scout bees corresponding to the artificial bee colony algorithm and the fitness values ​​corresponding to the nectar source;

[0107] Step S402, using a particle swarm optimization algorithm to adjust the position of the employed bees, and using the fitness value to determine the search direction of the employed bees;

[0108] Step S403: When the number of nectar source iterations exceeds a preset threshold, a scout bee is obtained, and a particle swarm optimization algorithm is used to calculate a new nectar source corresponding to the scout bee;

[0109] Step S404: obtain the new fitness value corresponding to the new nectar source in real time, stop iteration when the new fitness value converges, and obtain the current new fitness value.

[0110] The particle swarm optimization algorithm acceleration process includes: randomly generating the initial positions of employed bees, observer bees, and scout bees, and calculating the fitness values; then using the particle swarm optimization algorithm's speed update formula to adjust the positions of employed bees. If the new solution is better, the original nectar source is replaced; then a roulette wheel strategy is used to select the nectar source based on the fitness value, and the search direction is optimized in combination with the global optimal information of the particle swarm optimization algorithm; when the number of nectar source iterations exceeds a threshold, scout bees are generated, and the global optimal solution of the particle swarm optimization algorithm is used to generate a new nectar source; when the maximum number of iterations is reached or the fitness converges, the algorithm stops and outputs the optimal solution.

[0111] a) Optionally, after obtaining the initial positions of employed bees, observer bees, and scout bees corresponding to the artificial bee colony algorithm, as well as the fitness values ​​corresponding to the nectar sources, the method further includes: creating multiple threads based on the search tasks corresponding to the employed bees, observer bees, and scout bees, respectively, so that each thread independently performs the search task. In actual processing, multithreading technology can be used to improve parallel efficiency. Multiple threads are created using the POSIX thread library, each of which independently performs the search tasks of the employed bees, observer bees, and scout bees in the artificial bee colony algorithm. Local optimal solutions are then cached in shared memory, and the global optimal solution is synchronized through atomic operations to avoid thread contention. Finally, the number of threads is dynamically allocated to adapt to hardware resources, thereby improving the parallel efficiency of the algorithm.

[0112] The ultrasonic echo signal processing method in this embodiment simplifies the traditional five-parameter model into a single sampling delay that only needs to be optimized. Two attenuation coefficients ( and ), the other parameters (amplitude β, frequency and The method is quickly obtained through direct calculation, significantly reduces the calculation complexity, and improves the model convergence speed.

[0113] In addition, a nonlinear decreasing selection probability formula is introduced into the artificial bee colony algorithm , the priority of the observation bee selecting the honey source is dynamically adjusted, the balance between strong global search in the early stage and strong local search in the late stage of the algorithm is realized, multi-thread parallel processing is realized based on pthread, the optimization result is synchronized and optimized through shared memory and atomic operation, the speedup ratio of the algorithm on a multi-core CPU is improved, and the acceleration mechanism of the particle swarm optimization algorithm is introduced, so that the optimization performance of the artificial bee colony algorithm is significantly improved.

[0114] In a high-noise environment, through the synergistic effect of parameter optimization and signal reconstruction, the interference of noise is effectively suppressed, and the real-time requirement of industrial detection and medical imaging scenes is met. Figure 5 As shown in the iteration process diagram of the ultrasonic echo signal processing method, it can be seen that the iteration number of the method is less than 20 times to reach convergence.

[0115] As shown in the effect comparison diagram of the ultrasonic echo signal processing method, the red curve is the original signal curve collected in real time, that is, the ultrasonic waveform received in real time, and the green curve is the signal curve after parameter estimation and reconstruction of the signal data, that is, the waveform after reconstruction by the artificial bee colony algorithm. Figure 6 As shown in the effect comparison diagram of the ultrasonic echo signal processing method, the red curve is the original signal curve collected in real time, that is, the ultrasonic waveform received in real time, and the green curve is the signal curve after parameter estimation and reconstruction of the signal data, that is, the waveform after reconstruction by the artificial bee colony algorithm.

[0116] As can be seen from the ultrasonic echo signal processing method mentioned in the above embodiment, the method only optimizes the sampling delay time and the attenuation coefficient of the ultrasonic echo signal, reduces the complexity of the calculation, introduces a nonlinear iteration process into the artificial bee colony algorithm, and improves the performance of the artificial bee colony algorithm by using the particle swarm optimization algorithm, so as to realize efficient signal reconstruction and effectively suppress the interference of noise in the ultrasonic echo signal.

[0117] Corresponding to the ultrasonic echo signal processing method provided in the foregoing embodiment, the embodiment of the present application provides an ultrasonic echo signal processing system, as shown in Figure 7 The system comprises:

[0118] The model construction processing module 710 is configured to construct a mathematical model corresponding to the ultrasonic echo signal according to the sampling time, amplitude, frequency, sampling delay time and attenuation coefficient of the ultrasonic echo signal, and determine the sampling delay time and the attenuation coefficient as the parameters to be optimized of the mathematical model.

[0119] The global optimization processing module 720 is configured to perform nonlinear selection iteration processing on the to-be-optimized parameters based on the mathematical model and by using the improved artificial bee colony algorithm, and perform accelerated calculation on the iteration processing procedure and obtain the fitness value of the to-be-optimized parameters in real time by using the particle swarm optimization algorithm.

[0120] The signal reconstruction processing module 730 is configured to stop the iteration processing procedure when the fitness value meets the preset convergence condition, and reconstruct the ultrasonic echo signal by using the current sampling delay time and the attenuation coefficient.

[0121] It can be learned from the ultrasonic echo signal processing system mentioned in the foregoing embodiments that the system only performs parameter optimization on the sampling delay time and the attenuation coefficient of the ultrasonic echo signal, reduces the complexity of calculation, introduces a nonlinear iteration procedure into the artificial bee colony algorithm, and improves the performance of the artificial bee colony algorithm by using the particle swarm optimization algorithm, thereby realizing efficient signal reconstruction and effectively suppressing the interference of noise in the ultrasonic echo signal.

[0122] The ultrasonic echo signal processing system provided in the embodiments of the present application has the same implementation principle and technical effects as the foregoing ultrasonic echo signal processing method embodiments, and for brevity of description, the part of the system embodiments not mentioned can be referred to the corresponding content in the foregoing ultrasonic echo signal processing method embodiments.

[0123] The present embodiment also provides an electronic device, a structure diagram of which is shown in Figure 8 The device includes a processor 101 and a memory 102; the memory 102 is configured to store one or more computer instructions, and the one or more computer instructions are executed by the processor to implement the steps of the foregoing ultrasonic echo signal processing method.

[0124] Figure 8 The electronic device shown in the figure also includes a bus 103 and a communication interface 104, and the processor 101, the communication interface 104 and the memory 102 are connected through the bus 103.

[0125] The memory 102 can include a high-speed random access memory (RAM) and can also include a non-volatile memory, for example, at least one disk memory. The bus 103 can be an ISA bus, a PCI bus or an EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the convenience of representation, Figure 8 In the figure, only one bidirectional arrow is used to represent the bus, but it does not mean that there is only one bus or only one type of bus.

[0126] The communication interface 104 is used to connect to at least one user terminal and other network units through a network interface, and send the encapsulated IPv4 message or IPv4 message to the user terminal through the network interface.

[0127] The processor 101 may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by an integrated logic circuit of hardware in the processor 101 or by instructions in the form of software. The above-mentioned processor 101 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The various methods, steps, and logic block diagrams disclosed in the embodiments of the present disclosure can be implemented or executed. The general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc. The steps of the method disclosed in conjunction with the embodiments of the present disclosure can be directly embodied as being executed by a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium well-known in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. The storage medium is located in memory 102, and processor 101 reads information in memory 102 and, in conjunction with its hardware, completes the steps of the method of the aforementioned embodiment.

[0128] An embodiment of the present invention further provides a storage medium storing a computer program. When the computer program is executed by a processor, the steps of the ultrasonic echo signal processing method in the aforementioned embodiment are executed.

[0129] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, equipment and methods can be implemented in other ways. The system embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interface, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0130] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0131] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0132] If the functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage media include various media capable of storing program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0133] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the above-described embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily conceive of changes to the technical solutions described in the above-described embodiments within the technical scope disclosed by the present invention, or replace some of the technical features therein with equivalents. Such modifications, changes, or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A method for processing ultrasonic echo signals, characterized in that: The method comprises: Constructing a mathematical model corresponding to the ultrasonic echo signal according to the sampling time, amplitude, frequency, sampling delay time and attenuation coefficient of the ultrasonic echo signal, and determining the sampling delay time and the attenuation coefficient as parameters to be optimized of the mathematical model; Based on the mathematical model and using an improved artificial bee colony algorithm, a nonlinear selection iterative process is performed on the parameters to be optimized, and a particle swarm optimization algorithm is used to accelerate the iterative process and obtain the fitness value of the parameters to be optimized in real time; When the fitness value satisfies a preset convergence condition, the iterative processing flow is stopped, and the ultrasonic echo signal is reconstructed using the current sampling delay time and the attenuation coefficient; The particle swarm optimization algorithm is used to accelerate the iterative process and obtain the fitness value of the parameter to be optimized in real time, including: Obtaining the initial positions of employed bees, observer bees, and scout bees corresponding to the artificial bee colony algorithm and the fitness values ​​corresponding to the nectar sources; Using a particle swarm optimization algorithm to adjust the position of the employed bees, and using the fitness value to determine the search direction of the employed bees; When the number of iterations of the nectar source exceeds a preset threshold, the scout bee is obtained, and the particle swarm optimization algorithm is used to calculate a new nectar source corresponding to the scout bee; A new fitness value corresponding to the new nectar source is obtained in real time, and when the new fitness value converges, iteration is stopped and the current new fitness value is obtained.

2. The ultrasonic echo signal processing method according to claim 1, characterized in that: When the sampling time is not less than the sampling delay time, the mathematical model is: ； in, The sampling time is The ultrasonic echo signal at ; is the amplitude of the ultrasonic echo signal; is the first attenuation coefficient; is the sampling delay time; is the first frequency of the ultrasonic echo signal.

3. The ultrasonic echo signal processing method according to claim 1, characterized in that: When the sampling time is less than the sampling delay time, the mathematical model is: ； in, The sampling time is The ultrasonic echo signal at ; is the amplitude of the ultrasonic echo signal; is the second attenuation coefficient; is the sampling delay time; is the second frequency of the ultrasonic echo signal.

4. The ultrasonic echo signal processing method according to claim 1, wherein: Before constructing a mathematical model corresponding to the ultrasonic echo signal according to the sampling time, amplitude, frequency, sampling delay time and attenuation coefficient of the ultrasonic echo signal, the method further includes: Acquiring a data set corresponding to the ultrasonic echo signal, and determining the amplitude based on a maximum value of a signal in the data set; Calculating and obtaining the frequency based on the wave number and sampling frequency between preset adjacent sampling points of the ultrasonic echo signal; The sampling time is calculated and obtained according to the sampling frequency and the mark value of the sampling point.

5. The ultrasonic echo signal processing method according to claim 1, characterized in that: Based on the mathematical model and using an improved artificial bee colony algorithm, a nonlinear selection iterative process is performed on the parameters to be optimized, including: Randomly generating an initial nectar source corresponding to the parameter to be optimized in the artificial bee colony algorithm based on the parameter to be optimized of the mathematical model, and obtaining multiple nectar sources corresponding to the parameter to be optimized based on the initial nectar source; Calculating the normalized fitness value corresponding to the nectar source, and using the normalized fitness value to calculate the sum of the normalized fitness values ​​corresponding to all nectar sources; Obtaining the number of iterations corresponding to the normalized fitness value, and calculating a nonlinear attenuation factor using the number of iterations; Calculating the probability value of the nectar source being selected using the normalized fitness sum and the nonlinear attenuation factor; The parameters to be optimized corresponding to the nectar source are updated and iterated according to the probability value.

6. The ultrasonic echo signal processing method according to claim 5, characterized in that: The probability value is calculated by the following formula: ； in, The number of iterations is When selecting The probability value corresponding to each of the nectar sources; For the The normalized fitness value corresponding to each of the nectar sources; is the sum of the normalized fitness; The number of iterations is The nonlinear attenuation factor when , and ; The normalized fitness value Calculated by the following formula: ； in, ; For the The original fitness value corresponding to each of the nectar sources, and are the original signal function value and the reconstructed signal function value corresponding to the parameter to be optimized at the kth iteration respectively; is the sampling time.

7. The ultrasonic echo signal processing method according to claim 1, characterized in that: After the step of obtaining the initial positions of employed bees, observer bees, and scout bees corresponding to the artificial bee colony algorithm and the fitness values ​​corresponding to the nectar sources, the method further includes: Multiple threads are created based on the search tasks corresponding to the employed bees, the observer bees, and the scout bees, so that each thread independently executes the search task.

8. An ultrasonic echo signal processing system, characterized in that: The system comprises: a model construction processing module, configured to construct a mathematical model corresponding to the ultrasonic echo signal according to the sampling time, amplitude, frequency, sampling delay time, and attenuation coefficient of the ultrasonic echo signal, and determine the sampling delay time and the attenuation coefficient as parameters to be optimized for the mathematical model; A global optimization processing module is used to perform nonlinear selection iterative processing on the parameters to be optimized based on the mathematical model and using an improved artificial bee colony algorithm, accelerate the iterative processing process using a particle swarm optimization algorithm, and obtain the fitness value of the parameters to be optimized in real time; a signal reconstruction processing module, configured to stop the iterative processing flow when the fitness value satisfies a preset convergence condition, and reconstruct the ultrasonic echo signal using the current sampling delay time and the attenuation coefficient; In the process of using the particle swarm optimization algorithm to accelerate the calculation of the iterative processing flow and obtain the fitness value of the parameter to be optimized in real time, the global optimization processing module is also used to: obtain the initial positions of the employed bees, observer bees and scout bees corresponding to the artificial bee colony algorithm and the fitness value corresponding to the nectar source; adjust the position of the employed bees using the particle swarm optimization algorithm, and determine the search direction of the employed bees using the fitness value; when the number of iterations of the nectar source exceeds a preset threshold, obtain the scout bee, and calculate the new nectar source corresponding to the scout bee using the particle swarm optimization algorithm; obtain the new fitness value corresponding to the new nectar source in real time, stop the iteration when the new fitness value converges, and obtain the current new fitness value.

9. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores computer-executable instructions that can be executed by the processor, and the processor executes the computer-executable instructions to implement the steps of the ultrasonic echo signal processing method according to any one of claims 1 to 7.

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