A method, system and computer program product for analyzing transducer uniformity based on a DTW algorithm

Abstract

The application relates to a method and system for analyzing transducer consistency based on a DTW algorithm and a computer, the method comprising the following steps: acquiring second waveform data output by a second transducer within a first preset time to obtain a second waveform data set; acquiring first waveform data output by a first transducer within a second preset time to obtain a first waveform data set; respectively generating time sequence data by using data in the first waveform data set and data in the second waveform data set to correspondingly obtain a first time sequence data column and a second time sequence data column; calculating a regular path distance of the first time sequence data column and the second time sequence data column by using a DTW algorithm; and analyzing the consistency of the two transducers according to the regular path distance. The application can quickly and efficiently evaluate the consistency of the transient characteristics of the transducer by collecting echo signals of the transducer and performing waveform similarity evaluation and analysis, and the DTW algorithm can process two non-equal-length time sequences, which is superior to a Euclidean distance calculation method.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of dynamic characteristic analysis of ultrasonic transducers, and particularly relates to a method and system for analyzing transducer consistency based on a DTW algorithm and a computer. BACKGROUND

[0002] With the continuous development of ultrasonic flowmeter measurement technology, as a non-contact instrument with wide range ratio, high measurement accuracy, small pressure loss and other characteristics, the demand is also increasing year by year. The transducer is a device for energy conversion. In the field of ultrasonic detection, the ultrasonic transducer is an important sensing component in the ultrasonic flowmeter, and its function is to realize the mutual conversion of acoustic energy and electric energy, which plays an important role in the performance of the ultrasonic flowmeter.

[0003] At present, the related research on the consistency method of the transducer is mainly for the consistency of the transducer in the steady-state performance, and few researches are conducted on the dynamic characteristics. The dynamic performance of the transducer is the transmission characteristic in the actual working state, and the dynamic performance research of the transducer is to analyze the sensitivity and waveform consistency of the transducer under the condition of simulating the actual working state.

[0004] The dynamic analysis of the transducer in the prior art is mainly to collect the waveforms of multiple transducers under the same working environment by manual use of an oscilloscope and the like, and to analyze the dynamic performance of the multiple transducers by observing the differences in the waveforms. However, the efficiency of analyzing the dynamic performance of the transducer by manually observing the waveforms is low, and the consistency and accuracy of the analysis results are poor due to the influence of the experience of the human. SUMMARY

[0005] In order to solve the technical problems in the prior art that the dynamic performance analysis results of the transducer are influenced by the experience of the human, and the consistency and accuracy of the analysis results are poor, the present application provides a method and system for analyzing transducer consistency based on a DTW algorithm and a computer.

[0006] The technical solutions of the present application for solving the above technical problems are as follows:

[0007] A method for analyzing transducer consistency based on a DTW algorithm, comprising the following steps:

[0008] Obtaining second waveform data output by a second transducer within a first preset time to obtain a second waveform data set; wherein a first transducer sends a first input signal to the second transducer under the excitation of an excitation signal, and the waveform data output by the second transducer after receiving the first input signal is the second waveform data;

[0009] acquire first waveform data output by the first transducer within a second preset time, to obtain a first waveform data set; Wherein, the second transducer sends a second input signal to the first transducer under the excitation of the excitation signal, and the waveform data output by the first transducer after receiving the second input signal is the first waveform data;

[0010] respectively using the data in the first waveform data set and the data in the second waveform data set to generate time series data, to obtain a first time series data column and a second time series data column;

[0011] calculate the warping path distance between the first time series data column and the second time series data column based on the DTW algorithm;

[0012] According to the warping path distance, analyze the consistency of the two transducers; Wherein, the smaller the warping path distance, the higher the consistency of the two transducers.

[0013] The beneficial effects of the present application are: the signal waveform of a pair of transducers is equivalent to two time series when self-generating and self-receiving, and the consistency of piezoelectric transducer transient characteristics is evaluated and analyzed efficiently. According to the echo signals obtained by different transducers, the time series is converted, and the optimal mapping between two time series points in the echo signals of different transducers is determined by dynamic optimization of the DTW algorithm, that is, the warping path, so as to calculate the shortest warping path length, that is, the DTW distance, to study the consistency between transducers. The present application can quickly and efficiently evaluate the consistency of the transient characteristics of the transducer by collecting the echo signals of the transducer and performing waveform similarity evaluation and analysis. The DTW algorithm can process two non-equal-length time series, which is superior to the Euclidean distance calculation method. When studying the consistency of the transducer, the dynamic performance difference between the transducers can be more efficiently, intuitively and reliably analyzed. The traditional Euclidean distance calculation method needs to obtain two completely equal-length time series data. If the lengths of the two time series data cannot be completely equal, the results of the analysis will have a large deviation. However, in the actual data acquisition process, it is impossible to obtain two completely synchronous time series data. Therefore, the DTW algorithm can process two non-equal-length time series, and the analysis results are more accurate than the traditional Euclidean distance calculation method.

[0014] On the basis of the above technical scheme, the present application can also be improved as follows.

[0015] Further, the first transducer and the second transducer are of the same model.

[0016] Further, the warping path distance between the first time series data column and the second time series data column is calculated based on the DTW algorithm, comprising the following steps:

[0017] calculating distance values between each data point in the first time series data and each data point in the second time series data to obtain a local distance data set;

[0018] establishing a distance matrix using the data in the local distance data set; wherein the number of rows of the distance matrix is the length of the first time series data, and the number of columns of the distance matrix is the length of the second time series data;

[0019] finding a warping path of the distance matrix based on a DTW algorithm;

[0020] summing all distance values corresponding to the warping path in the distance matrix to obtain a warping path distance.

[0021] Further, calculating distance values between each data point in the first time series data and each data point in the second time series data to obtain a local distance data set, comprising the following steps:

[0022] defining the first time series data and the second time series data as time series Q and time series C respectively; wherein time series Q and time series C are represented as follows:

[0023] Q = {q1, q2, …, qm} m}

[0024] C = {c1, c2, …, cn} n}

[0025] q1 represents the first data in time series Q, q2 represents the second data in time series Q, q m represents the mth data in time series Q; c1 represents the first data in time series C, c2 represents the second data in time series C, c n represents the nth data in time series C;

[0026] calculating distance values between each data point in time series Q and each data point in time series C to obtain the local distance data set; wherein the formula for calculating distance values between each data point in time series Q and each data point in time series C is as follows:

[0027] d(q i , c j ) = (q i - c j ) 2

[0028] d(q i , c j ) represents the local distance data set, q i represents the ith data in time series Q, C jrepresents the jth data in the time series C, i is less than or equal to m, and j is less than or equal to n.

[0029] Further, a distance matrix is established using the data in the data set, including the following steps:

[0030] A distance matrix D is established, wherein the number of rows of the distance matrix D is m, and the number of columns of the distance matrix D is n.

[0031] All data in the local distance data set are imported into the distance matrix D, wherein the elements in the distance matrix D are the data in the local distance data set.

[0032] Further, a regular path of the distance matrix is found based on the DTW algorithm, including the following steps:

[0033] Two data points in the first time series data and the second time series data having similar change trends are corresponded by the DTW algorithm and based on the principle that the change characteristics of each data point in the first time series data and each data point in the second time series data are similar, wherein one of the two data points having similar change trends is in the first time series data, and the other is in the second time series data.

[0034] All corresponding elements of the two data points are found from the distance matrix D, wherein the corresponding elements of the two data points are the regular path of the distance matrix D.

[0035] Further, the sum of all distance values corresponding to the regular path in the distance matrix is calculated to obtain the regular path distance, and the specific steps are as follows: the sum of all corresponding elements of the two data points in the distance matrix D is calculated to obtain the regular path distance.

[0036] To solve the above technical problems, the application also provides a system for analyzing the consistency of a transducer based on a DTW algorithm, and the specific technical solutions are as follows:

[0037] A system for analyzing the consistency of a transducer based on a DTW algorithm, comprising a data acquisition module, a data processing module, a calculation module, and an analysis module.

[0038] The data acquisition module is configured to acquire second waveform data output by the second transducer within a preset time to obtain a second waveform data set, wherein the first transducer sends a first input signal to the second transducer under the excitation of an excitation signal, and the waveform data output by the second transducer after receiving the first input signal is the second waveform data.

[0039] The data acquisition module is further configured to acquire first waveform data output by the first transducer within a preset time, to obtain a first waveform data set; wherein the second transducer sends a second input signal to the first transducer under the excitation of the excitation signal, and the waveform data output by the first transducer after receiving the second input signal is the first waveform data;

[0040] The data processing module is configured to generate time sequence data by using data in the first waveform data set and data in the second waveform data set, respectively, to obtain a first time sequence data column and a second time sequence data column;

[0041] The calculation module is configured to calculate a warping path distance between the first time sequence data column and the second time sequence data column based on the DTW algorithm.

[0042] The analysis module is configured to analyze the consistency of the two transducers according to the warping path distance.

[0043] To solve the above technical problems, the present application further provides a computer, and the specific technical solutions are as follows:

[0044] A computer comprises a memory and one or more processors, the memory stores executable codes, and the one or more processors execute the executable codes to realize the steps of the method for analyzing the consistency of the transducers based on the DTW algorithm.

[0045] To solve the above technical problems, the present application further provides a transducer pairing method, and the specific technical solutions are as follows:

[0046] A transducer pairing method is used to match the transducers by analyzing the consistency of the two transducers, wherein the consistency of the two transducers is obtained by the method for analyzing the consistency of the transducers based on the DTW algorithm. BRIEF DESCRIPTION OF DRAWINGS

[0047] Figure 1 The flowchart of the method for analyzing the consistency of the transducers based on the DTW algorithm in the embodiment of the present application is shown in the figure.

[0048] Figure 2 The structural diagram of the system for analyzing the consistency of the transducers based on the DTW algorithm in the embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0049] The principles and characteristics of the present application are described below in combination with the drawings, and the examples are only used to explain the present application, and are not used to limit the scope of the present application.

[0050] DTW, full name Dynamic time warping, means dynamic time warping or dynamic time warping or dynamic time warping or dynamic time warping; It is an algorithm for measuring the best arrangement between two sequences. Linear sequence data such as time series, audio, video can be analyzed by this method. DTW finds the best match between two digital sequence data by local stretching and compression, and can also calculate the distance between these sequences.

[0051] Embodiment 1

[0052] As Figure 1 shown, the embodiment provides a method for analyzing transducer consistency based on DTW algorithm, comprising the following steps:

[0053] S1, obtaining the second waveform data output by the second transducer within a first preset time, to obtain a second waveform data set; wherein the first transducer sends a first input signal to the second transducer under the excitation of an excitation signal, and the waveform data output by the second transducer after receiving the first input signal is the second waveform data; The first transducer and the second transducer are of the same type.

[0054] S2, obtaining the first waveform data output by the first transducer within a second preset time, to obtain a first waveform data set; wherein the second transducer sends a second input signal to the first transducer under the excitation of the excitation signal, and the waveform data output by the first transducer after receiving the second input signal is the first waveform data.

[0055] S3, respectively using the data in the first waveform data set and the second waveform data set to generate time sequence data, to obtain a first time sequence data column and a second time sequence data column;

[0056] S4, calculating the warping path distance between the first time sequence data column and the second time sequence data column based on the DTW algorithm;

[0057] Specifically, calculating the warping path distance between the first time sequence data column and the second time sequence data column based on the DTW algorithm comprises the following steps:

[0058] S401, calculating the distance value between each data point in the first time sequence data and each data point in the second time sequence data to obtain a local distance data set.

[0059] Specifically, calculating the distance value between each data point in the first time sequence data and each data point in the second time sequence data to obtain a local distance data set comprises the following steps:

[0060] The first time series data and the second time series data are defined as time series Q and time series C respectively; wherein, time series Q and time series C are represented as follows:

[0061] Q = {q1, q2, …, qm} m}

[0062] C = {c1, c2, …, cn} n}

[0063] q1 represents the first data in time series Q, q2 represents the second data in time series Q, q m represents the mth data in time series Q; c1 represents the first data in time series C, c2 represents the second data in time series C, c n represents the nth data in time series C;

[0064] The distance values between each data point in time series Q and each data point in time series C are calculated to obtain the local distance data set; wherein, the formula for calculating the distance values between each data point in time series Q and each data point in time series C is as follows:

[0065] d(q i , c j ) = (q i - C j ) 2

[0066] d(q i , c j ) represents the local distance data set, q i represents the ith data in time series Q, C j represents the jth data in time series C, i is less than or equal to m, and j is less than or equal to n.

[0067] S402, a distance matrix is established using the data in the distance data set; wherein, the number of rows of the distance matrix is the length of the first time series data column, and the number of columns of the distance matrix is the length of the second time series data column.

[0068] Specifically, establishing a distance matrix using the data in the distance data set includes the following steps:

[0069] A distance matrix D is established; wherein, the number of rows of the distance matrix D is m, and the number of columns of the distance matrix D is n;

[0070] The data in the local distance data set is imported into the distance matrix D; wherein, the elements in the distance matrix D are the data in the local distance data set.

[0071] S403, find a warping path of the distance matrix based on the DTW algorithm.

[0072] Specifically, the warping path of the distance matrix is found based on the DTW algorithm, including the following steps:

[0073] Through the DTW algorithm, and taking the similarity of the change characteristics of each data point in the first time series data and each data point in the second time series data as the principle, the two data points in the first time series data and the second time series data with similar change trends are corresponded; wherein one of the two data points with similar change trends is in the first time series data, and the other is in the second time series data.

[0074] All corresponding elements corresponding to the two data points are found from the distance matrix D; wherein all corresponding elements corresponding to the two data points are the warping path of the distance matrix D.

[0075] Specifically, define a warping path W, and the warping path W represents the warping path of the distance matrix D, and the representation of the warping path W is as follows:

[0076] W={w1, w2,..., w k ,..., w k |max(m, n)≤k<(m+n-1)}

[0077] W represents the warping path of the distance matrix D, and specifically represents the element set corresponding to all corresponding two data points in the distance matrix D, w1 represents the first element on the warping path in the distance matrix D, w2 represents the second element on the warping path in the distance matrix D, and w k represents the kth element on the warping path in the distance matrix D, the kth element on the warping path in the distance matrix D is the element corresponding to the i th data of the time series Q and the j th data of the time series C, and the element corresponding to the i th data of the time series Q and the j th data of the time series C is the last data point on the warping path in the distance matrix D, and k represents the length of the warping path of the distance matrix D.

[0078] The warping path W requires to meet the boundary condition, continuity condition and monotonicity condition in the calculation process;

[0079] The boundary condition is that the warping path must start from w1=(1,1) and end at w k =(m, n); wherein w1=(1,1) represents the element corresponding to the 1st data of the time series Q and the 1st data of the time series C in the distance matrix D; w k= (m, n) represents the element in the distance matrix D that corresponds to the i-th data of the time series Q and the j-th data of the time series C.

[0080] Continuity condition: if w k-1 = (i', j'), then the next point w k = (i, j) of the regular path in the distance matrix D must satisfy (i - i') < 1 and (j - j') < 1. That is, it cannot jump over a point to match, but can only align with its adjacent point. In this way, it can be ensured that each coordinate in the time series Q and the time series C appears in the regular path W.

[0081] Monotonicity condition: if w k-1 = (i', j'), then the next point w k = (i, j) of the regular path in the distance matrix D must satisfy (i - i') > 0 and (j - j') > 0. That is, it is limited that the points on the regular path W must be monotonically increasing over time, so as to ensure that the mapping line between the time series Q and the time series C will not intersect.

[0082] Through the continuity and monotonicity constraints, the path of each grid point in the distance matrix D has only three directions. For example, if the path has passed through the grid point (i, j), then the next grid point that can be passed through is only one of the following three cases: (i+1, j), (i, j+1) or (i+1, j+1).

[0083] S404, sum all distance values corresponding to the regular path in the distance matrix to obtain the regular path distance. Specifically, sum all distance values corresponding to the regular path in the distance matrix to obtain the regular path distance, and the specific steps are as follows: sum all elements corresponding to two data points in the distance matrix D to obtain the regular path distance.

[0084] Specifically, the calculation method of each path point in the regular path W is as follows:

[0085] The data curve corresponding to the time series Q and the data curve of the time series C are respectively extended and shortened to obtain the shortest distance between the two time series, that is, the most similar distortion. The shortest distance is also the final distance measure of the two time series, and what needs to be done is to select a path so that the total distance obtained is the smallest.

[0086] The formula for calculating the shortest distance DTW(Q, C) between the time series Q and C is as follows:

[0087]

[0088] In the formula, γ(i,j) represents the distance from (q1,c1) to (q) in the distance matrix D. i c j The cumulative distance of local distances along the path; (q1, c1) represents the element corresponding to the first data point of time series Q and the first data point of time series C in distance matrix D; (q i c j ) represents the element corresponding to the i-th data point of time series Q and the j-th data point of time series C in the distance matrix D, and DTW(Q, C) is the distance corresponding to the optimal regular path in the regular path W between time series Q and C.

[0089] S5. Analyze the consistency of the two transducers based on the regularized path distance; wherein, the smaller the regularized path distance, the higher the consistency of the two transducers. Specifically, the smaller the regularized path distance between the two transducers, the higher the consistency of the two transducers.

[0090] This invention efficiently evaluates the consistency of piezoelectric transducer transient characteristics by equating the signal waveforms of a pair of transducers during self-emission and self-reception to two time series. Based on the echo signals obtained from different transducers, these are converted into time series. The DTW algorithm is used to dynamically optimize and determine the optimal mapping (i.e., the regularized path) between points in the two time series of the transducer echo signals. The shortest regularized path length, i.e., the DTW distance, is then calculated to study the consistency between transducers. This invention, by collecting transducer echo signals and performing waveform similarity evaluation analysis, can quickly and efficiently evaluate the consistency of transducer transient characteristics. Furthermore, the DTW algorithm can handle two non-equal time series, which is superior to the Euclidean distance calculation method. When conducting transducer consistency studies, the dynamic performance differences between transducers can be analyzed more efficiently, intuitively, and reliably. Traditional Euclidean distance calculation methods require two time series data of completely equal length. If the lengths of the two time series data are not completely equal, the analysis results will have a large deviation. However, in the actual data acquisition process, it is impossible to obtain two completely synchronized time series data. Therefore, the DTW algorithm can process two non-equal time series, and its analysis results are more accurate than those of traditional Euclidean distance calculation methods.

[0091] Example 2

[0092] like Figure 2 As shown in Embodiment 1, this embodiment provides a system for analyzing transducer consistency based on the DTW algorithm, including a data acquisition module, a data processing module, a calculation module, and an analysis module;

[0093] The data acquisition module is configured to acquire second waveform data output by the second transducer within a preset time, to obtain a second waveform data set; wherein the first transducer sends a first input signal to the second transducer under the excitation of the excitation signal, and the waveform data output by the second transducer after receiving the first input signal is the second waveform data;

[0094] The data acquisition module is further configured to acquire first waveform data output by the first transducer within a preset time, to obtain a first waveform data set; wherein the second transducer sends a second input signal to the first transducer under the excitation of the excitation signal, and the waveform data output by the first transducer after receiving the second input signal is the first waveform data;

[0095] The data processing module is configured to generate time series data by using data in the first waveform data set and data in the second waveform data set, respectively, to obtain a first time series data column and a second time series data column;

[0096] The calculation module is configured to calculate a warping path distance between the first time series data column and the second time series data column based on a DTW algorithm.

[0097] The analysis module is configured to analyze the consistency of the two transducers according to the warping path distance; wherein the smaller the warping path distance is, the higher the consistency of the two transducers is.

[0098] The embodiment of the present application efficiently evaluates and analyzes the consistency of the transient characteristics of the piezoelectric transducer by equivalently converting the signal waveforms of a pair of transducers into two time series when the transducers are self-excited and self-received. According to the echo signals obtained by self-excitation and self-reception of different transducers, the echo signals are converted into time series, and the optimal mapping between two time series points in the echo signals of different transducers, i.e., the warping path, is determined by dynamic optimization through the DTW algorithm, so as to calculate the shortest warping path length, i.e., the DTW distance, to study the consistency between the transducers. The present application can quickly and efficiently evaluate the consistency of the transient characteristics of the transducer by collecting the echo signals of the transducer and performing waveform similarity evaluation and analysis. The DTW algorithm can process two non-equal-length time series, which is superior to the Euclidean distance calculation method. When studying the consistency of the transducers, the dynamic performance difference between the transducers can be more efficiently, intuitively and reliably analyzed. The traditional Euclidean distance calculation method needs to obtain two completely equal-length time series. If the lengths of the two time series cannot be completely equal, the results obtained by the analysis have a large deviation. However, it is impossible to obtain two completely synchronous time series in the actual data acquisition process. Therefore, the DTW algorithm can process two non-equal-length time series, and the analysis results are more accurate than the traditional Euclidean distance calculation method.

[0099] Embodiment 3

[0100] Based on embodiment 1, this embodiment provides a computer comprising a memory and one or more processors, wherein the memory stores executable code, and the one or more processors execute the executable code to implement the steps of the method for analyzing consistency of transducers based on the DTW algorithm in embodiment 1.

[0101] The memory can be an internal storage unit of any data processing-capable device, such as a hard disk or a memory, as described in any of the preceding embodiments. The memory can also be an external storage device of any data processing-capable device, such as a plug-in hard disk, a smart memory card, an SD card, a flash memory card, and the like, provided on the device. Further, the memory can include both an internal storage unit and an external storage device of any data processing-capable device. The memory is used to store the computer program and other programs and data required by the data processing-capable device, and can also be used to temporarily store data that has been output or will be output.

[0102] Embodiment 4

[0103] Based on embodiment 1, this embodiment provides a transducer pairing method, which pairs transducers by analyzing the consistency of two transducers; wherein the consistency of the two transducers is obtained by the method for analyzing consistency of transducers based on the DTW algorithm according to any one of claims 1 to 7.

[0104] The present application analyzes the consistency of two transducers, selects two transducers with better consistency for pairing in the transducer pairing process, so as to improve the data conversion performance and data conversion accuracy of the transducers.

[0105] The above description is only the preferred embodiments of the present application and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. within the concept and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for analyzing transducer uniformity based on DTW algorithm, characterized in that, The method comprises the following steps: obtaining second waveform data output by a second transducer within a first preset time, to obtain a second waveform data set; wherein a first transducer sends a first input signal to the second transducer under the excitation of an excitation signal, and waveform data output by the second transducer after receiving the first input signal is the second waveform data; obtaining first waveform data output by the first transducer within a second preset time, to obtain a first waveform data set; wherein the second transducer sends a second input signal to the first transducer under the excitation of the excitation signal, and waveform data output by the first transducer after receiving the second input signal is the first waveform data; generating time series data by using data in the first waveform data set and data in the second waveform data set respectively, to obtain a first time series data column and a second time series data column correspondingly; calculating a warping path distance between the first time series data column and the second time series data column based on a DTW algorithm; analyzing consistency of the two transducers according to the warping path distance; wherein the smaller the warping path distance is, the higher the consistency of the two transducers is; calculating a warping path distance between the first time series data column and the second time series data column based on a DTW algorithm, comprising the following steps: calculating distance values between each data point in the first time series data column and each data point in the second time series data column, to obtain a local distance data set; establishing a distance matrix by using data in the local distance data set; wherein the number of rows of the distance matrix is the length of the first time series data column, and the number of columns of the distance matrix is the length of the second time series data column; finding a warping path of the distance matrix based on the DTW algorithm; summing all distance values corresponding to the warping path in the distance matrix, to obtain the warping path distance.

2. The method for analyzing transducer uniformity based on DTW algorithm according to claim 1, characterized in that, The first transducer and the second transducer are of the same type.

3. The method for analyzing transducer uniformity based on DTW algorithm according to claim 1, characterized in that, calculating distance values between each data point in the first time series data and each data point in the second time series data, to obtain a local distance data set, comprising the following steps: The first time series data and the second time series data are defined as time series Q and time series C, respectively; wherein the time series and the time series are represented as follows: represents a first data in a time series , represents a second data in a time series , represents a third data in a time series , ; represents a fourth data in a time series , , represents a fifth data in a time series , , represents a sixth data in a time series , ; computing a time series distance values between each data point in the time series distance values between each data point in the time series distance values between each data point in the time series The formula for computing the distance values between each data point in the time series is as follows: denotes the local distance dataset, denotes the time series of the first data, denotes the time series of the first data, is less than or equal to , is less than or equal to .

4. The method for analyzing transducer uniformity based on DTW algorithm according to claim 3, characterized in that, establishing a distance matrix by using data in the local distance data set, comprising the following steps: establishing a distance matrix ; wherein the distance matrix has a number of rows , and the distance matrix has a number of columns ; importing all data in the local distance dataset into the distance matrix wherein the elements in the distance matrix are data in the local distance dataset.

5. The method for analyzing transducer uniformity based on DTW algorithm according to claim 4, characterized in that, finding a warping path of the distance matrix based on the DTW algorithm, comprising the following steps: corresponding two data points in the first time series data and the second time series data having similar change trends by the DTW algorithm and on the principle that the change characteristics of each data point in the first time series data column and each data point in the second time series data column are similar; wherein one of the two data points having similar change trends is in the first time series data, and the other is in the second time series data; finding all corresponding elements of two data points from the distance matrix wherein all corresponding elements of two data points are a regular path of the distance matrix ​ 6. The method for analyzing transducer uniformity based on DTW algorithm according to claim 5, characterized in that, Sums all distance values corresponding to the regular path in the distance matrix to obtain the regular path distance. The specific steps are as follows: sum all elements corresponding to two data points corresponding to each other in the distance matrix corresponding to the regular path distance.

7. A system for analyzing transducer uniformity based on a DTW algorithm, characterized by, The method for analyzing consistency of transducers based on the DTW algorithm comprises a data acquisition module, a data processing module, a calculation module and an analysis module. The data acquisition module is configured to acquire second waveform data output by the second transducer within a preset time, to obtain a second waveform data set; wherein the first transducer sends a first input signal to the second transducer under the excitation of the excitation signal, and the waveform data output by the second transducer after receiving the first input signal is the second waveform data; The data acquisition module is further configured to acquire first waveform data output by the first transducer within a preset time, to obtain a first waveform data set; wherein the second transducer sends a second input signal to the first transducer under the excitation of the excitation signal, and the waveform data output by the first transducer after receiving the second input signal is the first waveform data; The data processing module is configured to generate time series data by using data in the first waveform data set and data in the second waveform data set, respectively, to obtain a first time series data column and a second time series data column; The calculation module is configured to calculate a warping path distance between the first time series data column and the second time series data column based on a DTW algorithm; The analysis module is configured to analyze the consistency of the two transducers according to the warping path distance; wherein the smaller the warping path distance is, the higher the consistency of the two transducers is.

8. A computer, characterized in that The device comprises a memory and one or more processors, the memory stores executable code, and the one or more processors execute the executable code to implement the steps of the method for analyzing the consistency of the transducers based on the DTW algorithm according to any one of claims 1 to 6.

9. A transducer pairing method, characterized by, The transducers are matched by analyzing the consistency of the two transducers; wherein the consistency of the two transducers is obtained by the method for analyzing the consistency of the transducers based on the DTW algorithm according to any one of claims 1 to 6.

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