Method and system for optimizing liquid medium through ultrasonic probe based on big data

Abstract

The invention discloses a method and a system for optimizing a liquid medium by an ultrasonic probe based on big data. The method comprises the steps that a three-dimensional first matrix is constructed, the first matrix is used for representing the states of bubble radiuses corresponding to different probe working frequencies in adjacent probe working time points, a three-dimensional first convolution kernel is adopted to conduct convolution on the three-dimensional matrix, the change conditions of the adjacent time points are judged, and liquid medium bubble characteristics are obtained; by adopting the associated neural network, the data of the working frequency of the probe, the working time point of the probe and the type of the liquid medium can be directly input in the subsequent judgment to obtain the bubble characteristics of the liquid medium with the corresponding bubble radius change, so that the detection time is shortened, and the parameters of the associated neural network can be updated in real time. Therefore, in the clustering process, the liquid medium category enabling the bubble radius to change stably is found out, and the corresponding change cavitation threshold value is found out.

Description

Technical Field

[0001] The present invention relates to the field of ultrasound, and in particular to a method and system for optimizing a liquid medium using an ultrasound probe based on big data. Background Art

[0002] At present, ultrasonic testing is performed using variable frequency ultrasonic instruments, low frequency for biofilm lysis and drug penetration promotion, high frequency for sterilization, etc. During operation, the handheld ultrasonic probe used will produce cavitation reaction when inserted into the liquid medium. The cavitation threshold is related to the radius of the bubble in the medium. The smaller the bubble radius, the higher the cavitation threshold. The bubble size can be optimized to promote the generation of cavitation effect by adjusting the composition of the liquid medium or adding appropriate microbubbles. The cavitation threshold is related to the operating frequency. The higher the frequency, the higher the cavitation threshold, and the more difficult it is to produce cavitation. Therefore, when improving the liquid medium, it is necessary to consider the operating frequency of the host and select appropriate medium components to match the frequency, so that the cavitation effect is more likely to occur. Summary of the invention

[0003] The purpose of the present invention is to provide a method and system for optimizing liquid media using an ultrasonic probe based on big data, so as to solve the above-mentioned problems existing in the prior art.

[0004] In a first aspect, an embodiment of the present invention provides a method for optimizing a liquid medium using an ultrasonic probe based on big data, comprising: Acquire multiple liquid medium categories, multiple probe working frequencies, multiple probe working time points and corresponding bubble radii; the liquid medium category indicates the category of the liquid medium in which the handheld ultrasonic probe is located; the probe working frequency indicates the frequency of the handheld ultrasonic probe; the probe working time point indicates the time length of the handheld ultrasonic probe in the liquid medium; the bubble radius indicates the radius of the bubble caused by the handheld ultrasonic probe; Based on the multiple liquid medium categories, multiple probe working frequencies, multiple probe working time points and corresponding medium bubble radii, adjacent time data change characteristics and multiple liquid medium bubble characteristics are obtained; the adjacent time data change characteristics represent the changes in the liquid medium categories and probe working frequencies at adjacent probe working time points; the liquid medium bubble characteristics represent the changes in the bubble radii corresponding to different probe working frequencies at adjacent probe working time points; By associating neural networks, the correlation between the changing characteristics of adjacent time data and the characteristics of multiple liquid medium bubbles is constructed; Clustering is performed according to the characteristics of the multiple liquid medium bubbles to obtain a detected liquid medium category; the detected liquid medium category is the cluster center; The bubble radius that is larger than the other bubble radius in the detected liquid medium category is used as the first bubble radius; The probe operating frequency corresponding to the first bubble radius is used as the optimal probe operating frequency, and the corresponding probe operating time point is used as the optimal probe operating time point; A variable cavitation threshold is obtained according to the first bubble radius.

[0005] Optionally, the obtaining of the bubble characteristics of the liquid medium by associating a neural network based on the liquid medium type, the probe working frequency, the probe working time point and the corresponding bubble radius includes: Based on multiple liquid medium categories, multiple probe working frequencies, multiple probe working time points and corresponding bubble radii, multiple first matrices are obtained; the first matrix is ​​a three-dimensional matrix; one liquid medium category corresponds to one first matrix; Based on multiple liquid medium categories, multiple probe operating frequencies, multiple probe operating durations and the first matrix, a second matrix is ​​obtained; the second matrix is ​​a two-dimensional matrix; Based on the first matrix, a liquid medium bubble characteristic is obtained; a plurality of first matrices correspond to a plurality of liquid medium bubble characteristics; Based on the second matrix, the adjacent time data variation characteristics are obtained.

[0006] Optionally, the constructing of the correlation relationship between the adjacent time data change characteristics and the liquid medium bubble characteristics by associating a neural network includes: Sort multiple liquid medium bubble features according to the order of liquid medium categories in the adjacent time data change features to obtain liquid medium fusion features; Constructing a second associative neural network; wherein the structure of the second associative neural network is the same as the structure of the associative neural network; Based on the changing characteristics of adjacent time data and the fusion characteristics of the liquid medium, a second associative neural network is used to adjust the parameters in the associative neural network.

[0007] Optionally, the step of using a second associative neural network to adjust parameters in the associative neural network based on the adjacent time data variation characteristics and the liquid medium fusion characteristics includes: Inputting the adjacent time data change characteristics into the associated neural network, fusing the characteristics with the liquid medium to obtain the loss, and training the associated neural network; The positions of the same probe working time points corresponding to the adjacent time data variation characteristics and the liquid medium fusion characteristics are jointly adjusted to train the associated neural network; Inputting the adjacent time data change characteristics into a second association neural network, fusing the characteristics with the liquid medium to obtain the loss, and training the second association neural network; The parameters of the second associative neural network and the corresponding positions of the associative neural network are merged to adjust the associative neural network.

[0008] Optionally, the method of jointly adjusting the positions of the same probe working time points corresponding to the adjacent time data variation characteristics and the liquid medium fusion characteristics, and training the associated neural network, comprises: A plurality of position pairs are obtained; the position pairs include a first position and a second position; the first position is a random position in the adjacent time data change feature; the second position is a position in the adjacent time data change feature other than the second position; sequentially exchanging the values ​​of multiple position pairs in the adjacent time data change feature to obtain a transformed adjacent time data change feature; sequentially exchanging the values ​​of multiple position pairs in the liquid medium fusion feature to obtain a transformed liquid medium fusion feature; The transformed adjacent time data change characteristics are input into the associated neural network, and the loss is calculated by fusing the transformed liquid medium characteristics to train the first neural network.

[0009] Optionally, obtaining the bubble characteristics of the liquid medium based on the first matrix includes: Obtain a first convolution kernel; the first convolution kernel is a 2*n*m three-dimensional convolution kernel; n is the number of columns of the first matrix; m is the number of pages of the first matrix; On the rows of the first matrix, the first convolution kernel is convolved on the first matrix with a step size of 1 to obtain the liquid medium.

[0010] Optionally, performing convolution based on the second matrix to obtain adjacent time data change characteristics includes: Get the second convolution kernel of 2*2; On the rows of the second matrix, the second convolution kernel is convolved on the first matrix with a step size of 1 to obtain the adjacent time data change characteristics.

[0011] Optionally, the multiple first matrices are obtained based on multiple liquid medium categories, multiple probe operating frequencies, multiple probe operating time points and corresponding bubble radii, including: The probe working time points from small to large are sequentially associated with the rows with small to large subscripts in the first matrix, the probe working frequencies from small to large are sequentially associated with the pages with small to large subscripts in the first matrix, and the bubble radii from small to large are sequentially associated with the columns with small to large subscripts in the first matrix; A plurality of first matrices are obtained corresponding to a plurality of liquid medium categories; In the first matrix corresponding to the liquid medium category, the positions of the probe working frequency, the probe working time point and the corresponding bubble radius are marked as 1.

[0012] Optionally, the multiple second matrices are obtained based on the multiple liquid medium categories, the multiple probe operating frequencies, the multiple probe operating durations and the first matrix, including: The probe working time points from small to large are sequentially associated with the rows with subscripts from small to large in the second matrix, the liquid medium category is associated with the first column of the second matrix, and the probe working frequency is associated with the second column of the second matrix; A plurality of second matrices are obtained corresponding to a plurality of liquid medium categories; Using the liquid medium category corresponding to the first matrix as the first liquid medium category; Filling the first column of the second matrix with the first liquid medium category; Fill the probe operating frequency in the first matrix into the second column of the same row as in the second matrix.

[0013] In a second aspect, an embodiment of the present invention provides a system for optimizing a liquid medium using an ultrasonic probe based on big data, comprising: An acquisition module is used to acquire multiple liquid medium categories, multiple probe operating frequencies, multiple probe operating time points and corresponding bubble radii; the liquid medium category indicates the category of the liquid medium in which the handheld ultrasonic probe is located; the probe operating frequency indicates the frequency of the handheld ultrasonic probe; the probe operating time point indicates the time length of the handheld ultrasonic probe in the liquid medium; and the bubble radius indicates the radius of the bubble caused by the handheld ultrasonic probe; A feature extraction module, for obtaining adjacent time data change characteristics and multiple liquid medium bubble characteristics based on the multiple liquid medium categories, multiple probe working frequencies, multiple probe working time points and corresponding medium bubble radii; the adjacent time data change characteristics represent changes in liquid medium categories and probe working frequencies at adjacent probe working time points; the liquid medium bubble characteristics represent changes in bubble radii corresponding to different probe working frequencies at adjacent probe working time points; A feature association module is used to construct an association relationship between the change characteristics of adjacent time data and the characteristics of multiple liquid medium bubbles through an association neural network; A clustering module, used for clustering the plurality of liquid medium bubble features to obtain a detected liquid medium category; the detected liquid medium category is a clustering center; A detection module, used to use a bubble radius greater than other bubble radii in the detected liquid medium category as a first bubble radius; use a probe operating frequency corresponding to the first bubble radius as an optimal probe operating frequency, and use a corresponding probe operating time point as an optimal probe operating time point; The cavitation detection module is used to obtain a variable cavitation threshold according to the first bubble radius.

[0014] Compared with the prior art, the embodiments of the present invention achieve the following beneficial effects: The embodiments of the present invention also provide a method and system for optimizing liquid media using an ultrasonic probe based on big data.

[0015] In the present invention, a three-dimensional first matrix is ​​constructed according to multiple probe working frequencies, multiple probe working time points and corresponding medium bubble radius; multiple liquid medium categories correspond to multiple first matrices. The first matrix is ​​used to represent the state of the bubble radius corresponding to different probe working frequencies in adjacent probe working time points. The three-dimensional first convolution kernel is used to convolve the three-dimensional matrix, and the change of adjacent time points is judged to obtain the liquid medium bubble characteristics. By constructing an associative neural network, the liquid medium bubble characteristics are associated with the changes in data such as the probe working frequency, the probe working time point, and the liquid medium category. By adopting the associative neural network, not only can the liquid medium bubble characteristics of the corresponding bubble radius change be obtained by directly inputting the data of the probe working frequency, the probe working time point, and the liquid medium category in the subsequent judgment, the detection time is reduced, and the parameters of the associative neural network can be updated in real time, making the detection more accurate. The associative neural network is also constructed more accurately by adjusting the data and adding a second associative neural network as a network for adjusting the associative neural network. By using multiple liquid medium bubble characteristics and adjacent time data change characteristics for feature matching, the change of two adjacent time points can be used for matching, which can make the matching more accurate and more in line with the change requirements. Therefore, through clustering, the liquid medium category that makes the bubble radius change stably can be found, achieving the technical effect of finding the changing cavitation threshold more accurately. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a flow chart of a method for optimizing liquid medium using an ultrasonic probe based on big data provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] The present invention will be described in detail below in conjunction with the accompanying drawings.

[0018] Example 1 like Figure 1 As shown, an embodiment of the present invention provides a method for optimizing liquid medium using an ultrasonic probe based on big data, the method comprising: S101: Acquire multiple liquid medium categories, multiple probe operating frequencies, multiple probe operating time points and corresponding bubble radii; the liquid medium category indicates the category of the liquid medium in which the handheld ultrasonic probe is located; the probe operating frequency indicates the frequency of the handheld ultrasonic probe; the probe operating time point indicates the length of time the handheld ultrasonic probe is in the liquid medium; the bubble radius indicates the radius of the bubble caused by the handheld ultrasonic probe.

[0019] S102: Based on the multiple liquid medium categories, multiple probe working frequencies, multiple probe working time points and corresponding medium bubble radii, adjacent time data change characteristics and multiple liquid medium bubble characteristics are obtained; the adjacent time data change characteristics represent changes in liquid medium categories and probe working frequencies at adjacent probe working time points; the liquid medium bubble characteristics represent changes in bubble radii corresponding to different probe working frequencies at adjacent probe working time points.

[0020] S103: constructing a correlation relationship between the adjacent time data change characteristics and the characteristics of multiple liquid medium bubbles through an associative neural network.

[0021] The associative neural network can be updated in real time. The input values ​​of the associative neural network are multiple probe operating frequencies, multiple probe operating time points and multiple liquid medium categories, and the output values ​​of the associative neural network represent the characteristics of the change of the bubble radius at adjacent time points corresponding to the data of the input values.

[0022] S104: Clustering is performed according to the plurality of liquid medium bubble features to obtain a detected liquid medium category; the detected liquid medium category is a cluster center.

[0023] In this embodiment, k-means is used to calculate the Euclidean distance of the value corresponding to each position in the bubble feature of the liquid medium for clustering.

[0024] S105: taking a bubble radius in the detected liquid medium category that is larger than other bubble radii as a first bubble radius.

[0025] The first bubble radius represents the largest bubble radius among multiple bubble radii corresponding to the detected liquid medium category.

[0026] S106: Taking the probe operating frequency corresponding to the first bubble radius as the optimal probe operating frequency, and taking the corresponding probe operating time point as the optimal probe operating time point.

[0027] Among them, the optimal probe operating frequency, the optimal probe operating time point and the type of detected liquid medium are output.

[0028] S107: Obtaining a variable cavitation threshold according to the first bubble radius.

[0029] In this embodiment, the variable cavitation threshold is obtained by calculating the ultrasonic cavitation frequency threshold as follows: f = (2σ / ρ)^(1 / 2) / (πR). f represents the variable cavitation threshold, σ represents the surface tension of the liquid, ρ represents the density of the liquid, and R represents the first bubble radius.

[0030] The variable cavitation threshold value represents a value at which a cavitation reaction can occur.

[0031] Optionally, obtaining adjacent time data variation characteristics and liquid medium bubble characteristics based on the multiple liquid medium categories, multiple probe operating frequencies, multiple probe operating time points and corresponding bubble radii includes: Based on multiple liquid medium categories, multiple probe working frequencies, multiple probe working time points and corresponding bubble radii, multiple first matrices are obtained; the first matrix is ​​a three-dimensional matrix; one liquid medium category corresponds to one first matrix; Based on multiple liquid medium categories, multiple probe operating frequencies, multiple probe operating durations and the first matrix, a second matrix is ​​obtained; the second matrix is ​​a two-dimensional matrix; Based on the first matrix, a liquid medium bubble characteristic is obtained; a plurality of first matrices correspond to a plurality of liquid medium bubble characteristics; Based on the second matrix, the adjacent time data variation characteristics are obtained.

[0032] Optionally, the constructing of the correlation relationship between the adjacent time data change characteristics and the liquid medium bubble characteristics by associating a neural network includes: The plurality of liquid medium bubble features are sorted according to the order of the liquid medium categories in the adjacent time data change features to obtain the liquid medium fusion feature.

[0033] A second associative neural network is constructed; the structure of the second associative neural network is the same as the structure of the associative neural network.

[0034] In this embodiment, the associative neural network and the second associative neural network are long short-term memory neural networks (LSTM).

[0035] Based on the changing characteristics of adjacent time data and the fusion characteristics of the liquid medium, a second associative neural network is used to adjust the parameters in the associative neural network.

[0036] Optionally, the step of using a second associative neural network to adjust parameters in the associative neural network based on the adjacent time data variation characteristics and the liquid medium fusion characteristics includes: The adjacent time data change characteristics are input into the associated neural network, and the loss is calculated by fusing the characteristics with the liquid medium to train the associated neural network.

[0037] The adjacent time data change characteristics are input into the associated neural network to obtain the first prediction data, the first prediction data is fused with the liquid medium characteristics to obtain the loss, and the associated neural network is trained.

[0038] Among them, the cross entropy loss function is used to calculate the loss.

[0039] The positions of the same probe working time points corresponding to the adjacent time data variation characteristics and the liquid medium fusion characteristics are jointly adjusted to train the associated neural network.

[0040] The adjacent time data change characteristics are input into the second associative neural network, and the loss is calculated by fusing the characteristics with the liquid medium to train the second associative neural network.

[0041] The variation characteristics of the adjacent time data are input into the second associative neural network to obtain the third prediction data; the loss is calculated by fusing the third prediction data with the liquid medium characteristics to train the second associative neural network.

[0042] Among them, the cross entropy loss function is used to calculate the loss.

[0043] The parameters of the second neural network and the corresponding positions of the first neural network are merged, and the first neural network is adjusted to obtain an associated neural network.

[0044] In this embodiment, an averaging method is used for fusion.

[0045] Optionally, the method of jointly adjusting the positions of the same probe working time points corresponding to the adjacent time data variation characteristics and the liquid medium fusion characteristics, and training the associated neural network, comprises: A plurality of position pairs are obtained; the position pairs include a first position and a second position; the first position is a random position in the adjacent time data change feature; the second position is a position in the adjacent time data change feature other than the second position.

[0046] Among them, as in this embodiment, the position pairs are [2,7], [4,9], [5,8] [3,6].

[0047] The values ​​of multiple position pairs in the adjacent time data change feature are exchanged in sequence to obtain the transformed adjacent time data change feature.

[0048] Among them, as in this embodiment, the value with subscript 2 in the adjacent time data change feature is exchanged with the value with subscript 7 to obtain the first transformed adjacent time data change feature; the value with subscript 4 in the first transformed adjacent time data change feature is exchanged with the value with subscript 9 to obtain the second transformed adjacent time data change feature; the value with subscript 5 in the second transformed adjacent time data change feature is exchanged with the value with subscript 8 to obtain the third transformed adjacent time data change feature; the value with subscript 3 in the third transformed adjacent time data change feature is exchanged with the value with subscript 8 to obtain the transformed adjacent time data change feature.

[0049] The values ​​of multiple position pairs in the liquid medium fusion feature are exchanged in sequence to obtain a transformed liquid medium fusion feature.

[0050] Among them, as in this embodiment, the value with subscript 2 in the liquid medium fusion feature is exchanged with the value with subscript 7 to obtain the first transformation liquid medium fusion feature; the value with subscript 4 in the first transformation liquid medium fusion feature is exchanged with the value with subscript 9 to obtain the second transformation liquid medium fusion feature; the value with subscript 5 in the second transformation liquid medium fusion feature is exchanged with the value with subscript 8 to obtain the third transformation liquid medium fusion feature; the value with subscript 3 in the third transformation liquid medium fusion feature is exchanged with the value with subscript 8 to obtain the transformation liquid medium fusion feature.

[0051] The transformed adjacent time data change characteristics are input into the associated neural network, and the loss is calculated by fusing the transformed liquid medium characteristics to train the first neural network.

[0052] The change characteristics of the transformed adjacent time data are input into the associated neural network to obtain the second prediction data; the loss is calculated by fusing the second prediction data with the transformed liquid medium characteristics to train the first neural network.

[0053] Among them, the cross entropy loss function is used to calculate the loss.

[0054] Optionally, obtaining the bubble characteristics of the liquid medium based on the first matrix includes: A first convolution kernel is obtained; the first convolution kernel is a three-dimensional convolution kernel of 2*n*m; n is the number of columns of the first matrix; and m is the number of pages of the first matrix.

[0055] Wherein, n and m are positive integers.

[0056] On the rows of the first matrix, the first convolution kernel is convolved on the first matrix with a step size of 1 to obtain the bubble characteristics of the liquid medium.

[0057] Among them, convolution is performed through the convolution method in 3D Convolutional Neural Networks (3D CNN).

[0058] Optionally, performing convolution based on the second matrix to obtain adjacent time data change characteristics includes: Get the second convolution kernel of 2*2; On the rows of the second matrix, the second convolution kernel is convolved on the first matrix with a step size of 1 to obtain the adjacent time data change characteristics.

[0059] Among them, convolution is performed through the convolution method in Convolutional Neural Networks (CNN).

[0060] Optionally, the multiple first matrices are obtained based on multiple liquid medium categories, multiple probe operating frequencies, multiple probe operating time points and corresponding bubble radii, including: The probe working time points from small to large are sequentially associated with the rows with small to large subscripts in the first matrix, the probe working frequencies from small to large are sequentially associated with the pages with small to large subscripts in the first matrix, and the bubble radii from small to large are sequentially associated with the columns with small to large subscripts in the first matrix; A plurality of first matrices are obtained corresponding to a plurality of liquid medium categories; In the first matrix corresponding to the liquid medium category, the positions of the probe working frequency, the probe working time point and the corresponding bubble radius are marked as 1.

[0061] Among them, in the first matrix corresponding to the liquid medium category, in the two-dimensional matrix composed of the columns and pages of the first matrix corresponding to a probe working time point, the position where 1 is located represents the bubble radius that changes with the probe working frequency.

[0062] Optionally, the multiple second matrices are obtained based on the multiple liquid medium categories, the multiple probe operating frequencies, the multiple probe operating durations and the first matrix, including: The probe working time points from small to large are sequentially associated with the rows with subscripts from small to large in the second matrix, the liquid medium category is associated with the first column of the second matrix, and the probe working frequency is associated with the second column of the second matrix.

[0063] A plurality of second matrices are obtained corresponding to a plurality of liquid medium categories; The liquid medium category corresponding to the first matrix is ​​used as the first liquid medium category.

[0064] Fill the first column of the second matrix with the first liquid medium category.

[0065] Fill the probe operating frequency in the first matrix into the second column of the same row as in the second matrix.

[0066] Example 2 You can also consider the duration of insonation: The cavitation threshold is related to the duration of insonation. When designing the debridement procedure, you need to consider the duration of the insonation to ensure that there is enough time for the cavitation effect to occur.

[0067] Enhance the cavitation effect of the liquid medium: The cavitation effect in the working fluid can be enhanced by increasing the power output of the ultrasonic vibration device. This can be achieved by adjusting the composition of the liquid medium or adding specific additives so that cavitation bubbles are more easily generated at a given power.

[0068] Optimizing fluid circulation: Improving electrolyte circulation can enhance the cavitation effect. This can be achieved by designing a more efficient fluid circulation system or by adding ingredients that promote fluid flow.

[0069] Utilizing micro-gas core cavitation bubbles: Ultrasonic cavitation is based on micro-gas core cavitation bubbles in liquid. The cavitation effect can be promoted by adding an appropriate amount of micro-bubbles to the liquid medium or selecting a liquid containing appropriate micro-gas cores.

[0070] Adjusting sound pressure: When the sound pressure reaches a certain value, the cavitation bubbles will go through a process of growth-acceleration-closure-rupture. The acoustic properties of the liquid medium can be adjusted to make it easier to reach the required sound pressure value at a given host frequency, thereby enhancing the cavitation effect.

[0071] Example 3 Based on the above-mentioned method of optimizing liquid medium by ultrasonic probe based on big data, an embodiment of the present invention also provides a system for optimizing liquid medium by ultrasonic probe based on big data, the system including an acquisition module, a feature extraction module, a feature association module, a clustering module, a detection module, and a cavitation detection module.

[0072] An acquisition module is used to acquire multiple liquid medium categories, multiple probe operating frequencies, multiple probe operating time points and corresponding bubble radii; the liquid medium category indicates the category of the liquid medium in which the handheld ultrasonic probe is located; the probe operating frequency indicates the frequency of the handheld ultrasonic probe; the probe operating time point indicates the time length of the handheld ultrasonic probe in the liquid medium; and the bubble radius indicates the radius of the bubble caused by the handheld ultrasonic probe; A feature extraction module, for obtaining adjacent time data change characteristics and multiple liquid medium bubble characteristics based on the multiple liquid medium categories, multiple probe working frequencies, multiple probe working time points and corresponding medium bubble radii; the adjacent time data change characteristics represent changes in liquid medium categories and probe working frequencies at adjacent probe working time points; the liquid medium bubble characteristics represent changes in bubble radii corresponding to different probe working frequencies at adjacent probe working time points; A feature association module is used to construct an association relationship between the change characteristics of adjacent time data and the characteristics of multiple liquid medium bubbles through an association neural network; A clustering module, used for clustering the plurality of liquid medium bubble features to obtain a detected liquid medium category; the detected liquid medium category is a clustering center; A detection module, used to use a bubble radius greater than other bubble radii in the detected liquid medium category as a first bubble radius; use a probe operating frequency corresponding to the first bubble radius as an optimal probe operating frequency, and use a corresponding probe operating time point as an optimal probe operating time point; The cavitation detection module is used to obtain a variable cavitation threshold according to the first bubble radius.

[0073] Regarding the system in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0074] The algorithm and display provided herein are not inherently related to any particular computer, virtual system or other device. Various general purpose systems can also be used together with the teachings based on this. According to the above description, it is obvious that the structure required for constructing such systems. In addition, the present invention is not directed to any specific programming language either. It should be understood that various programming languages ​​can be utilized to realize the content of the present invention described herein, and the description of the above specific languages ​​is for disclosing the best mode of the present invention.

[0075] In the description provided herein, a large number of specific details are described. However, it is understood that embodiments of the present invention can be practiced without these specific details. In some instances, well-known methods, structures and techniques are not shown in detail so as not to obscure the understanding of this description.

[0076] The various component embodiments of the present invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. It should be understood by those skilled in the art that a microprocessor or a digital signal processor (DSP) may be used in practice to implement some or all of the functions of some or all of the components in the apparatus according to an embodiment of the present invention. The present invention may also be implemented as a device or device program (e.g., a computer program and a computer program product) for executing part or all of the methods described herein. Such a program implementing the present invention may be stored on a computer-readable medium, or may be in the form of one or more signals. Such a signal may be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

Claims

1. A method for optimizing liquid medium using an ultrasonic probe based on big data, characterized in that: include: Obtain multiple liquid medium categories, multiple probe operating frequencies, multiple probe operating time points and corresponding bubble radii; The liquid medium category indicates the category of the liquid medium in which the handheld ultrasonic probe is located; the probe operating frequency indicates the frequency of the handheld ultrasonic probe; the probe operating time point indicates the time length of the handheld ultrasonic probe in the liquid medium; the bubble radius indicates the radius of the bubble caused by the handheld ultrasonic probe; Based on the multiple liquid medium categories, multiple probe working frequencies, multiple probe working time points and corresponding medium bubble radii, adjacent time data change characteristics and multiple liquid medium bubble characteristics are obtained; The adjacent time data change characteristics represent the changes in the liquid medium type and the probe working frequency at adjacent probe working time points; the liquid medium bubble characteristics represent the changes in the bubble radius corresponding to different probe working frequencies at adjacent probe working time points; By associating neural networks, the correlation between the changing characteristics of adjacent time data and the characteristics of multiple liquid medium bubbles is constructed; Clustering is performed according to the plurality of liquid medium bubble characteristics to obtain a detected liquid medium category; The detected liquid medium category is the cluster center; The bubble radius that is larger than the other bubble radius in the detected liquid medium category is used as the first bubble radius; The probe operating frequency corresponding to the first bubble radius is used as the optimal probe operating frequency, and the corresponding probe operating time point is used as the optimal probe operating time point; A variable cavitation threshold is obtained according to the first bubble radius.

2. The method for optimizing liquid medium using an ultrasonic probe based on big data according to claim 1, characterized in that: The liquid medium bubble characteristics are obtained by associating a neural network based on the liquid medium type, the probe working frequency, the probe working time point and the corresponding bubble radius, including: Based on multiple liquid medium categories, multiple probe working frequencies, multiple probe working time points and corresponding bubble radii, multiple first matrices are obtained; the first matrix is ​​a three-dimensional matrix; one liquid medium category corresponds to one first matrix; Based on multiple liquid medium categories, multiple probe operating frequencies, multiple probe operating durations and the first matrix, a second matrix is ​​obtained; the second matrix is ​​a two-dimensional matrix; Based on the first matrix, a liquid medium bubble characteristic is obtained; a plurality of first matrices correspond to a plurality of liquid medium bubble characteristics; Based on the second matrix, the adjacent time data variation characteristics are obtained.

3. The method for optimizing liquid medium using ultrasonic probe based on big data according to claim 1, characterized in that: The method of constructing the correlation relationship between the adjacent time data change characteristics and the liquid medium bubble characteristics through the correlation neural network includes: Sort multiple liquid medium bubble features according to the order of liquid medium categories in the adjacent time data change features to obtain liquid medium fusion features; Constructing a second associative neural network; wherein the structure of the second associative neural network is the same as the structure of the associative neural network; Based on the changing characteristics of adjacent time data and the fusion characteristics of the liquid medium, a second associative neural network is used to adjust the parameters in the associative neural network.

4. The method for optimizing liquid medium using ultrasonic probe based on big data according to claim 3, characterized in that: The method of using a second associative neural network to adjust parameters in the associative neural network based on the adjacent time data variation characteristics and the liquid medium fusion characteristics includes: Inputting the adjacent time data change characteristics into the associated neural network, fusing the characteristics with the liquid medium to obtain the loss, and training the associated neural network; The positions of the same probe working time points corresponding to the adjacent time data variation characteristics and the liquid medium fusion characteristics are jointly adjusted to train the associated neural network; Inputting the adjacent time data change characteristics into a second association neural network, fusing the characteristics with the liquid medium to obtain the loss, and training the second association neural network; The parameters of the second associative neural network and the corresponding positions of the associative neural network are merged to adjust the associative neural network.

5. The method for optimizing liquid medium using ultrasonic probe based on big data according to claim 4, characterized in that: The method of jointly adjusting the positions of the same probe working time points corresponding to the adjacent time data variation characteristics and the liquid medium fusion characteristics, and training the associated neural network, comprises: A plurality of position pairs are obtained; the position pairs include a first position and a second position; the first position is a random position in the adjacent time data change feature; the second position is a position in the adjacent time data change feature other than the second position; sequentially exchanging the values ​​of multiple position pairs in the adjacent time data change feature to obtain a transformed adjacent time data change feature; sequentially exchanging the values ​​of multiple position pairs in the liquid medium fusion feature to obtain a transformed liquid medium fusion feature; The transformed adjacent time data change characteristics are input into the associated neural network, and the loss is calculated by fusing the transformed liquid medium characteristics to train the first neural network.

6. The method for optimizing liquid medium using ultrasonic probe based on big data according to claim 2, characterized in that: The step of obtaining the bubble characteristics of the liquid medium based on the first matrix includes: Obtain a first convolution kernel; the first convolution kernel is a 2*n*m three-dimensional convolution kernel; n is the number of columns of the first matrix; m is the number of pages of the first matrix; On the rows of the first matrix, the first convolution kernel is convolved on the first matrix with a step size of 1 to obtain the liquid medium.

7. The method for optimizing liquid medium using ultrasonic probe based on big data according to claim 2, characterized in that: The convolution is performed based on the second matrix to obtain the adjacent time data change characteristics, including: Get the second convolution kernel of 2*2; On the rows of the second matrix, the second convolution kernel is convolved on the first matrix with a step size of 1 to obtain the adjacent time data change characteristics.

8. The method for optimizing liquid medium using ultrasonic probe based on big data according to claim 2, characterized in that: Based on the multiple liquid medium categories, the multiple probe working frequencies, the multiple probe working time points and the corresponding bubble radii, multiple first matrices are obtained, including: The probe working time points from small to large are sequentially associated with the rows with small to large subscripts in the first matrix, the probe working frequencies from small to large are sequentially associated with the pages with small to large subscripts in the first matrix, and the bubble radii from small to large are sequentially associated with the columns with small to large subscripts in the first matrix; A plurality of first matrices are obtained corresponding to a plurality of liquid medium categories; In the first matrix corresponding to the liquid medium category, the positions of the probe working frequency, the probe working time point and the corresponding bubble radius are marked as 1.

9. The method for optimizing liquid medium using ultrasonic probe based on big data according to claim 2, characterized in that: The method of obtaining a plurality of second matrices based on a plurality of liquid medium categories, a plurality of probe operating frequencies, a plurality of probe operating durations and a first matrix includes: The probe working time points from small to large are sequentially associated with the rows with subscripts from small to large in the second matrix, the liquid medium category is associated with the first column of the second matrix, and the probe working frequency is associated with the second column of the second matrix; A plurality of second matrices are obtained corresponding to a plurality of liquid medium categories; Using the liquid medium category corresponding to the first matrix as the first liquid medium category; Filling the first column of the second matrix with the first liquid medium category; Fill the probe operating frequency in the first matrix into the second column of the same row as in the second matrix.

10. A system for optimizing liquid medium using ultrasonic probe based on big data, characterized in that: include: An acquisition module, used to acquire multiple liquid medium categories, multiple probe operating frequencies, multiple probe operating time points and corresponding bubble radii; The liquid medium category indicates the category of the liquid medium in which the handheld ultrasonic probe is located; the probe operating frequency indicates the frequency of the handheld ultrasonic probe; the probe operating time point indicates the time length of the handheld ultrasonic probe in the liquid medium; the bubble radius indicates the radius of the bubble caused by the handheld ultrasonic probe; A feature extraction module, for obtaining adjacent time data variation features and multiple liquid medium bubble features based on the multiple liquid medium categories, multiple probe operating frequencies, multiple probe operating time points and corresponding medium bubble radii; The adjacent time data change characteristics represent the changes in the liquid medium type and the probe working frequency at adjacent probe working time points; the liquid medium bubble characteristics represent the changes in the bubble radius corresponding to different probe working frequencies at adjacent probe working time points; A feature association module is used to construct an association relationship between the change characteristics of adjacent time data and the characteristics of multiple liquid medium bubbles through an association neural network; A clustering module, used for clustering the plurality of liquid medium bubble features to obtain a detected liquid medium category; The detected liquid medium category is the cluster center; A detection module, used to use a bubble radius greater than other bubble radii in the detected liquid medium category as a first bubble radius; use a probe operating frequency corresponding to the first bubble radius as an optimal probe operating frequency, and use a corresponding probe operating time point as an optimal probe operating time point; The cavitation detection module is used to obtain a variable cavitation threshold according to the first bubble radius.

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