CN-121432446-B - High-precision multi-beam acoustic three-dimensional imaging method based on big data

CN121432446BCN 121432446 BCN121432446 BCN 121432446BCN-121432446-B

Abstract

The invention relates to the technical field of three-dimensional imaging, in particular to a high-precision multi-beam acoustic three-dimensional imaging method based on big data, which comprises the steps of determining imaging parameters, arranging a transducer array according to the imaging parameters, controlling the transducer array to output multi-beams to scan a target area and receiving echo signals; the method comprises the steps of preprocessing echo signals, carrying out noise reduction and filtering, mapping the preprocessed echo data to a three-dimensional space coordinate system to generate an initial three-dimensional imaging model, carrying out matching and volume estimation on characteristics of a missing area in the initial three-dimensional imaging model based on big data, calculating an imaging missing rate, determining whether current imaging is qualified or not based on the imaging missing rate, and determining imaging failure reasons based on complexity of the initial three-dimensional imaging model when the current imaging is failed.

Inventors

Zhou Gaochen
WANG YUQUAN
LI CHENJIE
WANG BAOJIANG

Assignees

中科探海(苏州)海洋科技有限责任公司

Dates

Publication Date: 20260505
Application Date: 20251231

Claims (8)

1. The high-precision multi-beam acoustic three-dimensional imaging method based on big data is characterized by comprising the following steps of: step S1, imaging parameters are determined, wherein the imaging parameters comprise the distance between adjacent transducers in a transducer array, the beam parameters of single beams output by each transducer and data processing standard parameters; Step S2, arranging a transducer array according to the imaging parameters, controlling the transducer array to output multiple beams to scan a target area, and receiving echo signals; step S3, preprocessing the echo signals by noise reduction and filtering, mapping the preprocessed echo data to a three-dimensional space coordinate system, and generating an initial three-dimensional imaging model; Step S4, carrying out matching and volume estimation on the characteristics of a missing region in the initial three-dimensional imaging model based on big data, determining an estimated missing volume, and calculating an imaging missing rate based on the estimated missing volume; Step S5, determining whether the current imaging is qualified or not based on the imaging missing rate, determining imaging failure reasons based on the complexity of the initial three-dimensional imaging model when the current imaging is failed, and generating corresponding imaging parameter correction instructions according to the imaging failure reasons; in step S5, the determining whether the current imaging is qualified based on the imaging failure rate includes: if the imaging missing rate is smaller than the preset imaging missing rate, determining that the current imaging is qualified, and outputting a final three-dimensional imaging model; If the imaging missing rate is greater than or equal to the preset imaging missing rate, determining that the current imaging is unqualified, and determining the reason of the unqualified imaging based on the complexity of the initial three-dimensional imaging model; in step S5, determining a cause of imaging failure based on the complexity of the initial three-dimensional imaging model, including: Step S51, calculating the complexity based on the total number of triangular patches contained in the initial three-dimensional imaging model and the area regularity of each triangular patch; step S52, comparing the complexity with a preset complexity threshold; step S53, if the complexity is greater than or equal to the preset complexity threshold, determining that the imaging failure source is insufficient in beam coverage caused by the complex terrain to be detected; And step S54, if the complexity is smaller than the preset complexity threshold, determining that the imaging failure cause is environmental interference.
2. The high-precision multi-beam acoustic three-dimensional imaging method based on big data according to claim 1, wherein in step S4, the imaging deletion rate is a ratio of an estimated deletion volume to a terrain volume acquired by scanning.
3. The high-precision multi-beam acoustic three-dimensional imaging method based on big data according to claim 1, wherein in step S51, calculating the complexity based on the total number of triangular patches contained in the initial three-dimensional imaging model and the area regularity of each triangular patch comprises: step S511, counting the total number of all the discrete triangular patches in the initial three-dimensional imaging model, and taking the total number as a first calculation factor; step S512, calculating the ratio of the actual area of each triangular patch in the model to a preset standard area, and calculating the arithmetic average value of all the ratios as a second calculation factor; In step S513, an intermediate complexity value is calculated, and a correction coefficient determined according to the historical data is used to adjust the intermediate complexity value, so as to obtain a final complexity.
4. The high-precision multi-beam acoustic three-dimensional imaging method based on big data according to claim 1, characterized in that in step S53, when it is determined that the imaging failure causes insufficient beam coverage due to the complex topography to be measured, the vertical angular width in the beam parameters of the single beam is adjusted; And determining the adjustment amplitude of the vertical angle width based on the complexity of the initial three-dimensional imaging model and the average depth gradient of the edge points of each missing area, wherein the higher the complexity is, the larger the average depth gradient is, and the larger the increase amplitude of the vertical angle width is.
5. The method according to claim 4, wherein in step S53, in adjusting the vertical angular width, the maximum distance between adjacent transducers in the transducer array is corrected based on the total estimated volume of all missing areas, and wherein the larger the total estimated volume is, the smaller the reduction of the maximum distance is.
6. The high-precision multi-beam acoustic three-dimensional imaging method based on big data according to claim 4, wherein in step S53, the steps S2 to S5 are re-executed according to the adjusted parameters, and if the imaging failure rate is still greater than or equal to the preset imaging failure rate, and the determined imaging failure cause is still insufficient coverage of the beam caused by complex topography to be measured, the normal angle of the output beam of each transducer is adjusted; determining an initial adjustment angle based on the included angle between the original normal angle of each wave beam and the connecting line of the central point of the corresponding missing area, and correcting the initial adjustment angle based on the vertical angle width after current adjustment to obtain a final adjustment angle; The larger the vertical angle width after the current adjustment is, the larger the reduction amplitude of the initial adjustment angle is.
7. The high-precision multi-beam acoustic three-dimensional imaging method based on big data according to claim 1, characterized in that in step S54, when determining that the imaging failure cause is environmental interference, it comprises: detecting the resolution of the initial three-dimensional imaging model; if the resolution is smaller than a preset resolution threshold, adjusting preprocessing parameters; And if the resolution is greater than or equal to the preset resolution threshold, increasing the angular width along the direction in the beam parameters of the single beam.
8. The high-precision multi-beam acoustic three-dimensional imaging method based on big data according to claim 7, characterized in that in step S54, the magnitude of increase in the angular width along direction in the beam parameters of the single beam is determined based on the ratio of the current model resolution to the preset resolution threshold and the imaging deletion rate.

Description

High-precision multi-beam acoustic three-dimensional imaging method based on big data Technical Field The invention relates to the technical field of three-dimensional imaging, in particular to a high-precision multi-beam acoustic three-dimensional imaging method based on big data. Background The multi-beam sonar system is core equipment for submarine topography mapping and underwater target detection, and a three-dimensional submarine model is constructed by forming a plurality of narrow beams through a transmitting array and receiving echo signals. However, in practical applications, obtaining high-precision, high-integrity three-dimensional imaging still faces many challenges. Firstly, due to the limitations of sound wave propagation characteristics, topographic shading effect and transducer beam width, a detection blind area or a weak signal missing area inevitably exists in scanned data, so that a generated three-dimensional model has holes or geometric distortion, and the integrity and the reliability of imaging are affected. Conventional interpolation or extrapolation methods have difficulty in accurately estimating the true morphology and volume of these missing regions. CN105259557a discloses a multi-frequency transmitting beam forming method, which comprises (1) dividing the beam direction of the transmitting array in the cross array into a plurality of sectors, sequentially transmitting a series of fan-shaped sonar beam signals with different frequencies in each sector, wherein the fan-shaped sonar beam signals with each frequency point to one beam direction in the corresponding sector, (2) receiving sonar echo signals by using the receiving array in the cross array after the transmission of the fan-shaped sonar beam signals with all frequencies in each sector is finished, extracting the frequency information corresponding to all the fan-shaped sonar beam signals in each sector by discrete fourier transform, and performing beam forming calculation in the frequency domain corresponding to the frequency information to obtain the beam intensity result. The invention can reduce the emission time of the cross array, obtain the beam performance similar to the two-dimensional triangular patch receiving array, and reduce the complexity of the underwater real-time three-dimensional acoustic imaging system. However, the prior art has the following problems: The method lacks the capability of automatic and quantitative evaluation of imaging quality, cannot intelligently diagnose the root cause of the imaging defect, and accordingly performs self-adaptive parameter optimization, so that the integrity and the precision of three-dimensional imaging are difficult to ensure in a complex scene. Disclosure of Invention Therefore, the invention provides a high-precision multi-beam acoustic three-dimensional imaging method based on big data, which is used for solving the problems that in the prior art, the automatic and quantitative evaluation capability of imaging quality is lacking, the root cause of imaging defects cannot be diagnosed intelligently, and the self-adaptive parameter optimization is carried out accordingly, so that the integrity and precision of three-dimensional imaging are difficult to ensure in a complex scene. In order to achieve the above purpose, the invention provides a high-precision multi-beam acoustic three-dimensional imaging method based on big data. Comprising the following steps: step S1, imaging parameters are determined, wherein the imaging parameters comprise the distance between adjacent transducers in a transducer array, the beam parameters of single beams output by each transducer and data processing standard parameters; Step S2, arranging a transducer array according to the imaging parameters, controlling the transducer array to output multiple beams to scan a target area, and receiving echo signals; step S3, preprocessing the echo signals by noise reduction and filtering, mapping the preprocessed echo data to a three-dimensional space coordinate system, and generating an initial three-dimensional imaging model; Step S4, carrying out matching and volume estimation on the characteristics of a missing region in the initial three-dimensional imaging model based on big data, determining an estimated missing volume, and calculating an imaging missing rate based on the estimated missing volume; and S5, determining whether the current imaging is qualified or not based on the imaging failure rate, determining imaging failure reasons based on the complexity of the initial three-dimensional imaging model when the current imaging is failed, and generating corresponding imaging parameter correction instructions according to the imaging failure reasons. Further, in step S4, the imaging missing rate is a ratio of the estimated missing volume to the scanned acquired topographic volume. Further, in step S5, the determining whether the current imaging is qualified based on the imaging failure ra