CN117434295B - Intelligent processing and evaluating method for acoustic chromatographic signal intensity data - Google Patents
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Abstract
The invention discloses an intelligent processing and evaluating method for acoustic chromatography signal intensity data, which comprises the following steps: (1) Calculating river noise background data, (2) sending measurement signals at different detection points of a river channel, and respectively calculating and analyzing received acoustic chromatographic signals from three dimension reference quantities; (3) Calculating flow results obtained under the acoustic chromatographic signal data by using an acoustic chromatographic data processing method, comparing the flow results with the navigation ADCP flow data respectively, and calculating corresponding errors; (4) Performing correlation test on the inverse of the error and each signal evaluation value to obtain each corresponding evaluation index value; (5) And calculating the weight occupied by each scoring result, and carrying out weighted average on each scoring result to finally obtain a comprehensive score so as to evaluate the quality of the acoustic chromatographic signal intensity. The invention is an evaluation method with strong comprehensiveness and more accurate data calculation, and can provide a stable and universal evaluation index of the acoustic chromatographic signal intensity.
Description
Technical Field
The invention relates to the field of intelligent analysis of river flow measurement sound data, in particular to an intelligent processing and evaluating method of sound chromatography signal intensity data.
Background
The river acoustic chromatography flow measurement technology is a technology for measuring the river flow rate by utilizing the speed difference of sound in the river, and the intensity of sound signals has great influence on the accuracy of flow rate measurement. Therefore, when the acoustic tomography apparatus is installed, a constructor needs to detect the signal intensity of the place. However, because of complex water wave and topography, the signal contour shape is irregular, and the visual judgment of the intensity is difficult.
The difference of the acoustic tomography signal and the common acoustic signal is that the quality evaluation is special, and the intensity of the acoustic tomography signal is evaluated mainly according to the experience judgment of the maximum value and the shape of the signal. However, in different watersheds, different signals with different temperatures in different seasons, different river bottom terrains, sand, waterweed, stone-like piles and the like are added, sound is transmitted along different sound lines, attenuation is large, clutter is large, sound signals of different sound lines are mutually overlapped to form signal data, if only the existing measuring technology is used, delay or inaccurate measurement is caused, and therefore the existing measuring index cannot be a stable and general index for judging signal intensity.
The prior closest technique is an coastal acoustic chromatography flow measurement method, the application number is CN202310314955.3, which comprises the steps of sequentially setting n data acquisition points along the sea area; the n data acquisition points mutually transmit and receive sound signals between every two mutually so as to acquire mutual correlation data; and establishing a two-dimensional flow field between two points through collected drainage basin data, reciprocal transmission of sound waves and acoustic Doppler effect, and establishing a temperature field between the two points through collected cross-correlation data and NRLII sound velocity empirical formula. The maximum cross-correlation peak position is obtained through a cross-correlation algorithm, and the ratio of the maximum cross-correlation peak position to the sampling rate is the propagation time of the acoustic signal in the direction from the transmitting data acquisition point to the receiving data acquisition point according to the property of the cross-correlation function. The method is to set equidistant data acquisition points in advance, cannot find the acquisition point with the strongest signal, has large measurement interference, needs to build a two-dimensional flow field and a temperature field, has a complex data model, and has long construction time, time and labor waste.
Disclosure of Invention
In order to overcome the problems in the prior art, the invention provides the intelligent processing and evaluating method for the acoustic chromatography signal intensity data, which forms an evaluating method with strong comprehensiveness and more accurate data calculation, can provide a stable and universal acoustic chromatography signal intensity evaluating index, and provides more accurate reference for the installation of river flow measuring equipment, signal debugging and flow velocity measurement.
The invention relates to an intelligent processing evaluation method of acoustic chromatography signal intensity data, which comprises the following steps:
(1) Receiving river noise background data in a non-signal transmission period, and calculating a noise intensity level;
(2) Transmitting measurement signals at different detection points of a river channel, and respectively calculating and analyzing the received acoustic chromatographic signals from three dimension reference quantities to obtain signal quality evaluation;
(3) According to the signal quality evaluation, calculating a flow result obtained under the acoustic chromatographic signal data by using an acoustic chromatographic data processing method, comparing the flow result with the navigation ADCP flow data respectively, and calculating a corresponding error;
(4) And carrying out correlation test on the inverse of the error and each signal evaluation value to obtain each corresponding evaluation index value.
(5) Calculating the weight of each scoring result according to each scoring index valueWeighting and averaging the scoring results to finally obtain the comprehensive score +.>The quality of the acoustic chromatographic signal intensity was evaluated.
In the step (1), the three-dimensional reference quantity of the acoustic tomography signal refers to signal-to-noise ratio, pulse shape similarity matching degree and concentration degree analysis of the signal, and the signal is scored respectively.
Further, in the step (2), the signal to noise ratio of the received acoustic chromatographic signal is calculated through a signal to noise ratio formula, and the larger the signal to noise ratio is, the better the signal is.
In the step (2), pulse shape similarity matching degree analysis is adopted, pure signal data is used as a template, a template matching filtering algorithm is carried out on the pure signal data, and matching degree results of the pure signal data and the pure signal data are obtained, so that the data are matched with the pure signal data, and a theoretical optimal ideal matching value is obtained;
performing template matching filtering operation on the received acoustic chromatographic signal and pure signal data to obtain a matching result of the data, wherein the matching result represents the receiving condition of the acoustic chromatographic signal;
finally, taking the ideal matching value as a template, and carrying out template matching filtering operation on the actual matching result to obtain the matching degree of the two; if the matching result is higher, the receiving condition of the acoustic chromatography signal is closer to the ideal condition, and the signal is better.
In step (2), a normal distribution curve is used for fitting the contour curve of the signal matched filtering result, and the variance sigma of the normal distribution can be obtained through fitting 2 The smaller the variance, the sharper the contour, the more concentrated the sound signal and the better the signal.
The method also comprises the step (6) of setting a standard threshold for judging the strength of the signal, and when the comprehensive score of the acoustic chromatographic signal is higher than the standard threshold, the acoustic chromatographic signal is judged to be the acoustic chromatographic signal with excellent strength, and the method is suitable for establishing a river flow measurement base station.
The relevance evaluation specifically comprises: calculating a flow result obtained under the signal data by using an acoustic chromatography data processing method, comparing the flow result with the navigation ADCP flow data, and calculating an error;
and carrying out correlation test on the inverse of the error and each signal evaluation value to obtain each corresponding evaluation index value, wherein the larger the correlation is, the more accurate the evaluation method is.
The intelligent processing evaluation method provided by the invention has the following technical advantages:
1) The evaluation method is a relatively visual quantitative data method, evaluates the quality of the measured sound signals, immediately knows the specific evaluation result in the field, is beneficial to signal intensity measurement during the point selection and investigation of the early acoustic chromatography station, and is also beneficial to improving the accuracy of flow velocity calculation. The method overcomes the defects of the existing single evaluation sound information intensity method, such as different sound propagation capacities and environmental noise in different flow domains, wherein a certain signal-to-noise value is a poor signal in one flow domain, but a strong signal possibly exists in the other flow domain, and the single evaluation index has poor applicability.
2) According to the evaluation method, the advantages of various basic evaluation methods are integrated, correlation test is carried out on the inverse of various evaluation calculation errors and various signal quality evaluation indexes, and the difference of evaluation values of different quantities is fused, so that the uniformity of various evaluation indexes is achieved. Finally, each evaluation index value is weighted and averaged, so that the error is greatly reduced, the accuracy and the scientificity of the evaluation are improved, and compared with the existing single evaluation technology, the accuracy is improved by 6%.
3) The basic evaluation methods included in the evaluation method are not fixed in number, the fusion operation has an expansion space, the inclusion is good, other sub-methods for evaluating the signal quality can be added, and the weight of the sub-method is obtained by calculating the correlation value of the score of the sub-method and the result error. The sub-method is integrated into the method, so that an evaluation result with wider applicability and higher accuracy is obtained.
4) The evaluation method is simple and quick to calculate, data are transmitted to a server side for modeling analysis without networking, and calculation evaluation can be completed in real time when the data are received at the field river side site. The algorithm program directly exists in the measuring equipment processor, and the algorithm program is directly led in and called by the measuring data, so that a two-dimensional flow field, a temperature field and other data system model are not required to be established, the time for pre-constructing the system model is saved by at least 3-5 days, and time and labor are saved.
Drawings
FIG. 1 is a diagram showing the result of self-matching of pure signals in the method of the present invention.
Fig. 2 is a schematic diagram of the waveform of the strong signal of the sound of the acoustic tomography in the method of the present invention.
Fig. 3 is a schematic diagram of weak signal waveforms of a voice pattern of the method of the present invention.
FIG. 4 is a graph showing the result of normal distribution curve fitting in the method of the present invention.
FIG. 5 is a schematic flow chart of the method of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the present embodiment.
As shown in FIG. 5, an embodiment of the present invention provides an intelligent processing and evaluating method for acoustic tomography signal intensity data, including
Step (1) receives river noise background data in a non-signal transmission period, and calculates a noise intensity level. Basic noise background data of river environments in different regions can be collected, and a perfect and various database is built for subsequent calculation and comparison data.
And (2) transmitting measurement signals at different detection points of the river channel, and respectively calculating and analyzing the received acoustic chromatographic signals from three-dimensional reference quantities.
The three-dimensional reference quantity of the acoustic tomography signal refers to signal-to-noise ratio, pulse shape similarity matching degree and concentration degree analysis of the signal, and the signal is scored respectively. The specific calculation process of the reference amount of each dimension is specifically described below.
When calculating the signal-to-noise ratio of the analysis signal, the received acoustic chromatography signal is passed through the signal-to-noise ratio formulaCalculating the signal-to-noise ratio of the signal, whichWherein P represents the average power of the signal, which can also be represented by the symbol S; n represents the average power of noise, and the signal-to-noise ratio algorithm can evaluate the intensity of the signal relative to the noise, and the larger the signal-to-noise ratio is, the better the signal intensity is.
When the pulse shape similarity matching degree analysis is adopted, namely template matching filtering analysis, pure signal data is used as a template, and the pure signal data is subjected to a template matching filtering algorithm to obtain a matching degree result of the pure signal data, and a theoretical ideal matching value is obtained due to the fact that the data is matched with the pure signal data, as shown in fig. 1, the abscissa of fig. 1 represents sampling interval time, and the ordinate represents voiceprint signal strength. The larger the template matching filter result value, the more similar the template is to the matched person. When the received sound signal is identical to the transmitted measurement signal, the most desirable signal data will be obtained.
Carrying out template matching filtering operation again on template matching filtering results of the received acoustic chromatographic signals, such as the strong signal waveform in fig. 2 or the weak signal waveform in fig. 3, and the template matching filtering results are respectively matched with pure signal data to obtain matching results of the data, wherein the matching results represent the receiving condition of the acoustic chromatographic signals;
then, taking the ideal matching value as a template, and carrying out template matching filtering operation on the actual matching result to obtain the matching degree of the two; if the matching result value is higher, the receiving condition of the acoustic chromatographic signal is closer to the ideal condition, and the signal strength is better.
When calculating the concentration of the analysis signals. As shown in fig. 4, a normal distribution curve is used to fit the profile curve of the signal matching filtering result, so as to obtain a normal distribution curve fitting result. The normal distribution function isWhere μ is the distribution expectation and σ is the distribution standard deviation.
The standard deviation sigma of normal distribution can be obtained through fitting, and the smaller the standard deviation is, the sharper the outline is indicated, the more concentrated the sound signals are, and the better the signal intensity is. Thus, the strong signal gathering part can be rapidly positioned by the acoustic tomography signal processing, the contrast ratio between the strong signal gathering part and the background noise signal is improved, and the success rate of accurately analyzing the signal is further improved.
And (3) calculating each piece of sound signal data by using a sound chromatography data processing method according to the signal quality evaluation to obtain a flow result, comparing each result with the navigation ADCP flow data, and calculating the corresponding error. And simultaneously, each piece of sound signal data is evaluated by using the sub-evaluation method of the reference quantity from three dimensions, so as to obtain a corresponding evaluation value.
And (4) performing correlation test on the inverse of the error and each signal quality evaluation value to obtain each corresponding evaluation index value.
The correlation calculation formula is:where x is an evaluation value obtained by each evaluation method, y is an error size, i is an ith evaluation method, j is jth signal data, and r is a correlation value. From the aspect of probability statistics accuracy, scientific calculation analysis is carried out, and if the correlation is larger, the evaluation method is more accurate.
The navigation ADCP flow data is the most accurate flow measurement method accepted by the industry at present, and can be regarded as the flow value closest to reality, and is usually used as an evaluation reference value of the accuracy of a flow result.
Because the magnitudes of the evaluation values obtained by the three sub-evaluation methods have larger differences, researches show that the differences of the magnitudes of the scores are needed to be removed firstly in actual calculation and then data fusion is carried out, so that errors can be eliminated to the greatest extent, and the method is unique.
Step (5) calculating the weight of each scoring result according to each scoring index valueWeighting and averaging the scoring results to finally obtain the comprehensive score +.>To evaluate therebyThe quality of the signal intensity of the price and sound chromatography.
And (6) setting a standard threshold for judging the strength of the signal, and judging the signal to be an excellent strength acoustic chromatographic signal when the comprehensive score of the acoustic chromatographic signal is higher than the standard threshold, thereby being suitable for establishing a river flow measurement base station.
As shown in fig. 2 and 3, fig. 2 shows a waveform of a strong acoustic signal for acoustic tomography, and fig. 3 shows a waveform of a weak acoustic signal for acoustic tomography. The strong signal data energy can be concentrated in one peak, the peak value is high, no other obvious secondary peaks exist, and the accurate sound arrival time is easy to calculate; the weak signal data has scattered energy, slightly lower peak value and a plurality of obvious secondary peaks, and the accurate time for reaching the target sound is difficult to find, so that the accuracy of flow velocity calculation is influenced.
The intelligent evaluation method overcomes the defects of the prior art, such as inaccurate single signal-to-noise ratio evaluation method when a plurality of underwater sound background interference factors are encountered. If the river width and the shoal are large, the concentration of a single measured waveform is low, the waveform profile is flat, and the measurement inaccuracy is caused by difficult sampling.
The method of the invention carries out comprehensive intelligent processing on the acoustic chromatographic signal intensity from three dimension reference quantities, overcomes the technical difficulty of fusion analysis, takes the advantages of the method, minimizes the influence of the defects, adopts probability statistics, normal distribution curve fitting and unique correlation calculation formulas, eliminates the influence of uneven river bottom topography, large water layer temperature difference, water wave clutter interference and the like on the signal, gives more accurate results than the traditional signal detection method from the aspects of calculation analysis of the detected signal wave, has convenient measurement operation, simple measurement equipment and low cost, does not need to detect a plurality of sections along the river by opening a ship like the prior river surface measurement, and is time-consuming and labor-consuming.
According to the method, the respective weight values can be dynamically adjusted according to the measurement environment condition and the correlation calculation formula, so that different measurement and evaluation advantages are reflected. For example, in a calm village, a straight flowing river has small background noise and small interference, the weight of the signal to noise ratio calculation sub-method can be increased, and the accuracy of measuring the sound signal intensity is improved. For river channel bending, the background noise is large and clutter is large, the weight for calculating the concentration degree of the analysis signals can be increased, the strong signal concentration position can be rapidly positioned, the contrast ratio between the strong signal concentration position and the background noise signal is improved, the accurate analysis signal intensity is further improved, and the optimal measurement point is determined.
The invention relates to an intelligent processing evaluation method of acoustic chromatography signal intensity data, which is a specific calculation, analysis and evaluation process and comprises the following steps of
1. Calculating the signal-to-noise ratio of the two received measurement signal data according to a signal-to-noise ratio formula to obtain a first signal-to-noise ratio of 107.20 and a second signal-to-noise ratio of 71.37;
2. calculating the matching condition of the two measurement signal data and the pure signal matching result to obtain a first signal matching value of 9.67 x 10 12 The second signal matching value is 6.09 x 10 12 ；
3. Calculating fitting conditions of the two signal data to a normal distribution curve to obtain a first signal fitting distribution standard deviation of 4.943 and a second signal fitting distribution standard deviation of 8.891;
4. comparing the flow velocity error with the navigation ADCP data, the flow velocity error calculated by the two signal data is respectively 3% and 5%. Performing correlation calculation on the inverse of the error and two scores obtained by the three seed evaluation methods respectively to obtain correlation coefficients of 0.9973, 0.9994 and 0.9987 respectively; the inaccurate corresponding sub-evaluation method is reflected, and the higher the coefficient value is.
5. The weights of the three scores are 0.3329, 0.3337 and 0.3334 respectively through weight formula calculation and are analyzed from the signal-to-noise ratio, the pulse shape similarity matching degree and the concentration degree. Because the magnitude of the evaluation values obtained by the three sub-evaluation methods has larger difference, the magnitude difference of each score needs to be removed before fusion in actual calculation. Wherein the range of the signal to noise ratio value is [15,120 ]]Nearby, the signal matching value range is [10 ] 12 ，10 13 ]In the vicinity, the inverse range of the normal distribution fitting standard deviation is [0.10,0.25 ]]Nearby. Dividing the signal matching value by 10 11 Fitting the inverse of standard deviation to normal distribution multiplied by 500 to achieve the aim of evaluating all fingersThe target orders are unified, and the final score is obtained by weighted average. The final composite score of the two measurement signals is: 101.68 and 62.84.
When 90 is set as a standard threshold value for judging the signal intensity, namely a strong and weak signal dividing line; 70 is divided into a boundary line for judging whether the point needs to be reselected or not, and a scoring standard for judging the strength of the signal can be obtained. An evaluation value of more than 90 points is the optimal river flow measurement base station, and a measurement point of less than 70 points is needed to be replaced for measurement. The method is beneficial to signal intensity measurement during the point selection and investigation of the acoustic chromatography station in the early stage of river flow, can rapidly and accurately select the best installation point of the measuring equipment, and provides more accurate reference basis for later signal debugging and data comparison.
The number of basic evaluation methods included in the evaluation method is not fixed, namely, the acoustic chromatographic signal intensity is comprehensively and intelligently processed from three-dimensional reference quantity, the expansion space is provided, the inclusion is good, other signal intensity evaluation sub-methods can be added, the weight of the sub-method is obtained by calculating the correlation value of the score and the result error of the sub-method, and the sub-method is integrated into the method, so that the evaluation result with wider applicability and higher accuracy is obtained.
Claims (2)
1. An intelligent processing and evaluating method for acoustic tomography signal intensity data is characterized by comprising the following steps:
(1) Receiving river noise background data in a non-signal transmission period, and calculating a noise intensity level;
(2) Transmitting measurement signals at different detection points of a river channel, and respectively calculating and analyzing the received acoustic chromatographic signals from three dimension reference quantities to obtain signal quality evaluation; the three-dimensional reference quantity of the acoustic chromatographic signal refers to signal-to-noise ratio, pulse shape similarity matching degree and concentration degree analysis of the signal, and the signal is scored respectively;
calculating the signal-to-noise ratio of the received acoustic chromatographic signal through a signal-to-noise ratio formula, wherein the larger the signal-to-noise ratio is, the better the signal is;
adopting pulse shape similarity matching degree analysis, taking pure signal data as a template, and performing a template matching filtering algorithm on the pure signal data to obtain a matching degree result of the pure signal data and the pure signal data, wherein the data is matched with the pure signal data, so that a theoretical optimal ideal matching value can be obtained; performing template matching filtering operation on the received acoustic chromatographic signal and pure signal data to obtain a matching result of the data, wherein the matching result represents the receiving condition of the acoustic chromatographic signal;
taking the ideal matching value as a template, and performing template matching filtering operation on an actual matching result to obtain the matching degree of the two; the higher the matching result is, the closer the receiving condition of the acoustic chromatography signal is to the ideal condition, and the better the signal is;
fitting the contour curve of the signal matching filtering result by using a normal distribution curve, and obtaining the variance sigma of the normal distribution through fitting 2 The smaller the variance, the sharper the contour, the more concentrated the sound signal, the better the signal;
(3) According to the signal quality evaluation, calculating a flow result obtained under the acoustic chromatographic signal data by using an acoustic chromatographic data processing method, comparing the flow result with the navigation ADCP flow data respectively, and calculating a corresponding error;
(4) Performing correlation test on the inverse of the error and each signal evaluation value to obtain each corresponding evaluation index value; the correlation calculation formula is:wherein x is an evaluation value obtained by each evaluation method, y is an error size, i is an ith evaluation method, j is jth signal data, and r is a correlation value;
(5) Calculating the weight of each scoring result according to each scoring index valueWherein r is a correlation value, and each scoring result is weighted and averaged to finally obtain the comprehensive score ++of the acoustic tomography signal>x is an evaluation value obtained by each evaluation method, and the quality of the acoustic chromatographic signal intensity is evaluated.
2. The method for intelligently processing and evaluating the intensity data of the acoustic tomography signal according to claim 1, further comprising the step (6) of setting a standard threshold for evaluating the intensity of the signal, wherein when the integrated score of the acoustic tomography signal is higher than the standard threshold, the acoustic tomography signal is judged to have excellent intensity, and the method is suitable for establishing a river flow measurement base station.
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Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2006118921A (en) * | 2004-10-20 | 2006-05-11 | Arata Kaneko | Real-time type hydroacoustic tomography device, and underwater sound tomography system |
| JP2012098093A (en) * | 2010-10-29 | 2012-05-24 | Hiroshima Univ | Acoustic tomography measuring system and acoustic tomography measuring method |
| RU2477498C1 (en) * | 2011-11-25 | 2013-03-10 | Учреждение Российской академии наук Тихоокеанский океанологический институт им. В.И. Ильичева Дальневосточного отделения РАН (ТОИ ДВО РАН) | Method of monitoring vertical distribution of sound speed in shallow water conditions |
| CN105891541A (en) * | 2016-06-13 | 2016-08-24 | 南京世海声学科技有限公司 | ADCP surface layer blind area flow velocity estimation method based on interlayer correlation |
| KR20210044079A (en) * | 2019-10-14 | 2021-04-22 | 경원산업 주식회사 | Remote coastal acoustic tomography system based on directional array sensor |
| CN112782705A (en) * | 2020-12-31 | 2021-05-11 | 闽南师范大学 | Ultrasonic acoustic tomography flow field imaging method and device thereof |
| CN113466872A (en) * | 2021-05-17 | 2021-10-01 | 浙江大学 | Small-scale layered horizontal two-dimensional flow field observation method |
| CN113917185A (en) * | 2021-10-22 | 2022-01-11 | 浙江天禹信息科技有限公司 | Acoustic full-section flow meter and working method thereof |
| CN115407404A (en) * | 2022-11-03 | 2022-11-29 | 自然资源部第二海洋研究所 | Self-adaptive marine mobile sound chromatography device |
| CN115578227A (en) * | 2022-09-13 | 2023-01-06 | 西安市智慧环保综合指挥中心 | Method for determining atmospheric particulate pollution key area based on multi-source data |
| CN115600044A (en) * | 2022-11-28 | 2023-01-13 | 湖南大学（Cn） | River section flow calculation method, device, equipment and storage medium |
| CN116256761A (en) * | 2022-12-19 | 2023-06-13 | 浙江大学 | Temperature field acoustic tomography observation method for small-scale horizontal section |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111325165B (en) * | 2020-02-26 | 2023-05-05 | 中南大学 | Urban remote sensing image scene classification method considering spatial relationship information |
-
2023
- 2023-10-24 CN CN202311391628.4A patent/CN117434295B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2006118921A (en) * | 2004-10-20 | 2006-05-11 | Arata Kaneko | Real-time type hydroacoustic tomography device, and underwater sound tomography system |
| JP2012098093A (en) * | 2010-10-29 | 2012-05-24 | Hiroshima Univ | Acoustic tomography measuring system and acoustic tomography measuring method |
| RU2477498C1 (en) * | 2011-11-25 | 2013-03-10 | Учреждение Российской академии наук Тихоокеанский океанологический институт им. В.И. Ильичева Дальневосточного отделения РАН (ТОИ ДВО РАН) | Method of monitoring vertical distribution of sound speed in shallow water conditions |
| CN105891541A (en) * | 2016-06-13 | 2016-08-24 | 南京世海声学科技有限公司 | ADCP surface layer blind area flow velocity estimation method based on interlayer correlation |
| KR20210044079A (en) * | 2019-10-14 | 2021-04-22 | 경원산업 주식회사 | Remote coastal acoustic tomography system based on directional array sensor |
| CN112782705A (en) * | 2020-12-31 | 2021-05-11 | 闽南师范大学 | Ultrasonic acoustic tomography flow field imaging method and device thereof |
| CN113466872A (en) * | 2021-05-17 | 2021-10-01 | 浙江大学 | Small-scale layered horizontal two-dimensional flow field observation method |
| CN113917185A (en) * | 2021-10-22 | 2022-01-11 | 浙江天禹信息科技有限公司 | Acoustic full-section flow meter and working method thereof |
| CN115578227A (en) * | 2022-09-13 | 2023-01-06 | 西安市智慧环保综合指挥中心 | Method for determining atmospheric particulate pollution key area based on multi-source data |
| CN115407404A (en) * | 2022-11-03 | 2022-11-29 | 自然资源部第二海洋研究所 | Self-adaptive marine mobile sound chromatography device |
| CN115600044A (en) * | 2022-11-28 | 2023-01-13 | 湖南大学（Cn） | River section flow calculation method, device, equipment and storage medium |
| CN116256761A (en) * | 2022-12-19 | 2023-06-13 | 浙江大学 | Temperature field acoustic tomography observation method for small-scale horizontal section |
Non-Patent Citations (3)
| Title |
|---|
| 利用声层析的实时流量监测方法和系统的研究;郑黎明;《中国优秀硕士学位论文全文数据库基础科学辑》;20180715(第7期);第15-32页 * |
| 海洋声层析观测技术和方法;廖光洪 等;《海洋学报》;20100531;第32卷(第3期);第14-20页 * |
| 高频声学流速层析研究;俞晓牮 等;《中国科学:物理学 力学 天文学》;20210228;第51卷(第2期);第1-7页 * |
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