Ultrasonic echo signal processing method and system and electronic equipment

Abstract

The invention provides an ultrasonic echo signal processing method and system and electronic equipment, and relates to the technical field of ultrasonic signal processing, and the method only carries out parameter optimization on the sampling delay time and attenuation coefficient of an ultrasonic echo signal, and reduces the calculation complexity; a non-linear iteration process is introduced into an artificial bee colony algorithm, and the performance of the artificial bee colony algorithm is improved by using a particle swarm optimization algorithm, so that efficient signal reconstruction is realized, and noise interference in an 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: 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; 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 optimization parameters in real time. 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.

[0005] Optionally, when the sampling time is not less than the sampling delay time, the mathematical model is: ; in, The sampling time is Ultrasonic echo signal at the time; 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.

[0006] Optionally, when the sampling time is less than the sampling delay time, the mathematical model is: ; in, The sampling time is Ultrasonic echo signal at the time; 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.

[0007] Optionally, 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: Acquire a data set corresponding to the ultrasonic echo signal, and determine the amplitude based on the maximum value of the signal in the data set; Calculate and obtain 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 based on the sampling frequency and the mark value of the sampling point.

[0008] Optionally, a nonlinear selection iterative process is performed on the optimization parameters based on a mathematical model and using an improved artificial bee colony algorithm, including: The parameters to be optimized based on the mathematical model are randomly generated according to the initial nectar source in the artificial bee colony algorithm, and multiple nectar sources corresponding to the parameters to be optimized are obtained based on the initial nectar source; Calculate the normalized fitness value corresponding to the nectar source, and use the normalized fitness value to calculate the sum of the normalized fitness corresponding to all nectar sources; Obtain the number of iterations corresponding to the normalized fitness value, and use the number of iterations to calculate the nonlinear attenuation factor; The probability of the nectar source being selected is calculated 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.

[0009] Optionally, the probability value is calculated using the following formula: ; 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 is ; Normalized fitness value Calculated by the following formula: ; 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.

[0010] 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: 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; 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; 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; 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.

[0011] 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: 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.

[0012] In a second aspect, the present invention provides an ultrasonic echo signal processing system, the system comprising: 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; 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; 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.

[0013] 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.

[0014] 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.

[0015] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or understood by practicing the present invention. The purposes and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0016] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1A flowchart of an ultrasonic echo signal processing method provided by an embodiment of the present invention; Figure 2 This is a flowchart before step S101 of an ultrasonic echo signal processing method provided by an embodiment of the present invention; Figure 3 A flowchart of step S102 of an ultrasonic echo signal processing method provided by an embodiment of the present invention, wherein a nonlinear selection iterative process is performed on the optimization parameters based on a mathematical model and using an improved artificial bee colony algorithm; Figure 4 A flowchart of using a particle swarm optimization algorithm to accelerate the iterative processing process and obtain the fitness value of the parameter to be optimized in real time in step S102 of an ultrasonic echo signal processing method provided by an embodiment of the present invention; Figure 5 A schematic diagram of an iterative process of an ultrasonic echo signal processing method provided by an embodiment of the present invention; Figure 6 A comparison diagram of the effects of an ultrasonic echo signal processing method provided by an embodiment of the present invention; Figure 7 A schematic structural diagram of an ultrasonic echo signal processing system provided by an embodiment of the present invention; Figure 8 A schematic structural diagram of an electronic device provided by an embodiment of the present invention.

[0019] icon: 710-model building processing module; 720-global optimization processing module; 730-signal reconstruction processing module; 101 - processor; 102 - memory; 103 - bus; 104 - communication interface. DETAILED DESCRIPTION

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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: Step S101 : 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 attenuation coefficient as parameters to be optimized in the mathematical model.

[0024] Among the five parameters involved in this mathematical model, namely sampling time, amplitude, frequency, sampling delay time and attenuation coefficient, only the sampling delay time and attenuation coefficient are determined as the parameters to be optimized in 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.

[0025] Optionally, when the sampling time is not less than the sampling delay time, the mathematical model is: ; When the sampling time is less than the sampling delay time, the mathematical model is: ; in, The sampling time is Ultrasonic echo signal at the time; 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.

[0026] In the above mathematical model, the only parameter to be optimized is the sampling delay time , attenuation coefficient and , the remaining parameters are as follows 、 、 It can be directly calculated, and the calculation process will be introduced later.

[0027] In step S102 , based on the mathematical model and using the improved artificial bee colony algorithm, nonlinear selection iterative processing is performed on the parameters to be optimized, and 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.

[0028] After obtaining the mathematical model, the artificial bee colony algorithm is used to 、 、 Global optimization involves a nonlinear decreasing selection strategy, which allows for nonlinear selection and iteration of the optimization parameters. The core steps of the artificial bee colony algorithm include: Initialization: Randomly generate an initial population of nectar sources (solutions).

[0029] Hired bee stage: Hired bees search for new nectar sources (new solutions) near the current nectar source and select better nectar sources based on quality (fitness function).

[0030] Observer bee stage: Observer bees select nectar sources and conduct further searches based on the information shared by employed bees.

[0031] Scouting bee stage: If a nectar source has not improved after multiple rounds of iterations, scout bees will be sent out to explore new nectar sources.

[0032] Iteration phase: Repeat the above steps until the termination condition (such as number of iterations, solution quality, etc.) is reached.

[0033] This step introduces nonlinear selection iterative processing into the artificial bee colony algorithm, dynamically adjusts the priority of the observer bees in selecting nectar sources, and achieves a balance between strong global search in the early stage of the algorithm and strong local search in the later stage. By introducing the particle swarm optimization algorithm to accelerate the iterative processing process and obtain the fitness values of the parameters to be optimized in real time, the optimization performance of the particle swarm optimization algorithm is significantly improved.

[0034] Step S103: 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 attenuation coefficient.

[0035] After the fitness value in the iterative processing process meets the specific convergence conditions, the optimal parameters of the parameters to be optimized can be obtained, and the ultrasonic echo signal is reconstructed using the sampling delay time and attenuation coefficient in the current parameters to be optimized, thereby obtaining the denoised ultrasonic echo signal.

[0036] Optionally, 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, such as Figure 2 As shown, the method further includes: Step S201, obtaining a data set corresponding to an ultrasonic echo signal, and determining an amplitude based on a maximum value of a signal in the data set; Step S202, calculating and obtaining the frequency based on the wave number and sampling frequency between preset adjacent sampling points of the ultrasonic echo signal; Step S203: Calculate and obtain the sampling time according to the sampling frequency and the mark value of the sampling point.

[0037] Amplitude of ultrasonic echo signal The maximum value in the collected ultrasonic signal data set is calculated and determined as the amplitude of the ultrasonic echo signal. .

[0038] Due to the frequency and Corresponding to the frequencies in the two scenarios respectively, for the convenience of description, the upper level is described as , the frequency is obtained by the reciprocal of the period T, and T is the number of sampling points multiplied by the reciprocal of the sampling frequency, as follows: ; in, is the horizontal coordinate corresponding to the first sampling point, is the horizontal coordinate corresponding to the second sampling point, and the difference between the two horizontal coordinates is the number of corresponding sampling points. N is the wave number between the two sampling points, is the inverse of the sampling frequency. Assume =201, =1, and The number of waves N is 4, and the sampling frequency is 20MHz, then milliseconds, then the above formula can be used to obtain T=400 milliseconds, and then the frequency of the wave can be obtained. ,Right now: .

[0039] The sampling time t is obtained by To calculate, where i represents the i-th sampling point.

[0040] Optionally, based on the mathematical model and using the improved artificial bee colony algorithm, nonlinear selection and iteration processing is performed on the optimization parameters, such as Figure 3 Shown, including: Step S301: randomly generating an initial nectar source corresponding to the parameters to be optimized in the artificial bee colony algorithm based on the parameters to be optimized of the mathematical model, and obtaining multiple nectar sources corresponding to the parameters to be optimized based on the initial nectar source; Step S302, 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; 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; Step S304, calculating the probability value of the nectar source being selected using the normalized fitness sum and the nonlinear attenuation factor; Step S305: update and iterate the parameters to be optimized corresponding to the nectar source according to the probability value.

[0041] 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: ; in, ; For the The original fitness value corresponding to each nectar source is 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.

[0042] 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: ; in, is the sum of normalized fitness.

[0043] Then the nonlinear attenuation factor is calculated according to the current iteration number i ,in .

[0044] The final probability value is calculated using the following formula: ; 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.

[0045] 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: 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; 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; 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; 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.

[0046] 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.

[0047] 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.

[0048] 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 ) can be quickly obtained through direct calculation, significantly reducing computational complexity and improving model convergence speed.

[0049] In addition, a nonlinear decreasing selection probability formula is introduced into the artificial bee colony algorithm , dynamically adjusts the priority of the observer bees in selecting nectar sources, achieving a balance between strong global search in the early stages of the algorithm and strong local search in the later stages. At the same time, multi-threaded parallel processing is implemented based on pthreads, and optimization results are synchronized through shared memory and atomic operations, improving the algorithm's speedup ratio on multi-core CPUs. Furthermore, the introduction of the acceleration mechanism of the particle swarm optimization algorithm significantly improves the optimization performance of the artificial bee colony algorithm.

[0050] In a high-noise environment, the synergy between parameter optimization and signal reconstruction can effectively suppress noise interference and meet the real-time requirements of industrial detection and medical imaging. Figure 5 As shown in the schematic diagram of the iterative process of the ultrasonic echo signal processing method, it can be seen that the method of the present invention reaches convergence when the number of iterations is less than 20.

[0051] like Figure 6 The following figure compares the effects of ultrasonic echo signal processing methods. The red curve represents the original signal curve collected in real time, that is, the real-time ultrasonic waveform received, and the green curve represents the signal curve after parameter estimation and reconstruction of the signal data, that is, the waveform reconstructed using the artificial bee colony algorithm. It can be seen that the method of the present invention has a good denoising effect for real-time industrial detection in complex environments.

[0052] From the ultrasonic echo signal processing method mentioned in the above embodiment, it can be seen that this method 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.

[0053] Corresponding to the ultrasonic echo signal processing method provided in the above embodiment, an embodiment of the present invention provides an ultrasonic echo signal processing system, such as Figure 7 As shown, the system includes: The model building processing module 710 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; The global optimization processing module 720 is used to perform nonlinear selection iterative processing on the optimization parameters based on the mathematical model and using the improved artificial bee colony algorithm, and to accelerate the iterative processing process using the particle swarm optimization algorithm and obtain the fitness value of the optimization parameters in real time; The signal reconstruction processing module 730 is used to stop the iterative processing flow when the fitness value meets the preset convergence condition, and reconstruct the ultrasonic echo signal using the current sampling delay time and attenuation coefficient.

[0054] From the ultrasonic echo signal processing system mentioned in the above embodiment, it can be seen that the system 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.

[0055] The ultrasonic echo signal processing system provided in the embodiment of the present invention has the same implementation principle and technical effects as those of the aforementioned ultrasonic echo signal processing method embodiment. For the sake of brief description, for matters not mentioned in the system embodiment, reference may be made to the corresponding content in the aforementioned ultrasonic echo signal processing method embodiment.

[0056] This embodiment also provides an electronic device. The structural diagram of the electronic device is as follows: Figure 8 As shown, the device includes a processor 101 and a memory 102; wherein the memory 102 is used 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 above-mentioned ultrasonic echo signal processing method.

[0057] Figure 8The electronic device shown further includes a bus 103 and a communication interface 104 , and the processor 101 , the communication interface 104 and the memory 102 are connected via the bus 103 .

[0058] The memory 102 may include a high-speed random access memory (RAM) and may also include a non-volatile memory, such as at least one disk storage. The bus 103 may be an ISA bus, a PCI bus, or an EISA bus. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 8 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0059] 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.

[0060] 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.

[0061] 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.

[0062] 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.

[0063] 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.

[0064] 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.

[0065] 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.

[0066] 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.

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: 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.

8. The ultrasonic echo signal processing method according to claim 7, 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.

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

10. 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 8.

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