Ultra-shallow water area measurement method and system based on obliquely-installed single-beam depth finder

Abstract

The invention relates to the technical field of single-beam sounder ultra-shallow water area measurement, in particular to a single-beam sounder ultra-shallow water area measurement method and system based on inclined installation. The method comprises the following steps: acquiring operation data of a single-beam depth sounder, identifying a measurement route, collecting an environment change condition, and generating depth sounding simulation data in combination with a simulation depth sounding process; according to environment change and sounding angle disturbance response data, environment measurement coupling interference intensity is calculated, and a dynamic abnormal condition of a sound wave propagation path is identified; detecting a sound wave frequency drift phenomenon in an extremely shallow water area, performing depth finder error extension prediction according to the sound wave frequency drift phenomenon, obtaining error extension growth data, evaluating the deviation degree of the inclined depth finder based on an error extension condition, and generating deviation degree data; performing correction processing on the original sounding data to obtain a corrected sounding value; the ultra-shallow water area measurement of the single-beam depth finder is corrected through the ultra-shallow water area measurement of the single-beam depth finder, so that the ultra-shallow water area measurement of the single-beam depth finder is more accurate and efficient.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of single-beam bathymetry in extremely shallow water, and particularly relates to a single-beam bathymetry method and system based on an inclined installation in extremely shallow water. BACKGROUND

[0002] In the environment of extremely shallow water, the water depth changes dramatically, the water bottom structure is complex, and wave interference is frequent. The traditional single-beam bathymetry often faces problems such as a decrease in measurement accuracy, signal distortion, and data deviation in such an environment. Especially in the state of an inclined installation of the device, the sound wave emission direction deviates from the normal of the water surface, which easily causes phenomena such as abnormal sound wave incidence angle, reflection path distortion, enhanced multi-path interference, and serious waveform overlap, resulting in an enlarged error of the bathymetric data and failing to meet the requirement of high-precision measurement. The commonly used single-beam bathymetry system mainly relies on a fixed installation and calculates the water depth based on an idealized vertical incidence model, and lacks the ability of adaptive modeling and error correction for environmental factors such as actual sailing posture changes, inclined installation angles, and wave disturbances. Especially when operating in extremely shallow water or complex nearshore areas, the accumulated error leads to a deviation in the judgment of information such as the bottom profile and the structure of the sediment layer, and even causes a collision risk. However, the single-beam bathymetry in extremely shallow water based on the traditional inclined installation has the problems of inaccurate detection of dynamic abnormal conditions of the sound wave propagation path and inaccurate detection of the coupling interference strength of the bathymeter environmental measurement. SUMMARY

[0003] Therefore, it is necessary to provide a single-beam bathymetry method and system based on an inclined installation in extremely shallow water to solve at least one of the above technical problems.

[0004] To achieve the above-mentioned purpose, a single-beam bathymetry method based on an inclined installation in extremely shallow water comprises the following steps: Step S1: obtaining single-beam bathymetry operation data; identifying single-beam bathymetry measurement route data according to the single-beam bathymetry operation data; collecting the environmental changes of the measurement route according to the single-beam bathymetry measurement route data; and obtaining single-beam bathymetry simulation data by performing bathymeter depth simulation based on the single-beam bathymetry measurement route data; Step S2: detecting depth angle dynamic disturbance response data from the single-beam bathymetry simulation data according to the environmental changes of the measurement route; determining the coupling interference strength of the bathymeter environmental measurement according to the environmental changes of the measurement route and the depth angle dynamic disturbance response data; and determining the dynamic abnormal conditions of the sound wave propagation path according to the coupling interference strength of the bathymeter environmental measurement; Step S3: detecting the frequency drift phenomenon of the sound wave in the extremely shallow water based on the dynamic abnormal conditions of the sound wave propagation path; and obtaining bathymeter error expansion growth data by performing bathymeter error expansion prediction based on the frequency drift phenomenon of the sound wave in the extremely shallow water. Step S4: based on the error spread growth data of the depth sounder, the degree of tilt depth sounder deviation is evaluated to obtain the degree of tilt depth sounder deviation data; the single-beam depth sounder depth value is corrected based on the degree of tilt depth sounder deviation data to obtain the single-beam depth sounder depth value correction data.

[0005] The present application can accurately restore the measurement track and the operation track by obtaining the single-beam depth sounder operation data and identifying the measurement route data, and can enhance the environmental perception ability of the depth measurement process; the influence of factors such as waves and ship attitude on the change of incident angle can be effectively reflected by collecting the environmental change of the measurement route and analyzing the dynamic disturbance response of the depth angle based on the depth simulation data, thereby improving the accuracy of abnormal identification; the environmental coupling interference strength of the depth sounder and the dynamic abnormality of the sound wave propagation path can be determined according to the disturbance data, which can enhance the response ability of the system to the complex changes of the sound wave propagation path and reduce the source of depth measurement error; further detection of frequency drift and error expansion prediction can improve the prediction and response level of the system to the error caused by signal instability in extremely shallow water; the degree of tilt depth sounder deviation is evaluated and the depth value is corrected, which can realize real-time optimization of measurement data, ensure the depth accuracy and system stability in non-standard installation and complex water environment, and significantly improve the reliability and practicality of the single-beam depth sounder in extremely shallow water application. Therefore, the present application is an optimized processing of the traditional single-beam depth sounder measurement in extremely shallow water, which solves the problems of inaccurate detection of dynamic abnormality of sound wave propagation path and inaccurate detection of environmental measurement coupling interference strength of depth sounder in traditional single-beam depth sounder measurement in extremely shallow water, and improves the accuracy of detection of dynamic abnormality of sound wave propagation path and the accuracy of detection of environmental measurement coupling interference strength of depth sounder.

[0006] The present application also provides a single-beam depth sounder measurement system in extremely shallow water based on tilt installation, which is used to execute the single-beam depth sounder measurement method in extremely shallow water based on tilt installation as described above, and the single-beam depth sounder measurement system in extremely shallow water based on tilt installation comprises: The depth sounder depth simulation module is used to obtain the single-beam depth sounder operation data; the single-beam depth sounder measurement route data is identified according to the single-beam depth sounder operation data; the measurement route environmental change is collected according to the single-beam depth sounder measurement route data; the depth sounder depth simulation is performed based on the single-beam depth sounder measurement route data to obtain the single-beam depth sounder depth simulation data; The propagation path dynamic abnormality determination module is used to detect the depth angle dynamic disturbance response data of the single-beam depth sounder depth simulation data according to the measurement route environmental change; the depth sounder environmental measurement coupling interference strength is determined according to the measurement route environmental change and the depth angle dynamic disturbance response data; the sound wave propagation path dynamic abnormality is determined according to the depth sounder environmental measurement coupling interference strength; The error propagation prediction module is used for detecting the frequency drift phenomenon of the sound wave in the extremely shallow water based on the dynamic abnormal condition of the sound wave propagation path; and the error propagation prediction of the depth finder is carried out based on the frequency drift phenomenon of the sound wave in the extremely shallow water, so as to obtain the error propagation growth data of the depth finder. The depth value correction processing module is used for evaluating the deviation degree of the tilt depth finder based on the error propagation growth data of the depth finder, so as to obtain the deviation degree data of the tilt depth finder; and the depth value correction processing of the single-beam depth finder is carried out based on the deviation degree data of the tilt depth finder, so as to obtain the depth value correction data of the single-beam depth finder.

[0007] The single-beam depth finder extremely shallow water measurement system based on the tilt installation can realize any single-beam depth finder extremely shallow water measurement method based on the tilt installation, and is used for combining the operation and signal transmission between the modules to complete the single-beam depth finder extremely shallow water measurement method based on the tilt installation. The modules in the system cooperate with each other, and the depth precision, stability and data reliability of the tilt installation single-beam depth finder in the extremely shallow water complex environment are improved. BRIEF DESCRIPTION OF DRAWINGS

[0008] Figure 1 It is a step flowchart of a single-beam depth finder extremely shallow water measurement method based on tilt installation; Figure 2 It is a step flowchart of a single-beam depth finder extremely shallow water measurement method based on tilt installation; Figure 1 It is a detailed implementation step flowchart of step S3; Figure 3 It is a single-beam depth finder metal equipment diagram; The implementation, functional features and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0009] The technical method of the present application will be described clearly and completely in combination with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor belong to the protection scope of the present application.

[0010] In addition, the accompanying drawings are only schematic drawings of the present application, and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus repeated description thereof will be omitted. Some block diagrams shown in the drawings are functional entities, and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0011] It should be understood that, although the terms "first", "second" or the like can be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element without departing from the scope of the example embodiments. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0012] To achieve the above object, please refer to Figures 1 to 3 A single-beam bathymeter tilt-mounted shallow water measurement method, comprising the following steps: Step S1: Obtain single-beam bathymeter operation data; identify single-beam bathymeter measurement route data according to the single-beam bathymeter operation data; collect the environmental changes of the measurement route according to the single-beam bathymeter measurement route data; perform bathymeter depth simulation based on the single-beam bathymeter measurement route data to obtain single-beam bathymeter depth simulation data; In the embodiment of the present application, a single-beam depth sounder of HydroLite-TM model is used. The depth sounder is installed on the right side of the hull of the measuring ship, fixed on a rigid mounting bracket, and the preset installation angle is 11°. The installation angle is recorded in real time by an integrated inclination sensor, and after installation, calibration is performed and uploaded to the control processing unit. The depth sounder and the main control data acquisition unit communicate in real time through the RS-232 serial port, and collect operation data including attitude information (pitch, roll, yaw), working frequency, sound speed setting, signal gain, echo intensity, power voltage, current load, etc., and the sampling frequency is set to 5Hz. During the measurement process, the control unit synchronously receives the position information provided by the high-precision GPS module, and the positioning accuracy is 0.3 meters. Through the association of the GPS timestamp and the depth data timestamp, the alignment of the operation data and the spatial position is realized. In the path identification process, the moving average filtering method is used to smooth the GPS trajectory, and the trajectory jump caused by satellite shielding or reflection is eliminated. Then, based on the smoothed path, the line segment is segmented, and combined with the speed curve and the turning angle information, different path segments are divided. Along the measurement path, the control unit calls the seabed topographic map, hydrological data, wind wave field data, etc. stored in the geographic information system (GIS) of the region, and combines the data collected by the wave height sensor and the water quality sensor (including turbidity, bubble concentration, bottom type, etc.), to take an environmental sampling point every 100 meters on the path, and establish an environmental change database of the measurement route. Combined with the installation angle and attitude change data, the layered sound speed data measured by the sound speed profiler are used to construct the simulated depth path by the ray tracing method. In the simulation, the sound wave propagation path is calculated according to the refraction path of the oblique incidence angle and the water layer refraction ratio change, and the predicted echo time, path distance and depth value are output. The simulation results are bound to the position of each point on the path to form a "position-simulated depth value" mapping table, which is output as the depth simulation data.

[0013] Step S2: detecting depth angle dynamic disturbance response data according to the depth simulation data of the single-beam depth sounder based on the environmental change of the measurement route; determining the environmental measurement coupling interference strength of the depth sounder according to the environmental change of the measurement route and the depth angle dynamic disturbance response data; determining the dynamic abnormality of the sound wave propagation path according to the environmental measurement coupling interference strength of the depth sounder; In the embodiment of the present application, after the simulation data of depth measurement is generated, the attitude disturbance sequence of the measurement path is established by using the changes of the pitch angle and roll angle obtained by the three-axis gyroscope and accelerometer in the shipborne inertial navigation system (INS). The sampling frequency is 50 Hz, and a sliding window with a window width of 5 seconds is used for attitude change detection. In each window, the mean, variance and maximum change amplitude of the pitch angle and roll angle are calculated; when the pitch change amplitude in a certain window exceeds 1.2° and the roll exceeds 0.9°, it is defined as a dynamic disturbance event. For the identified disturbance events, the coupling mapping of disturbance and environmental change is established in combination with the meteorological monitoring data (anemometer), the wave intensity change identified by the water surface video stream and the measured water surface bubble concentration data. The disturbance coupling index I is used to represent the interference intensity, and the calculation method is: I=K×wave height change rate×bubble concentration×ship body inclination amplitude, wherein K is the amplification coefficient (experimentally determined as 3.2). If the value of I exceeds 1.0, it is determined that there is significant environmental coupling interference. The value of I in the current measurement section is 1.38, indicating that the environmental disturbance is amplified through the installation angle coupling, causing the attitude instability to be enhanced. Then according to the value of I, the sound wave path is simulated again in the ray tracing module to observe whether there are abnormal phenomena such as multipath, turning back, deviation, refraction angle mutation, etc. When the echo turning angle in the simulated path exceeds 15% of the incident angle, it is marked as path dynamic anomaly. Combined with the sound speed data and the change trend of the echo signal intensity, it is determined that there is a dynamic anomaly of the sound wave propagation path in the current navigation section, and the abnormal type and intensity data are output as the input of the next step.

[0014] Step S3: detecting the sound wave frequency drift phenomenon in the extremely shallow water area based on the dynamic anomaly of the sound wave propagation path; predicting the error expansion of the depth sounder based on the sound wave frequency drift phenomenon in the extremely shallow water area, to obtain the error expansion growth data of the depth sounder; In the embodiment of the present application, on the premise that it is identified in step S2 that there is a dynamic anomaly of the sound wave propagation path in the current measurement area, the recorded original echo signal data is analyzed point by point. The high-frequency sound wave acquisition module is used to extract data from each echo signal, including echo time delay, echo amplitude, waveform envelope, and original sound wave signal sequence in the corresponding time period. The above signal data is sent to the sound wave signal processing module, and the waveform stability of each sound wave signal is detected by using the waveform analysis processor embedded in the high-speed acquisition system. In the extremely shallow water area, because the water depth is less than 3 meters, the echo return speed is extremely fast, and the water surface disturbance intensifies to cause the incident angle to change frequently, and the signal path stability decreases. The system compares the echo characteristic curve under the same installation angle and the same sound speed in the normal working state to judge the abnormal change of the current data. It is found that part of the signal appears irregular extension, there is a delay echo and the energy concentration peak position moves backward. The signal characteristics no longer present a regular single-peak shape, but multiple secondary peaks or main peak drift appear, indicating that there is a frequency drift phenomenon. The frequency drift condition is combined with the sound wave path anomaly record and the sea surface disturbance data (wave height, wave frequency, water flow velocity) to input an error prediction model. The model uses a static neural network structure trained by historical error expansion samples. The model does not rely on real-time simulation, but directly outputs the error expansion growth rate and trend in the current period through classification decision rules. The model compares the current environmental disturbance level, path change intensity, wave impact period, and other input factors, combines the sound wave anomaly category, and evaluates the decline rate of the measurement accuracy of the depth sounder. The results show that the depth error has increased by about 2.4 meters in the past 15 minutes, and the error continues to rise. The prediction data shows that the error will increase by 0.8 meters in the next 10 minutes, and will affect all depth points in the current path segment. The prediction error expansion result is output as an error expansion growth data table with time and space labels, including the time stamp, geographic location, error trend category, error growth value, and other fields of each measurement point, for use in step S4.

[0015] Step S4: Based on the depth sounder error expansion growth data, the inclination of the depth sounder bias degree is evaluated to obtain the inclination of the depth sounder bias degree data; the inclination of the depth sounder bias degree data is processed to correct the single-beam depth sounder depth value, and the single-beam depth sounder depth value correction data is obtained.

[0016] In the embodiment of the present application, after the data containing the error spread growth of each measurement point is obtained in step S3, the installation angle information of the tilt depth sounder and the corresponding pitch angle and roll angle history sequence are called. Through time alignment with the echo data in the path segment, one-to-one matching of the angle data and the error spread data is realized. For each measurement point, the system calls the ship body attitude change curve in the path, combines the installation tilt angle (such as 11°) and the actual dynamic attitude angle change amplitude, and calculates the potential observation offset caused by the tilt at this position. Then, by comparing with the error spread data, it is identified which offset is caused by the deviation accumulation due to the coupling of the attitude tilt and the error. Further, all the deviation data are classified according to the path segment to form the deviation degree statistical result. For example, in the paragraph with path number R13, the error spread growth value is generally higher than 2.5 meters, combined with the record of frequent pitch angle change (more than 1.5° within 1 minute), the path segment is marked as a high deviation area. The system automatically calculates the average deviation degree of the paragraph and marks it as a "high-risk deviation section". The above statistical results are organized into a tilt depth sounder deviation degree data set according to the path segment number, coordinates, and time stamp, and the output includes fields such as deviation level (low, medium, high), average deviation value, maximum deviation, signal stability level, etc. After obtaining the deviation degree data, the correction process is entered. Instead of simple linear regression, a preset correction rule table is used in the correction process. The rule table is built according to the empirical correction coefficients obtained from multiple water areas and multiple attitude installation combinations, and is mapped in multiple dimensions according to the tilt angle range of the depth sounder, the pitch angle change amplitude, and the water depth interval. The system reads the error data of each measurement point, the installation angle, the current attitude angle, and the characteristics of the path segment, automatically matches the corresponding correction rule, and applies the correction value to replace each original depth value. The corrected data is bound with the original position and time stamp and output to form a corrected single-beam depth sounder depth value correction data file. The file format conforms to the XYZ three-column standard format and can be imported into surveying and mapping post-processing software for three-dimensional terrain reconstruction, contour line extraction, or error analysis operations. The correction process is fully traceable, supports precision verification and data review, and meets the national standard requirement of ±0.2m for water depth accuracy.

[0017] Preferably, step S1 comprises the following steps: Step S11: Obtain single-beam depth sounder operation data; In the embodiment of the present application, the single-beam sounding system integrated on the ship body collects the running data in real time. The core components of the system include the master control unit, the data acquisition unit, the attitude sensor (IMU), the GPS positioning module, and the sound wave transmitting and receiving device. The process of running data collection synchronously records the following contents at a frequency of seconds: the current timestamp of the sounder, the real-time ship speed (unit: knots), the heading angle (unit: degrees), the sounder pitch angle and roll angle, the sound wave transmission frequency, the echo receiving time difference, the transmission power intensity, the sounding distance (unit: meters), the water depth value, the positioning coordinates (WGS-84 format), etc. All data are communicated through the CAN bus and the high-speed Ethernet, and are preliminarily filtered and stored by the embedded data processing platform.

[0018] Step S12: determining the single-beam sounder installation data according to the single-beam sounder running data; In the embodiment of the present application, the average installation attitude state of the sounder during the entire measurement period is extracted by using the attitude sensor output information contained in the running data collected in step S11. The installation data refers to the installation angle of the single-beam sounder relative to the ship body coordinate system, including the longitudinal inclination angle (pitch direction offset angle) and the transverse inclination angle (roll direction offset angle), and the unit is degree. By selecting the attitude data segment in the stable navigation stage of the ship body (the speed change rate is less than 0.2 knots per second, and the direction change is less than 1 degree per second), the included angle between the direction of the sounding head and the theoretical vertical direction in the time period is calculated. For example, in a certain operation path, the ship body maintains uniform linear motion for 5 minutes, and the attitude angle is stable at a pitch angle of 11.5° and a roll angle of 1.8°. The calculation shows that the sounder installation exists 11.5° forward inclination and 1.8° right inclination relative to the vertical direction, that is, the inclination installation state.

[0019] Step S13: identifying the single-beam sounder measurement route data according to the single-beam sounder running data; In the embodiment of the present application, in the original operation data collected in step S11, the GPS data provided by the positioning module gives the longitude and latitude coordinates corresponding to each time point in time sequence. These coordinate data are imported into the GIS data processing platform to form the flight path trajectory on the map base map. In order to ensure the continuity of the path and the spatial resolution, 1-second interval sampling data are used to generate a high-precision measurement path point sequence with time markers. A spatial coordinate trajectory fitting method (such as a five-point Bezier curve fitting) is used to eliminate the slight path jumps caused by GPS signal jitter, so that the path is smooth and can be used for subsequent environment parameter registration. At the same time, according to the flight segment division rule, the whole operation path is segmented and marked, and the path segment numbers such as R1, R2, R3, etc. are generated according to the principle that the length of each segment is not less than 100 meters, and the start and end points, path length, average speed, average attitude angle, etc. of each segment are recorded. The output of this step is a complete "measurement route data set" for subsequent environment change analysis and simulation.

[0020] Step S14: Collecting measurement route environment change information according to single-beam depth sounder measurement route data In the embodiment of the present application, after obtaining the measurement route data, the matching multi-source environment monitoring equipment is called to extract the environmental elements along the path. The environment collection system includes a wave height radar, a wind speed and direction sensor, a water turbidity meter, a water temperature sensor, and a water flow direction / speed measuring device. The system performs paired sampling on the navigation time interval of each measurement path segment, and records the following data: wave height (unit: meters), average wave period (unit: seconds), main wave direction (unit: degrees), water turbidity (unit: NTU), wind speed (unit: m / s), water surface bubble density (unit: pieces / m²), etc. The data collection process records a group every 5 seconds, and each group of data contains a space label and a path segment label. For example, in the path R3 flight segment, the average wave height is 0.8 meters, the main wave direction is 212 degrees, the wind speed is 4.3 m / s, and the water surface bubble density is about 56 pieces / m². Combined with the sound wave penetration ability of the depth sounder, these data are used as environmental disturbance input parameters to form an environmental change database for subsequent data support for sound wave propagation path disturbance and incident angle fluctuation.

[0021] Step S15: Depth sounder depth simulation based on single-beam depth sounder measurement route data and single-beam depth sounder installation data to obtain single-beam depth sounder depth simulation data.

[0022] In the embodiment of the present application, according to the measurement route data obtained in step S13 and the installation data of step S12, the path segment, the ship speed, the installation inclination angle are taken as inputs, combined with the sound wave emission parameters (the emission frequency is 200 kHz, and the sound speed is 1500 m / s), and the simulation process for backstepping the sound wave path is constructed. The propagation path analysis method of ray tracing is adopted to generate the theoretical sounding value by calculating the direction angle of the sound wave from the emission point (affected by the inclination angle), the incident point position (calculated by the linkage of the ship body posture and the water depth), and the echo path (including one reflection and multi-path return). During the simulation process of the sound wave path, the environmental change disturbance parameters such as the wave surface disturbance angle ±2.1 degrees, the water surface reflection loss coefficient 0.25, and the wave impact frequency 0.18 Hz are introduced to correct the sound wave incidence angle and simulate the propagation trajectory of the sound wave in the dynamic water area. During the simulation process, all the echo point positions, the propagation time, and the expected sounding value are recorded at a high resolution (0.01 meter precision). In the path segment R3, the simulation calculation shows that the original installation angle is 11.5°, the average disturbance angle of the wave surface is ±2.1°, the sound wave incidence angle dynamically changes in the range [9.4°, 13.8°], and the water depth value difference of the echo point under different angles is within ±0.7 meters. The step outputs the single-beam echo sounder sounding simulation data table, the fields of which include the path segment number, the theoretical sounding value, the incidence angle, the echo time, the wave correction value, etc., to provide basic data support for subsequent sounding deviation analysis and error prediction.

[0023] Preferably, step S15 comprises the following steps: Step S151: identifying the single-beam echo sounder installation posture data based on the single-beam echo sounder installation data; In the embodiment of the present application, according to the single-beam echo sounder installation data determined in step S12, the three-axis acceleration and angular velocity data collected by the posture sensor (inertial measurement unit, IMU) during the measurement are extracted. The original signals of the IMU sensor are filtered and fused by using a posture solving algorithm (such as Kalman filtering algorithm) to eliminate the vibration interference and obtain accurate posture angle information. The posture data includes the pitch angle (Pitch), the roll angle (Roll), and the yaw angle (Yaw), and the angle unit is degree. By using these data, the installation posture data time sequence is formed to identify the stable interval and the change trend of the installation posture. Combined with the ship body geometric model, the installation posture of the echo sounder relative to the ship body coordinate system is determined, that is, the included angle relationship between the echo sounder and the longitudinal direction, the lateral direction, and the vertical direction of the ship body. The time sequence file format of the posture data is JSON, and the structure includes the time stamp, the pitch angle, the roll angle, and the yaw angle fields. The data provides basic input for subsequent installation angle measurement.

[0024] Step S152: measuring the installation angle of the echo sounder according to the single-beam echo sounder installation posture data to obtain the installation angle data of the echo sounder; In the embodiment of the present application, the installation attitude data obtained in step S151 is statistically analyzed to filter the stable attitude data in the measurement time period, that is, to exclude the instantaneous abnormal or large fluctuation values, and to retain the continuous time period with the pitch angle and roll angle fluctuation range less than ±0.3 degrees. The mean value of the pitch angle and roll angle in the continuous interval is calculated to obtain the overall inclination installation angle of the depth sounder. Here, the pitch angle represents the forward and backward inclination of the depth sounder relative to the vertical line of the ship body, and the roll angle represents the left and right inclination. Assuming that the calculated result is a pitch angle of 11.3 degrees and a roll angle of 2.1 degrees. According to the angles, in combination with the ship body reference coordinate system, the installation direction deviation of the depth sounder is determined.

[0025] Step S153: determining the installation ship body trajectory change of the depth sounder according to the single-beam depth sounder measurement route data; In the embodiment of the present application, the track trajectory of the ship body on which the depth sounder is installed is extracted according to the measurement route data identified in step S13. The track data is composed of the longitude and latitude coordinates collected by the GPS module once per second, in combination with the heading angle and speed data, the trajectory curvature, speed change rate and heading angle change rate of the ship body are calculated through spatial interpolation and filtering processing. The curve fitting technology is used to smooth the track trajectory points and eliminate the GPS signal jitter error. Then, according to the time sequence of the track points, the dynamic change indicators of the ship body trajectory are calculated, including the heading change rate (unit: degree / second), the speed change rate (unit: knots / second) and the trajectory curvature radius (unit: meters). For example, the average heading change rate is measured to be 0.15 degrees / second, the trajectory curvature radius is about 300 meters, and the speed change rate is less than 0.1 knots / second. Through the data, the dynamic motion characteristics of the ship body in the measurement process are determined to provide a basis for the vertical offset of the depth sounder incidence angle.

[0026] Step S154: when the installation ship body trajectory change and the inclination installation angle data of the depth sounder exceed 9.5°, the vertical offset of the depth sounder incidence angle is measured to obtain the vertical offset data of the depth sounder incidence angle; In the embodiment of the present application, the step S152 determines whether the measured installation angle (mainly the pitch angle) exceeds the threshold value of 9.5 degrees. If the threshold value is exceeded, the incident angle vertical offset measurement process is started. According to the dynamic index of the ship trajectory change measured in step S153, combined with the installation tilt angle of the depth sounder, the actual offset angle of the incident sound wave relative to the vertical direction is calculated by using the geometric projection method. The specific method is: the dynamic offset of the instantaneous sound wave incident angle is obtained by superimposing the depth sounder tilt angle and the ship heading change angle; the actual offset angle of the sound wave incident angle in the vertical plane is calculated by using the trigonometric function, and the unit is degree. According to the dynamic index of the ship trajectory change measured in step S153, combined with the installation tilt angle of the depth sounder, the actual offset angle of the incident sound wave relative to the vertical direction is calculated by using the geometric projection method. The specific method is: the dynamic offset of the instantaneous sound wave incident angle is obtained by superimposing the depth sounder tilt angle and the ship heading change angle; the actual offset angle of the sound wave incident angle in the vertical plane is calculated by using the trigonometric function, and the unit is degree.

[0027] Step S155: determining the probe sound velocity echo path according to the depth sounder incident angle vertical offset data to obtain probe sound velocity echo path data; In the embodiment of the present application, based on the incident angle vertical offset data obtained in step S154, the sound wave echo path is determined by using the acoustic propagation path tracking method. According to the sound wave propagation principle, combined with the seawater sound velocity profile (the sound velocity curve with depth change is determined by the water depth detection and temperature and salinity data), the actual path of the sound wave from the emission point to the seabed and reflected back to the receiver is simulated. According to the dynamic offset of the sound wave incident angle, the propagation distance and path shape of the sound wave at each moment are calculated, including the direct path, the reflection path and the multi-path interference path. By using the ray tracing algorithm, the refraction and reflection process of the sound wave in the water medium is simulated, and the propagation time and distance of each path are obtained. These echo path data are arranged in time sequence to form a probe sound velocity echo path data set, and the fields include path type, propagation time, propagation distance and corresponding incident angle. This data provides core acoustic path information for the subsequent depth sounder simulation model.

[0028] Step S156: constructing a single-beam depth sounder simulation model according to the probe sound velocity echo path data and the installation ship trajectory change of the depth sounder; In this embodiment of the present invention, the acoustic echo path data from step S155 is integrated with the dynamic change data of the ship's trajectory from step S153 to construct a comprehensive bathymetric simulation model. This simulation model utilizes a numerical simulation platform based on the integration of physical acoustics and motion dynamics to achieve full dynamic simulation of the bathymetric measurement process. Model parameters include: sound velocity profile parameters, acoustic wave emission frequency and power, ship's motion speed and attitude change data, bathymetric installation angle, and dynamic incident angle offset data. The simulation process simulates the propagation and echo reception of each acoustic wave emission cycle in time periods to calculate the expected bathymetric value and its error distribution. The simulation results provide the theoretical bathymetric value at each time point, the impact of incident angle offset on bathymetric error, and measurement deviation caused by variable acoustic wave paths. The simulation model is compiled using the Simulink platform, and the output data format is a time series structure containing fields such as timestamp, theoretical water depth, and path error.

[0029] Step S157: performing a depth sounder simulation based on the single-beam depth sounder simulation model to obtain single-beam depth sounder simulation data.

[0030] In an embodiment of the present invention, the simulation model established in step S156 is used to run simulation calculations step by step according to the time sequence of the measurement route. By inputting the real-time trajectory and attitude data of the hull and combining it with the acoustic propagation path, the actual propagation distance of the sound wave and the expected sounding value at each moment are calculated. During the simulation process, the influence of the depth sounder's tilt angle, the vertical offset of the incident angle, and the dynamic movement of the hull on the sound wave propagation are considered in real time, and the sounding error is calculated to obtain a complete sounding simulation data set, including sounding time, theoretical sounding value, error compensation value, sound wave path information and dynamic disturbance index. The data is saved in CSV format with clear fields and complete structure, providing direct data support for subsequent error analysis and deviation correction. By comparing the measured data with the simulation data, the influence of the tilted installation on the sounding accuracy can be effectively verified, and accurate measurement of extremely shallow waters can be achieved.

[0031] Preferably, in step S2, detecting the dynamic disturbance response data of the sounding angle from the single-beam echo sounder sounding simulation data according to the change of the measurement route environment includes: Analyze the time series changes of the measurement route environment according to the changes of the measurement route environment; In the embodiment of the present application, based on the measurement route environment change data collected in step S1, the time sequence analysis is performed on the environment parameters in the measurement time sequence. The environment parameters include water surface wind speed, wind direction, sea wave height, wave period, air temperature, salinity, and seawater temperature profile, etc. These data are provided by fixed environment monitoring sensors (such as wave buoys, weather stations) and underwater acoustic detection equipment, and the sampling frequency is 1 Hz to 10 Hz. The time sequence environment data are subjected to smoothing filter processing, and the abnormal mutation points are removed, and the moving average method and low-pass filter are used to maintain the data continuity. Based on the time sequence statistical analysis method, such as autocorrelation function and power spectral density analysis, the change trend and periodic characteristics of the environment parameters are identified. The time-varying mean and variance of the environment parameters are calculated to form the environment change trend curve. The analysis result outputs the environment time sequence change data file, which contains the time stamp and the corresponding change value of each environment parameter. This data provides the time and space environment basis for determining the ship body disturbance factor.

[0032] Based on the measurement route environment time sequence change data, the ship body combined disturbance factor data are determined; In the embodiment of the present application, the environment time sequence change data are used to calculate the combined disturbance factor in combination with the ship body dynamics characteristics. The ship body combined disturbance factor mainly includes wind wave action force, wind load moment, ship body acceleration caused by wave and inertial force, etc. Based on the classical marine engineering theory, the Morison equation and ship body response spectrum analysis method are used to calculate the wind wave disturbance force. The specific method is as follows: according to the wave height and period data, the time variation characteristics of the wave force applied to different parts of the ship body are calculated; according to the wind speed and wind direction data, the wind load moment is calculated. In combination with the natural frequency and damping characteristics of the ship body, the motion response of the ship body is obtained. According to the three-degree-of-freedom motion model of the ship body, the acceleration and angular velocity of the ship body in the pitch (around the transverse axis), roll (around the longitudinal axis) and yaw directions are calculated. The combined disturbance factor is expressed in the form of time sequence, which contains the dynamic disturbance amplitude and change rate in each direction. This data provides a quantitative basis for identifying the installation attitude change of the depth sounder.

[0033] According to the installation ship body combined disturbance factor data, the installation attitude change of the single-beam depth sounder is identified from the depth sounding simulation data of the depth sounder; In the embodiment of the present application, based on the above-mentioned joint disturbance factor, the attitude change of the ship body on which the depth finder is installed is analyzed. The attitude data of the depth finder is synchronized with the time stamp of the joint disturbance factor of the ship body, and the angular velocity and acceleration data of the depth finder collected by the inertial measurement unit (IMU) are combined. Through kinematics derivation, the mapping relationship between the attitude change of the depth finder and the disturbance of the ship body is established. The frequency spectrum characteristics of the angular velocity data of the depth finder are analyzed by using Fourier transform, and the low-frequency ship body swing and the high-frequency mechanical vibration are distinguished. The time variation curves of the pitch angle and the roll angle are extracted. In the identification process, the threshold method is used to screen the time period with significant attitude change, the instantaneous change amplitude and the change rate of the attitude angle are calculated, and the attitude change data set of the ship body on which the depth finder is installed is formed. This data provides input for subsequent swing direction division.

[0034] The ship body swing direction division processing is performed on the attitude change of the ship body on which the depth finder is installed, and the ship body swing data around the horizontal axis and the ship body tilt data around the vertical axis are obtained. In the embodiment of the present application, the attitude change data obtained in the previous step is directionally divided according to the coordinate axes. Specifically, the attitude change is decomposed into two components: around the horizontal axis (ship body forward and backward swing) and around the vertical axis (ship body left and right tilt). A three-dimensional coordinate transformation matrix is used to convert the attitude angle of the depth finder from the sensor coordinate system to the ship body coordinate system. Through matrix operation, the angle change around the horizontal axis and the angle change around the vertical axis are extracted. The forward and backward swing angles and the left and right tilt angles corresponding to different time points are calculated. The maximum swing amplitude and the average swing angular velocity in the two directions are counted, and two time series of the ship body swing data around the horizontal axis and the ship body tilt data around the vertical axis are formed. This division data provides a basis for detecting the changes of the pitch angle and the roll angle.

[0035] The single-beam depth finder pitch angle change is detected according to the ship body swing data around the horizontal axis. In the embodiment of the present application, the ship body swing time series data around the transverse axis obtained in the previous step is used to extract the single-beam echo sounder pitch angle change signal. The specific operation is to collect the rotation angle change of the ship body around the transverse axis in real time through the inertial measurement unit (IMU) sensor installed on the ship body. The obtained pitch angle time series reflects the dynamic angle fluctuation of the echo sounder around the transverse axis of the ship body. The pitch angle time series signal is subjected to first-order differential operation to obtain the pitch angle change rate curve, which is used to quantify the speed and dynamic characteristics of the angle change. At the same time, the pitch angle data is compared with the acceleration data collected by the IMU sensor in time synchronization, and the short-time abnormal signals caused by factors such as ship body vibration or water wave are removed through filtering algorithm, so as to ensure that the extracted pitch angle change signal accurately reflects the real ship body swing movement around the transverse axis. Further, statistical parameters of the processed pitch angle data are calculated, including maximum value, minimum value, mean value and standard deviation, to form a complete pitch angle change statistical report. The report describes in detail the deflection of the incident angle of the echo sounder when emitting sound waves in the up-down direction, quantifies the pitch angle change amplitude and dynamic characteristics of the sound wave emission direction of the echo sounder, and provides necessary angle deflection parameters and time series basis for the comprehensive calculation of the change degree of the sound wave emission direction in the subsequent steps.

[0036] According to the ship body tilt data around the longitudinal axis, the single-beam echo sounder roll angle change is detected. In the embodiment of the present application, the left-right tilt angle data of the ship body around the longitudinal axis is obtained, and a digital filtering technology combining high-pass filtering and low-pass filtering is used to preprocess the data to obtain the roll angle change of the echo sounder. Random noise and environmental interference from the sensor are effectively removed to ensure the smoothness and continuity of the roll angle signal. Then, the instantaneous change value of the filtered roll angle data is calculated to reflect the dynamic tilt amplitude at any time point. The roll angle amplitude distribution characteristics are analyzed through statistical methods to reveal the concentration trend and dispersion degree of the tilt amplitude. Polynomial fitting or curve fitting technology is used to mathematically model the roll angle change trend to describe its change law with time. Key parameters including the maximum roll angle value and the standard deviation are extracted from the fitting result as important indicators for evaluating the dynamic fluctuation amplitude of the roll angle. A complete roll angle change time series data is formed to reflect in detail the tilt dynamic characteristics of the echo sounder in the longitudinal axis direction of the ship body, provide accurate input data for the calculation of the sound wave emission direction offset in the subsequent steps, and ensure the scientific accuracy of the quantitative analysis basis of the tilt influence.

[0037] Based on the pitch angle change of the single-beam echo sounder and the roll angle change of the single-beam echo sounder, the change degree of the sound wave emission direction of the echo sounder is determined. In the embodiment of the present application, the actual deflection angle of the sound wave emission direction is calculated by using the space vector superposition method combined with the change data of the pitch angle and the roll angle. The specific method is to convert the pitch angle and the roll angle into angle offset components in two orthogonal directions respectively, and then perform vector synthesis to obtain the deflection angle of the sound wave emission direction in three-dimensional space. According to the time series data, the change amplitude of the sound wave emission direction at each time is calculated, and the change rate is analyzed. By comparing the emission direction in the static installation with the direction difference in the dynamic situation, the change degree of the sound wave emission direction is obtained. The sound wave emission direction change degree data set is output, and the fields include timestamp, total deflection angle, pitch contribution angle and roll contribution angle. The data provides a key indicator for dynamic disturbance response detection.

[0038] According to the sound wave emission direction change degree detection of the depth finder, the dynamic disturbance response data of the depth angle is obtained.

[0039] In the embodiment of the present application, the sound wave emission direction change degree data obtained in the previous step is subjected to time series statistical analysis, so as to accurately depict the dynamic disturbance response of the depth angle. The specific operation process is as follows: the sequence of the deflection angle of the sound wave emission direction changing with time is subjected to discrete time sampling processing, so as to ensure that the sampling frequency can cover the dynamic disturbance frequency band of interest. Subsequently, by setting a dynamic disturbance response threshold, which is usually determined based on historical data experience or field calibration results, when the deflection angle exceeds the threshold, it is considered that the disturbance event starts, and when the deflection angle is below the threshold, the disturbance ends, so as to accurately determine the starting and ending time points of each dynamic disturbance. Then, for the disturbance interval, the disturbance amplitude curve is extracted, the envelope line of the disturbance signal is calculated to capture the overall amplitude change trend, and the fast Fourier transform (FFT) method is used to calculate the frequency spectrum density to identify the main frequency component and energy distribution characteristics of the disturbance signal. Through spectrum analysis, different disturbance types can be distinguished, for example, periodic changes with lower frequency and larger amplitude are classified as low-frequency swing disturbance, and changes with higher frequency and larger amplitude are classified as high-frequency vibration disturbance. In order to further quantify the disturbance characteristics, the maximum amplitude peak value, the disturbance duration and the timestamp of the disturbance event during the disturbance period are also required to be counted, and all the disturbance parameters are integrated to form a depth angle dynamic disturbance response data set, which contains key indicators such as the starting time, the ending time, the duration, the peak amplitude, the main frequency and the energy distribution of each disturbance. The data set is the core input for determining the environmental measurement coupling interference intensity of the depth finder in the subsequent step, which ensures the accurate response analysis of the depth finder to the angle disturbance in the complex marine environment.

[0040] Preferably, the determination of the environmental measurement coupling interference intensity of the depth finder according to the environmental change of the measurement route and the dynamic disturbance response data of the depth angle in step S2 comprises: determining the environmental wave enhancement trend of the measurement route according to the environmental change of the measurement route; In the embodiment of the present application, when determining the wave enhancement trend of the measurement route environment according to the change of the measurement route environment, based on the obtained measurement route environment data, including time series data such as sea surface wind speed, wind direction, wave height and period, statistical analysis and trend fitting method is used to quantitatively analyze the dynamic change of the environmental wave. The wave energy distribution is calculated by using the wave spectrum analysis technology, and the trend characteristic parameters of wave enhancement are determined, such as wave height growth rate, period change trend and wave direction change. By comparing the environmental data of different time periods, it is determined whether the wave energy presents an increasing trend, and the wave enhancement trend data is obtained, which is used as the basis input for subsequent coupling interference analysis.

[0041] Estimating the growth of the water surface bubble layer based on the wave enhancement trend of the measurement route environment; In the embodiment of the present application, when estimating the growth of the water surface bubble layer based on the wave enhancement trend of the measurement route environment, according to the known principle of ocean physics, the bubble generation model is applied for calculation combined with the wave enhancement trend parameters. Specifically, the bubble generation rate and distribution density are calculated through the correlation between sea surface turbulence intensity and wave breaking frequency, and the change curves of bubble layer thickness and volume fraction are derived. Combined with the sound speed profile data observed on site, the bubble layer estimation result is further corrected to obtain the growth data of the water surface bubble layer based on the wave enhancement trend, which is used to evaluate the influence on the sound wave propagation.

[0042] Detecting the wear growth of the single-beam echo sounder according to the wave enhancement trend of the measurement route environment; In the embodiment of the present application, when detecting the wear growth of the single-beam echo sounder according to the wave enhancement trend of the measurement route environment, the relationship between the wave enhancement trend and the force on the equipment is analyzed, and the mechanical vibration data of the installation position of the echo sounder is collected by using the vibration sensor and the accelerometer. The periodicity and intensity of wear aggravation are determined through the matching analysis of vibration amplitude and wave frequency. Combined with the use time of the equipment and the cumulative amplitude of vibration, the wear growth curve model is established, and the wear degree change of the key components of the echo sounder such as the probe surface and the shell is calculated to form the wear growth data, which provides a quantitative basis for subsequent structure damage assessment.

[0043] Verifying the damage of the fastener structure of the echo sounder based on the wear growth of the single-beam echo sounder and the dynamic disturbance response data of the sounding angle; In the embodiment of the application, when the single-beam depth sounder wear growth and depth angle dynamic disturbance response data are used to check the damage of the fastener structure of the depth sounder, ultrasonic detection and structure health monitoring technology are adopted to non-destructively detect the fastener connection part. Through vibration mode recognition and dynamic response analysis, the structure loosening, crack initiation and expansion are detected. Combined with the wear growth data, the mechanical property change of the fastener is evaluated, and the structure damage probability index is formed. At the same time, the dynamic disturbance data are used to analyze the stress state of the fastener under different dynamic loads, and the overall detection of the fastener structure damage is realized.

[0044] The erosion intensification condition of the probe head surface is detected according to the bubble layer growth condition of the route water surface. In the embodiment of the application, when the erosion intensification condition of the probe head surface is detected according to the bubble layer growth condition of the route water surface, high-frequency acoustic scattering technology is used to detect the probe head surface in detail. The technology transmits acoustic waves of a specific frequency and receives reflected signals to obtain the scattering characteristics of the probe head surface structure and generate high-resolution sonar images. The probe head surface data in multiple measurement periods are continuously collected to ensure that the time interval is sufficient to capture the dynamic changes of corrosion development. The sonar images at different time points are compared and analyzed, the image processing algorithm is used to identify the surface texture changes, the number, area and distribution position of corrosion pits, and the quantitative evaluation of the corrosion degree of the probe head is realized. Trend analysis is performed on the multi-period image data to reveal the relationship between the corrosion expansion speed and the bubble layer thickness and density. The chemical parameters of the water environment are synchronously collected, including dissolved oxygen concentration, pH value, salinity and water temperature, combined with the water flow rate and turbulence intensity data, the physical and chemical mechanism of the probe head surface corrosion is comprehensively analyzed, and the promoting effect of the bubble layer on the corrosion reaction rate is investigated. By establishing a time sequence model of corrosion intensification, the influence of bubble layer growth on the corrosion rate of the probe head material is quantitatively described, and the erosion intensification condition data with time and space resolution are formed. The data provides a solid foundation for the subsequent comprehensive evaluation of the environmental disturbance amplification condition of the depth sounder, and supports accurate judgment of the influence degree of environmental factors on the equipment performance.

[0045] The environmental disturbance amplification condition of the tilt depth sounder is estimated based on the wave enhancement trend of the measurement route environment. In the embodiment of the present application, when estimating the environmental disturbance amplification of the tilt-sounding instrument based on the measurement route environmental wave enhancement trend, the wave enhancement trend data in the measurement route is collected, which is collected by the wave sensor installed around the measurement ship body, including wave height, wave period and wave direction parameters. The water surface bubble layer distribution and concentration data are collected synchronously, and the acoustic Doppler current profiler (ADCP) and acoustic scattering instrument are used to monitor the water surface bubble layer characteristics in real time to obtain bubble density and size distribution information. At the same time, the measurement instrument itself is installed with high-precision acceleration sensor and vibration sensor to collect the mechanical vibration data of the equipment, including vibration frequency, vibration amplitude and vibration direction. The above three types of data are time-synchronized to ensure the consistency of the time stamps of different data sources to ensure the accuracy of subsequent analysis. The frequency response analysis method is used to analyze the spectrum of wave, bubble layer and mechanical vibration signals, and the main vibration frequency component and its amplitude characteristics are extracted by fast Fourier transform (FFT). Combined with the inherent frequency and damping characteristics of the sounding instrument structure, a coupled vibration transmission model is established, which simulates how the mechanical vibration caused by waves and bubbles is transmitted through the support structure and mounting device of the sounding instrument, and focuses on calculating the vibration amplification factor and dynamic response amplitude. The model input includes wave excitation force, bubble-induced acoustic pressure wave and equipment vibration state, and the response amplitude of the sounding instrument under the action of different frequencies is obtained by solving the structure dynamics equation. The output result is the environmental disturbance amplification data, which quantitatively reflects the degree of vibration enhancement of the sounding instrument caused by the transmission of wave and bubble disturbance through the structure. This data is an important indicator of environmental coupling interference intensity, which provides key parameter support for subsequent error correction and sounding value correction.

[0046] The environmental measurement coupling interference intensity of the sounding instrument is determined according to the erosion intensification condition of the probe head surface and the environmental disturbance amplification of the tilt-sounding instrument.

[0047] In the embodiment of the present application, when determining the environmental measurement coupling interference intensity of the depth sounder according to the surface erosion intensification condition of the probe head and the environmental disturbance amplification of the tilt-sounding depth sounder, the collected probe head surface corrosion data and the environmental disturbance amplification data of the tilt-sounding depth sounder are synchronously integrated. The probe head corrosion data includes corrosion area, depth distribution, corrosion rate and corrosion morphology characteristics, and the environmental disturbance amplification data covers the mechanical vibration amplification coefficient caused by waves, the sound wave scattering influence caused by the bubble layer and the structure resonance enhancement effect. A multivariate comprehensive evaluation method is adopted, the corrosion index and the disturbance amplification index are taken as input variables by establishing a multi-dimensional feature space, the historical maintenance records and actual operation parameters of the equipment are introduced, such as equipment use time, previous maintenance cycle, operation environment temperature and salinity, and a quantitative model of the coupling effect of environmental factors and equipment aging is constructed. The model adopts weight distribution and normalization technology to standardize each variable, ensuring the comparability of data with different dimensions, and forming a comprehensive evaluation function by weighted superposition. Statistical regression analysis and machine learning regression algorithm are used to calibrate the model parameters combined with the actual deviation data of the equipment performance monitoring and depth error in previous years, so as to realize accurate prediction of the coupling interference intensity. The environmental measurement coupling interference intensity index generated is a comprehensive numerical value, which clearly reflects the comprehensive influence degree of the environmental disturbance and wear of the depth sounder under specific environmental conditions and equipment state. The index is used as an input parameter to directly assist in adjusting the weight distribution of the depth value correction algorithm in the depth error correction module, thereby improving the accuracy and reliability of the measurement in the extremely shallow water area.

[0048] Please refer to Figure 3 , which is a schematic diagram of the detection of the metal fatigue degree of the single-beam depth sounder in the present application; Preferably, the determination of the dynamic abnormal condition of the sound wave propagation path according to the environmental measurement coupling interference intensity of the depth sounder in step S2 comprises: The resonance intensification condition of the single-beam depth sounder is estimated according to the environmental measurement coupling interference intensity of the depth sounder. In the embodiment of the present application, the resonance intensification condition of the single-beam depth sounder is estimated based on the environmental measurement coupling interference intensity data of the depth sounder. Specifically, the mechanical vibration signals are collected in real time by arranging vibration sensors and accelerometers at key structural parts of the depth sounder. The frequency component decomposition of the collected vibration signals is performed by using the frequency spectrum analysis technology, and the energy change trend in the resonance frequency interval is identified. The continuous rise of vibration energy and the concentration of peak frequency reflect the resonance intensification state. This process is recorded by a high-speed data acquisition device, and the vibration-environment coupling relationship curve is established combined with the environmental coupling interference intensity data, so as to obtain the quantitative estimation value of the resonance intensification.

[0049] The metal fatigue degree of the single-beam depth sounder is detected based on the resonance intensification condition of the single-beam depth sounder. In the embodiment of the present application, based on the resonance aggravation condition data, the metal fatigue degree of the single-beam depth finder is further detected. In the implementation process, the ultrasonic detection technology is used to periodically scan the surface and internal structure of the key metal parts of the depth finder, and the acoustic signal reflection intensity and scattering characteristics of the metal fatigue cracks are collected. Combined with the distribution of the stress concentration area caused by resonance, the generation and expansion degree of the metal fatigue cracks are inferred by the acoustic emission analysis method. The fatigue degree quantization adopts the fatigue damage accumulation model, and combined with the resonance state parameters, the current metal fatigue degree index is obtained, reflecting the proximity degree of the safety boundary of the device structure.

[0050] The single-beam depth finder metal fatigue degree is used to predict the single-beam depth finder micro-crack accumulation situation; In the embodiment of the present application, the implementation of the single-beam depth finder micro-crack accumulation situation according to the metal fatigue degree is described in detail as follows: combined with the fracture mechanics theory in material mechanics, the fatigue crack propagation rate is used as a basic parameter, and according to the material characteristics and historical operation fatigue data of the key metal parts of the depth finder, a micro-crack growth prediction model is constructed. The model comprehensively considers the stress intensity factor range, the stress field distribution of the crack tip and the relationship between the crack propagation rate and the cyclic load, and accurately describes the evolution process of the micro-crack under the action of repeated stress. In order to obtain real-time dynamic information of the crack, the multi-frequency ultrasonic phased array detection technology is used to continuously monitor the metal structure of the depth finder. This technology realizes high-resolution crack positioning and morphology recognition by adjusting the ultrasonic wave emission frequency and phase, and effectively enhances the detection ability of early micro-cracks. In the detection process, the spatial size change data of the crack length, width and depth are collected to form a time series data set. Advanced time series analysis methods are applied to the time series data, including trend analysis, spectrum analysis and prediction algorithm, to accurately infer the future development trend of the crack propagation and identify the acceleration or deceleration stage of the crack propagation rate. Combined with the prediction model and the measured dynamic data, the micro-crack accumulation situation is finely predicted, and a micro-crack accumulation prediction data set is formed, which contains the crack size change trajectory, growth rate and predicted future crack propagation amplitude. This provides a scientific basis for safety evaluation and maintenance plan of the depth finder structure, and ensures the operation stability and reliability of the key parts of the depth finder.

[0051] According to the depth finding simulation data of the single-beam depth finder, the collision probability data of the depth finder in the extremely shallow water area is calculated; In the embodiment of the present application, the water depth distribution, sound wave reflection intensity and detection blind area of the current working area of the depth sounder are comprehensively analyzed based on the depth sounding simulation data, and the spatial distribution range of the potential obstacles in the extremely shallow water area is accurately defined. The position and dynamic change of the underwater obstacle, including the drift speed, direction and deformation information, are further identified by using the environmental measurement coupled interference intensity parameter and combining the underwater acoustic signal attenuation characteristics. The trajectory data and real-time speed data of the depth sounder installed on the ship body are synchronously collected and processed, and the real-time spatial position relationship of the depth sounder relative to the obstacle is determined by the high-precision navigation system. The uncertainty of the position of the underwater obstacle and the dynamic change of the navigation trajectory of the depth sounder are included in the collision risk analysis framework by using the probability and statistics method. Specifically, a large number of navigation path samples are randomly generated by using the Monte Carlo simulation technology, and the path deviation caused by environmental interference and the attitude change of the depth sounder are combined to simulate the motion state of the depth sounder under different conditions in the extremely shallow water area. The spatial position of each sample path and the obstacle is subjected to collision determination, and the number of collision events and the time distribution thereof are counted. Through statistical analysis of the collision events of all sample paths, the frequency and probability distribution of the collision events are calculated, and the collision probability data reflecting the collision risk of the depth sounder are formed. The data specifically include the numerical value of the collision probability, the risk fluctuation in the corresponding time period and the spatial distribution of the high-risk area, and provide scientific and quantitative index support for the safe operation risk management of the depth sounder. The collision probability data can be used as the input parameter of the subsequent depth data correction and risk warning system, and the safety guarantee and dynamic risk control of the depth sounding operation in the extremely shallow water area are realized.

[0052] According to the collision probability data of the depth sounder in the extremely shallow water area and the cumulative condition of the micro-cracks of the single-beam depth sounder, the micro-pore water infiltration condition of the depth sounder is estimated; In the embodiment of the present application, the micro-pore water infiltration condition of the depth sounder is estimated according to the collision probability data of the depth sounder in the extremely shallow water area and the cumulative condition of the micro-cracks. The water infiltration of the key structure of the depth sounder is analyzed by using the finite element method, the influence of the micro-cracks on the integrity of the waterproof layer is considered, the permeability and micro-pore size distribution parameters of the material are combined, and the water infiltration process through the micro-cracks is simulated. The water infiltration rate and penetration depth are calculated in combination with the structure deformation and crack propagation caused by collision. The water infiltration risk is graded and evaluated, and the micro-pore water infiltration condition data are generated, which provide a basis for subsequent maintenance decision.

[0053] According to the micro-pore water infiltration condition of the depth sounder and the dynamic disturbance response data of the depth angle, the dynamic abnormal condition of the sound wave propagation path is determined.

[0054] In the embodiment of the present application, based on the data of the water seepage condition of the depth finder micro hole and the dynamic disturbance response data of the depth angle, the dynamic abnormal condition of the sound wave propagation path is comprehensively determined. By using the multi-source data fusion technology, the structural changes caused by the water seepage influence and the sound wave deviation data caused by the angle disturbance are combined, and the abnormal characteristics of the sound wave propagation path are identified by the time and space synchronous analysis method. The abnormal path is classified, and the abnormal conditions such as reflection enhancement, multi-path propagation and signal distortion are identified, and the quantitative description data of the dynamic abnormal condition of the sound wave propagation path is formed. The data is used as the core input for the subsequent depth error prediction and correction, and the measurement accuracy of the depth finder in the extremely shallow water environment is ensured.

[0055] Preferably, step S3 comprises the following steps: Step S31: determining the multi-path abnormal interference condition of the depth finder based on the dynamic abnormal condition of the sound wave propagation path; In the embodiment of the present application, the multi-path abnormal interference condition of the depth finder is determined based on the dynamic abnormal condition of the sound wave propagation path, the dynamic abnormal parameters of the sound wave propagation path obtained by the foregoing steps are combined with the sound velocity distribution, water depth change and bottom reflection characteristics in the actual measurement environment, and a multi-path model of sound wave propagation is constructed. Through the time and space change analysis of the sound wave propagation path, the reflection surface and refraction interface causing the multi-path interference are identified, including the water surface, the sea bottom and the interface of different density layers of the water body. A high-resolution acoustic signal processing device is used to analyze the received echo signal in time and frequency domain, detect the overlap and time delay distribution of the signal, and identify the abnormal waveform and interference strength caused by the multi-path signal. Through quantitative calculation of the amplitude attenuation, phase change and time delay difference of the multi-path interference signal, multi-path abnormal interference feature data is formed, which provides basic input for subsequent signal distortion prediction. The data reflects the dynamic abnormal condition of the sound wave propagation in the complex environment of the extremely shallow water in detail, and contains indexes such as the number of interference paths, signal superposition strength and time distribution.

[0056] Step S32: predicting the signal distortion condition of the single-beam depth finder according to the multi-path abnormal interference condition of the depth finder; In the embodiment of the present application, the signal distortion condition of the single-beam depth finder is predicted according to the multi-path abnormal interference condition of the depth finder, the multi-path abnormal interference feature data obtained in step S31 is combined with the signal receiving mechanism of the depth finder, the influence of the multi-path interference on the amplitude, phase and frequency response of the echo signal is analyzed. Through a high-precision sonar signal analyzer, the nonlinear distortion component, distortion index and signal-to-noise ratio change of the signal are extracted. Combined with the sound wave propagation characteristics under the special hydrological conditions of the extremely shallow water, the signal time delay expansion and spectrum broadening phenomenon caused by the multi-path effect are identified. By comparing and analyzing the historical depth data, the corresponding relationship between the signal distortion and the multi-path interference is established, the signal distortion trend and amplitude range under the current environment are predicted. A prediction data set containing the signal distortion level, distortion duration and frequency band influence range is formed, which provides support for the frequency drift detection of the sound wave in the extremely shallow water.

[0057] Step S33: detecting the sound wave frequency drift phenomenon in the extremely shallow water area according to the multi-path abnormal interference of the depth finder and the signal distortion condition of the single-beam depth finder; In the embodiment of the present application, the sound wave frequency drift phenomenon in the extremely shallow water area is detected according to the multi-path abnormal interference of the depth finder and the signal distortion condition of the single-beam depth finder, and the frequency stability of the sound wave signal is analyzed by using the multi-path interference and signal distortion data. A high-resolution spectrum analyzer is used to monitor the instantaneous frequency change and frequency offset trend of the echo signal. The influence of the environmental factors on the frequency drift is decoupled by combining the sound speed variation caused by the water temperature, salinity and flow change in the extremely shallow water area. The frequency offset amplitude and change rate are accurately identified by comparing the theoretical sound wave transmission frequency with the actual receiving frequency. The time series analysis is performed on the continuous measurement data to distinguish the periodic and non-periodic characteristics of the frequency drift. The frequency drift phenomenon data including the frequency drift amplitude, drift rate and time distribution are formed to provide a quantitative basis for the depth error expansion prediction.

[0058] Step S34: performing the depth finder error expansion prediction based on the sound wave frequency drift phenomenon in the extremely shallow water area to obtain the depth finder error expansion growth data.

[0059] In the embodiment of the present application, the depth finder error expansion prediction is performed based on the sound wave frequency drift phenomenon in the extremely shallow water area to obtain the depth finder error expansion growth data. The frequency drift parameters detected in step S33 are used to calculate the error influence of the frequency drift on the depth measurement result in combination with the conversion relationship between the sound wave propagation time and the water depth. The expansion trend of the depth error is inferred by numerically analyzing the cumulative effect of the frequency drift error with time. The time-resolved error prediction method is used to form the error growth curve and to clearly define the dynamic evolution law of the error with the operation time and the environmental change. The error expansion growth data is refined into the error amplitude, growth rate and critical error time point to form a complete error expansion prediction data set. The data serves as the basis for the measurement correction of the single-beam depth finder installed at an inclination in the extremely shallow water area and supports the implementation of the subsequent depth value correction processing.

[0060] Preferably, step S31 comprises the following steps: Step S311: detecting the irregular surface reflection enhancement condition based on the abnormal sound wave propagation path; In the embodiment of the present application, the water surface irregular reflection enhancement condition is detected based on the abnormality of the sound wave propagation path. Specifically, the water surface reflection waveform in the echo signal is analyzed in time domain and frequency domain by receiving the single-beam echo sounder echo signal. The abnormal change of the reflection intensity in the echo signal is captured by using a high-precision time-frequency analysis instrument, and the mutation of the reflection amplitude and the irregularity of the time delay are focused on. The dynamic change of the water surface wave shape is analyzed in combination with the measurement route environment data, and the influence of factors such as irregular waves, wind waves and surge waves on the sound wave reflection surface is particularly focused on. The time distribution and spatial expansion characteristics of the reflection enhancement phenomenon are evaluated by continuously monitoring the change trend of the water surface reflection signal. Quantitative data of the water surface irregular reflection enhancement is obtained in this step, which provides a basis for multiple reflection analysis.

[0061] Step S312: determining the sounding sound wave multiple reflection data based on the water surface irregular reflection enhancement condition; In the embodiment of the present application, the sounding sound wave multiple reflection data is determined based on the water surface irregular reflection enhancement condition. In the operation, the water surface reflection enhancement information obtained in step S311 is used in combination with the sound wave propagation path simulation technology to infer the occurrence frequency and reflection path of the sound wave in the water body. The time sequence distribution and energy attenuation of the multiple reflection wave in the echo signal are measured by using a precise time delay estimation device. The specific parameters of the multiple reflection signal such as the number of reflections, path length and reflection surface position are determined by analyzing the repeated structure of the echo signal. The multiple reflection data set is formed in this step, which covers the number of reflections and the corresponding reflection intensity, and provides data support for echo overlap and waveform distortion detection.

[0062] Step S313: determining the sounding sound wave echo overlap condition according to the sounding sound wave multiple reflection data and the water surface irregular reflection enhancement condition; In the embodiment of the present application, the sounding sound wave echo overlap condition is determined according to the sounding sound wave multiple reflection data and the water surface irregular reflection enhancement condition. The overlapping interval of different reflection waves on the time axis is identified according to the time sequence and intensity characteristics of the multiple reflection signal. The high-resolution signal deconvolution technology is used to separate the overlapping waveforms into independent echoes, and the interference degree of the overlapping signal on the sounding result is evaluated. The change trend of the overlapping interval and its influence on the sounding signal quality are analyzed in combination with the spatial dynamics of the water surface reflection enhancement. Detailed echo overlap data including the overlapping time, intensity superposition ratio and spatial distribution are formed, which provides a quantitative basis for waveform distortion analysis.

[0063] Step S314: verifying the sounding sound wave waveform distortion degree according to the sounding sound wave echo overlap condition; In the embodiment of the present application, the waveform distortion degree of the depth sounding sound wave is checked according to the overlapping condition of the depth sounding sound wave echo. In the specific implementation process, the signal processing device is used to perform waveform analysis on the overlapping echo signal, and to measure the waveform distortion parameters such as peak change, waveform stretching and phase shift. The time-frequency joint analysis method is used to accurately capture the time-varying characteristics of the waveform distortion. By comparing the deviation between the normal waveform and the actual received waveform, the percentage value of the waveform distortion degree is calculated. The waveform distortion degree data is output in this step, which is an important indicator for judging the severity of the multipath interference.

[0064] Step S315: checking the position offset degree of the depth sounder measuring point according to the abnormal condition of the sound wave propagation path; In the embodiment of the present application, the position offset degree of the depth sounder measuring point is checked according to the abnormal condition of the sound wave propagation path. In the operation process, the space-time abnormal data of the sound wave propagation path is used in combination with the known installation position of the depth sounder and the positioning system data to calculate the offset amount of the measuring point in space. By comparing the continuous measurement data, the position drift of the measuring point caused by the multipath reflection and propagation abnormality is identified. The three-dimensional positioning and trajectory reconstruction technology is used to accurately determine the offset distance and direction of the measuring point. The position offset degree data of the measuring point is formed to reflect the influence of the sound wave propagation abnormality on the depth position accuracy.

[0065] Step S316: determining the multipath abnormal interference condition of the depth sounder when the waveform distortion degree of the depth sounding sound wave is greater than 8% and the position offset degree of the depth sounder measuring point is determined.

[0066] In the embodiment of the present application, when the waveform distortion degree of the depth sounding sound wave is greater than 8% and the position offset degree of the depth sounder measuring point exceeds the set threshold, the multipath abnormal interference condition of the depth sounder is determined by comprehensively considering the above data. The waveform distortion degree and the measuring point offset data are weighted and integrated by the data fusion algorithm to generate a multipath abnormal interference evaluation index. The index quantifies the intensity and influence range of the multipath interference, and provides a basis for subsequent signal processing and error correction. The multipath abnormal interference condition data of the depth sounder is formed to clearly define the interference level and its space-time distribution, which provides technical support for the accuracy guarantee of the depth data in the extremely shallow water measurement environment.

[0067] Especially important is that step S32 includes the following steps: Step S321: determining the irregular peak change condition of the signal according to the multipath abnormal interference condition of the depth sounder; In the embodiment of the present application, based on the "bathymeter multi-path abnormal interference condition" data set obtained in the previous step S31, the data contains the number of multi-path reflection signals, time delay deviation, intensity ratio and superposition degree and other information. For each echo event, the time domain waveform of the received echo signal is extracted and peak value identification processing is performed. A high-precision time-frequency signal analyzer is used to detect the main peak in each echo at each time, and the peak amplitude and occurrence time are obtained. Then, peak fluctuation analysis is performed to calculate the difference between the standard deviation and the mean value of the main peak amplitude in the echo signal, as well as the jump of the main peak occurrence time. By comparing the statistical benchmark of the main peak amplitude variation range (such as ±0.2dB) under normal environment, the interval of irregular change of the peak under multi-path abnormal influence is identified, for example, the amplitude deviation is more than ±0.5dB or the delay peak time offset is more than 0.1ms. The output "peak irregular change data set" includes the main peak amplitude and time difference change sequence in each echo sequence, and the abnormal wave band identification.

[0068] Step S322: determining the irregular change of signal wave trough according to the bathymeter multi-path abnormal interference condition; In the embodiment of the present application, the same source of multi-path abnormal interference characteristics is used to identify and analyze the wave trough (local minimum value) in the echo signal. Through time domain signal processing, the first valley position and amplitude after obvious reflection in each echo are extracted after the echo waveform is normalized. By comparing the average wave trough depth (such as -30dB) and position parameters (corresponding to the main peak after 30-40 microseconds) in the normal environment, the wave trough offset and depth fluctuation are calculated. If the wave trough amplitude offset caused by multi-path interference is more than ±3dB, or multiple troughs appear instead of a single trough, it is considered that "wave trough irregular change" occurs. After the wave trough fluctuation time stamp, amplitude deviation and position change value are calculated, the "wave trough irregular change data set" is generated for subsequent error judgment.

[0069] Step S323: verifying the echo peak value judgment error condition according to the irregular change of signal wave trough and the irregular change of signal wave peak; In the embodiment of the present application, the data output by steps S321 and S322 is jointly analyzed. The peak irregular change interval and the trough abnormal interval are compared in time and space to identify the time period when the peak amplitude offset and the trough structure abnormality occur at the same time. In these time periods, the system compares the peak value identification processing result with the actual waveform structure to determine whether the peak value extraction error occurs. By calculating the ratio of the main peak to the second main peak amplitude and the time delay difference between the peaks (for example, the second main peak intensity is more than 80% of the main peak and the time difference is less than 5 microseconds), it is determined that there is a peak value identification error. These error time periods are recorded as "echo peak value judgment error" events to form an error data set containing the start and end time of the event, the peak amplitude deviation, the corresponding wave trough and the peak-to-peak time difference, which is used for subsequent distortion prediction.

[0070] Step S324: predicting the monobeam sounder signal distortion condition based on the echo peak value misjudgment condition.

[0071] In the embodiment of the present application, the echo peak value misjudgment events obtained in S323 are used to predict the signal distortion trend. The frequency, duration and average peak value deviation amplitude of all misjudgment events are counted. Combined with the signal-to-noise ratio data of the echo signal (usually less than 10 dB indicating that noise has a serious impact), the distortion level is quantified, such as: mild distortion (deviation <0.1 meters), moderate distortion (deviation 0.1-0.5 meters), severe distortion (deviation >0.5 meters). In the event set, the prediction of the signal distortion condition will be moderate or severe distortion frequently alternating. The output "signal distortion prediction data set" includes the following data content: predicted start and end time, high distortion frequency interval, signal deviation level distribution and cumulative distortion time ratio. As the input of the subsequent sounding error expansion (S34), it ensures accurate identification of the sounding data variation caused by multipath interference in the very shallow water measurement process.

[0072] Especially important is that step S34 includes the following steps: Step S341: determining the echo signal frequency component deviation condition according to the frequency drift phenomenon of sound waves in very shallow water; In the embodiment of the present application, in the very shallow water area, due to the small water depth, strong interface reflection and frequent water body disturbance, the sound wave is subjected to complex interference during propagation, and the frequency deviation phenomenon occurs. In this step, by obtaining the original echo signal data returned by the tilt-mounted monobeam sounder in different flight segments, a time-frequency analysis device is used for real-time demodulation and spectrum extraction. The echo data is processed by short-time window segmentation, the main frequency component in each time period is extracted by combining the spectrum scanner, and compared with the standard set transmission frequency. Once a measurable deviation is found between the received frequency and the standard frequency, it is judged that the frequency component has drifted. The drift result is recorded in the form of a frequency component deviation record table, including time point, echo segment number, reference frequency value, actual frequency value and deviation degree.

[0073] Step S342: detecting the echo signal peak value time point deviation degree based on the echo signal frequency component deviation condition; In the embodiment of the present application, based on the frequency component offset data obtained in the previous step, the time domain variation of the signal waveform is further analyzed. In this process, the echo signal is compared with the standard waveform template by the signal comparison analysis module, the actual occurrence time of the signal peak value is judged by point-by-point scanning, and is compared with the theoretical propagation time, so as to extract the offset value of the received peak time. The offset degree represents the time domain distortion degree caused by the frequency instability in the depth measurement signal propagation process. The result is expressed in the form of a received signal peak time offset table, which includes each received time point, corresponding frequency offset, wave peak actual arrival time and its deviation from the theoretical time.

[0074] Step S343: estimating the depth measurement error expansion according to the received signal peak time point offset degree; In the embodiment of the present application, by analyzing the wave peak offset time data, the depth measurement error variation caused by the offset is evaluated. In the specific operation, each time offset value is converted into a corresponding depth error growth value, and is converted according to the direct conversion relationship between the sound wave propagation speed in water and the time deviation. The time offset data of each measuring point is processed in sequence to form a corresponding error value data set, and is arranged in time sequence to observe the error expansion trend. The processed result generates a depth measurement error expansion table, which includes time points, wave peak time offset values and corresponding depth error values. After visualizing the data, the time characteristics of error expansion are further identified.

[0075] Step S344: estimating the error accumulation effect data based on the depth measurement error expansion; In the embodiment of the present application, the depth measurement error expansion data is processed in sequence, and the accumulation effect of the error in the continuous measurement process is analyzed. In the execution process, the error values of each leg or measuring point are accumulated and counted to form an error total value sequence which increases with the measurement time. Further, the time series analysis method is combined to analyze the change rate and growth law of the accumulated error, and to identify whether there is an accelerated growth phenomenon or a mutation point. The error accumulation effect data is presented in the form of a table and a curve, including measurement number, single point error value, accumulated error value, error growth rate and the like, which are used to quantify the error accumulation degree in the extremely shallow water environment.

[0076] Step S345: predicting the depth measurement error expansion based on the error accumulation effect data to obtain the depth measurement error expansion growth data.

[0077] In the embodiment of the present application, on the basis of grasping the error accumulation trend, the error growth is predicted and evaluated for future measurement tasks. In this step, the historical accumulated error data is used to fit the trend, and the growth law of the error with time or the number of measurement points is extracted. By observing the trend of the accumulated error curve, it is determined whether the error growth presents linear growth, exponential growth or periodic fluctuation, and the trend extrapolation result of the future error is presented in the error expansion growth prediction table according to the error value and the change amplitude of the error growth. The prediction data can provide a reference basis for subsequent tilt angle correction, signal compensation and flight path planning.

[0078] Preferably, step S4 comprises the following steps: Step S41: verifying the depth measurement precision gradient attenuation condition based on the echo sounder error expansion growth data; In the embodiment of the present application, the depth measurement precision gradient attenuation condition is verified based on the echo sounder error expansion growth data. In this step, the error expansion growth data is obtained by using a high-precision depth measurement device, and the change of the error with the measurement distance and time is recorded in detail. By constructing an error gradient curve, the attenuation trend of the error in the space and time dimensions is quantified. In the specific operation, the error data is statistically segmented by using a data analysis tool, and the error amplitude rate and the gradient change with the distance are calculated. Combined with the echo sounder measurement frequency and the sound wave propagation environment parameters, the spatial distribution law of the error expansion is determined to form the depth measurement precision gradient attenuation data, which reflects the actual influence of the error expansion on the depth measurement precision and provides basic data for subsequent performance attenuation analysis.

[0079] Step S42: determining the echo sounder detection performance attenuation trend according to the depth measurement precision gradient attenuation condition and the echo sounder error expansion growth data; In the embodiment of the present application, the echo sounder detection performance attenuation trend is determined according to the depth measurement precision gradient attenuation condition and the echo sounder error expansion growth data. By integrating the error gradient attenuation curve and the error expansion data, the detection performance change of the echo sounder in the very shallow water environment is analyzed. By using the statistical regression analysis method, the attenuation model of the detection performance with time and distance is fitted to clearly determine the speed and amplitude of the performance decline. In this step, the detection performance indicators such as signal-to-noise ratio, echo intensity and depth repeatability are used to comprehensively evaluate the detection ability of the echo sounder. The echo sounder detection performance attenuation trend data is outputted to provide a technical basis for subsequent tilt echo sounder bias evaluation.

[0080] Step S43: performing tilt echo sounder bias degree evaluation according to the echo sounder detection performance attenuation trend to obtain tilt echo sounder bias degree data; In the embodiment of the present application, the inclination of the depth finder is evaluated according to the performance attenuation trend of the depth finder, and the inclination of the depth finder is obtained. The operation combines the performance attenuation trend with the installation inclination angle data of the depth finder to analyze the influence of the inclination installation on the depth error. The error propagation analysis method is used to calculate the depth error increase caused by the inclination angle. The accuracy of the error model is verified by comparing the measured data. The spatial distribution characteristics of the inclination degree are quantified by combining the mechanical structure parameters of the depth finder. The inclination of the depth finder is obtained as the key input for the depth error correction.

[0081] Step S44: The inclination of the depth finder is corrected by the single-beam depth finder depth value correction processing, and the single-beam depth finder depth value correction data is obtained.

[0082] In the embodiment of the present application, the inclination of the depth finder is corrected by the single-beam depth finder depth value correction processing, and the single-beam depth finder depth value correction data is obtained. In specific implementation, the depth value correction algorithm is constructed according to the inclination of the depth finder, and the correction coefficient is calculated for different measurement angles and inclination degrees. The original measurement data of the depth finder and the correction coefficient are multiplied to adjust the depth result. The correction accuracy is verified by the field calibration data and the historical measurement record to ensure that the corrected depth value meets the actual water depth. The depth value correction data output by this step provides accurate basic data for subsequent depth data analysis and application, and ensures the accuracy and reliability of the measurement in the extremely shallow water area.

[0083] The present application also provides a single-beam depth finder for measuring in extremely shallow water area based on inclination installation, which is used to execute the single-beam depth finder for measuring in extremely shallow water area based on inclination installation as described above, and comprises: The depth finder depth simulation module is used to obtain the single-beam depth finder operation data, identify the single-beam depth finder measurement route data according to the single-beam depth finder operation data, collect the measurement route environment change according to the single-beam depth finder measurement route data, and perform the depth finder depth simulation based on the single-beam depth finder measurement route data to obtain the single-beam depth finder depth simulation data. The propagation path dynamic anomaly determination module is used to detect the depth angle dynamic disturbance response data according to the single-beam depth finder depth simulation data based on the measurement route environment change, determine the depth finder environment measurement coupling interference intensity according to the measurement route environment change and the depth angle dynamic disturbance response data, and determine the sound wave propagation path dynamic anomaly condition according to the depth finder environment measurement coupling interference intensity. The error propagation prediction module is configured to detect a sound wave frequency drift phenomenon in the extremely shallow water based on a dynamic anomaly of a sound wave propagation path, and to predict error propagation of the depth sounder based on the sound wave frequency drift phenomenon in the extremely shallow water, so as to obtain error propagation growth data of the depth sounder. The depth value correction processing module is configured to evaluate a bias degree of the tilt depth sounder based on the error propagation growth data of the depth sounder, so as to obtain bias degree data of the tilt depth sounder, and to perform depth value correction processing on the bias degree data of the tilt depth sounder, so as to obtain depth value correction data of the single-beam depth sounder.

[0084] The above description is merely a specific implementation of the present application, and enables those skilled in the art to understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown herein, but is intended to conform to the widest range of equivalents consistent with the principles and novel features disclosed herein.

Claims

1. A method for measuring extremely shallow waters using an inclined single-beam echo sounder, characterized in that: The following steps are involved: Step S1: acquiring single-beam echo sounder operation data; identifying single-beam echo sounder measurement route data based on the single-beam echo sounder operation data; collecting measurement route environment changes based on the single-beam echo sounder measurement route data; performing echo sounder depth simulation based on the single-beam echo sounder measurement route data to obtain single-beam echo sounder depth simulation data; Step S2: detecting the dynamic disturbance response data of the sounding angle on the single-beam echo sounding simulation data according to the environmental changes along the measurement route; determining the coupling interference intensity of the echo sounder environmental measurement according to the environmental changes along the measurement route and the dynamic disturbance response data of the sounding angle; and determining the dynamic abnormality of the sound wave propagation path according to the coupling interference intensity of the echo sounder environmental measurement; Step S3: detecting the sound wave frequency drift phenomenon in extremely shallow waters based on the dynamic abnormality of the sound wave propagation path; predicting the depth sounder error expansion based on the sound wave frequency drift phenomenon in extremely shallow waters to obtain depth sounder error expansion growth data; Step S4: evaluating the degree of deviation of the tilt sounder based on the sounder error expansion growth data to obtain the degree of deviation of the tilt sounder; The single-beam echo sounder sounding value correction processing is performed on the deviation degree data of the inclined installation echo sounder to obtain the single-beam echo sounder sounding value correction data.

2. The extremely shallow water measurement method based on the tilted installation single beam echo sounder according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: Acquire single-beam echo sounder operating data; Step S12: determining the single-beam echo sounder installation data according to the single-beam echo sounder operation data; Step S13: identifying single-beam echo sounder measurement route data according to the single-beam echo sounder operation data; Step S14: collecting measurement route environment changes based on the single-beam echo sounder measurement route data; Step S15: performing a depth sounding simulation based on the single-beam depth sounder measurement route data and the single-beam depth sounder installation data to obtain single-beam depth sounder simulation data.

3. The extremely shallow water measurement method based on the tilted installation single beam echo sounder according to claim 2 is characterized in that: Step S15 The following steps are involved: Step S151: identifying single-beam echo sounder installation posture data based on the single-beam echo sounder installation data; Step S152: measuring the installation angle of the echo sounder according to the installation posture data of the single-beam echo sounder to obtain the tilt installation angle data of the echo sounder; Step S153: determining the trajectory change of the ship on which the echo sounder is installed based on the measurement route data of the single-beam echo sounder; Step S154: When the depth sounder installation hull trajectory changes and the depth sounder tilt installation angle data exceeds 9.5°, the depth sounder incident angle vertical offset is measured to obtain depth sounder incident angle vertical offset data; Step S155: determining the sound velocity echo path of the detector according to the vertical offset data of the depth sounder incident angle to obtain the sound velocity echo path data of the detector; Step S156: constructing a single-beam echo sounder simulation model based on the sound velocity echo path data of the detector and the trajectory change of the hull on which the echo sounder is installed; Step S157: performing a depth sounder simulation based on the single-beam depth sounder simulation model to obtain single-beam depth sounder simulation data.

4. The extremely shallow water measurement method based on the tilted installation single beam echo sounder according to claim 1 is characterized in that: In step S2, the dynamic disturbance response data of the sounding angle of the single-beam echo sounder sounding simulation data is detected according to the change of the measurement route environment, including: Analyze the time series changes of the measurement route environment according to the changes of the measurement route environment; Determine the installation hull joint disturbance factor data based on the time series changes of the measurement route environment; According to the joint disturbance factor data of the installation hull, the single-beam echo sounder sounding simulation data is used to identify the change in the posture of the hull where the echo sounder is installed; The hull posture change of the echo sounder is divided into the hull swing direction to obtain the hull swing data around the transverse axis and the hull tilt data around the longitudinal axis; Detect the change of the single-beam echo sounder's pitch angle based on the data of the ship's forward and backward swing around the transverse axis; Detect the change of the single-beam echo sounder's roll angle based on the left and right tilt data of the hull around the longitudinal axis; Determine the degree of change in the sound wave emission direction of the echo sounder based on the change in the pitch angle and the roll angle of the single-beam echo sounder; The dynamic disturbance response data of the sounding angle is detected according to the degree of change in the sound wave emission direction of the sounder.

5. The extremely shallow water measurement method based on the tilted installation single beam echo sounder according to claim 1 is characterized in that: In step S2, determining the coupling interference intensity of the sounder environment measurement according to the measurement route environment change and the sounding angle dynamic disturbance response data includes: Determine the wave strengthening trend along the measurement route based on the changes in the measurement route environment; Estimate the growth of the bubble layer on the water surface based on the wave enhancement trend of the measurement route; Detect the wear growth of the single-beam echo sounder based on the wave strengthening trend of the measurement route environment; The structural damage of the sounder fasteners was checked based on the wear growth of the single-beam sounder and the dynamic disturbance response data of the sounding angle; Detect the aggravation of the surface erosion of the probe according to the growth of the bubble layer on the water surface along the route; Estimate the environmental disturbance amplification of the inclined echo sounder based on the environmental wave enhancement trend along the measurement route; The coupling interference intensity of the echo sounder environmental measurement is determined based on the aggravated erosion of the probe surface and the amplification of the environmental disturbance of the inclination echo sounder.

6. The extremely shallow water measurement method based on the tilted installation single beam echo sounder according to claim 1 is characterized in that: Determining the dynamic abnormality of the sound wave propagation path according to the coupled interference intensity measured by the echo sounder environment in step S2 includes: The resonance aggravation of the single beam echo sounder is estimated based on the coupled interference intensity measured in the echo sounder environment; Detect the metal fatigue degree of the single-beam echo sounder based on the aggravated resonance condition of the single-beam echo sounder; Predicting the accumulation of microcracks in single-beam depth sounders based on their metal fatigue level; Calculate the collision probability data of the echosounder in extremely shallow waters based on the single-beam echosounder sounding simulation data; Estimate the micropore water seepage condition of the echo sounder based on the echo sounder collision probability data in extremely shallow waters and the accumulation of microcracks in the single-beam echo sounder; The dynamic abnormality of the sound wave propagation path is determined based on the micropore water seepage status of the depth sounder and the dynamic disturbance response data of the depth sounding angle.

7. The extremely shallow water measurement method based on an inclined-mounted single-beam echo sounder according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: determining the abnormal multipath interference of the depth sounder based on the dynamic abnormality of the sound wave propagation path; Step S32: predicting the single-beam echo sounder signal distortion condition based on the echo sounder multipath abnormal interference condition; Step S33: detecting the acoustic frequency drift phenomenon in extremely shallow waters based on the abnormal multipath interference of the echo sounder and the signal distortion of the single-beam echo sounder; Step S34: performing a depth sounder error expansion prediction based on the sound wave frequency drift phenomenon in extremely shallow waters to obtain depth sounder error expansion growth data.

8. The extremely shallow water measurement method based on the tilted installation single beam echo sounder according to claim 7 is characterized in that: Step S31 The following steps are involved: Step S311: detecting irregular reflection enhancement on the water surface based on abnormal conditions of the sound wave propagation path; Step S312: determining multiple reflection data of the sounding sound wave based on the irregular reflection enhancement condition of the water surface; Step S313: determining the echo overlap of the sounding sound waves based on the multiple reflection data of the sounding sound waves and the irregular reflection enhancement status of the water surface; Step S314: checking the distortion degree of the sounding sound wave waveform according to the overlapping condition of the sounding sound wave echoes; Step S315: checking the degree of position deviation of the depth sounder measuring point according to the abnormality of the sound wave propagation path; Step S316: When the sounding sound wave waveform distortion degree is greater than 8% and the sounder measuring point position offset degree is determined to be abnormal multipath interference of the sounder.

9. The extremely shallow water measurement method based on an inclined-mounted single-beam echo sounder according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: checking the sounding accuracy gradient attenuation condition based on the sounder error expansion growth data; Step S42: determining the attenuation trend of the sounder detection performance according to the deep precision gradient attenuation status and the sounder error expansion growth data; Step S43: evaluating the degree of deviation of the inclined echo sounder according to the attenuation trend of the echo sounder detection performance, and obtaining the degree of deviation data of the inclined echo sounder; Step S44: performing single-beam echo sounder depth value correction processing on the tilted-mounted echo sounder deviation degree data to obtain single-beam echo sounder depth value correction data.

10. An extremely shallow water measurement system based on an inclined-mounted single-beam echo sounder, characterized in that: For executing the extremely shallow water measurement method based on the tilted installation single-beam echo sounder according to claim 1, the extremely shallow water measurement system based on the tilted installation single-beam echo sounder comprises: The echo sounder simulation module is used to obtain the single-beam echo sounder operation data; identify the single-beam echo sounder measurement route data based on the single-beam echo sounder operation data; collect the measurement route environment changes based on the single-beam echo sounder measurement route data; perform echo sounder simulation based on the single-beam echo sounder measurement route data to obtain the single-beam echo sounder simulation data; The propagation path dynamic anomaly determination module is used to detect the dynamic disturbance response data of the sounding angle from the single-beam echo sounding simulation data according to the changes in the measurement route environment; determine the coupling interference intensity of the echo sounder environment measurement according to the changes in the measurement route environment and the dynamic disturbance response data of the sounding angle; and determine the dynamic anomaly of the sound wave propagation path according to the coupling interference intensity of the echo sounder environment measurement; The error expansion prediction module is used to detect the sound wave frequency drift phenomenon in extremely shallow waters based on the dynamic abnormal conditions of the sound wave propagation path; the error expansion of the echo sounder is predicted based on the sound wave frequency drift phenomenon in extremely shallow waters, and the echo sounder error expansion growth data is obtained; The depth sounding value correction processing module is used to evaluate the deviation degree of the inclined depth sounder based on the depth sounder error expansion growth data to obtain the deviation degree data of the inclined depth sounder; and to perform single-beam depth sounder depth sounding value correction processing on the deviation degree data of the inclined installed depth sounder to obtain single-beam depth sounder depth sounding value correction data.

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