Method, device and equipment for three-dimensional sonar imaging reconstruction of underwater environment of inspection well

Abstract

The application relates to the field of electronic technology and discloses a sonar three-dimensional imaging reconstruction method, device and equipment for the underwater environment of an inspection well, which projects sonar ring scanning data corresponding to each depth to a target plane based on target posture data of each depth to obtain first sonar ring scanning data corresponding to each depth; calculates first centroid position coordinates corresponding to each depth based on the first sonar ring scanning data of each depth, and calculates second centroid position coordinates corresponding to each depth based on reference sonar ring scanning data; determines a translation vector corresponding to each depth based on the first centroid position coordinates and the second centroid position coordinates of each depth; corrects the first sonar ring scanning data by using the translation vector of each depth to obtain target sonar ring scanning data corresponding to each depth; and determines an underwater three-dimensional model of the inspection well based on the target sonar ring scanning data of each depth, pixel quantity information, pixel spacing information and target posture data, so that the complex geometric shape of the whole inside of the inspection well can be restored completely and with high precision.

Description

Technical Field

[0001] This invention relates to the field of electronic technology, and more specifically to a method, apparatus, and equipment for sonar three-dimensional imaging reconstruction of the underwater environment of inspection wells. Background Technology

[0002] As a crucial urban infrastructure, the operation and maintenance of urban drainage networks hinges on effective monitoring. Inspection wells, as core nodes connecting urban drainage pipes and constructing underground drainage networks, are key structures for monitoring pipeline transfer status, identifying structural problems, and conducting routine maintenance. However, inspection wells are constantly exposed to high water levels, and the water is extremely turbid due to the high concentration of suspended particles and contaminants. In practice, conventional optical inspection equipment such as pipe periscopes (QVs) are limited by light penetration, making it difficult to penetrate the turbid water and obtain clear images. This results in the spatial structure, pipe connection status, and well wall defects becoming "visual blind spots" in network inspections. While single-beam or multi-beam sonar technology can be used for underwater detection, mainstream equipment typically only outputs discrete two-dimensional cross-sectional scans, lacking in-depth spatial correlation. This two-dimensional slice cannot intuitively recreate the complex spatial geometry inside the inspection well (such as pipe diameter orientation and the spatial distribution of damage locations), failing to meet the technical requirements of modern, refined pipeline maintenance for "digital twins" or three-dimensional reality models. Summary of the Invention

[0003] This invention provides a sonar three-dimensional imaging reconstruction method, device, and equipment for underwater environment reconstruction of inspection wells, in order to solve the problem that two-dimensional slices cannot intuitively restore the complex spatial geometry inside the inspection well in related technologies.

[0004] In a first aspect, the present invention provides a sonar three-dimensional imaging reconstruction method for the underwater environment of a manhole. The method includes: acquiring sonar ring scan data at different depths underwater, target attitude data, reference sonar ring scan data of the manhole, pixel count information, and pixel spacing information of the sonar ring scan data at each depth; projecting the sonar ring scan data at each depth onto a target plane based on the target attitude data at each depth to obtain first sonar ring scan data at the corresponding depth; calculating the first centroid position coordinates at the corresponding depth based on the first sonar ring scan data at each depth, and calculating the second centroid position coordinates at the corresponding depth based on the reference sonar ring scan data; determining the translation vector at the corresponding depth based on the first centroid position coordinates and the second centroid position coordinates at each depth; correcting the first sonar ring scan data based on the translation vector at each depth to obtain second sonar ring scan data; restoring the second sonar ring scan data based on the target attitude data at each depth to obtain target sonar ring scan data at the corresponding depth; and determining the underwater three-dimensional model of the manhole based on the target sonar ring scan data at each depth, pixel count information, pixel spacing information, and target attitude data.

[0005] The sonar 3D imaging reconstruction method for underwater environment of inspection wells provided by this invention integrates multi-depth sonar ring scan data, target attitude data, reference calibration data, pixel quantity information, and pixel spacing information to form a complete multi-source data system. First, the spatial plane projection transformation of the corresponding ring scan data is completed through the target attitude data at each depth to establish the basic spatial orientation reference of the data. Then, by comparing and analyzing the centroid of the projection data at each depth with the centroid of the reference data, the spatial translation vector specific to each depth is obtained. This is used to correct the offset distortion of the projection data at each layer, and from the root, a unified and continuous spatial coordinate relationship is established between the ring scan data at different depths. This solves the core problem of discrete and fragmented data at each layer and lack of depth spatial relationship in related technologies. Meanwhile, this invention combines pixel quantity information and pixel spacing information to achieve accurate scale conversion from image pixels to real physical space coordinates. It combines the target sonar ring scan data after full depth correction with attitude parameters to complete full-domain three-dimensional fusion reconstruction. It can completely and accurately restore all complex geometric shapes such as the overall well structure, pipeline orientation, and spatial distribution of defects in the well wall of the inspection well. It can directly generate an underwater three-dimensional real scene model of the inspection well that can be applied in engineering. It can provide reliable three-dimensional data support for the construction of digital twins of underground pipe networks, accurate defect location and assessment, and refined operation and maintenance of pipe networks throughout their entire life cycle.

[0006] In one optional implementation, the step of determining the underwater three-dimensional model of the inspection well based on the target sonar ring scan data, pixel quantity information, pixel spacing information, and target attitude data at each depth includes: calculating the true coordinate information of the target sonar ring scan data based on the pixel quantity information and pixel spacing information at each depth; determining the global coordinate information of the corresponding depth based on the true coordinate information and target attitude data at each depth; and constructing the underwater three-dimensional model of the inspection well based on the global coordinate information at each depth.

[0007] The method provided by this optional implementation realizes the accurate mapping of pixel data to real physical space, establishes a unified global spatial coordinate benchmark, solves the problem of discrete and spatially unrelated two-dimensional sonar data, and can efficiently and accurately construct underwater three-dimensional models of inspection wells, completely restore the spatial structure inside the well, and meet the three-dimensional modeling requirements of refined pipeline maintenance and digital twins.

[0008] In one optional implementation, the step of projecting sonar ring scan data at each depth onto a target plane based on target attitude data at each depth to obtain first sonar ring scan data at the corresponding depth includes: performing noise reduction processing on the sonar ring scan data at each depth to obtain noise-reduced data at the corresponding depth; and projecting the noise-reduced data onto a target plane based on target attitude data at each depth to obtain first sonar ring scan data at the corresponding depth.

[0009] The method provided in this optional implementation first denoises the sonar ring scan data to remove noise interference caused by the underwater turbid environment and retain the real and effective detection data; then it combines the target attitude data with the projection onto the target plane to unify the data projection reference and eliminate the influence of attitude deviation, effectively improving the data accuracy and reliability, and laying a high-quality data foundation for subsequent steps such as centroid calculation and translation correction.

[0010] In one optional implementation, the sonar ring scan data includes multiple sonar images. Noise reduction processing is performed on the sonar ring scan data at each depth to obtain noise-reduced data for the corresponding depth. This includes: performing temporal statistics on the intensity values ​​of each pixel in the multiple sonar images at each depth to obtain the mean and standard deviation of the intensity of each pixel at the corresponding depth; determining a dynamic filtering threshold for the corresponding pixel based on the mean and standard deviation of the intensity of each pixel at each depth; identifying the target sonar image at the corresponding depth from the multiple sonar images at each depth; and filtering noise from the target sonar image based on the dynamic filtering threshold for the multiple pixels at each depth to obtain noise-reduced data for the corresponding depth.

[0011] The method provided in this optional implementation uses time-series statistics to calculate and generate dynamic filtering thresholds pixel by pixel. It can adaptively filter out random noise such as suspended particles and bubbles in the turbid water of the inspection well, accurately retain the real structural signals of the well wall, pipelines, etc. The noise reduction effect is more in line with the complex environment downhole, and can significantly improve the purity and accuracy of sonar data, providing reliable data support for subsequent projection, correction and 3D modeling.

[0012] In one optional implementation, the target pose data at each depth is determined through the following steps: acquiring the initial pose data and corresponding timestamp information at each depth; determining the first pose data within the target time period before and after the timestamp information at each depth; concatenating the first pose data and the initial pose data at each depth to obtain the second pose data; using a preset algorithm to remove extreme values ​​from the second pose data at each depth to obtain the third pose data at the corresponding depth; and extracting the median from the third pose data at each depth to obtain the denoised target pose data.

[0013] The method provided by this optional implementation extracts attitude data for the corresponding time period based on the timestamp and completes data stitching. It removes extreme values ​​in the data through a preset algorithm and then extracts the median to determine the final attitude data. This can effectively eliminate attitude data anomalies caused by equipment descent vibration and water flow disturbance, improve the stability and accuracy of attitude data, and provide a stable and reliable attitude reference for subsequent data projection, correction and 3D modeling.

[0014] Secondly, the present invention provides a sonar three-dimensional imaging reconstruction device for the underwater environment of a manhole. The device includes: an acquisition module for acquiring sonar ring scan data at different depths underwater, target attitude data, reference sonar ring scan data of the manhole, pixel count information, and pixel spacing information of the sonar ring scan data at each depth; a projection module for projecting the sonar ring scan data at each depth onto a target plane based on the target attitude data at each depth, to obtain the first sonar ring scan data at the corresponding depth; and a calculation module for calculating the first centroid position coordinates at the corresponding depth based on the first sonar ring scan data at each depth, and based on the reference sonar ring scan data. The system is divided into three modules: a first determining module, a second determining module, and a third determining module. The first determining module is used to determine the translation vector for each depth based on the first and second centroid coordinates. The second determining module is used to correct the first sonar ring scan data based on the translation vectors for each depth to obtain the second sonar ring scan data. The third determining module is used to restore the second sonar ring scan data based on the target attitude data for each depth to obtain the target sonar ring scan data for the corresponding depth. The fourth determining module is used to determine the underwater three-dimensional model of the inspection well based on the target sonar ring scan data for each depth, pixel count information, pixel spacing information, and target attitude data.

[0015] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the sonar three-dimensional imaging reconstruction method for the underwater environment of a manhole as described in the first aspect or any corresponding embodiment.

[0016] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the sonar three-dimensional imaging reconstruction method for the underwater environment of a manhole according to the first aspect or any corresponding embodiment described above.

[0017] Fifthly, the present invention provides a computer program product, including computer instructions for causing a computer to execute the sonar three-dimensional imaging reconstruction method for the underwater environment of a manhole according to the first aspect or any corresponding embodiment described above. Attached Figure Description

[0018] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0019] Figure 1This is a schematic diagram of an application scenario according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the first process of a sonar three-dimensional imaging reconstruction method for the underwater environment of an inspection well according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the second process of a sonar three-dimensional imaging reconstruction method for the underwater environment of an inspection well according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the sonar circumferential scanning device in the embodiments of this application; Figure 5 This is a schematic diagram of the sonar image before noise reduction in an embodiment of this application; Figure 6 This is a schematic diagram of the noise-reduced sonar image in an embodiment of this application; Figure 7 This is a schematic diagram of the third process of a sonar three-dimensional imaging reconstruction method for the underwater environment of an inspection well according to an embodiment of the present invention; Figure 8 This is a structural block diagram of a sonar three-dimensional imaging reconstruction device for the underwater environment of an inspection well according to an embodiment of the present invention. Figure 9 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0022] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0023] As an optional application scenario of this invention, the specific application environment architecture or specific hardware architecture on which the execution of the sonar three-dimensional imaging reconstruction method for the underwater environment of inspection wells depends is described herein. For example... Figure 1 As shown, the architecture system may include at least one terminal device and at least one server. Figure 1 The example shows that the system includes a computer 101, a mobile terminal 102, and a server 103, and the terminal devices such as the computer 101 and the mobile terminal 102 are connected to the server 103 through a network 110.

[0024] Specifically, the terminal device can be a smartphone, tablet, laptop, PDA, desktop computer, game console, smart TV, smart wearable device, in-vehicle terminal, VR (Virtual Reality) device, AR (Augmented Reality) device, etc. Server 103 can be a standalone physical server, a server cluster, a distributed system, or a cloud server providing cloud services. Network 110 can be a wired or wireless network, examples of which include, but are not limited to, the Internet, corporate intranet, local area network, wide area network, mobile communication network, and combinations thereof.

[0025] In related technologies, single-beam or multi-beam sonar technology can be used for underwater detection, but mainstream equipment on the market can usually only output discrete two-dimensional cross-sectional scans, lacking depth spatial correlation. Such two-dimensional slices cannot intuitively restore the complex spatial geometry inside the inspection well (such as pipe diameter orientation, spatial distribution of damage locations, etc.), making it difficult to meet the technical requirements of modern pipeline network refined management for "digital twins" or three-dimensional reality models.

[0026] In view of this, the sonar 3D imaging reconstruction method for underwater environment of inspection wells provided in this application embodiment can be applied to a server to realize 3D reconstruction of the underwater environment of inspection wells. The method provided in this application embodiment integrates multi-depth sonar ring scan data, target attitude data, reference calibration data, pixel quantity information and pixel spacing information to form a complete multi-source data system. First, the spatial plane projection transformation of the corresponding ring scan data is completed through the target attitude data at each depth to establish the basic spatial orientation reference of the data. Then, through the comparative analysis of the centroid of the projection data at each depth and the centroid of the reference data, the spatial translation vector specific to each depth is obtained, thereby completing the offset distortion correction of the projection data at each layer. From the root, a unified and continuous spatial coordinate relationship is established between the ring scan data at different depths, solving the core problem of discrete and fragmented data at each layer and lack of depth spatial relationship in related technologies. Meanwhile, this invention combines pixel quantity information and pixel spacing information to achieve accurate scale conversion from image pixels to real physical space coordinates. It combines the target sonar ring scan data after full depth correction with attitude parameters to complete full-domain three-dimensional fusion reconstruction. It can completely and accurately restore all complex geometric shapes such as the overall well structure, pipeline orientation, and spatial distribution of defects in the well wall of the inspection well. It can directly generate an underwater three-dimensional real scene model of the inspection well that can be applied in engineering. It can provide reliable three-dimensional data support for the construction of digital twins of underground pipe networks, accurate defect location and assessment, and refined operation and maintenance of pipe networks throughout their entire life cycle.

[0027] According to an embodiment of the present invention, a method for sonar three-dimensional imaging reconstruction of the underwater environment of an inspection well is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0028] This embodiment provides a sonar three-dimensional imaging reconstruction method for inspecting the underwater environment of a well, which can be used in the aforementioned server. Figure 2 This is a flowchart of a sonar three-dimensional imaging reconstruction method for the underwater environment of an inspection well according to an embodiment of the present invention, as follows: Figure 2 As shown, the process includes the following steps: Step S201: Obtain sonar ring scan data at different underwater depths of the inspection well, target attitude data, reference sonar ring scan data of the inspection well, pixel count information and pixel spacing information of sonar ring scan data at each depth.

[0029] For example, the sonar circumferential scan data at different depths underwater in the inspection well consists of raw two-dimensional sonar image data acquired by lowering the circumferential scan sonar probe to different depths underwater in the inspection well. Target attitude data is the azimuth angle, roll angle, and pitch angle data corresponding to each depth probe, acquired by an attitude sensor. The reference sonar circumferential scan data is the sonar circumferential scan image acquired when the probe has just entered the water. The pixel count information for each depth's sonar circumferential scan data includes the width and height pixel values ​​of the sonar circumferential scan image. Pixel spacing information includes the actual physical distance corresponding to a single pixel in the sonar image, including lateral and longitudinal spacing.

[0030] Step S202: Based on the target attitude data at each depth, project the sonar ring scan data at the corresponding depth onto the target plane to obtain the first sonar ring scan data at the corresponding depth.

[0031] For example, the target plane may include, but is not limited to, the XY plane. In this embodiment, the sonar swathe data may be a pre-selected sonar image. For the sonar image Pi, according to the elevation angle... Roll angle and azimuth Projecting the data onto the XY plane yields the first sonar ring scan data, as shown in the following formula:

[0032]

[0033] in, This represents the elevation angle of the sonar probe at the acquisition time corresponding to the i-th pixel; This represents the roll angle of the sonar probe at the time of acquisition corresponding to the i-th pixel. This represents the azimuth angle of the sonar probe at the time of acquisition corresponding to the i-th pixel; This represents the physical coordinate of the i-th pixel after it has undergone a three-axis attitude rotation transformation and been projected onto the unified XY reference target plane, which is the horizontal coordinate of the first sonar ring scan data. The vertical axis represents the first sonar ring scan data. , This represents the physical coordinates of the i-th pixel within the local scan plane of the sonar probe.

[0034] Step S203: Calculate the first centroid position coordinates at the corresponding depth based on the first sonar ring scan data at each depth, and calculate the second centroid position coordinates at the corresponding depth based on the reference sonar ring scan data.

[0035] For example, in this embodiment of the application, the coordinates of the first centroid position of the first sonar ring scan data at each depth can be calculated by the following formula:

[0036]

[0037] in, The x-coordinate representing the position of the first centroid. The ordinate representing the position of the first centroid. This indicates the number of coordinate points in the first sonar ring scan data; the meanings of the other variables will not be elaborated further.

[0038] Similarly, the coordinates of the second centroid position at the corresponding depth are calculated from the baseline sonar ring scan data. , ).

[0039] Step S204: Determine the translation vector for the corresponding depth based on the first centroid position coordinates and the second centroid position coordinates for each depth.

[0040] For example, in the embodiments of this application, the translation vector for each depth can be represented by the following formula:

[0041]

[0042] in,( , ) represents the translation vector, and the meanings of the other variables will not be elaborated further.

[0043] Step S205: Correct the first sonar ring scan data based on the translation vectors at each depth to obtain the second sonar ring scan data.

[0044] For example, in this embodiment of the application, the first sonar circumferential scan data can be corrected using the following formula:

[0045]

[0046] in,( , () represents the pixel coordinates in the corrected second sonar ring scan data.

[0047] Step S206: The second sonar ring scan is restored based on the target attitude data at each depth to obtain the target sonar ring scan data at the corresponding depth.

[0048] For example, in the embodiments of this application, the corrected sonar circumferential scan data ( , According to pitch angle Roll angle and azimuth The data was restored to obtain the target sonar ring scan data:

[0049]

[0050]

[0051] in, , This represents the pixel coordinates in the target sonar ring scan data; the meanings of the other variables will not be elaborated further.

[0052] Step S207: Determine the underwater 3D model of the inspection well based on the target sonar ring scan data, pixel quantity information, pixel spacing information, and target attitude data at each depth.

[0053] For example, in this embodiment of the application, based on the pixel quantity information and pixel spacing information at each depth, the pixel coordinates of the target sonar ring scan data are accurately converted to the real physical plane coordinates in the probe coordinate system; then, the spatial rotation transformation is completed by combining the target attitude data at each depth, and the physical coordinates are mapped to a unified global coordinate system by combining the elevation parameters at each depth to obtain the global three-dimensional spatial coordinates of each point; finally, the three-dimensional coordinate point clouds of all depth layers are fused to complete the inter-layer data stitching and integration, and a complete underwater three-dimensional model of the inspection well is constructed.

[0054] The sonar 3D imaging reconstruction method for underwater environment of inspection wells provided in this embodiment integrates multi-depth sonar ring scan data, target attitude data, reference calibration data, pixel quantity information, and pixel spacing information to form a complete multi-source data system. First, the spatial plane projection transformation of the corresponding ring scan data is completed through the target attitude data at each depth to establish the basic spatial orientation reference of the data. Then, by comparing and analyzing the centroid of the projection data at each depth with the centroid of the reference data, the spatial translation vector specific to each depth is obtained. This is used to correct the offset distortion of the projection data at each layer, and from the root, a unified and continuous spatial coordinate relationship is established between the ring scan data at different depths. This solves the core problem of discrete and fragmented data at each layer and lack of depth spatial relationship in related technologies. Meanwhile, this invention combines pixel quantity information and pixel spacing information to achieve accurate scale conversion from image pixels to real physical space coordinates. It combines the target sonar ring scan data after full depth correction with attitude parameters to complete full-domain three-dimensional fusion reconstruction. It can completely and accurately restore all complex geometric shapes such as the overall well structure, pipeline orientation, and spatial distribution of defects in the well wall of the inspection well. It can directly generate an underwater three-dimensional real scene model of the inspection well that can be applied in engineering. It can provide reliable three-dimensional data support for the construction of digital twins of underground pipe networks, accurate defect location and assessment, and refined operation and maintenance of pipe networks throughout their entire life cycle.

[0055] This embodiment provides a sonar three-dimensional imaging reconstruction method for inspecting the underwater environment of a well, which can be used in the aforementioned server. Figure 3This is a flowchart of a sonar three-dimensional imaging reconstruction method for the underwater environment of an inspection well according to an embodiment of the present invention, as follows: Figure 3 As shown, the process includes the following steps: Step S301: Acquire sonar ring scan data at different underwater depths of the inspection well, target attitude data, reference sonar ring scan data of the inspection well, pixel count information, and pixel spacing information for the sonar ring scan data at each depth. For details, please refer to [link to relevant documentation]. Figure 1 Step S201 of the illustrated embodiment will not be described again here.

[0056] In some alternative implementations, the target pose data at each depth is determined through the following steps: Step a1: Obtain the initial pose data and corresponding timestamp information for each depth.

[0057] For example, in the embodiments of this application, during the data acquisition stage at various depths underwater in the inspection well, the original initial three-axis attitude data output in real time by the attitude sensor is acquired simultaneously, and the precise acquisition time information corresponding to each set of initial attitude data is retained.

[0058] Step a2: Determine the first pose data of the target time period before and after the timestamp information corresponding to each depth.

[0059] For example, in this embodiment of the application, the pitch angle within the time interval dt before and after the current image timestamp is extracted. Roll angle and azimuth .

[0060] Step a3: The first pose data and the initial pose data at each depth are stitched together to obtain the second pose data.

[0061] For example, the original initial attitude data corresponding to each depth is sequentially spliced ​​and integrated with the first attitude data obtained from the aforementioned time interval to form a complete and continuous attitude data sequence, thereby obtaining the second attitude data for the corresponding depth.

[0062] Step a4: Use a preset algorithm to remove extreme values ​​from the second pose data at each depth to obtain the third pose data at the corresponding depth.

[0063] For example, in this embodiment of the application, a preset outlier removal algorithm is used to perform a full-domain traversal detection on the second attitude data sequence after time-series stitching at each depth. This identifies and removes extreme outlier data in the data caused by underwater disturbances and instantaneous sensor errors, eliminating interference from distorted bad values ​​and retaining continuous and stable valid attitude information. This yields the processed third attitude data at the corresponding depth, providing a clean and reliable data sample for subsequent median noise reduction optimization of attitude data. The outlier removal algorithm may include, but is not limited to, the Interquartile Range (IQR) algorithm.

[0064] Step a5: Extract the median from the third pose data corresponding to each depth to obtain the denoised target pose data.

[0065] For example, in this embodiment of the application, the median of each angle in the third attitude data corresponding to each depth is recorded as the pitch angle corresponding to the current sonar image. Roll angle and azimuth .

[0066] Step S302: Based on the target attitude data at each depth, project the sonar ring scan data at the corresponding depth onto the target plane to obtain the first sonar ring scan data at the corresponding depth.

[0067] Specifically, step S302 includes: Step S3021: Denoise reduction processing is performed on the sonar ring scan data at each depth to obtain the denoised data at the corresponding depth.

[0068] In some alternative implementations, the sonar swathe data includes multiple sonar images acquired by a sonar swathe device, the structure of which is shown in the schematic diagram below. Figure 4 As shown, the system includes a telescopic rod 1, a ring-scan sonar probe 2, an attitude sensor 3, a depth sensor 4 (for recording water depth), a first platform 5, a level gauge 6, and a second platform 7. The attitude sensor 3 records the orientation angle (along the Z-axis), roll angle (along the Y-axis), and pitch angle (along the X-axis). The bottom of the depth gauge should extend beyond the ring-scan sonar probe, and the distance between the bottom of the depth gauge and the ring-scan sonar probe is h. a The sonar is aligned with the positive Y-axis of the attitude sensor. Specific data acquisition steps include: (1) Use RTK to record the center coordinates (X0, Y0) and elevation Hs of the inspection well. Set up a second platform 7 at the wellhead and use a level gauge to record the distance D from the well surface to the liquid surface. w The water surface elevation H is obtained. w = H s – D w .

[0069] (2) Place the telescopic rod with the first platform 5 inspection well in the water and turn on the ring scan sonar probe, attitude and water pressure sensor.

[0070] (3) When the platform just enters the water, store the sonar circumferential scan image and record it as P0. At this time, according to the data of the attitude sensor, the pitch angle and attitude angle should be 0 as much as possible, and the center of the sonar head should be in the center of the inspection well, and the direction angle should be pointing due north.

[0071] (4) Hold the telescopic pole and gradually lower the platform. Record the sonar circumferential scan image as Pi every 10cm. At the same time, record the accurate water depth h using a water pressure gauge. i Corresponding elevation H i = H w – h i - h a Pitch angle recorded by attitude sensor Roll angle and azimuth During recording, data from multiple sensors are fused, and real-time sonar scanning images are observed during the descent. If a pipe opening or damaged area is found, encrypted slices can be prepared.

[0072] The above step S3021 includes: Step b1: Based on multiple sonar images at each depth, perform time-series statistics on the intensity values ​​of each pixel in the multiple sonar images to obtain the mean and standard deviation of the intensity of each pixel at the corresponding depth.

[0073] For example, in the embodiments of this application, for multiple continuous sonar images acquired at each depth, all pixel points in the images are traversed, the echo intensity data of a single pixel in all time-series images are extracted and time-domain statistical operations are performed, and then the mean and standard deviation of the signal intensity corresponding to each pixel point at each depth are obtained.

[0074] Step b2: Determine the dynamic filtering threshold for each pixel based on the mean intensity and standard deviation of each pixel at each depth.

[0075] For example, in this embodiment of the application, the dynamic filtering threshold can be determined by the following formula:

[0076] in, Represents pixels The dynamic filtering threshold, Represents pixels The average intensity Represents pixels The strength standard deviation.

[0077] Step b3: Identify the target sonar image at the corresponding depth among multiple sonar images at each depth.

[0078] For example, in the embodiments of this application, the target sonar image is the reference sonar image to be processed at the corresponding depth, which is generally the middle frame or the last frame of the time sequence.

[0079] Step b4: Based on the dynamic filtering threshold corresponding to multiple pixels at each depth, noise is filtered out from the target sonar image to obtain the noise-reduced data at the corresponding depth.

[0080] For example, if the sonar echo signal strength of a pixel is less than the corresponding dynamic filtering threshold, the pixel strength of that pixel is set to 0; if the sonar echo signal strength of a pixel is greater than or equal to the corresponding dynamic filtering threshold, the original pixel strength is retained, thus obtaining the denoised data. In this embodiment, the sonar image before denoising is as follows: Figure 5 As shown, the denoised sonar image data is as follows: Figure 6 As shown.

[0081] Step S3022: Based on the target attitude data at each depth, the noise reduction data is projected onto the target plane to obtain the first sonar ring scan data at the corresponding depth.

[0082] For example, the noise reduction data is projected onto the XY plane. The specific projection process will not be described in detail in this application embodiment.

[0083] Step S303: Calculate the first centroid position coordinates at each depth based on the first sonar circular scan data, and calculate the second centroid position coordinates at the corresponding depth based on the reference sonar circular scan data. For details, please refer to [link to relevant documentation]. Figure 2 Step S203 of the illustrated embodiment will not be described again here.

[0084] Step S304: Determine the translation vector for the corresponding depth based on the coordinates of the first and second centroids at each depth. For details, please refer to [link to details]. Figure 2 Step S204 of the illustrated embodiment will not be described again here.

[0085] Step S305: Correct the first sonar ring scan data based on the translation vectors at each depth to obtain the second sonar ring scan data. For details, please refer to [link to details]. Figure 2 Step S205 of the illustrated embodiment will not be described again here.

[0086] Step S306: Based on the target attitude data at each depth, the second sonar circular scan is reconstructed to obtain the target sonar circular scan data at the corresponding depth. For details, please refer to [link to details]. Figure 2 Step S206 of the illustrated embodiment will not be described again here.

[0087] Step S307 determines the underwater 3D model of the inspection well based on the target sonar ring scan data at each depth, pixel count information, pixel spacing information, and target attitude data. For details, please refer to [link to details]. Figure 2 Step S207 of the illustrated embodiment will not be described again here.

[0088] This embodiment provides a sonar three-dimensional imaging reconstruction method for inspecting the underwater environment of a well, which can be used in the aforementioned server. Figure 7 This is a flowchart of a sonar three-dimensional imaging reconstruction method for the underwater environment of an inspection well according to an embodiment of the present invention, as follows: Figure 7 As shown, the process includes the following steps: Step S701: Acquire sonar ring scan data at different underwater depths of the inspection well, target attitude data, reference sonar ring scan data of the inspection well, pixel count information, and pixel spacing information for the sonar ring scan data at each depth. For details, please refer to [link to relevant documentation]. Figure 3 Step S301 of the illustrated embodiment will not be described again here.

[0089] Step S702: Based on the target attitude data at each depth, project the sonar circular scan data at the corresponding depth onto the target plane to obtain the first sonar circular scan data at the corresponding depth. For details, please refer to [link to details]. Figure 3 Step S302 of the illustrated embodiment will not be described again here.

[0090] Step S703: Calculate the first centroid position coordinates at each depth based on the first sonar circular scan data, and calculate the second centroid position coordinates at the corresponding depth based on the reference sonar circular scan data. For details, please refer to [link to relevant documentation]. Figure 3 Step S303 of the illustrated embodiment will not be described again here.

[0091] Step S704: Determine the translation vector for the corresponding depth based on the coordinates of the first and second centroids at each depth. For details, please refer to [link to relevant documentation]. Figure 3 Step S304 of the illustrated embodiment will not be described again here.

[0092] Step S705: Correct the first sonar ring scan data based on the translation vectors at each depth to obtain the second sonar ring scan data. For details, please refer to [link to details]. Figure 3 Step S305 of the illustrated embodiment will not be described again here.

[0093] Step S706: Based on the target attitude data at each depth, the second sonar circular scan is reconstructed to obtain the target sonar circular scan data at the corresponding depth. For details, please refer to [link to details]. Figure 3 Step S306 of the illustrated embodiment will not be described again here.

[0094] Step S707: Determine the underwater 3D model of the inspection well based on the target sonar ring scan data, pixel quantity information, pixel spacing information, and target attitude data at each depth.

[0095] Specifically, step S707 includes: Step S7071: Calculate the true coordinate information of the target sonar ring scan data based on the pixel quantity information and pixel spacing information at each depth.

[0096] For example, in this embodiment of the application, the actual coordinate information can be determined by the following formula:

[0097]

[0098] in, Indicates the width in pixels. Indicates the height in pixels. This represents the actual physical distance corresponding to a single pixel in the horizontal (x-direction) of the image. This represents the actual physical distance corresponding to a single pixel in the vertical (y-direction) of the image; This represents the x-coordinate of a single pixel in a sonar circular scan image. This represents the original image pixel ordinate of a single pixel in a sonar circular scan image. , This represents the actual physical coordinates of the pixel in the local scanning plane of the probe after the conversion, and i represents the traversal index of the pixel.

[0099] Step S7072: Determine the global coordinate information for the corresponding depth based on the real coordinate information and target attitude data for each depth.

[0100] For example, in this embodiment of the application, a rotation matrix for the corresponding depth is determined based on the target pose data at each depth. The rotation matrix can be represented by the following formula:

[0101]

[0102]

[0103]

[0104]

[0105]

[0106]

[0107]

[0108]

[0109] The meanings of each variable will not be elaborated further.

[0110] Global coordinate information can be determined by the following formula:

[0111]

[0112]

[0113] Here, X, Y, and Z represent global coordinates, and the meanings of the other variables will not be elaborated further.

[0114] Step S7073: Construct an underwater 3D model of the inspection well based on the global coordinate information at each depth.

[0115] For example, in this embodiment of the application, the global three-dimensional coordinates of all pixels calculated at each depth layer are summarized in the whole domain to form a discrete point cloud set covering the entire underwater space of the inspection well. Based on the spatial position correlation of the data at each depth, data fusion and spatial surface reconstruction are completed to accurately fit the complete spatial morphology of the inspection well wall outline, the direction and size of the connecting pipes, and the internal structural defects, thereby generating a three-dimensional model that can completely represent the real underwater structure of the inspection well.

[0116] This embodiment also provides a sonar three-dimensional imaging reconstruction device for inspecting the underwater environment of a well. This device is used to implement the above embodiments and preferred embodiments, and details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0117] This embodiment provides a sonar three-dimensional imaging reconstruction device for inspecting the underwater environment of a well, such as... Figure 8 As shown, it includes: The acquisition module 801 is used to acquire sonar ring scan data at different underwater depths of the inspection well, target attitude data, reference sonar ring scan data of the inspection well, pixel quantity information and pixel spacing information of sonar ring scan data at each depth; The projection module 802 is used to project the sonar ring scan data at the corresponding depth onto the target plane based on the target attitude data at each depth, so as to obtain the first sonar ring scan data at the corresponding depth. The calculation module 803 is used to calculate the first centroid position coordinates at the corresponding depth based on the first sonar ring scan data at each depth, and to calculate the second centroid position coordinates at the corresponding depth based on the reference sonar ring scan data. The first determining module 804 is used to determine the translation vector for the corresponding depth based on the first centroid position coordinates and the second centroid position coordinates at each depth. The correction module 805 is used to correct the first sonar ring scan data based on the translation vectors at each depth to obtain the second sonar ring scan data. The restoration module 806 is used to restore the second sonar ring scan based on the target attitude data at each depth to obtain the target sonar ring scan data at the corresponding depth. The second determining module 807 is used to determine the underwater three-dimensional model of the inspection well based on the target sonar ring scan data at each depth, pixel quantity information, pixel spacing information, and target attitude data.

[0118] In some alternative implementations, the second determining module 807 includes: The calculation submodule is used to calculate the true coordinate information of the target sonar ring scan data based on the pixel count information and pixel spacing information at each depth; The first determination submodule is used to determine the global coordinate information of the corresponding depth based on the real coordinate information of each depth and the target attitude data; The construction submodule is used to build an underwater 3D model of the inspection well based on global coordinate information at each depth.

[0119] In some alternative implementations, the projection module 802 includes: The noise reduction submodule is used to perform noise reduction processing on the sonar ring scan data at various depths to obtain the noise-reduced data at the corresponding depth. The projection submodule is used to project the noise-reduced data onto the target plane based on the target attitude data at each depth, so as to obtain the first sonar ring scan data at the corresponding depth.

[0120] In some alternative implementations, the noise reduction submodule includes: The statistical unit is used to perform time-series statistics on the intensity values ​​of each pixel in multiple sonar images at various depths, and obtain the mean and standard deviation of the intensity of each pixel at the corresponding depth. The first determining unit is used to determine the dynamic filtering threshold of the corresponding pixel based on the mean intensity and standard deviation of each pixel at each depth. The second determining unit is used to determine the target sonar image at the corresponding depth among multiple sonar images at each depth. The noise reduction unit is used to filter out noise from the target sonar image based on the dynamic filtering threshold of multiple pixels corresponding to each depth, so as to obtain the noise reduction data corresponding to the depth.

[0121] In some alternative implementations, the target pose data at each depth is determined through the following steps: Obtain the initial pose data and corresponding timestamp information for each depth; Determine the first pose data of the target time period before and after the timestamp information corresponding to each depth; The first pose data and the initial pose data at each depth are concatenated to obtain the second pose data; The extreme values ​​in the second pose data at each depth are removed using a preset algorithm to obtain the third pose data at the corresponding depth. The median of the third pose data corresponding to each depth is extracted to obtain the denoised target pose data.

[0122] The sonar three-dimensional imaging reconstruction device for underwater environment of inspection wells provided in this embodiment of the invention can execute the sonar three-dimensional imaging reconstruction method for underwater environment of inspection wells provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects for executing the method. Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0123] Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0124] The following is a detailed reference. Figure 9 This diagram illustrates a structural schematic suitable for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, graphics processor, etc.) 901, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 902 or a program loaded from memory 908 into random access memory (RAM) 903. The RAM 903 also stores various programs and data required for the operation of the electronic device. The processor 901, ROM 902, and RAM 903 are interconnected via a bus 904. An input / output (I / O) interface 905 is also connected to the bus 904.

[0125] Typically, the following devices can be connected to I / O interface 905: input devices 906 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 907 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 908 including, for example, magnetic tapes, hard disks, etc.; and communication devices 909. Communication device 909 allows electronic devices to exchange data via wireless or wired communication with other devices. Although Figure 9 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0126] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 909, or installed from a memory 908, or installed from a ROM 902. When the computer program is executed by the processor 901, it performs the functions defined in the sonar three-dimensional imaging reconstruction method for the underwater environment of a manhole according to embodiments of the present invention.

[0127] Figure 9 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0128] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the sonar three-dimensional imaging reconstruction method for the underwater environment of inspection wells shown in the above embodiments is implemented.

[0129] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0130] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A sonar three-dimensional imaging reconstruction method for inspecting the underwater environment of a well, characterized in that, The method includes: Acquire sonar ring scan data, target attitude data, reference sonar ring scan data of the inspection well at different underwater depths, pixel count information and pixel spacing information of sonar ring scan data at each depth; Based on the target attitude data at each depth, the sonar ring scan data at the corresponding depth is projected onto the target plane to obtain the first sonar ring scan data at the corresponding depth. Calculate the first centroid position coordinates at the corresponding depth based on the first sonar ring scan data at each depth, and calculate the second centroid position coordinates at the corresponding depth based on the reference sonar ring scan data. The translation vector for each depth is determined based on the coordinates of the first centroid position and the coordinates of the second centroid position at each depth. The first sonar ring scan data is corrected based on the translation vectors at each depth to obtain the second sonar ring scan data; The second sonar ring scan is restored based on the target attitude data at each depth to obtain the target sonar ring scan data at the corresponding depth. The underwater 3D model of the inspection well is determined based on the target sonar ring scan data at each depth, the pixel count information, the pixel spacing information, and the target attitude data.

2. The method according to claim 1, characterized in that, The steps for determining the underwater 3D model of the inspection well based on the target sonar ring scan data at each depth, the pixel count information, the pixel spacing information, and the target attitude data include: The true coordinate information of the target sonar ring scan data is calculated based on the pixel quantity information and pixel spacing information at each depth. Based on the actual coordinate information at each depth and the target pose data, determine the global coordinate information at the corresponding depth; An underwater 3D model of the inspection well is constructed based on global coordinate information at each depth.

3. The method according to claim 1, characterized in that, The step of projecting sonar circular scan data at corresponding depths onto the target plane based on the target attitude data at each depth to obtain the first sonar circular scan data at the corresponding depth includes: The sonar ring scan data at each depth are denoised to obtain the denoised data for the corresponding depth. Based on the target attitude data at each depth, the noise reduction data is projected onto the target plane to obtain the first sonar ring scan data at the corresponding depth.

4. The method according to claim 3, characterized in that, The sonar circumferential scan data includes multiple sonar images. The noise reduction processing of the sonar circumferential scan data at each depth to obtain noise-reduced data for the corresponding depth includes: Based on the multiple sonar images at each depth, the intensity values ​​of each pixel in the multiple sonar images are statistically analyzed over time to obtain the mean and standard deviation of the intensity of each pixel at the corresponding depth. The dynamic filtering threshold for each pixel is determined based on the mean intensity and standard deviation of each pixel at each depth. Identify the target sonar image at the corresponding depth from multiple sonar images at various depths; The target sonar image is noise-filtered based on a dynamic filtering threshold corresponding to multiple pixels at each depth to obtain noise-reduced data at the corresponding depth.

5. The method according to claim 4, characterized in that, The target pose data at each depth is determined through the following steps: Obtain the initial pose data and corresponding timestamp information for each depth; Determine the first pose data of the target time period before and after the timestamp information corresponding to each depth; The first pose data at each depth and the initial pose data are concatenated to obtain the second pose data; The extreme values ​​in the second pose data at each depth are removed using a preset algorithm to obtain the third pose data at the corresponding depth. The median of the third pose data corresponding to each depth is extracted to obtain the denoised target pose data.

6. A sonar three-dimensional imaging reconstruction device for inspecting the underwater environment of a well, characterized in that, The device includes: The acquisition module is used to acquire sonar ring scan data, target attitude data, reference sonar ring scan data of the inspection well at different underwater depths, pixel count information and pixel spacing information of sonar ring scan data at each depth; The projection module is used to project the sonar ring scan data at the corresponding depth onto the target plane based on the target attitude data at each depth, so as to obtain the first sonar ring scan data at the corresponding depth. The calculation module is used to calculate the first centroid position coordinates at the corresponding depth based on the first sonar ring scan data at each depth, and to calculate the second centroid position coordinates at the corresponding depth based on the reference sonar ring scan data. The first determining module is used to determine the translation vector at the corresponding depth based on the first centroid position coordinates and the second centroid position coordinates at each depth. The correction module is used to correct the first sonar ring scan data based on the translation vector at each depth to obtain the second sonar ring scan data. The restoration module is used to restore the second sonar ring scan based on the target attitude data at each depth to obtain the target sonar ring scan data at the corresponding depth. The second determining module is used to determine the underwater three-dimensional model of the inspection well based on the target sonar ring scan data at each depth, the pixel quantity information, the pixel spacing information, and the target attitude data.

7. An electronic device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the sonar three-dimensional imaging reconstruction method for the underwater environment of a manhole as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the sonar three-dimensional imaging reconstruction method for the underwater environment of the inspection well as described in any one of claims 1 to 5.

9. A computer program product, characterized in that, It includes computer instructions for causing a computer to execute the sonar three-dimensional imaging reconstruction method for the underwater environment of a manhole as described in any one of claims 1 to 5.

Images