Ultrasonic echo signal processing methods, systems, and ultrasonic measuring equipment

Abstract

This invention provides an ultrasonic echo signal processing method, system, and ultrasonic measuring device, relating to the field of signal processing. The method utilizes an improved artificial bee colony algorithm and introduces differential evolution, enhancing the development capability of the artificial bee colony and alleviating the problem of the artificial bee colony easily getting trapped in local optima. It can stably, accurately, and quickly acquire the signal parameters of the echo signal in low signal-to-noise ratio scenarios, thereby improving the resolution of the ultrasonic echo.

Description

Technical Field

[0001] This invention relates to the field of signal processing, and in particular to an ultrasonic echo signal processing method, system, and ultrasonic measuring device. Background Technology

[0002] Ultrasonic echo signal analysis, as a core tool in nondestructive testing, medical imaging, and industrial monitoring, directly determines the reliability of defect identification, material characterization, and biological tissue evaluation based on the accuracy of its parameter estimation. Traditional time-domain or frequency-domain analysis methods struggle to accurately decouple key physical parameters such as amplitude, time delay, and attenuation coefficients implicit in echo signals under conditions of strong noise interference, multipath effects, and signal aliasing. Especially in complex media environments, the nonlinear and non-stationary characteristics of signals transform the parameter estimation problem into a high-dimensional, strongly coupled, non-convex optimization problem, significantly increasing the difficulty of echo signal parameter processing. While existing technologies employ genetic algorithms (GA), particle swarm optimization (PSO), differential evolution (DE), and artificial bee colony optimization (ABC) to invert echo signal parameters, significant bottlenecks remain. For example, traditional artificial bee colony optimization struggles to balance global search and local optimization capabilities, easily getting trapped in local optima; the fixed mutation strategy of differential evolution algorithms results in insufficient adaptive capability in high-dimensional spaces. These issues lead to slow convergence speed, low accuracy, and poor stability, resulting in low resolution of ultrasonic echoes. Summary of the Invention

[0003] In view of this, the purpose of the present invention is to provide an ultrasonic echo signal processing method, system and ultrasonic measuring device. The method utilizes an improved artificial bee colony algorithm and introduces differential evolution, which enhances the development capability of the artificial bee colony and alleviates the situation where the artificial bee colony is prone to getting trapped in local optima. It can stably, accurately and quickly acquire the signal parameters of the echo signal in low signal-to-noise ratio scenarios, thereby improving the resolution of the ultrasonic echo.

[0004] In a first aspect, embodiments of the present invention provide an ultrasonic echo signal processing method, which is used to determine the echo signal in an ultrasonic measuring device; the method includes:

[0005] The echo parameter vector is determined based on the characteristic parameters corresponding to the echo signal, and the target conditions and echo data corresponding to the echo signal are determined using the echo parameter vector.

[0006] The artificial bee colony algorithm is constructed using echo data to establish the corresponding stages of the hired bee, follower bee, and scout bee phases. In the hired bee phase, the target nectar source undergoes differential evolution based on at least four differentiating information points. In the follower bee phase, the search direction is determined by the difference information between target nectar sources. In the scout bee phase, the location parameters of the target nectar source are updated using chaotic mapping and the search direction.

[0007] The control echo data sequentially executes the mercenary bee stage, the follower bee stage, and the scout bee stage, and acquires the rate of change of the position parameters in real time;

[0008] When the rate of change is detected to meet the target condition, the echo signal is determined based on the echo parameter vector corresponding to the target honey source under the current location parameters.

[0009] Optionally, the steps of determining the echo parameter vector based on the characteristic parameters corresponding to the echo signal, and using the echo parameter vector to determine the target conditions and echo data corresponding to the echo signal, include:

[0010] After receiving the echo signal using the ultrasonic receiving transducer in the ultrasonic measuring equipment, the characteristic parameters are determined based on the amplitude coefficient, bandwidth factor, time parameter when the echo signal arrives at the ultrasonic receiving transducer, center frequency and phase parameter.

[0011] The echo model corresponding to the ultrasonic receiving transducer is constructed by using characteristic parameters, and the echo parameter vector and its corresponding echo data are determined based on the echo model.

[0012] Obtain the actual measurement data corresponding to the echo signal under preset test conditions and parameters, and determine the target conditions corresponding to the echo signal based on the difference between the actual measurement data and the echo data.

[0013] Optionally, the corresponding mercenary bee stage in the artificial bee colony algorithm can be constructed using echo data, including:

[0014] The target honey source corresponding to the echo parameter vector is constructed by using echo data, and four distinct solutions are randomly obtained using the solution vector corresponding to the honey source.

[0015] The first differential mutation vector corresponding to the target honey source is constructed using the distinct solutions, and the crossover strategy corresponding to the first differential mutation vector and the solution vector is determined according to the preset crossover probability parameters.

[0016] The first experimental solution corresponding to the first difference mutation vector and the solution vector is determined by the crossover strategy, and the first replacement strategy corresponding to the solution vector is determined based on the fitness numerical result corresponding to the first experimental solution.

[0017] If the first experimental solution satisfies the first replacement strategy, then the solution vector is replaced with the first experimental solution.

[0018] Optionally, the corresponding follower bee stage in the artificial bee colony algorithm can be constructed using echo data, including:

[0019] The selection probability of the target nectar source is calculated using the fitness values ​​corresponding to the solution vectors.

[0020] Select sub-honey sources with a probability greater than a preset random dimension threshold from the target honey source, and randomly select the optimal honey source from the preset set of optimal honey source solutions;

[0021] Calculate the first difference between two random distinct solutions of the sub-honey source, and calculate the second difference between the sub-honey source and the optimal honey source;

[0022] The second differential mutation vector corresponding to the target honey source is constructed by using a preset scaling factor, a first difference, and a second difference. The second replacement strategy corresponding to the optimal honey source is determined based on the fitness value result corresponding to the second differential mutation vector.

[0023] If the second difference mutation vector satisfies the second replacement strategy, then the optimal honey source is replaced with the target honey source corresponding to the second difference mutation vector.

[0024] Optionally, after replacing the optimal honey source with the target honey source corresponding to the second difference mutation vector, it also includes:

[0025] The sampling weight values ​​are determined using a scaling factor; where the sampling weight values ​​follow a normal distribution.

[0026] Calculate the average value of the sampling weights based on the number of target nectar sources;

[0027] The weight values ​​are updated using the average value, and the scaling factor is then updated using the updated weight values.

[0028] Optionally, the corresponding scout bee stage in the artificial bee colony algorithm can be constructed using echo data, including:

[0029] Real-time tracking of the number of times the target nectar source's location changes;

[0030] If the number of times the location changes meets the preset threshold condition, the target honey source is abandoned; if the number of times the location changes does not meet the preset threshold condition, the target honey source is retained.

[0031] The logistic sequence solution corresponding to the target honey source is obtained based on the chaotic mapping, and the third difference mutation vector is constructed using the logistic sequence solution;

[0032] Obtain three random distinct solutions corresponding to the target honey source, and construct a fourth difference mutation vector using the scaling factor and the random distinct solutions;

[0033] The second experimental solution corresponding to the target honey source is generated using the third and fourth difference mutation vectors, and the third replacement strategy corresponding to the target honey source is determined based on the fitness value results corresponding to the second experimental solution.

[0034] If the second experimental solution satisfies the third replacement strategy, then the solution vector corresponding to the target honey source is replaced with the second experimental solution.

[0035] Optionally, the steps of controlling the echo data to sequentially execute the mercenary bee stage, the follower bee stage, and the scout bee stage, and to acquire the rate of change of the position parameters in real time, include:

[0036] After sequentially executing the mercenary bee stage, follower bee stage, and scout bee stage to control the echo data, the echo data is updated using the updated position parameters.

[0037] The updated echo data is controlled to sequentially execute the hired bee stage, the follower bee stage, and the scout bee stage, and the number of times the echo data is executed in real time is obtained, and the change rate is obtained in real time using the change value of the position parameter.

[0038] Optionally, when the rate of change is detected to meet the target condition, the step of determining the echo signal based on the echo parameter vector corresponding to the target honey source under the current location parameters includes:

[0039] When the rate of change is detected to meet the target condition and the number of iterations is less than the preset iteration threshold, the echo data corresponding to the target honey source under the current location parameters is obtained.

[0040] The echo signal is determined using the echo parameter vector corresponding to the echo data.

[0041] In a second aspect, the present invention provides an ultrasonic echo signal processing system for determining echo signals in an ultrasonic measuring device; the system includes:

[0042] The initialization module is used to determine the echo parameter vector based on the characteristic parameters corresponding to the echo signal, and to determine the target conditions and echo data corresponding to the echo signal using the echo parameter vector;

[0043] The phase construction module is used to construct the corresponding mercenary bee stage, follower bee stage, and scout bee stage in the artificial bee colony algorithm using echo data. In the mercenary bee stage, the target nectar source is differentially evolved using at least four differential information. In the follower bee stage, the search direction is determined by the differential information between target nectar sources. In the scout bee stage, the position parameters of the target nectar source are updated using chaotic mapping and search direction.

[0044] The iterative processing module is used to control the echo data to sequentially execute the hired bee stage, the follower bee stage, and the scout bee stage, and to obtain the rate of change of the position parameters in real time.

[0045] The echo signal determination module is used to determine the echo signal based on the echo parameter vector corresponding to the target honey source under the current location parameters when the rate of change meets the target conditions.

[0046] Thirdly, embodiments of the present invention also provide an ultrasonic measuring device, which includes a controller; the controller includes a processor and a memory, the memory storing computer-executable instructions that can be executed by the processor, and the processor executing the computer-executable instructions to implement the ultrasonic echo signal processing method mentioned in the first aspect.

[0047] This invention provides an ultrasonic echo signal processing method, system, and ultrasonic measuring device. In determining the echo signal in the ultrasonic measuring device, the method first determines an echo parameter vector based on the characteristic parameters corresponding to the echo signal, and then uses the echo parameter vector to determine the target conditions and echo data corresponding to the echo signal. Next, it constructs the corresponding hired bee stage, follower bee stage, and scout bee stage in the artificial bee colony algorithm using the echo data. In the hired bee stage, the target nectar source undergoes differential evolution through at least four differentiating information points. In the follower bee stage, the search direction is determined through the differentiating information between target nectar sources. In the scout bee stage, the position parameters of the target nectar source are updated using chaotic mapping and the search direction. Subsequently, the echo data is controlled to sequentially execute the hired bee stage, follower bee stage, and scout bee stage, and the rate of change corresponding to the position parameters is acquired in real time. When the rate of change is detected to meet the target conditions, the echo signal is determined based on the echo parameter vector corresponding to the target nectar source under the current position parameters. This method utilizes an improved artificial bee colony algorithm and introduces differential evolution, which enhances the development capability of the artificial bee colony and alleviates the problem of the artificial bee colony easily getting trapped in local optima. It can stably, accurately and quickly acquire the signal parameters of the echo signal in low signal-to-noise ratio scenarios, thereby improving the resolution of the ultrasonic echo.

[0048] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained in accordance with the structures particularly pointed out in the description, claims and drawings.

[0049] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0050] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0051] Figure 1A flowchart of an ultrasonic echo signal processing method provided in an embodiment of the present invention;

[0052] Figure 2 A flowchart of step S101 of an ultrasonic echo signal processing method provided in an embodiment of the present invention;

[0053] Figure 3 In step S102 of the ultrasonic echo signal processing method provided in this embodiment of the invention, a flowchart of the corresponding hired bee stage in the artificial bee colony algorithm is constructed using echo data.

[0054] Figure 4 In step S102 of the ultrasonic echo signal processing method provided in this embodiment of the invention, a flowchart of the corresponding follower bee stage in the artificial bee colony algorithm is constructed using echo data.

[0055] Figure 5 In step S102 of the ultrasonic echo signal processing method provided in this embodiment of the invention, the flowchart after replacing the optimal honey source with the target honey source corresponding to the second differential variation vector is shown.

[0056] Figure 6 In step S103 of the ultrasonic echo signal processing method provided in this embodiment of the invention, a flowchart is constructed using echo data to construct the corresponding scout bee stage in the artificial bee colony algorithm.

[0057] Figure 7 This is a flowchart of step S103 in an ultrasonic echo signal processing method provided in an embodiment of the present invention;

[0058] Figure 8 This is a flowchart of step S104 in an ultrasonic echo signal processing method provided in an embodiment of the present invention;

[0059] Figure 9 A signal curve of an ultrasonic echo reference signal;

[0060] Figure 10 The signal curve of the ultrasonic echo signal obtained by the traditional artificial bee colony algorithm;

[0061] Figure 11 This is a signal curve of an ultrasonic echo signal obtained using an ultrasonic echo signal processing method according to an embodiment of the present invention.

[0062] Figure 12 The image shows the convergence curve of an ultrasonic echo signal obtained using an ultrasonic echo signal processing method according to an embodiment of the present invention.

[0063] Figure 13 This is a schematic diagram of the structure of an ultrasonic surface density measurement system provided in an embodiment of the present invention;

[0064] Figure 14 This is a schematic diagram of the controller in an ultrasonic measuring device provided in an embodiment of the present invention.

[0065] icon:

[0066] 1310 - Initialization module; 1320 - Stage construction module; 1330 - Iterative processing module; 1340 - Echo signal determination module;

[0067] 101 - Processor; 102 - Memory; 103 - Bus; 104 - Communication interface. Detailed Implementation

[0068] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0069] To facilitate understanding of this embodiment, a method for processing ultrasonic echo signals disclosed in this embodiment of the invention will first be described in detail. This method is used to determine the echo signal in an ultrasonic measuring device; such as Figure 1 As shown, the method includes:

[0070] Step S101: Determine the echo parameter vector based on the characteristic parameters corresponding to the echo signal, and use the echo parameter vector to determine the target conditions and echo data corresponding to the echo signal.

[0071] This step is the initialization step, mainly involving the construction of parameter vectors and basic data. First, the core characteristic parameters of the ultrasonic echo signal (including amplitude coefficient, levitation factor, center frequency, phase, and other key physical parameters) are identified. Based on these characteristic parameters, an echo parameter vector representing the core attributes of the echo signal is constructed. Then, based on this echo parameter vector, the target conditions for echo signal parameter estimation are further defined (such as parameter estimation accuracy thresholds, convergence criteria, etc.). Simultaneously, the raw echo data acquired by the ultrasonic measuring equipment is extracted and organized to provide basic data support for subsequent algorithm iteration and optimization.

[0072] Step S102: Construct the corresponding mercenary bee stage, follower bee stage, and scout bee stage in the artificial bee colony algorithm using echo data; wherein, in the mercenary bee stage, the target nectar source is differentially evolved using at least four differential information; in the follower bee stage, the search direction is determined by the differential information between target nectar sources; and in the scout bee stage, the location parameters of the target nectar source are updated using chaotic mapping and search direction.

[0073] This step constructs and improves the three stages of the artificial bee colony algorithm. Specifically, based on the processed echo data, an artificial bee colony algorithm framework incorporating a differential evolution mechanism is built, clarifying the operational logic of the three core stages: hired bees, follower bees, and scout bees. Specifically, in the hired bee stage, the target nectar source (i.e., candidate solutions for parameter optimization) needs to undergo differential evolution through at least four dimensions of difference information (ensuring the diversity of the initial search and laying the foundation for subsequent optimization). The follower bee stage no longer relies on single nectar source information but instead analyzes the difference information between different target nectar sources to accurately lock in a better search direction, improving local optimization efficiency. The scout bee stage introduces a chaotic mapping mechanism, dynamically updating the position parameters (i.e., candidate solutions) of the target nectar source based on the search direction determined by the follower bees, effectively enhancing global search capabilities and alleviating the problem of traditional algorithms easily getting trapped in local optima.

[0074] Step S103: Control the echo data to sequentially execute the hired bee stage, the follower bee stage, and the scout bee stage, and obtain the change rate of the position parameters in real time.

[0075] This step mainly implements multi-stage iterative processing and parameter monitoring. The control echo data is processed iteratively in the order of hired bee stage - follower bee stage - scout bee stage. In each round of iteration, the rate of change of the target nectar source location parameters is collected and calculated in real time (this rate of change directly reflects the convergence trend of parameter optimization and is the core indicator for judging whether the optimization is in place).

[0076] Step S104: When the rate of change is detected to meet the target condition, the echo signal is determined based on the echo parameter vector corresponding to the target honey source under the current location parameters.

[0077] This step mainly involves convergence determination and echo signal identification. The rate of change of position parameters is continuously monitored. When the rate of change meets the target condition preset in step S101 (i.e., parameter optimization has reached convergence, and the candidate solution is close to the true parameter value), the iterative loop stops. Based on the echo parameter vector corresponding to the target honey source in the current convergence state, the final ultrasonic echo signal is derived and determined in reverse, achieving high-precision extraction of the echo signal in low signal-to-noise ratio scenarios.

[0078] Optionally, step S101, which involves determining the echo parameter vector based on the characteristic parameters corresponding to the echo signal and using the echo parameter vector to determine the target conditions and echo data corresponding to the echo signal, is as follows: Figure 2 As shown, it includes:

[0079] Step S201: After receiving the echo signal using the ultrasonic receiving transducer in the ultrasonic measuring equipment, determine the characteristic parameters based on the amplitude coefficient, bandwidth factor, time parameter when the echo signal reaches the ultrasonic receiving transducer, center frequency, and phase parameter.

[0080] Step S201 is the feature parameter extraction stage: First, the ultrasonic echo signal is acquired using the ultrasonic receiving transducer mounted on the ultrasonic measuring equipment; based on the physical propagation characteristics of the ultrasonic echo signal and the signal characterization requirements, core feature parameters are extracted from the received signal, specifically including: the amplitude coefficient reflecting the signal energy intensity. Bandwidth factor describing the frequency distribution range of a signal Arrival time parameter characterizing signal propagation delay (i.e., the timestamp information of the echo signal arriving at the receiving transducer), which determines the center frequency of the signal's main frequency characteristics. and phase parameters that reflect the phase state of the signal. This forms a complete set of feature parameters, providing basic input for subsequent echo model construction.

[0081] Step S202: Construct the echo model corresponding to the ultrasonic receiving transducer through the feature parameters, and determine the echo parameter vector and its corresponding echo data based on the echo model.

[0082] Step S202 involves the echo model construction and core data generation: based on the feature parameters extracted in step S201, an ultrasonic echo mathematical model is constructed that can accurately characterize the response characteristics of the ultrasonic receiving transducer (this model must satisfy the consistency between the time-domain / frequency-domain response and the actual physical laws of ultrasonic propagation). For example, the model is: Where t is the time parameter.

[0083] By embedding feature parameters into the undetermined parameter terms of the echo model, a structured echo parameter vector is formed. (This vector is an ordered set of feature parameters, directly mapping to the core representation dimension of the model, such as...) Echo data is obtained based on the constructed echo model and echo parameter vector. It satisfies: .

[0084] In specific scenarios, ultrasound waves undergo multiple reflections. Therefore, ultrasound receiving transducers typically receive multiple echoes, and these echo signals... ;in, is the echo signal obtained in the k-th iteration; m is the number of iterations for the echo signal; This represents the noise component corresponding to the echo data.

[0085] Step S203: Obtain the actual measurement data corresponding to the echo signal under the preset test conditions and parameters, and determine the target conditions corresponding to the echo signal based on the difference between the actual measurement data and the echo data.

[0086] Step S203 is the step of establishing the target conditions (convergence criterion): First, preset test condition parameters are set (which can be consistent with the characteristic parameters). Under these parameters, actual measurement data of the echo signal are collected using an ultrasonic measuring device. The actual measurement data is then compared point by point with the theoretical echo data generated in step S202, and the difference between the two is calculated. This can be achieved through the following formula:

[0087] ;

[0088] Where N is the number of discrete points contained in one period; This represents the echo data corresponding to the echo parameter vector after the Kth iteration. This is an echo signal; This is based on actual measurement data. The least squares function constructed using this formula yields the target conditions, such as when the function value (echo signal value)... When the value reaches its minimum, the echo signal is finally determined using the current echo parameter vector.

[0089] In the artificial bee colony algorithm, hired bees generate new solutions within the neighborhood of the current food source, responsible for local development and information sharing. Follower bees, based on a roulette wheel mechanism, select food sources with higher fitness and perform local searches on the selected food sources. If a food source does not improve after multiple generations of selection, scout bees generate new random solutions, escaping the local optimum. They randomly explore new regions to avoid premature convergence. Although the artificial bee colony algorithm has strong exploration capabilities, its development capabilities are insufficient. Near the global optimum, its search ability significantly decreases. To enhance the development capabilities of the artificial bee colony algorithm and balance exploration and development capabilities during the evolutionary process, this scheme improves the existing artificial bee colony algorithm. First, it sets the population size SN, the maximum number of iterations MaxIter, the threshold limit, and the differential evolution scaling factor F (usually...). Crossover probability CR (usually) ).

[0090] Based on this, optionally, the corresponding mercenary bee stage in the artificial bee colony algorithm can be constructed using echo data, such as... Figure 3 As shown, it includes:

[0091] Step S301: Construct the target honey source corresponding to the echo parameter vector through the echo data, and randomly obtain four distinct solutions using the solution vector corresponding to the honey source.

[0092] Step S301 is the honey source initialization and distinct solution selection stage in the hired bee phase: First, based on the echo data generated in step S202, the echo parameter vector is mapped to the target honey source (i.e., the candidate solution set of the parameter optimization problem) in the artificial bee colony algorithm. Each target honey source corresponds to a solution vector representing the combination of echo parameters. To ensure the diversity of subsequent mutation operations and global search capability, four distinct solutions (referred to as "distinct solutions") are selected from the solution vector space of the target honey source based on a random sampling mechanism. This ensures the difference of each solution in the parameter dimension and provides basic data support for the mutation strategy of incorporating differential evolution.

[0093] For example, for each target honey source Four distinct solutions are randomly selected as follows: , , , ;in, .

[0094] Step S302: Construct the first differential mutation vector corresponding to the target honey source using the distinct solutions, and determine the crossover strategy corresponding to the first differential mutation vector and the solution vector according to the preset crossover probability parameters.

[0095] Step S302 is the differential mutation vector construction and crossover strategy determination stage: Based on the mutation mechanism of differential evolution, using the four distinct solutions selected in step S301, the first differential mutation vector corresponding to the target honey source is constructed through relevant formulas, realizing global perturbation of the solution vector and expansion of the search space. For example, the first differential mutation vector... It can be constructed using the following formula:

[0096] ;

[0097] in, For the current solution A new solution generated within the neighborhood; i is the i-th honey source; j is the j-th dimension of the solution; k is a randomly selected honey source index different from i; for Random numbers; for Random numbers; , , , These are randomly selected nectar sources. Therefore, this formula involves two difference terms, namely: and This design has several advantages: First, it increases the diversity of the search by utilizing the differences among the four nectar sources; second, it expands the search space, avoiding being limited to the small domain of the current nectar source; third, it introduces more random factors, reducing the probability of getting stuck in local optima; and fourth, it adapts to complex problems. For high-dimensional, multimodal problems, the basic strategy may not be sufficient, while the extended strategy can better adapt.

[0098] In addition, this step introduces a preset crossover probability parameter CR (algorithm hyperparameter, used to balance the proportion of mutation and retention of original high-quality genes). Based on the value of this parameter (e.g., 0.8~1.0), the crossover strategy (e.g., binary crossover, exponential crossover, etc.) between the first differential mutation vector and the original solution vector is determined, clarifying the gene exchange rules of the two vectors in each parameter dimension, laying the foundation for generating new candidate solutions.

[0099] Step S303: Determine the first experimental solution corresponding to the first difference mutation vector and the solution vector through the crossover strategy, and determine the first replacement strategy corresponding to the solution vector based on the fitness numerical result corresponding to the first experimental solution.

[0100] Step S303 is the experimental solution generation and replacement strategy determination step: according to the crossover strategy determined in step S302, the first difference mutation vector and the original solution vector are subjected to dimension-by-dimensional gene exchange to generate a new candidate solution (i.e., the first experimental solution). Specifically, the first experimental solution can be obtained using the following formula:

[0101] ;

[0102] in, The first difference mutation vector in the j-th dimension ; For the target honey source in the j-th dimension rand represents the random dimension; For random dimension indexing, ensure that at least one dimension comes from .

[0103] Based on the error evaluation index (such as mean square error and sum of squared errors) set in step S203, calculate the fitness value corresponding to the first experimental solution (fitness is negatively correlated with error, and the higher the value, the better the quality of the solution); use the fitness value as the core judgment criterion to establish the first replacement strategy (usually a greedy replacement strategy, that is, comparing the fitness difference between the first experimental solution and the original solution vector), and clarify the judgment rule for whether to update the original solution vector.

[0104] Step S304: If the first experimental solution satisfies the first replacement strategy, then the solution vector is replaced with the first experimental solution.

[0105] Step S304 is the solution vector update execution stage: Based on the first replacement strategy established in step S303, the fitness values ​​of the first experimental solution and the original solution vector are compared; if the fitness value of the first experimental solution is better than the original solution vector (i.e., it meets the core condition of the replacement strategy, indicating that the newly generated candidate solution is closer to the optimal parameter combination), then the original solution vector is replaced with the first experimental solution, completing the local optimization update of the target nectar source in the hired bee stage; if the replacement condition is not met, then the original solution vector is retained (if...). Its fitness is better than Then use replace Otherwise, retain the original solution and proceed to the next follower bee stage for further optimization.

[0106] Optionally, the corresponding follower bee stage in the artificial bee colony algorithm can be constructed using echo data, such as... Figure 4 As shown, it includes:

[0107] Step S401: Calculate the selection probability of the target honey source using the fitness value corresponding to the solution vector.

[0108] This step involves calculating the probability of nectar source selection during the follower bee stage: based on the solution vectors corresponding to each target nectar source generated in the mercenary bee stage, a fitness-oriented probability allocation mechanism is used to assign the fitness value of each solution vector. (Fitness is negatively correlated with echo data error; a higher value indicates a better solution quality.) This is converted into a corresponding selection probability. The specific calculation logic can adopt the probability mapping rule of roulette wheel (i.e., the selection probability of a certain honey source = the fitness value of the honey source solution vector / the sum of the fitness values ​​of all honey source solution vectors), ensuring that honey sources with better fitness receive a higher selection probability, laying the foundation for subsequent precise optimization. For example, the selection probability can be calculated using the following formula: .

[0109] Step S402: Select sub-honey sources from the target honey sources whose probability is greater than the preset random dimension threshold, and randomly select the optimal honey source from the preset set of optimal honey source solutions.

[0110] This step involves selecting target sub-nectar sources and identifying the optimal nectar source: First, a preset random dimension threshold is set (the algorithm hyperparameter rand, used to control the stringency of sub-nectar source selection, balancing search diversity and optimization efficiency). From all target nectar sources, those with a selection probability greater than this threshold are selected. Define it as a sub-honey source to be optimized. (The filtered sub-honey source set focuses on high-quality candidate solutions, improving the targeting of local optimization); simultaneously, an optimal honey source is selected from the preset set of optimal honey source solutions (which stores the top-ranked honey source solution vectors discovered during the iteration process) through random sampling (selecting from the top p% of the best individuals). By leveraging its superior parameter characteristics, it guides subsequent mutation directions and enhances local development capabilities.

[0111] Step S403: Calculate the first difference between the two random distinct solutions of the sub-honey source, and calculate the second difference between the sub-honey source and the optimal honey source.

[0112] This step is the differential information extraction stage: for each sub-honey source selected in step S402 Two distinct solutions are randomly selected from their corresponding solution vector space. , (i.e., random distinct solutions) ), calculate the difference between the two in each parameter dimension, and obtain the first difference ( This reflects the local differences in the solution vectors within the sub-honey source, providing gradient directions for local search; simultaneously, it calculates the differences in each dimension between the solution vector corresponding to the sub-honey source and the optimal honey source solution vector obtained in step S402, obtaining the second difference ( This reflects the difference between the current sub-honey source and the global high-quality solution, providing guidance for convergence to the optimal solution. The core search basis for differential evolution is constructed through dual difference information.

[0113] Step S404: Construct the second differential mutation vector corresponding to the target honey source using a preset scaling factor, a first difference, and a second difference, and determine the second replacement strategy corresponding to the optimal honey source based on the fitness value result corresponding to the second differential mutation vector.

[0114] This step is the stage for establishing the second differential mutation vector construction and replacement strategy: a preset scaling factor F is introduced (used to adjust the influence of difference information on the differential mutation vector, usually ranging from 0.5 to 1.0, balancing global exploration and local development). Based on this scaling factor, the first and second differences are weighted and fused, and the second differential mutation vector corresponding to the target honey source is constructed through the mutation formula of differential evolution. (By integrating local differences with global high-quality solutions, precise perturbation of the solution vector is achieved).

[0115] The second difference mutation vector can be constructed using the following formula:

[0116] ;

[0117] in, For the current solution A new solution generated within the neighborhood; i is the i-th honey source; j is the j-th dimension of the solution; k is a randomly selected honey source index different from i; This is the scaling factor.

[0118] Follower bees search near food sources discovered by hired bees, choosing nectar sources based on a roulette wheel approach, which can easily lead to local optima. The above formula integrates the hired bee search process with a differential evolution algorithm and incorporates a parameter adaptation strategy. In the follower bee phase, a... As a search direction guide, it replaces the completely random search of traditional artificial bee colony algorithms. Follower bees no longer rely solely on searching within their current individual's domain, but instead move towards... The xpbest,j direction shift, combined with the mutation operator of differential evolution, generates directional candidate solutions.

[0119] Subsequently, the fitness value of the second difference mutation vector is calculated based on the error evaluation index set in step S203. The second replacement strategy is established by comparing the fitness value with the fitness value of the original optimal honey source (i.e., the greedy replacement criterion: if the fitness of the second difference mutation vector is better, the replacement is triggered; otherwise, the original optimal honey source is retained).

[0120] Step S405: If the second differential mutation vector satisfies the second replacement strategy, then the optimal honey source is replaced with the target honey source corresponding to the second differential mutation vector.

[0121] This step is the optimal nectar source update execution stage: based on the second replacement strategy established in step S404, the fitness values ​​of the second differential mutation vector and the original optimal nectar source are quantitatively compared; if the fitness value of the second differential mutation vector is better than that of the original optimal nectar source (i.e., the core judgment condition of the replacement strategy is met, indicating that the newly generated differential mutation vector is closer to the true optimal solution of the ultrasonic echo parameters), then the original optimal nectar source is replaced with the target nectar source corresponding to the second differential mutation vector, completing the local optimization update of the optimal solution in the follower bee stage; if the replacement condition is not met, then the original optimal nectar source is retained to ensure the stability of the high-quality solution (if...). Its fitness is better than Then use replace Otherwise, retain the original solution. Then, the global search process begins in the scout bee phase.

[0122] After replacing the optimal honey source with the target honey source corresponding to the second difference mutation vector, the current sampling weight value can be recorded, which can be used for subsequent updates. Optionally, after replacing the optimal honey source with the target honey source corresponding to the second difference mutation vector, as follows: Figure 5 As shown, it also includes:

[0123] Step S501: Determine the sampling weight value using the scaling factor; wherein the sampling weight value satisfies a normal distribution.

[0124] The scaling factor F controls the scaling of the difference vector, affecting the search step size and global search. The scaling factor can be calculated using the following formula:

[0125] .

[0126] It is worth mentioning that the above formula satisfies the normal distribution sampling.

[0127] Step S502: Calculate the average value of the sampling weights based on the number of target honey sources;

[0128] Step S503: Update the weight values ​​using the average value, and update the scaling factor using the updated weight values.

[0129] mean of normal distribution The following relationship must be satisfied:

[0130] ;

[0131] In the above formula, It is a weighted average of the previous mean and the current successful F-value. Adjusting the mean using the F-values ​​of successful cases helps retain effective parameters and allows the algorithm to automatically adjust F at different stages. Larger F-values ​​in the early stages of the search are helpful for exploration, while smaller F-values ​​in the later stages are helpful for development. The significance lies in collecting the F-values ​​of successful mutations, reflecting the effective scaling factors at different stages. And... It can dynamically adjust to avoid extreme values ​​and ensure that the direction of parameter adjustment matches the characteristics of the current problem. The smoothing coefficient c controls the update speed, is used to adjust the weight of historical mean and new samples, and determines the fusion ratio of historical experience and current iteration information. The smoothing coefficient can prevent parameter oscillation and improve the robustness of the algorithm.

[0132] Optionally, the corresponding scout bee stage in the artificial bee colony algorithm can be constructed using echo data, such as... Figure 6 As shown, it includes:

[0133] Step S601: Real-time acquisition of the number of times the target nectar source's location changes.

[0134] This step is the monitoring of stagnant nectar sources during the scout bee stage: it tracks and records the number of times each target nectar source (corresponding to the candidate solution of the echo parameter optimization) changes position during the iteration process in real time, providing the core judgment basis for subsequent nectar source screening.

[0135] Step S602: If the number of location changes meets the preset threshold condition, then the target honey source is abandoned; if the number of location changes does not meet the preset threshold condition, then the target honey source is retained.

[0136] This step involves screening and handling stagnant nectar sources, with threshold conditions set using the `limit` parameter. For example: if a nectar source... If the solution fails to improve after a certain number of consecutive limit (threshold) iterations, it is abandoned; if the threshold condition is not met, the honey source is retained and the process proceeds to the next mutation update.

[0137] Step S603: Obtain the logistic sequence solution corresponding to the target honey source based on the chaotic mapping, and construct the third difference mutation vector using the logistic sequence solution.

[0138] This step involves constructing the chaotic perturbation differential mutation vector: For the retained target honey source, a chaotic mapping mechanism is introduced (utilizing the randomness and ergodicity of chaotic sequences to enhance the diversity of the global search and break the constraints of local optima). A Logistic sequence solution (with uniformly distributed sequence values ​​and inherent randomness, avoiding the blindness of traditional random searches) is generated using the Logistic chaotic equation to match the dimension of the target honey source's solution vector. Subsequently, based on this Logistic sequence solution, combined with the target honey source... Based on the characteristics of the original solution vector, construct the third difference mutation vector. This enables global exploration-oriented mutation based on chaotic perturbations.

[0139] Step S604: Obtain three random distinct solutions corresponding to the target honey source, and construct a fourth difference mutation vector using the scaling factor and the random distinct solutions.

[0140] This step is the differential evolution-guided differential mutation vector construction stage: for the retained target honey source, three distinct solutions are randomly selected from its corresponding solution vector space (i.e., three randomly distinct solutions). , , , (To ensure the diversity of differential information); combined with the scaling factor F mentioned above, the difference between the three random distinct solutions is weighted and calculated using the mutation formula of differential evolution to construct a fourth differential mutation vector, thereby achieving precise perturbation based on differential information while taking into account the potential for local optimization.

[0141] Specifically, the fourth difference mutation vector is calculated using the following formula:

[0142] .

[0143] Step S605: Generate the second experimental solution corresponding to the target honey source using the third and fourth difference mutation vectors, and determine the third replacement strategy corresponding to the target honey source based on the fitness value results corresponding to the second experimental solution.

[0144] This step is the stage for establishing the strategy for generating and replacing the fusion-type experimental solution: the third difference mutation vector constructed in step S603 is used. (Chaos Global Exploration Guide) and the fourth difference mutation vector constructed in step S604 (Differential local precision guidance) is used to fuse solutions and generate a new candidate solution that balances global search and local optimization, namely the second experimental solution. For example, the second experimental solution can be calculated using the following formula:

[0145] .

[0146] Subsequently, the fitness value of the second experimental solution can be calculated based on the error evaluation index (such as mean square error and sum of squared errors) set in step S203. The third replacement strategy is established by comparing the fitness value with the fitness value of the original solution vector of the target honey source.

[0147] Step S606: If the second experimental solution satisfies the third replacement strategy, then replace the solution vector corresponding to the target honey source with the second experimental solution.

[0148] This step is the target nectar source update execution stage: Based on the third replacement strategy established in step S605, the fitness values ​​of the second experimental solution and the original solution vector of the target nectar source are quantitatively compared. If the fitness value of the second experimental solution is better than the original solution vector (i.e., the replacement strategy is satisfied, indicating that the new solution integrating chaos and difference mechanisms is closer to the true optimal solution of the ultrasonic echo parameters), then the original solution vector corresponding to the target nectar source is replaced with the second experimental solution, completing the activation and optimization update of stagnant risk nectar sources in the reconnaissance bee stage. If the replacement condition is not met, the original solution vector is retained to ensure the stability of high-quality solutions, and then the reconnaissance bee stage ends, entering the next round of algorithm iteration. Specifically, if the second experimental solution... If the fit is better, then a replacement is triggered. Replace with Conversely, the original solution vector is retained. .

[0149] As can be seen, the above steps embed the mutation strategy of the differential evolution algorithm into the scout bee stage, using the population difference vector to guide the exploration direction, effectively exploring the solution space boundary, improving the ability to escape local optima, and avoiding premature convergence. Simultaneously, introducing chaotic mapping into differential mutation avoids search stagnation. Random initialization may lead to uneven population distribution and miss regions of high-quality solutions. Chaotic sequences can cover all points in the solution space infinitely close to but without repetition, replacing traditional pseudo-random numbers. This operation allows for dynamic adjustment of the mutation direction, escaping local optima through chaotic perturbations.

[0150] Optionally, step S103 involves controlling the echo data to sequentially execute the mercenary bee stage, the follower bee stage, and the scout bee stage, and acquiring the rate of change of the position parameters in real time. Figure 7 As shown, it includes:

[0151] Step S701: After the echo data is executed sequentially through the mercenary bee stage, the follower bee stage, and the scout bee stage, the echo data is updated using the updated position parameters.

[0152] This step is the echo data synchronization update stage after the first round of three-stage execution: First, the initial echo data is strictly controlled to complete the first round of complete processing according to the established process of the hired bee stage - follower bee stage - scout bee stage; through this round of processing, the location parameters of each target nectar source (corresponding to the candidate solutions of echo parameters) will be optimized and updated. Then, based on these updated location parameters, the original echo data is synchronously iterated and updated to ensure that the updated echo data matches the current optimized parameter state, providing accurate input data that reflects the optimization results of the first round for subsequent iterative cycles, and avoiding optimization deviations caused by outdated data in subsequent iterations.

[0153] Step S702: Control the updated echo data to sequentially execute the mercenary bee stage, the follower bee stage, and the scout bee stage, obtain the number of times the echo data is executed in real time, and obtain the change rate in real time using the change value of the position parameter.

[0154] This step involves iterative progression and real-time acquisition of core monitoring indicators: controlling the echo data updated in step S701, continuously executing the complete process of the hired bee stage - follower bee stage - scout bee stage; during each cycle, on the one hand, the number of times the echo data completely traverses the three stages is statistically recorded in real time (i.e., the number of algorithm iterations, used to help judge whether the iteration process is reasonable and avoid over-iteration or under-iteration); on the other hand, the specific change values ​​of the target nectar source location parameters in adjacent iterations are captured in real time, and the change rate of the location parameters is calculated in combination with the iteration interval (this change rate directly quantifies the convergence speed and trend of parameter optimization, and is the core basis for determining whether the target conditions are met and terminating the iteration in subsequent steps).

[0155] Optionally, when the rate of change is detected to meet the target condition, step S104, which determines the echo signal based on the echo parameter vector corresponding to the target honey source under the current location parameters, is as follows: Figure 8 As shown, it includes:

[0156] Step S801: When the rate of change is detected to meet the target condition and the number of iterations is less than the preset iteration threshold, obtain the echo data corresponding to the target honey source under the current position parameters.

[0157] This step involves determining the pre-termination of the iteration and acquiring the current echo data: continuously monitoring two core indicators, the rate of change of location parameters and the number of iterations. When two conditions are met simultaneously: first, the rate of change of location parameters reaches the preset target condition (i.e., parameter optimization has entered a stable convergence state, and the candidate solution is close to the true parameter value, corresponding to the convergence condition); second, the number of iterations has not exceeded the preset iteration threshold (to avoid insufficient optimization due to premature termination of iterations). Then, the subsequent iterations are stopped. Subsequently, the echo data corresponding to the target honey source location parameters under the current optimization state is extracted and acquired (this data is the accurate echo data that matches the optimal candidate solution after multiple rounds of iterative optimization), providing core data support for the determination of the final echo signal.

[0158] In real-world scenarios, other conditions can also be set, such as: when the rate of change is detected to meet the target condition, or when the number of iterations reaches a preset threshold, subsequent steps can be executed.

[0159] Step S802: Determine the echo signal using the echo parameter vector corresponding to the echo data.

[0160] Step S802 is the final precise determination of the echo signal: Based on the echo data obtained in step S801, the echo parameter vector corresponding to the echo data is extracted (this vector is a combination of parameters that accurately characterizes the core physical properties of the echo signal after multiple rounds of optimization by hired bees, follower bees, and scout bees, including key parameters such as amplitude coefficient, bandwidth factor, and center frequency); based on this echo parameter vector, combined with the physical laws of ultrasonic echo propagation and the preset echo model, the target ultrasonic echo signal is deduced and finally determined, realizing high-precision extraction of ultrasonic echo signals in low signal-to-noise ratio and complex medium scenarios, providing a reliable signal foundation for subsequent applications such as defect identification and material characterization.

[0161] To verify the effectiveness of the ultrasonic echo signal processing method in the above embodiments, a simulation experimental platform was built using MATLAB, and the experimental parameters were set as follows:

[0162] True parameters of the echo signal: amplitude β = 0.8, bandwidth factor α = 12 (MHz) 2 Arrival time τ = 1.3 μs, center frequency fc = 5 MHz, phase =2rad;

[0163] Algorithm parameters: colony size SN=50, local loop count limit=20, maximum loop count MaxIter=200;

[0164] Signal-to-noise ratio scenarios: The initial signal-to-noise ratios are set to 0dB, 5dB, 10dB, and 20dB respectively, covering low-noise to strong-noise environments;

[0165] Performance metrics: Mean squared error (MSE), waveform similarity (NCC), and signal-to-noise ratio (SNR) are used to evaluate the algorithm performance.

[0166] The table below shows the parameter estimation results and performance comparison results of the traditional ABC (Artificial Bee Colony Algorithm) and the ultrasonic echo signal processing method in this embodiment (described as ideabc for ease of description) under four signal-to-noise ratio scenarios.

[0167] Table 1 Comparison of parameter estimation results when SNR=0dB

[0168]

[0169] Table 2 Comparison of parameter estimation results when SNR=5dB

[0170]

[0171] Table 3 Comparison of parameter estimation results when SNR=10dB

[0172]

[0173] Table 4 Comparison of parameter estimation results when SNR=20dB

[0174]

[0175] For different signal-to-noise ratio scenarios, comparison charts were generated showing the noisy signal, the estimated signal obtained using this method, and the original signal, as well as the algorithm convergence curve.

[0176] Figure 9 This is a signal curve of the ultrasonic echo reference signal, specifically a noisy reference signal generated under different initial signal-to-noise ratio conditions, which serves as a baseline curve.

[0177] Figure 10 The image shows the signal curve of the ultrasonic echo signal obtained from the traditional artificial bee colony (ABC) algorithm. Figure 10The values ​​(SNR=0 dB), (SNR=5 dB), (SNR=10 dB), and (SNR=20 dB) in the figure show that the signal estimated by the artificial bee colony algorithm deviates significantly from the original signal.

[0178] Figure 11 The signal curve of the ultrasonic echo signal obtained using this method is shown below. Figure 11 As shown in (SNR=0 dB), (SNR=5 dB), (SNR=10 dB), and (SNR=20 dB), the signal estimated by the improved differential evolution artificial bee colony algorithm used in this embodiment almost overlaps with the original signal, and the NCC value is close to 1, with significantly better waveform fidelity.

[0179] Figure 12 To show the convergence curve of the ultrasonic echo signal obtained using this method, from... Figure 12 As shown in (SNR=0 dB), (SNR=5 dB), (SNR=10 dB), and (SNR=20 dB), the signal convergence curve estimated by the improved differential evolutionary artificial bee colony algorithm used in this embodiment decreases rapidly in the early stages of iteration and reaches stability earlier.

[0180] Experimental results show that the improved differential evolutionary artificial bee colony algorithm used in this embodiment has the following four core advantages compared with the traditional artificial bee colony algorithm:

[0181] Higher estimation accuracy: The mean squared error (MSE) is significantly reduced. For example, when SNR=20 dB, the MSE of the improved differential evolution artificial bee colony algorithm (0.00064) is only 0.043% of that of the artificial bee colony algorithm (1.5025).

[0182] Stronger signal fidelity: The waveform similarity NCC value is closer to 1. For example, when SNR=0 dB, the waveform similarity NCC of the improved differential evolution artificial bee colony algorithm (0.999738) is 0.07% higher than that of the artificial bee colony algorithm (0.998997).

[0183] Outstanding noise reduction capability: The signal-to-noise ratio (SNR) is significantly improved. In various scenarios, the SNR of the improved differential evolution artificial bee colony algorithm is more than 2dB higher than that of the artificial bee colony algorithm, and the difference reaches 33.68dB when SNR=20dB.

[0184] Faster convergence speed: By introducing the differential mutation operator, the convergence speed is improved by more than 2 times, and the convergence stability is better, which can meet the needs of real-time parameter estimation.

[0185] As can be seen from the ultrasonic echo signal processing method mentioned in the above embodiments, this method utilizes an improved artificial bee colony algorithm and introduces differential evolution, which enhances the development capability of the artificial bee colony, alleviates the situation where the artificial bee colony is prone to getting trapped in local optima, and can stably, accurately and quickly obtain the signal parameters of the echo signal in low signal-to-noise ratio scenarios, thereby improving the resolution of the ultrasonic echo.

[0186] Corresponding to the ultrasonic echo signal processing method provided in the foregoing embodiments, this invention provides an ultrasonic echo signal processing system for determining echo signals in ultrasonic measuring equipment; such as Figure 13 As shown, the system includes:

[0187] The initialization module 1310 is used to determine the echo parameter vector based on the characteristic parameters corresponding to the echo signal, and to determine the target conditions and echo data corresponding to the echo signal using the echo parameter vector;

[0188] The phase construction module 1320 is used to construct the corresponding mercenary bee stage, follower bee stage, and scout bee stage in the artificial bee colony algorithm using echo data. In the mercenary bee stage, the target nectar source is differentially evolved using at least four differential information. In the follower bee stage, the search direction is determined by the differential information between target nectar sources. In the scout bee stage, the position parameters of the target nectar source are updated using chaotic mapping and search direction.

[0189] The iterative processing module 1330 is used to control the echo data to sequentially execute the hired bee stage, the follower bee stage, and the scout bee stage, and to obtain the rate of change of the position parameters in real time.

[0190] The echo signal determination module 1340 is used to determine the echo signal based on the echo parameter vector corresponding to the target honey source under the current position parameters when the rate of change meets the target conditions.

[0191] As can be seen from the ultrasonic echo signal processing system mentioned in the above embodiments, the system utilizes an improved artificial bee colony algorithm and introduces differential evolution, which enhances the development capability of the artificial bee colony, alleviates the situation where the artificial bee colony is prone to getting trapped in local optima, and can stably, accurately and quickly acquire the signal parameters of the echo signal in low signal-to-noise ratio scenarios, thereby improving the resolution of the ultrasonic echo.

[0192] The ultrasonic echo signal processing system provided in this embodiment of the invention has the same implementation principle and technical effect as the aforementioned ultrasonic echo signal processing method embodiment. For the sake of brevity, any parts not mentioned in the system embodiment can be referred to the corresponding content in the aforementioned ultrasonic echo signal processing method embodiment.

[0193] This embodiment also provides an ultrasonic measuring device, which includes a controller; such as Figure 14As shown, the controller includes a processor 101 and a memory 102; wherein, the memory 102 is used to store one or more computer-executable instructions, which are executed by the processor to implement the steps of the above-described ultrasonic echo signal processing method.

[0194] Figure 14 The error compensation control unit shown also includes a bus 103 and a communication interface 104. The processor 101, the communication interface 104 and the memory 102 are connected through the bus 103.

[0195] The memory 102 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage device. The bus 103 may be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 14 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0196] The communication interface 104 is used to connect to at least one user terminal and other network units through a network interface, and to send encapsulated IPv4 packets or IPv4 packets to the user terminal through the network interface.

[0197] Processor 101 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of processor 101 or by instructions in software form. The processor 101 can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can 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 gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this disclosure. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this disclosure can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software module can reside in a mature storage medium in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory 102, and processor 101 reads the information in memory 102 and, in conjunction with its hardware, completes the steps of the method described in the foregoing embodiments.

[0198] This invention also provides a storage medium storing a computer program, which, when run by a processor, executes the steps of the ultrasonic echo signal processing method described in the foregoing embodiments.

[0199] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, devices, and methods can be implemented in other ways. The system embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0200] The units described as separate components may or may not be physically separate. 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 the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0201] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0202] If the aforementioned 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 this invention, or the part 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 several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0203] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. 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 foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for processing ultrasonic echo signals, characterized in that, The method is used to determine the echo signal in an ultrasonic measuring device; the method includes: Based on the characteristic parameters corresponding to the echo signal, an echo parameter vector is determined, and the target conditions and echo data corresponding to the echo signal are determined using the echo parameter vector; The echo data is used to construct the corresponding mercenary bee stage, follower bee stage, and scout bee stage in the artificial bee colony algorithm. In the mercenary bee stage, the target nectar source is differentially evolved using at least four differentiating information. In the follower bee stage, the search direction is determined by the differentiating information between the target nectar sources. In the scout bee stage, the position parameters of the target nectar source are updated using chaotic mapping and the search direction. The echo data is controlled to sequentially execute the hired bee stage, the follower bee stage, and the scout bee stage, and the rate of change corresponding to the position parameters is acquired in real time; When the rate of change is detected to meet the target condition, the echo signal is determined based on the echo parameter vector corresponding to the target honey source under the current location parameters. The corresponding mercenary bee stage in the artificial bee colony algorithm is constructed using the echo data, including: The target honey source corresponding to the echo parameter vector is constructed using the echo data, and four distinct solutions are randomly obtained using the solution vector corresponding to the honey source. The first differential mutation vector corresponding to the target honey source is constructed using the mutually exclusive solutions, and the crossover strategy corresponding to the first differential mutation vector and the solution vector is determined according to the preset crossover probability parameters. The first experimental solution corresponding to the first difference mutation vector and the solution vector is determined by the crossover strategy, and the first replacement strategy corresponding to the solution vector is determined based on the fitness value result corresponding to the first experimental solution. If the first experimental solution satisfies the first replacement strategy, then the solution vector is replaced with the first experimental solution; Constructing the corresponding follower bee stage in the artificial bee colony algorithm using the echo data includes: The selection probability of the target honey source is calculated using the fitness value corresponding to the solution vector; Obtain the sub-honey sources among the target honey sources whose selection probability is greater than a preset random dimension threshold, and randomly select the optimal honey source from the preset set of optimal honey source solutions; Calculate the first difference between the two random distinct solutions of the sub-honey source, and calculate the second difference between the sub-honey source and the optimal honey source; A second differential mutation vector corresponding to the target honey source is constructed by using a preset scaling factor, the first difference, and the second difference, and a second replacement strategy corresponding to the optimal honey source is determined based on the fitness value result corresponding to the second differential mutation vector. If the second differential mutation vector satisfies the second replacement strategy, then the optimal honey source is replaced with the target honey source corresponding to the second differential mutation vector.

2. The ultrasonic echo signal processing method according to claim 1, characterized in that, The steps of determining the echo parameter vector based on the characteristic parameters corresponding to the echo signal, and determining the target conditions and echo data corresponding to the echo signal using the echo parameter vector, include: After receiving the echo signal using the ultrasonic receiving transducer in the ultrasonic measuring device, the characteristic parameters are determined based on the amplitude coefficient, bandwidth factor, time parameter when the echo signal arrives at the ultrasonic receiving transducer, center frequency, and phase parameter. The echo model corresponding to the ultrasonic receiving transducer is constructed using the feature parameters, and the echo parameter vector and its corresponding echo data are determined based on the echo model. Obtain the actual measurement data corresponding to the echo signal under preset test conditions and parameters, and determine the target conditions corresponding to the echo signal based on the difference between the actual measurement data and the echo data.

3. The ultrasonic echo signal processing method according to claim 1, characterized in that, After replacing the optimal honey source with the target honey source corresponding to the second differential mutation vector, the method further includes: The sampling weight values ​​are determined using the scaling factor; wherein the sampling weight values ​​follow a normal distribution. Calculate the average value of the sampling weights based on the number of the target honey sources; The weight value is updated using the average value, and the scaling factor is updated using the updated weight value.

4. The ultrasonic echo signal processing method according to claim 1, characterized in that, The echo data is used to construct the corresponding scout bee stage in the artificial bee colony algorithm, including: The number of times the target honey source's location changes in real time is obtained; If the number of times the location changes meets a preset threshold condition, the target honey source is abandoned; if the number of times the location changes does not meet the preset threshold condition, the target honey source is retained. The logistic sequence solution corresponding to the target honey source is obtained based on the chaotic mapping, and the third difference mutation vector is constructed using the logistic sequence solution; Obtain three random distinct solutions corresponding to the target honey source, and construct a fourth difference mutation vector using the scaling factor and the random distinct solutions; The third and fourth differential mutation vectors are used to generate a second experimental solution corresponding to the target honey source, and the third replacement strategy corresponding to the target honey source is determined based on the fitness value result corresponding to the second experimental solution. If the second experimental solution satisfies the third replacement strategy, then the solution vector corresponding to the target honey source is replaced with the second experimental solution.

5. The ultrasonic echo signal processing method according to claim 1, characterized in that, The steps of controlling the echo data to sequentially execute the mercenary bee stage, the follower bee stage, and the scout bee stage, and to acquire the rate of change corresponding to the position parameters in real time, include: After controlling the echo data to sequentially execute the mercenary bee stage, the follower bee stage, and the scout bee stage, the echo data is updated using the updated position parameters; The updated echo data is controlled to sequentially execute the hired bee stage, the follower bee stage, and the scout bee stage, and the number of times the echo data is executed in real time is obtained, and the change rate is obtained in real time using the change value of the position parameter.

6. The ultrasonic echo signal processing method according to claim 5, characterized in that, When the rate of change is detected to meet the target condition, the step of determining the echo signal based on the echo parameter vector corresponding to the target honey source under the current location parameters includes: When the rate of change is detected to meet the target condition and the number of iterations is less than the preset iteration threshold, the echo data corresponding to the target honey source under the current position parameters is obtained. The echo signal is determined using the echo parameter vector corresponding to the echo data.

7. An ultrasonic echo signal processing system, characterized in that, The system is used to determine echo signals in ultrasonic measuring equipment; the system includes: An initialization module is used to determine an echo parameter vector based on the feature parameters corresponding to the echo signal, and to determine the target conditions and echo data corresponding to the echo signal using the echo parameter vector; The phase construction module is used to construct the corresponding mercenary bee phase, follower bee phase, and scout bee phase in the artificial bee colony algorithm using the echo data; wherein, in the mercenary bee phase, the target nectar source is differentially evolved using at least four differential information; the follower bee phase determines the search direction using the differential information between the target nectar sources; and the scout bee phase updates the position parameters of the target nectar source using chaotic mapping and the search direction. An iterative processing module is used to control the echo data to sequentially and cyclically execute the hired bee stage, the follower bee stage, and the scout bee stage, and to obtain the rate of change corresponding to the position parameters in real time; An echo signal determination module is used to determine the echo signal based on the echo parameter vector corresponding to the target honey source under the current location parameters when the rate of change satisfies the target condition. In the process of constructing the corresponding hired bee stage in the artificial bee colony algorithm using the echo data, the stage construction module is further configured to: construct the target nectar source corresponding to the echo parameter vector using the echo data, and randomly obtain four distinct solutions using the solution vector corresponding to the nectar source; construct the first differential mutation vector corresponding to the target nectar source using the distinct solutions, and determine the crossover strategy corresponding to the first differential mutation vector and the solution vector according to a preset crossover probability parameter; determine the first experimental solution corresponding to the first differential mutation vector and the solution vector using the crossover strategy, and determine the first replacement strategy corresponding to the solution vector according to the fitness value result corresponding to the first experimental solution; if the first experimental solution satisfies the first replacement strategy, then replace the solution vector with the first experimental solution; In the process of constructing the follower bee stage in the artificial bee colony algorithm using the echo data, the stage construction module is further configured to: calculate the selection probability of the target nectar source using the fitness value corresponding to the solution vector; obtain sub-nectar sources among the target nectar sources whose selection probability is greater than a preset random dimension threshold, and randomly select the optimal nectar source from a preset set of optimal nectar source solutions; calculate the first difference between two random distinct solutions of the sub-nectar source, and calculate the second difference between the sub-nectar source and the optimal nectar source; construct the second difference mutation vector corresponding to the target nectar source using a preset scaling factor, the first difference, and the second difference, and determine the second replacement strategy corresponding to the optimal nectar source based on the fitness value result corresponding to the second difference mutation vector; if the second difference mutation vector satisfies the second replacement strategy, then replace the optimal nectar source with the target nectar source corresponding to the second difference mutation vector.

8. An ultrasonic measuring device, characterized in that, The ultrasonic measuring device is equipped with a controller; the controller includes a processor and a memory, the memory stores computer-executable instructions that can be executed by the processor, and the processor executes the computer-executable instructions to implement the ultrasonic echo signal processing method mentioned in any one of claims 1 to 6.

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