Denoising method and device for sound echo point cloud data of inner surface of water diversion tunnel

Abstract

The invention discloses a water diversion tunnel inner surface sound echo point cloud data denoising method and device, and belongs to the technical field of water tunnel disease detection and sonar data processing.The method comprises the steps that tunnel inner surface reflection echo data are collected, and direct wave noise data are filtered out by setting a boundary threshold value of a direct wave area; based on a density clustering method and a point cloud data rapid search strategy, a lower boundary threshold value and an upper boundary threshold value of a tunnel inner surface echo region are judged, a water body region, the tunnel inner surface echo region and a high-order echo region are divided, and high-order echo noise data in the high-order echo region are filtered out; and filtering noise data of the water body area by using echo data based on sound echo characteristics. According to the invention, the influence of noise on a detection result is reduced, the 3D visualization precision is improved, and effective monitoring of disaster-inducing factors such as surface cracks of hydraulic structures such as water diversion tunnels is realized.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of water diversion tunnel disease detection and sonar data processing, more specifically, it relates to a method and device for denoising acoustic echo point cloud data of the inner surface of a water diversion tunnel, which is used for safety monitoring and health management of the water diversion tunnel during its service period in water conservancy and hydropower engineering. BACKGROUND

[0002] The disease of the inner surface of the water diversion tunnel is an important factor threatening its safe operation and leading to major accidents. Regular inspection of the disease of the inner surface of the water diversion tunnel is a key means to ensure its safe operation and health management during its service period.

[0003] Due to the influence of factors such as turbidity, reverberation in the actual water environment inside the tunnel and the size of the tunnel itself, the optical imaging detection technology cannot obtain the expected detection results. On the contrary, since sound waves are effective carriers for long-distance transmission of underwater information, the acoustic detection method based on the principle of sound wave reflection is an effective means for detecting and identifying the disease of the inner surface of the tunnel in long-distance and turbid water bodies.

[0004] However, the detection method based on the principle of sound wave reflection forms acoustic echo data similar to point cloud by collecting the reflected echo data of the inner surface. Due to the influence of factors such as water environment, system noise of the collection equipment itself and multipath propagation, the collected echo point cloud data is inevitably contaminated by noise.

[0005] The existing denoising methods for acoustic echo data have two limitations: first, only single type noise can be filtered (such as only processing direct wave or high-order echo), and the complex scene of 'direct wave noise + high-order echo noise + water body interference noise' coexisting in the water body environment of the tunnel is not considered; second, when denoising in the water area, the effective echo of obstacles (such as pipeline fragments) in the water body is easily misjudged as noise and deleted, resulting in a decrease in the detection accuracy of diseases such as cracks and corrosion in the tunnel, and the method cannot meet the engineering-level safety monitoring requirements.

[0006] Therefore, there is an urgent need for a denoising method for acoustic echo point cloud data of the inner surface of a water diversion tunnel to denoise acoustic echo point cloud data and improve the accuracy of detection results and 3D visualization. SUMMARY

[0007] In view of the above or existing deficiencies in the prior art, the present application proposes a method and device for denoising acoustic echo point cloud data of the inner surface of a water diversion tunnel to reduce the noise influence of the existing detection method using the sound wave reflection mechanism on the detection process of the inner surface of the water diversion tunnel, improve the accuracy of detection results and 3D visualization, and realize effective monitoring of disaster-causing factors such as cracks on the surface of water conservancy structures such as water diversion tunnels.

[0008] To solve the above technical problems, the present application provides the following technical solutions:

[0009] In a first aspect, the present application provides a method for denoising point cloud data of an acoustic echo of an inner surface of a water diversion tunnel, comprising:

[0010] The method comprises collecting reflected echo data of the inner surface of the tunnel, setting a boundary threshold of a direct wave region to filter out direct wave noise data, and determining a lower boundary threshold and an upper boundary threshold of an inner surface echo region of the tunnel based on a density clustering method and a fast search strategy of point cloud data.

[0011] The method further comprises dividing a water body region, the inner surface echo region of the tunnel, and a high-order echo region, and filtering out high-order echo noise data in the high-order echo region.

[0012] The method further comprises filtering out noise data in the water body region using echo data based on acoustic echo characteristics.

[0013] As a further technical solution of the present application, the collecting of the reflected echo data of the inner surface of the tunnel and the setting of the boundary threshold of the direct wave region to filter out the direct wave noise data are specifically as follows:

[0014] The method comprises determining the boundary threshold of the direct wave region according to inherent physical size parameters of an imaging sonar probe, an echo collection period, and a sound propagation speed, and further dividing the direct wave region.

[0015] As a further technical solution of the present application, the boundary threshold of the direct wave region is determined by a width of a sound wave emitted by a sonar sensor, a sampling time, a coupling layer distance, and a sound speed parameter, and the calculation formula is as follows:

[0016] ;

[0017] Wherein, is the boundary threshold of the direct wave region, is the width of the emitted sound wave, in seconds; is the width of the coupling medium of the sonar, in meters; is the sampling period, in seconds; represents the sound speed, in meters per second; represents the upward integer; the direct wave region data is non-tunnel inner surface reflected echo data, which is filtered out by being set to zero.

[0018] As a further technical solution of the present application, the determination of the inner surface echo region of the tunnel and the high-order echo region based on the density clustering method and the fast search strategy of point cloud data, and the filtering out of high-order echo noise data in the high-order echo region are specifically as follows:

[0019] Using the cross-sectional echo data of the water diversion tunnel as the processing unit, the collected discrete data points of the cross-section are mapped one by one to the actual spatial location and represented in polar coordinates; the sampling point sequence number of the polar coordinate value is converted into distance information, and the angular coordinates are distributed at equal intervals according to the sonar angular resolution.

[0020] The lower and upper boundary thresholds of the echo region on the inner surface of the tunnel are found based on density clustering and KD tree search strategy.

[0021] By combining the boundary threshold of the direct wave region, the tunnel acoustic echo point cloud data is divided into the direct wave region, the water body region, the tunnel inner surface echo region, and the high-order echo region.

[0022] Non-zero data values ​​located after the echo region on the inner surface of the tunnel are considered as high-order echo noise data and are filtered out by setting them to zero.

[0023] As a further technical solution of the present invention, the method of filtering noise data in a water body area using echo data based on acoustic echo characteristics specifically includes:

[0024] The water body region is the area between the boundary threshold of the direct wave region and the lower boundary threshold of the echo region on the inner surface of the tunnel. The noise data of the water body region is filtered out by an echo data denoising method based on acoustic echo characteristics.

[0025] As a further technical solution of the present invention, the noise data of the water body area is filtered out by an echo data denoising method based on acoustic echo characteristics, specifically including:

[0026] Joint filtering is performed using the spatial location information of data points, the threshold for the number of adjacent consecutive non-zero points, and the intensity threshold of data points. The spatial location information includes the current data frame, adjacent data frames, and data frames at the corresponding positions within adjacent sections where the data point to be filtered is located. The threshold for the number of adjacent consecutive non-zero points is jointly determined by the imaging sonar acquisition cycle and imaging resolution. The intensity threshold of data points is determined by the average intensity value of the echo data points.

[0027] As a further technical solution of the present invention, the method of using the spatial location information of data points, the threshold for the number of adjacent consecutive non-zero value points, and the intensity threshold of data points for joint filtering is as follows:

[0028] Determine whether the number of adjacent consecutive non-zero value points of the data point to be filtered in the current data frame and whether the intensity value of the data point to be filtered is higher than the corresponding threshold. If all are higher than the threshold, the data point is a valid data point and is retained; if the number of adjacent consecutive non-zero value points of the data point to be filtered is lower than the corresponding threshold, the data point is a noise point.

[0029] If the number of adjacent consecutive non-zero points of a data point to be filtered is higher than the threshold and its intensity value is lower than the data point intensity threshold, the determination is further made based on the information of the data point at the corresponding position in the adjacent data frame; if the number of adjacent consecutive non-zero points and the intensity of the data point at the corresponding position in the adjacent data frame are both higher than the corresponding threshold, then the current data point to be filtered is a valid data point and is retained; if the number of adjacent consecutive non-zero points of the data point at the corresponding position in the adjacent data frame is lower than the corresponding threshold, then the current data point to be filtered is a noise point.

[0030] If the number of consecutive non-zero values ​​of data points at corresponding positions in adjacent data frames of the data point to be filtered is greater than the threshold and its intensity value is lower than the data point intensity threshold, the determination is further made based on the information of the data points at corresponding positions in adjacent sections; if the number of consecutive non-zero values ​​of data points at corresponding positions in adjacent sections is greater than the threshold and its intensity value is greater than the data point intensity threshold, then the current data point to be filtered is a valid data point and is retained; otherwise, the current data point to be filtered is determined to be a noise point.

[0031] Secondly, the present invention proposes a noise reduction device for surface acoustic echo point cloud data inside a water diversion tunnel, comprising:

[0032] The direct wave region noise processing unit collects reflected echo data from the inner surface of the tunnel and filters out direct wave noise data by setting the boundary threshold of the direct wave region.

[0033] The high-order echo noise processing unit, based on density clustering and point cloud data fast search strategy, determines the lower and upper boundary thresholds of the echo region on the inner surface of the tunnel, divides the water region, the echo region on the inner surface of the tunnel, and the high-order echo region, and filters out the high-order echo noise data in the high-order echo region.

[0034] The water body area noise processing unit uses echo data based on acoustic echo characteristics to filter out noise data in the water body area.

[0035] Thirdly, the present invention provides an electronic device, the electronic device comprising:

[0036] At least one processor, and a memory communicatively connected to said at least one processor;

[0037] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the method for denoising surface acoustic echo point cloud data inside the water diversion tunnel.

[0038] Fourthly, the present invention provides a computer-readable storage medium storing a computer program for enabling a processor to implement the method for denoising surface acoustic echo point cloud data inside a water diversion tunnel.

[0039] The beneficial effects of this invention are:

[0040] This invention fills the gap in current methods for removing noise from the three-dimensional acoustic point cloud formed during the identification and 3D visualization of surface defects in water diversion tunnels using the principle of acoustic wave reflection. It reduces the impact of noise on the detection results and improves the accuracy of 3D visualization, enabling effective monitoring of disaster-causing factors such as surface cracks in water diversion tunnels and other hydraulic structures. Attached Figure Description

[0041] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 This is a flowchart of the method for denoising surface acoustic echo point cloud data inside a water diversion tunnel proposed in this invention;

[0043] Figure 2 This is a specific flowchart in an embodiment of the present invention;

[0044] Figure 3 This is a trend chart of some experimental data from the front end in an embodiment of the present invention;

[0045] Figure 4 This is a trend chart of the front end of the 11th frame of experimental data in this embodiment of the invention.

[0046] Figure 5 This is a diagram showing the echo region division of the acoustic point cloud in an embodiment of the present invention;

[0047] Figure 6 This is a flowchart of a noise reduction algorithm based on acoustic echo characteristics in an embodiment of the present invention;

[0048] Figure 7 This is a denoising effect diagram of the denoising method in this embodiment of the invention;

[0049] Figure 8 This is a point cloud model of the inner surface of the tunnel formed after filtering by the denoising method in this embodiment of the invention;

[0050] Figure 9 This is a structural diagram of the noise reduction device for surface acoustic echo point cloud data inside the water diversion tunnel provided by the present invention. Detailed Implementation

[0051] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0052] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0053] It should be noted that, unless otherwise defined, the technical or scientific terms used in the embodiments of this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this disclosure pertains. The terms "comprising" or "including," and similar terms used in this disclosure, mean that the element or object preceding the term covers the element or object listed after the term and its equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Connections can be fixed, detachable, or integral; they can be mechanical or electrical; they can be direct or indirect through an intermediate medium; they can be internal communication between two elements or an interaction between two elements, unless otherwise explicitly defined. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances. Terms such as "upper," "lower," "left," and "right" are only used to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0054] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that mutually excludes other embodiments. It should be noted that the embodiments of the present invention can be applied to any applicable scenario.

[0055] Example 1

[0056] See Figure 1 This invention proposes a method for denoising surface acoustic echo point cloud data inside water diversion tunnels, including:

[0057] Step 101: Collect reflected echo data from the inner surface of the tunnel, and filter out direct wave noise data by setting a boundary threshold for the direct wave region;

[0058] Step 102: Based on the density clustering method and the point cloud data fast search strategy, determine the lower boundary threshold and upper boundary threshold of the echo region on the inner surface of the tunnel, divide the water body region, the echo region on the inner surface of the tunnel, and the high-order echo region, and filter out the high-order echo noise data in the high-order echo region.

[0059] Step 103: Use echo data based on acoustic echo characteristics to filter out noise data in the water body area.

[0060] This invention fills the gap in current methods for removing noise from the three-dimensional acoustic point cloud formed during the identification and 3D visualization of surface defects in water diversion tunnels using the principle of acoustic wave reflection. It reduces the impact of noise on the detection results and improves the accuracy of 3D visualization, enabling effective monitoring of disaster-causing factors such as surface cracks in water diversion tunnels and other hydraulic structures.

[0061] See Figure 2 In this embodiment of the invention, reflected echo data from the inner surface of the tunnel are collected, and direct wave noise data is filtered out by setting a boundary threshold for the direct wave region. Specifically, based on the inherent physical size parameters of the imaging sonar probe (including the width of the probe coupling medium and the acoustic wave diffusion width at the transmitting end), the echo acquisition period, and the sound propagation speed, the boundary threshold of the direct wave region is determined, and then the direct wave region is divided. The intensity value of the data points within the direct wave boundary threshold is set to zero, thereby filtering out the direct wave noise data.

[0062] The boundary threshold of the direct wave region is determined by the sonar sensor's emitted sound wave width, sampling time, coupling layer distance, and sound velocity parameters, and its calculation formula is shown below:

[0063] ;

[0064] in, For the direct wave region boundary threshold, The width of the emitted sound wave, measured in seconds (s). The width of the sonar coupling medium is in meters (m). The sampling period is expressed in seconds (s). Represents the speed of sound, measured in m / s; This indicates rounding up to the nearest integer; data in the direct wave region are non-tunnel inner surface reflected echo data, which are filtered out by setting it to zero.

[0065] See Figure 3 This invention collects multiple sets of experimental data for the analysis of direct-wave region data. Direct-wave data refers to data where the transmitted wave is directly received by the sensor at the moment of transmission or after reflection from the inner wall of the transmitter probe's protective cover, without reflection from the tunnel's inner surface. This is caused by the sonar sensor's own design factors and the data's synchronous sampling mechanism. The front end of some echo data frames in the experimental data is shown below. Figure 3As shown, the data change trends within different data frames are basically consistent, indicating the universality of the experimental data, meaning that any one frame of data is representative for analysis.

[0066] As shown in Figure 4, due to the universality of the experimental data, the first part of the 11th frame of data was analyzed to determine whether the data in this area belonged to the direct wave region. It can be seen that there is a small data value at sampling point S1, reaching its maximum value at sampling points S2, S3, and S4, and decreasing at sampling points S5 and S6; subsequently, it reaches its maximum value in the region from sampling points S7 to S11, and the data intensity gradually decreases in the region from sampling points S12 to S15. Due to the combined transmit and receive design and synchronous sampling mechanism of the sonar equipment, the receiver should acquire the data value at the moment of acoustic signal transmission, and the data value should gradually increase and then gradually decrease; subsequently, the echo signal reflected from the inner wall of the sonar probe's protective cover should have a similar trend in data value change as the directly acquired transmitted signal. Figure 4 The trend of data change in the region is highly consistent with the trend of direct wave data from sonar equipment; therefore, the data in this region is direct wave region data.

[0067] In this embodiment of the invention, based on density clustering and a fast point cloud data search strategy, the echo region and high-order echo region on the inner surface of the tunnel are determined, and high-order echo noise data in the high-order echo region is filtered out; specifically including:

[0068] Using the cross-sectional echo data of the water diversion tunnel as the processing unit, the collected discrete data points of the cross-section are mapped one by one to the actual spatial location and represented in polar coordinates; the sampling point sequence number of the polar coordinate value is converted into distance information, and the angular coordinates are distributed at equal intervals according to the sonar angular resolution.

[0069] The lower and upper boundary thresholds of the echo region on the inner surface of the tunnel are found based on density clustering and KD tree search strategy.

[0070] By combining the boundary threshold of the direct wave region, the tunnel acoustic echo point cloud data is divided into the direct wave region, the water body region, the tunnel inner surface echo region, and the high-order echo region.

[0071] Non-zero data values ​​located after the echo region on the inner surface of the tunnel are considered as high-order echo noise data and are filtered out by setting them to zero.

[0072] As shown in Figure 5, after determining the boundary threshold of the direct wave region, it is necessary to determine the high-order echo region to filter out high-order echo noise caused by multipath effects. Using the echo data from the water diversion tunnel section as the processing unit, the collected discrete data points of the section are mapped one by one to their actual spatial locations and represented in polar coordinates. The polar coordinate values ​​are the sampling point sequence numbers, which can be converted into distance information, and the angular coordinates are distributed at equal intervals according to the sonar angular resolution. For example... Figure 5 As shown, based on density clustering and KD-tree search strategy, the lower and upper boundary thresholds of the echo region on the tunnel inner surface are found. Combined with the direct wave region boundary threshold, the tunnel acoustic echo point cloud data is divided into four parts: the direct wave region, the water body region, the tunnel inner surface echo region, and the high-order echo region. Non-zero data values ​​located after the tunnel inner surface echo region are considered as high-order echo noise data and are filtered out by setting them to zero.

[0073] like Figure 6 As shown, compared to filtering out direct wave noise data and high-order echo noise data, processing noise data existing between the direct wave region and the echo region on the inner surface of the tunnel is more complex. This is because the non-zero data values ​​in this region may be reflected echo data from obstacles in the water, rather than noise signals. Treating them as noise signals would affect the accuracy of the measurement data. For noise data in this region, a denoising algorithm based on acoustic echo characteristics is used for filtering.

[0074] like Figure 6 As shown, noise data in the water body region is filtered out using echo data based on acoustic echo characteristics; specifically, the water body region is the area between the boundary threshold of the direct wave region and the lower boundary threshold of the echo region on the inner surface of the tunnel, and the noise data in the water body region is filtered out using an echo data denoising method based on acoustic echo characteristics.

[0075] Noise data in the water area is filtered out using an echo data denoising method based on acoustic echo characteristics. Specifically, this includes: joint filtering using spatial location information of data points, a threshold for the number of adjacent consecutive non-zero points, and a threshold for data point intensity. Spatial location information includes the current data frame, adjacent data frames, and data frames at corresponding locations within adjacent cross sections where the data point to be filtered is located. The threshold for the number of adjacent consecutive non-zero points is determined by the imaging sonar acquisition cycle and imaging resolution. The data point intensity threshold is determined by the average intensity value of the echo data points.

[0076] In this embodiment of the invention, joint filtering is performed using the spatial location information of data points, a threshold for the number of adjacent consecutive non-zero value points, and a threshold for data point intensity; specifically:

[0077] Determine whether the number of adjacent consecutive non-zero value points of the data point to be filtered in the current data frame and whether the intensity value of the data point to be filtered is higher than the corresponding threshold. If all are higher than the threshold, the data point is a valid data point and is retained; if the number of adjacent consecutive non-zero value points of the data point to be filtered is lower than the corresponding threshold, the data point is a noise point.

[0078] If the number of adjacent consecutive non-zero points of a data point to be filtered is higher than the threshold and its intensity value is lower than the data point intensity threshold, the determination is further made based on the information of the data point at the corresponding position in the adjacent data frame; if the number of adjacent consecutive non-zero points and the intensity of the data point at the corresponding position in the adjacent data frame are both higher than the corresponding threshold, then the current data point to be filtered is a valid data point and is retained; if the number of adjacent consecutive non-zero points of the data point at the corresponding position in the adjacent data frame is lower than the corresponding threshold, then the current data point to be filtered is a noise point.

[0079] If the number of consecutive non-zero values ​​of data points at corresponding positions in adjacent data frames of the data point to be filtered is greater than the threshold and its intensity value is lower than the data point intensity threshold, the determination is further made based on the information of the data points at corresponding positions in adjacent sections; if the number of consecutive non-zero values ​​of data points at corresponding positions in adjacent sections is greater than the threshold and its intensity value is greater than the data point intensity threshold, then the current data point to be filtered is a valid data point and is retained; otherwise, the current data point to be filtered is determined to be a noise point.

[0080] like Figure 7 As shown, through with Figure 5 In comparison, the denoising method of this invention, after filtering the original acoustic echo point cloud data, reveals that direct wave region data and high-order echo region data are filtered out, while the echo region data of the tunnel's inner surface is retained. Furthermore, most noise data located in the water body region is also filtered out, preserving the actual obstacle echo data information in the water body region. This denoising method provides high-precision and highly reliable real data for the detection and identification of defects on the inner surface of water diversion tunnels and for 3D visualization. Figure 8 This is a point cloud model of the inner surface of the tunnel after filtering using this denoising method.

[0081] Example 2

[0082] See Figure 9 The present invention also provides a device for denoising surface acoustic echo point cloud data inside a water diversion tunnel, comprising:

[0083] The direct wave region noise processing unit 201 collects reflected echo data from the inner surface of the tunnel and filters out direct wave noise data by setting the boundary threshold of the direct wave region.

[0084] The high-order echo noise processing unit 202, based on density clustering method and point cloud data fast search strategy, determines the lower boundary threshold and upper boundary threshold of the echo region on the inner surface of the tunnel, divides the water region, the echo region on the inner surface of the tunnel and the high-order echo region, and filters out the high-order echo noise data in the high-order echo region.

[0085] The water area noise processing unit 203 uses echo data based on acoustic echo characteristics to filter out noise data in the water area.

[0086] The various variations and specific examples of the surface acoustic echo point cloud data denoising method for water diversion tunnels in the foregoing embodiments are also applicable to the surface acoustic echo point cloud data denoising device for water diversion tunnels in this embodiment. Through the foregoing detailed description of the surface acoustic echo point cloud data denoising method for water diversion tunnels, those skilled in the art can clearly understand the surface acoustic echo point cloud data denoising device for water diversion tunnels in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here.

[0087] Example 3

[0088] This invention proposes an electronic device, the electronic device comprising:

[0089] At least one processor, and a memory communicatively connected to said at least one processor;

[0090] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the method for denoising surface acoustic echo point cloud data inside the water diversion tunnel.

[0091] A processor can be a central processing unit (CPU) or other form of processing unit with data processing and instruction execution capabilities, and can control other components in an electronic device to perform desired functions.

[0092] The memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and a processor may execute the program instructions to implement the methods of the software programs in the various embodiments of the present invention described above, as well as other desired functions. In addition, depending on the specific application, the electronic device may include any other suitable components.

[0093] Example 4

[0094] This invention proposes a computer-readable storage medium storing a computer program that enables a processor to execute the aforementioned method for denoising surface acoustic echo point cloud data inside a water diversion tunnel.

[0095] In addition to the methods and apparatus described above, embodiments of the present invention may also be computer program products, which include computer program instructions that, when executed by a processor, cause the processor to perform the steps in the methods according to various embodiments of the present invention described in the "Exemplary Methods" section above.

[0096] The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of the present invention. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0097] Furthermore, embodiments of the present invention may also be computer-readable storage media storing computer program instructions thereon, which, when executed by a processor, cause the processor to perform the steps of the methods according to various embodiments of the present invention described in the "Exemplary Methods" section above.

[0098] The computer-readable storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may include, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0099] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of the invention to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A method for denoising surface acoustic echo point cloud data inside a water diversion tunnel, characterized in that, include: Collect reflected echo data from the inner surface of the tunnel, and filter out direct wave noise data by setting a boundary threshold for the direct wave region; Based on density clustering and point cloud data fast search strategy, the lower and upper boundary thresholds of the echo region on the inner surface of the tunnel are determined, and the water region, the echo region on the inner surface of the tunnel and the high-order echo region are divided. High-order echo noise data in the high-order echo region is filtered out. Noise data in water bodies is filtered out using echo data based on acoustic echo characteristics.

2. The method for denoising surface acoustic echo point cloud data inside a water diversion tunnel according to claim 1, characterized in that, The method for collecting reflected echo data from the inner surface of the tunnel is to filter out direct wave noise data by setting a boundary threshold for the direct wave region. Specifically: Based on the inherent physical size parameters of the imaging sonar probe, the echo acquisition period, and the sound propagation speed, the boundary threshold of the direct wave region is determined, and then the direct wave region is divided. The intensity value of the data points within the direct wave boundary threshold is set to zero, thereby filtering out direct wave noise data.

3. The method for denoising surface acoustic echo point cloud data inside a water diversion tunnel according to claim 2, characterized in that, The boundary threshold of the direct wave region is determined by the sonar sensor's emitted sound wave width, sampling time, coupling layer distance, and sound velocity parameters, and its calculation formula is as follows: ； in, For the direct wave region boundary threshold, The width of the emitted sound wave, measured in seconds (s). The width of the sonar coupling medium is in meters (m). The sampling period is expressed in seconds (s). Represents the speed of sound, measured in m / s; This indicates rounding up to the nearest integer; data in the direct wave region are non-tunnel inner surface reflected echo data, which are filtered out by setting it to zero.

4. The method for denoising surface acoustic echo point cloud data inside a water diversion tunnel according to claim 1, characterized in that, Based on density clustering and a fast point cloud data search strategy, the echo regions and high-order echo regions on the tunnel surface are identified, and high-order echo noise data within the high-order echo regions is filtered out; specifically including: Using the cross-sectional echo data of the water diversion tunnel as the processing unit, the collected discrete data points of the cross-section are mapped one by one to the actual spatial location and represented in polar coordinates; the sampling point sequence number of the polar coordinate value is converted into distance information, and the angular coordinates are distributed at equal intervals according to the sonar angular resolution. The lower and upper boundary thresholds of the echo region on the inner surface of the tunnel are found based on density clustering and KD tree search strategy. By combining the boundary threshold of the direct wave region, the tunnel acoustic echo point cloud data is divided into the direct wave region, the water body region, the tunnel inner surface echo region, and the high-order echo region. Non-zero data values ​​located after the echo region on the inner surface of the tunnel are considered as high-order echo noise data and are filtered out by setting them to zero.

5. The method for denoising surface acoustic echo point cloud data inside a water diversion tunnel according to claim 1, characterized in that, The method of filtering noise data in water bodies using echo data based on acoustic echo characteristics specifically includes: The water body region is the area between the boundary threshold of the direct wave region and the lower boundary threshold of the echo region on the inner surface of the tunnel. The noise data of the water body region is filtered out by an echo data denoising method based on acoustic echo characteristics.

6. The method for denoising surface acoustic echo point cloud data inside a water diversion tunnel according to claim 5, characterized in that, Noise data from the water area is filtered out using an echo data denoising method based on acoustic echo characteristics, specifically including: Joint filtering is performed using the spatial location information of data points, the threshold for the number of adjacent consecutive non-zero points, and the intensity threshold of data points. The spatial location information includes the current data frame, adjacent data frames, and data frames at the corresponding positions within adjacent sections where the data point to be filtered is located. The threshold for the number of adjacent consecutive non-zero points is jointly determined by the imaging sonar acquisition cycle and imaging resolution. The intensity threshold of data points is determined by the average intensity value of the echo data points.

7. The method for denoising surface acoustic echo point cloud data inside a water diversion tunnel according to claim 6, characterized in that, The method involves joint filtering using spatial location information of data points, a threshold for the number of adjacent consecutive non-zero value points, and a data point intensity threshold; specifically: Determine the number of adjacent consecutive non-zero value points of the data point to be filtered in the current data frame and whether the intensity value of the data point to be filtered is higher than the corresponding threshold. If all are higher than the threshold, they are valid data points and are retained. If the number of consecutive non-zero values ​​of a data point to be filtered is less than the corresponding threshold, it is considered a noise point. If the number of adjacent consecutive non-zero points of the data point to be filtered is higher than the threshold and its own intensity value is lower than the data point intensity threshold, the determination is further made based on the information of the data points at the corresponding positions in the adjacent data frames. If the number and intensity of adjacent consecutive non-zero value points of a data point at a corresponding position in an adjacent data frame are both higher than the corresponding threshold, then the current data point to be filtered is a valid data point and is retained. If the number of consecutive non-zero values ​​of a data point at a corresponding position in an adjacent data frame is less than the corresponding threshold, then the current data point to be filtered is a noise point. If the number of consecutive non-zero values ​​of a data point at a corresponding position in an adjacent data frame is greater than a threshold and its intensity value is lower than a data point intensity threshold, the determination is further made based on the information of the data points at corresponding positions in adjacent sections; if the number of consecutive non-zero values ​​of a data point at a corresponding position in an adjacent section is greater than a threshold and its intensity value is higher than a data point intensity threshold, then the current data point to be filtered is a valid data point and is retained. Otherwise, the current data point to be filtered is determined to be a noise point.

8. A device for denoising surface acoustic echo point cloud data inside a water diversion tunnel, characterized in that, The method for denoising surface acoustic echo point cloud data inside a water diversion tunnel as described in any one of claims 1-7 includes: The direct wave region noise processing unit collects reflected echo data from the inner surface of the tunnel and filters out direct wave noise data by setting the boundary threshold of the direct wave region. The high-order echo noise processing unit, based on density clustering and point cloud data fast search strategy, determines the lower and upper boundary thresholds of the echo region on the inner surface of the tunnel, divides the water region, the echo region on the inner surface of the tunnel, and the high-order echo region, and filters out the high-order echo noise data in the high-order echo region. The water body area noise processing unit uses echo data based on acoustic echo characteristics to filter out noise data in the water body area.

9. An electronic device, characterized in that, The electronic device includes: At least one processor, and a memory communicatively connected to said at least one processor; The memory stores a computer program that can be executed by the at least one processor, which is then executed by the at least one processor to enable the at least one processor to perform the method for denoising surface acoustic echo point cloud data inside the water diversion tunnel as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that enables a processor to implement the method for denoising surface acoustic echo point cloud data inside the water diversion tunnel as described in any one of claims 1-7.

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