An underwater 3D visualization intelligent survey system

Abstract

The present invention discloses an underwater 3D visualization intelligent survey system, which relates to the technical field of industrial solid waste treatment material management, and includes an ultrasonic sensor and a sonar sensor detection module: a water pressure sensor, an ultrasonic sensor and a sonar sensor are integrated on an underwater survey device to obtain sensor data of underwater detection in real time, and then the sensor data collected in real time is transmitted to a cloud data interaction processing module through a communication protocol; the present invention realizes an intelligent function integrating ultrasonic sensors, sonar sensors, automatic orientation technology, automatic water surface keeping technology, GPS satellite positioning technology, information denoising technology, and on-site and cloud data instant interaction technology by setting ultrasonic sensors and sonar sensor detection modules, a sensor automatic orientation module, an automatic water surface keeping module, a cloud data interaction processing module, a GPS satellite positioning module, and a data fusion analysis and calculation module.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial solid waste treatment material management, and in particular to an underwater 3D visualization intelligent survey system. Background Art

[0002] In underwater dredging operations, traditional river dredging earthwork measurement and auditing are crucial. The traditional underwater survey method is to first demarcate an area to be measured, and then the surveyors sit on the boat and use simple measuring tools to measure the water depth in the area to be measured. The commonly used measuring tools are bamboo poles, ropes, and manual measurements with ropes and iron nets below to calculate the earthwork volume.

[0003] In the traditional measurement process, an iron net is sent underwater, and the iron net is taken out after a knot is tied on the rope at the water surface. The length from the iron net to the knot is measured as the depth of the water, and then the measurement is made every 100 meters. The average value is calculated after multiple measurements. Secondly, a measurement is made before dredging, and a water depth value is recorded. After dredging, the measurement is made again, and another water depth value is recorded. Finally, the two water depth values ​​are subtracted to obtain the height of the clear silt, and then multiplied by the area of ​​the designated area to obtain the dredged earthwork volume. However, this underwater survey method has the problem that the accuracy of the measurement results caused by human errors and simple tools is difficult to guarantee, and it is easy to produce large errors, resulting in extremely poor accuracy and inaccurate measurement results. Manual measurement requires a lot of time and manpower, which is not only labor-intensive and backward in technique, but also requires measurement at intervals, which makes the overall work efficiency low. In addition, complex natural factors underwater will interfere with the measurement process, and it is difficult to eliminate the influence of water flow and waves, which further affects the accuracy, reliability and stability of the measurement.

[0004] Therefore, the traditional underwater silt volume measurement method has significant deficiencies in accuracy, efficiency, anti-interference ability and labor intensity. It is urgent to develop an underwater 3D visualization intelligent survey system to improve the measurement accuracy and efficiency, reduce environmental interference, and reduce labor intensity, so as to achieve accurate auditing of silt volume during underwater dredging.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0006] The purpose of the present invention is to provide an underwater 3D visualization intelligent survey system. The present invention solves the problems in the above-mentioned background technology by setting a sensor automatic orientation module, an automatic water surface keeping module, a cloud data interaction processing module, a GPS satellite positioning module, a natural factor influence correction and compensation module and a data fusion analysis and calculation module.

[0007] In order to achieve the above-mentioned purpose, the present invention provides the following technical solutions: an underwater 3D visualization intelligent survey system, including an ultrasonic sensor and a sonar sensor detection module: a water pressure sensor, an ultrasonic sensor and a sonar sensor are integrated on an underwater survey device to obtain sensor data of underwater detection in real time, and then the real-time collected sensor data is transmitted to a cloud data interaction processing module through a communication protocol for further denoising and data storage;

[0008] Sensor automatic orientation module: The sensor data collected in real time by the ultrasonic sensor and sonar sensor detection module are automatically oriented using the Kalman filter algorithm that fuses the gyroscope and magnetometer data, and the stable direction and positioning of the underwater survey device are further adjusted through real-time feedback;

[0009] Automatic surface keeping module: The sensor data collected in real time by the ultrasonic sensor and sonar sensor detection module are used to automatically keep the sensor on the surface using the PID control algorithm, and the buoyancy and posture of the underwater survey device are adjusted underwater through real-time feedback;

[0010] Cloud data interaction processing module: uses the real-time data transmission http protocol on the cloud platform to receive the sensor data obtained from the ultrasonic sensor and sonar sensor detection modules, and performs data storage, denoising and reading analysis to generate denoising data;

[0011] GPS satellite positioning module: Install a GPS satellite locator on the underwater survey device, use differential GPS / DGPS technology for high-precision positioning, and obtain the positioning signal of the measured position, and then synchronously transmit the positioning signal to the data fusion analysis and calculation module;

[0012] Natural factors impact correction and compensation module: establish an environmental error compensation model, combine the impact of natural factors on sensor measurement data, compare the measured sensor data with the environmental error compensation model, perform correction calculations, generate environmental correction data, and then pass the environmental correction data to the data fusion analysis and calculation module;

[0013] Data fusion analysis and calculation module: receives the denoising data from the cloud data interaction processing module, the positioning signal from the GPS satellite positioning module, and the environmental correction data from the natural factor impact correction and compensation module, calculates the earthwork volume using a multi-source data fusion algorithm and a numerical integration algorithm based on volume calculation, generates a desilting earthwork volume report based on the earthwork volume, and transmits it to the cloud data interaction processing module and the cloud data interaction processing module for interactive processing;

[0014] Human-computer interaction interface module: Receives the desilting volume report from the data fusion analysis and calculation module through the communication interface, uses a graphical user interface GUI design, provides a visual display of measurement data and an operation interface, monitors the measurement process in real time, and enables operators to view measurement data and analysis reports on mobile phones or computers.

[0015] Optionally, the steps of acquiring the sensor data are as follows:

[0016] The sensor data includes the water pressure sensor measuring the draft data, the ultrasonic sensor measuring underwater to obtain the depth data in real time, and the sonar sensor obtaining the river topography and silt distribution information in real time. The sensor data is calibrated as SD.

[0017] The draft depth data is obtained by measuring the water pressure value through the water pressure sensor and calibrating it as P W , to calculate the water depth, calibrated as H W , using the density of water, calibrated as ρ W and the acceleration due to gravity, calibrated as g W The pressure is converted into depth. The water pressure sensor measures the water pressure value in real time, and obtains the draft depth data through the water pressure measurement formula and the water depth conversion formula. That is, the water pressure measurement formula is P W =ρ W ×g W ×H W +P g0 , and P g0 ≈101325Pa, ρ W ≈1000kg / m 3 and g W ≈9.81m / s 2 , where P W It is the water pressure value measured by the water pressure sensor, in Pa, ρ W It is expressed as the density of the water to be tested, in kilograms per cubic meter, g W Expressed as the acceleration due to gravity in meters per second squared, H W It is expressed as the draft depth in meters; the water depth conversion formula is: The water pressure value measured in real time by the water pressure sensor can obtain the draft depth data H W ;

[0018] Ultrasonic data is obtained through ultrasonic sensors. The water depth is calculated by emitting ultrasonic pulses and measuring the time it takes for the ultrasonic pulses to reflect back from the bottom of the water. The underwater measurement time of ultrasonic emission and reception is calibrated as t W , to convert the water depth data and calibrate it to h W , to obtain the actual measured ultrasonic data, that is, the ultrasonic measurement time formula is t W=2h W / v W , and v W ≈1500m / s, where t W It is expressed as the measurement time of the ultrasonic wave emitted by the ultrasonic sensor being transmitted and received underwater, in seconds, h W Represents the water depth data measured by the ultrasonic sensor in meters, v W It is expressed as the propagation speed of the ultrasonic wave emitted by the ultrasonic sensor in the water, in meters per second; the conversion formula for water depth data is: Through the real-time measurement time of the ultrasonic sensor, the water depth data can be obtained. W ;

[0019] The actual depth data measured underwater is the sum of the draft depth data obtained by the water pressure sensor and the water depth data obtained by the ultrasonic sensor. The actual underwater depth data is calibrated as H WT , H WT =H W +h W ;

[0020] The acquisition of river channel topography and silt distribution information is done through sonar sensors, which emit sound waves and measure the signal time and signal strength of the sound waves reflected from the river bottom and silt. The distance to the river bottom and silt layer can be measured and calibrated as d W , based on the distance data acquired in real time, a three-dimensional topographic map and silt distribution map are generated using a three-dimensional reconstruction algorithm. The calculation formula for the distance data is: And V W ≈1500m / s, where d W It is expressed as the distance that the sonar sensor sends sound waves to detect the river bottom and silt layer, in meters, T W It is the measurement time of the sound waves emitted by the sonar sensor being transmitted and received underwater, in seconds, V W It is expressed as the propagation speed of the ultrasonic wave emitted by the sonar sensor in water, in meters per second. Then the sensor data SD = {H W ,h W , d W}.

[0021] Optionally, the steps of automatically orienting the sensor are as follows:

[0022] A1. By reading the corresponding data of the gyroscope and magnetometer in real time in the sensor data and performing denoising, including the angular velocity data in the gyroscope and the geomagnetic field data in the magnetometer, the rotational motion information and azimuth information of the underwater survey device integrating the water pressure sensor, the ultrasonic sensor and the sonar sensor are obtained. The angular velocity data of the gyroscope is read and calibrated as ω0=[ω x ,ωy ,ω z ], read the geomagnetic field data of the magnetometer, calibrated as B0 = [B x , B y , B z ];

[0023] A2. Then use the Kalman filter algorithm for data fusion, including predicting the current attitude angle state of the underwater survey device based on the angular velocity data of the gyroscope, and correcting the predicted state using the geomagnetic field data of the magnetometer, and updating the attitude information of the geomagnetic field data. The calculation formula for the initial state estimation is: and Where f(x0) is represented by a discrete state transfer matrix composed of angular velocity data ω0 and geomagnetic field data B0. They represent the three initial degrees of freedom of position, velocity and acceleration respectively, T represents the transposition, x0 represents the initial quantity of the angular velocity data ω0 and the geomagnetic field data B0 input, ω0 represents the angular velocity data of the gyroscope, and B0 represents the geomagnetic field data of the magnetometer;

[0024] The calculation formula for the predicted state is h(x0) k =f(x0)×h(x0) k-1 +b(x0) k ×u(x0) k , where h(x0) k It is expressed as the prior state estimate of the initial quantity x0 at time k, h(x0) k-1 It is expressed as the posterior state estimate of the initial quantity x0 at time k-1, b(x0) k It is represented as the control input matrix of the initial quantity x0 at time k, u(x0) k It is expressed as the control input of the initial quantity x0 at time k, and the prediction formula of the error covariance is P(x0) k =f(x0)×P(x0) k-1 ×f(x0) T +Q(x0) k , where P(x0) k It is expressed as the prior error covariance matrix of the initial quantity x0 at time k, P(x0) k-1 It is expressed as the posterior error covariance matrix of the initial quantity x0 at time k-1, Q(x0) k It is expressed as the process noise covariance matrix of the initial quantity x0 at time k;

[0025] The calculation formula for updating the state is h′(x0) k =h(x0) k-1 +K(x0) k ×(z(x0)k -H(x0) k ×h(x0) k-1 ), and K(x0) k =P(x0) k ×H(x0) k T ×H(x0) k ×P(x0) k-1 ×H(x0) k T +R(x0) k -1 , where h′(x0) k Expressed as the posterior state estimate of the initial quantity x0 at time k, K(x0) k Expressed as the Kalman gain of the initial quantity x0 at time k, z(x0) k It is expressed as the measured value of the initial quantity x0 at time k, H(x0) k It is expressed as the measurement matrix of the initial quantity x0 at time k, P(x0) k 、P(x0) k-1 They are respectively expressed as the prior error covariance matrix of the initial quantity x0 at time k and the posterior error covariance matrix of the initial quantity x0 at time k-1, R(x0) k It is expressed as the measurement noise covariance matrix of the initial quantity x0 at time k, and the update formula of the error covariance is P′(x0) k =(1-K(x0) k ×H(x0) k )×P(x0) k , where P′(x0) k It is represented as the posterior error covariance matrix of the initial quantity x0 updated at time k;

[0026] A3. Based on the predicted data after fusion of Kalman filter algorithm, the roll angle of underwater survey device is calculated and calibrated using quaternion method. Pitch angle, calibrated as and yaw angle, calibrated as attitude angle changes, then according to the predicted data results of the Kalman filter algorithm, the expression of the quaternion method is q0 = [h(x0)1, h(x0)2, h(x0)3, h(x0)4], where q0 is represented by a quaternion, h(x0)1, h(x0)2, h(x0)3, and h(x0)4 are respectively represented by the four components of the quaternion, and correspond to the angle and direction of rotation, that is, h(x0)1 is the first component of the rotation size around the positive direction of the x-axis, h(x0)2 is the second component of the rotation size around the positive direction of the y-axis, h(x0)3 is the third component of the rotation size around the positive direction of the z-axis, and h(x0)4 is the fourth component of the rotation size around the orthogonal axis. The calculation formula for the roll angle is The formula for calculating the pitch angle is: The calculation formula for the yaw angle is:

[0027] A4. Implement feedback mechanism in real time to monitor the attitude changes of underwater survey equipment and Pitch Angle and yaw angle The attitude angle changes, the PID controller is used to adjust the working state of the propeller and the rudder, and the attitude and direction of the underwater survey device are further automatically controlled to ensure the stable function and optimal working state of the sensor's automatic orientation. Among them, the control formula of the PID controller for the attitude angle change is: and as well as In the formula, They are respectively represented by the PID controller for the roll angle Pitch Angle and yaw angle The control output, Respectively expressed as the roll angle Pitch Angle and yaw angle The predicted value of Compared with the actual measured value The error value between Respectively expressed as PID to roll angle Pitch Angle and yaw angle The proportional gain, Respectively expressed as PID to roll angle Pitch Angle and yaw angle The integral gain, Respectively expressed as PID to roll angle Pitch Angle and yaw angle The differential gain.

[0028] Optionally, the steps of the sensor automatically maintaining the water surface are as follows:

[0029] B1. Obtain the real underwater depth data H through the draft depth data obtained by the water pressure sensor and the water depth data obtained by the ultrasonic sensor WT , as the current water surface height value;

[0030] B2. Obtain the roll angle based on the real-time monitoring of the attitude changes of the underwater survey device and pitch angle As a method of providing the current attitude value of the underwater survey device;

[0031] B3. Set the target height value of the standard water level in the water area to be measured and calibrate it as H th By collecting sensor data in real time, the PID controller calculates the error value and updates the control output to adjust the buoyancy and posture of the underwater survey device underwater, and further control the sensor to automatically maintain a stable working state on the water surface. The error calculation formula is: In the formula, e(H) k It is expressed as the target height value of the standard water level of the water area to be measured at time k and the actual underwater depth data H WT The difference, It is expressed as the target height value of the standard water level of the water area to be measured at time k. It is represented by the actual underwater depth data actually measured by the water pressure sensor and ultrasonic sensor at time k. The calculation formula of the PID control algorithm is: In the formula, U(H) k It is represented as the control output of PID controller for water depth, Kp(H), Ki(H), Kp(H) respectively represent the proportional gain, integral gain and differential gain for water depth regulation.

[0032] Optionally, the steps of acquiring the denoising data are as follows:

[0033] Receive sensor data from water pressure, ultrasonic sensors, and sonar sensors using HTTP protocol, store them in the cloud platform database, and then perform denoising on the data;

[0034] The signal processing algorithm uses a low-pass filter to process the data collected by the water pressure sensor and the ultrasonic sensor. WT To remove high-frequency noise, the filtering calculation formula of the low-pass filter is: In the formula, H′ WTn Expressed as the input signal H WTn After filtering, the signal H WTn Represents the input signal collected by n water pressure sensors and ultrasonic sensors, α iExpressed as the corresponding input signal H WTn-i The filter coefficients, β j Expressed as the corresponding filtered signal H′ WTn-j The filter coefficients, H′ WTn-j Represented as the njth input signal H WTn The filtered signal, i and n represent the i-th input signal H WTn and input signal H WTn The total number of, j, m represent the jth input signal H WTn The filtered signal H′ WTn H′ WTn and input signal H WTn The filtered signal H′ WTn The total number of

[0035] The convolutional neural network (CNN) algorithm is a method for analyzing the data collected by the sonar sensor. W Denoising is performed, including convolutional layer feature extraction and pooling layer downsampling, and then the denoised data is output after integration through the fully connected layer. The calculation formula for convolutional layer feature extraction is: And the calculation formula for downsampling in the pooling layer is d′ W (x,y)=Max{i,j}×d W (i+x,j+y), where d′ W (x,y) represents the value of the output feature map at position (x,y), d W (i+x,j+y) represents all input data d within the range of position (i+x,j+y) W , W(i,j) represents the convolution kernel weight, is represented as the bias term, k is the convolution kernel size, i and j are the distances that can be translated at the position (x, y), respectively. The training data is used to train the CNN model, and the loss function is optimized to adjust the model parameters. Then, the new sonar sensor data can be input into the trained CNN model to obtain the denoised data.

[0036] The denoised data obtained by denoising and storing in the cloud is calibrated as Dpd, then

[0037] Optionally, the steps of acquiring the positioning signal are as follows:

[0038] A GPS receiver is used to receive satellite signals containing satellite positions and timestamps, and differential GPS / DGPS technology is applied to calculate the error between the actual measured position and the known position, and obtain the correction error information, which is transmitted to a mobile GPS receiver in the underwater survey device, so that the mobile GPS receiver in the underwater survey device can perform high-precision position calculation in combination with the satellite signal and the correction error information, and obtain the calculated measurement time, longitude and latitude data, which are calibrated as Mst, Lng, Lat, that is, the positioning signal, and calibrated as GPS, then GPS = {Mst, Lng, Lat}.

[0039] Optionally, the steps of establishing the environmental error compensation model are as follows:

[0040] C1. Use the acoustic wave sensor and sonar sensor to detect sensor data affected by natural factors under different environmental conditions, and collect and record the corresponding environmental condition data, including tidal height, ocean current speed and environmental conditions of swimming organisms;

[0041] C2. Clean and filter the collected data to remove outliers and noise, match and synchronize the sensor measurement data with the corresponding environmental condition data, analyze the error between the sensor measurement data and the true value, find out the influence of environmental factors on the measurement data, and generate error analysis results;

[0042] C3. Based on the error analysis results, the random forest algorithm is used to establish an environmental error compensation model to fit the relationship between the sensor measurement data and the environmental condition data;

[0043] C4. Divide the measurement data under different environmental conditions into a training set and a validation set, wherein the training set data is used to train the environmental error compensation model, adjust the parameters of the environmental error compensation model, and optimize the performance of the environmental error compensation model. At the same time, the validation set is used to verify the accuracy and generalization ability of the environmental error compensation model to evaluate the error compensation effect of the environmental error compensation model;

[0044] C5. Optimize and adjust the environmental error compensation model based on the verification results, and update the model regularly as environmental conditions in natural factors change and new data are input.

[0045] Optionally, the steps of acquiring the environmental correction data are as follows:

[0046] The sensor collects and measures K kinds of sensor data affected by natural factors such as tide height, ocean current velocity and swimming biological activities, and calibrates them as SD K , and collect and record the corresponding environmental condition data, calibrated as ED K ;

[0047] The sensor data and environmental condition data are matched and synchronized by time alignment and interpolation to obtain a measurement data set, calibrated as Y, and Y = {(t 1 , S.D. 1 , E.D. 1 ), (t 2 , S.D. 2 , E.D. 2 ),…,(t K , S.D. K , E.D. K )}, and the denoised data Dpd calculated by the sensor after denoising without the influence of natural factors is taken as the true value;

[0048] The error analysis result is calculated based on the measured data set Y and the true value Dpd. The calculation formula for the error analysis result is: And Y-Dpd=(SD 1 -Dpd)+(SD 2 -Dpd)+…+(SD K -D), where e SD It represents the error analysis result of natural factors, K represents the K data affected by natural factors collected by the sensor, Y represents the measurement data set collected by the sensor under the environmental conditions affected by K natural factors, and Dpd represents the true value calculated by the sensor after denoising without the influence of natural factors;

[0049] Use linear regression analysis algorithm to describe environmental condition data ED K The error analysis results SD The degree of influence of is calculated and the regression coefficient is obtained. The calculation formula of the regression coefficient is e SD =η0+η1×ED 1 +η2×ED 2 +…+η K ×ED K +E0, and η′=((ED K ) T ×ED K ) -1 ×(ED K ) T ×E0, where, ED 1 , ED 2 ,…,ED K They are respectively represented as environmental condition data affected by K kinds of natural factors, η0 is represented as a constant of the regression coefficient vector, η1, η2, …, η K Respectively expressed as the corresponding environmental condition data ED KThe regression coefficient vector, E0 represents the residual vector of linear regression, η′ represents the predicted regression coefficient in the linear regression analysis algorithm, and T represents the transpose;

[0050] By evaluating the fitting effect of the linear regression model, the environmental correction data is calculated and calibrated as Ecd to identify and reduce the impact of environmental factors on the error. The calculation formula for the environmental correction data is: Where Ecd represents the environmental correction data, E0 represents the residual vector of linear regression, η′ represents the predicted regression coefficient in the linear regression analysis algorithm, and e SD It is expressed as the error analysis result.

[0051] Optionally, the data processing steps of the multi-source data fusion algorithm are as follows:

[0052] Collect the denoising data Dpd, positioning signal GPS and environmental correction data Ecd, and perform data standardization respectively. The calculation formula for data standardization is: and Where Dpd 0 、GPS 0 、Ecd 0 They respectively represent the normalized data of the denoised data Dpd, the positioning signal Gps and the environmental correction data Ecd after data normalization processing, and Dpd, Gps and Ecd respectively represent the original data of the denoised data Dpd, the positioning signal Gps and the environmental correction data Ecd, They are respectively represented as the average values ​​of the original data of the denoised data Dpd, the positioning signal Gps and the environmental correction data Ecd, σ Dpd , σ Gps , σ Ecd They are respectively represented as the standard deviation of the original data of the denoised data Dpd, the positioning signal Gps and the environmental correction data Ecd;

[0053] Use the weighted average method to standardize data Dpd from different sources 0 、GPS 0 、Ecd 0 Fusion is performed to generate fused data. The calculation formula for fused data is FD = W1 × Dpd 0 +W2×Gps 0 +W3×Ecd 0 , and W1+W2+W3=1, where FD represents the standardized data Dpd from different sources 0 、GPS 0 、Ecd 0 The fusion data W1, W2, and W3 are represented as corresponding to the standardized data Dpd 0 、GPS 0、Ecd 0 The weight value of

[0054] Then use Kalman filtering to dynamically update and correct the fusion data FD, including prediction calculation and update calculation. The prediction calculation formula is H(FD) t =A FD ×H(FD) t-1 +B FD ×U(FD) t , and P(FD) t =A FD ×P(FD) t-1 ×A FD T +Q(FD) t , where H(FD) t It is represented as the state vector predicted by the fusion data FD at the current time step t, A FD It is represented as the state transfer matrix of the fused data FD, H(FD) t-1 It is represented as the state vector predicted by the fusion data FD at the previous time step t-1, B FD It is represented as the control matrix of the fused data FD, U(FD) t It is represented as the control input of the fusion data FD at the current time step t, P(FD) t It is represented as the covariance matrix of the fusion data FD prediction at the current time step t, P(FD) t-1 It is represented as the covariance matrix of the fusion data FD prediction at the previous time step t-1, Q(FD) t It is represented as the process noise covariance matrix of the fused data FD at the current time step t, T is represented as the transpose, and t and t-1 represent the current time step and the previous time step respectively;

[0055] The updated calculation formula is K(FD) t =P(FD) t ×L(FD) t T ×(L(FD) t ×P(FD) t ×L(FD) t T +R(FD) t ) -1 , and H(FD) t+1 =H(FD) t +K(FD) t ×(Z(FD) t -L(FD) t ×H(FD) t ), and P(FD) t+1 =(1-K(FD)t ×L(FD) t )×P(FD) t , where K(FD) t It is represented as the Kalman gain of the fused data FD at the current time step t, L(FD) t Represented as the observation matrix of the fused data FD at the current time step t, R(FD) t It is represented as the observation noise covariance matrix of the fused data FD at the current time step t, H(FD) t+1 It is represented as the state vector predicted by the fusion data FD at the next time step t+1 after the update, Z(FD) t It is represented as the observation vector of the fused data FD at the current time step t, P(FD) t+1 It is represented as the covariance matrix of the prediction of the fused data FD at the next time step t+1 after the update.

[0056] Optionally, the steps of calculating the earthwork volume value using the numerical integration algorithm are as follows:

[0057] The river topography and silt distribution information obtained by the sonar sensor is represented in the form of discrete point cloud data, and the discrete point area is divided into a number of unit grids, and the side length of each unit grid is represented by a and b;

[0058] Set the height of each grid's base surface to h th , calculate the height difference of each mesh vertex, then the height difference calculation formula is Δhd(u,v)=hd(u,v)-h th , where Δhd(u,v) represents the height difference, hd(u,v) represents the height of the (u,v)th grid vertex, and (u,v) represents the variable values ​​corresponding to the horizontal and vertical axes in the grid area;

[0059] The volume of each grid is calculated using the numerical integration method. The volume calculation formula is: Where Vg(u,v) represents the volume of the (u,v)th grid, hd(u,v), hd(u+1,v), hd(u,v+1), hd(u+1,v+1) represent the heights of the four vertices of a single grid, and a and b represent the side lengths of each unit grid respectively;

[0060] The total earthwork volume is obtained by summing up the volumes of several grids. The calculation formula for the total earthwork volume is: In the formula, Vte represents the total earthwork volume, Vg(u,v) represents the volume of the (u,v)th grid, rows and cols represent the number of rows and columns of the grid respectively;

[0061] The numerical calculation results of the total earthwork volume are collated to generate reports with visual topographic maps and volume change diagrams.

[0062] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0063] The present invention realizes the intelligent function of integrating ultrasonic sensors, sonar sensors, automatic orientation technology, automatic water surface maintenance technology, GPS satellite positioning technology, information denoising technology, and instant interaction technology between on-site and cloud data by arranging ultrasonic sensors and sonar sensor detection modules, sensor automatic orientation module, automatic water surface maintenance module, cloud data interaction processing module, GPS satellite positioning module, and data fusion analysis and calculation module, thereby improving the automatic analysis and calculation of collected data, and the ability to accurately, stably and efficiently perform lake bottom dredging measurement and calculation, further improving the highly friendly human-computer interaction, achieving the effect of completing the accurate measurement of river channel topography and silt distribution in an integrated manner, and generating a visualized dredging earthwork volume report for users to analyze and make decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 It is a module schematic diagram of the underwater 3D visualization intelligent survey system of the present invention.

[0065] Figure 2 It is a schematic diagram of the GUI on a mobile phone or computer in the human-computer interaction interface module of the present invention.

[0066] Figure 3 It is a schematic diagram of a 3D visualization report of the river topography and silt distribution of the present invention. DETAILED DESCRIPTION

[0067] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0068] The present invention provides Figure 1-3 An underwater 3D visualization intelligent survey system shown includes an ultrasonic sensor and a sonar sensor detection module: a water pressure sensor, an ultrasonic sensor and a sonar sensor are integrated on the underwater survey device to obtain sensor data of underwater detection in real time, the sensor data includes draft depth data measured by the water pressure sensor, depth data obtained in real time by the ultrasonic sensor during underwater measurement, and river topography and silt distribution information obtained in real time by the sonar sensor, and then the sensor data collected in real time is transmitted to the cloud data interaction processing module through a communication protocol for further denoising and data storage;

[0069] It is supplemented that the underwater survey device includes a wired and wireless communication unit, a water pressure sensor, an ultrasonic sensor, a sonar sensor, a float, a counterweight ball, a positioning and orientation device, a waterproof and corrosion-resistant shell and a battery structure, which is installed on a ship. During underwater measurement, each sensor on the underwater survey device measures the corresponding data, that is, the water pressure sensor measures the water depth data in real time. After the ultrasonic sensor is powered on and receives instructions, it transmits an ultrasonic signal and receives a reflected signal to obtain ultrasonic data for measuring the water depth. The sonar sensor controls the emission of sound wave signals and receives reflected signals through wireless communication to generate a sonar signal of river channel topography and silt distribution information. The sensor data is transmitted to the cloud data interaction processing module for denoising and storage through the communication protocol and the wired communication port connection. The battery provides power to the underwater survey device, and the wired and wireless communication unit is for the sensor to collect signals and transmit data to the cloud data interaction processing module. As for the float, counterweight ball, positioning and orientation device, and waterproof and corrosion-resistant shell, they protect the internal electronic components and sensors of the underwater survey device as a whole, adjust buoyancy, block water flow impact, and control the movement and direction of the device.

[0070] Specifically, the steps for obtaining sensor data are as follows:

[0071] The sensor data includes the water pressure sensor measuring the draft data, the ultrasonic sensor measuring underwater to obtain the depth data in real time, and the sonar sensor obtaining the river topography and silt distribution information in real time. The sensor data is calibrated as SD.

[0072] The draft depth data is obtained by measuring the water pressure value through the water pressure sensor and calibrating it as P W , to calculate the water depth, calibrated as H W , using the density of water, calibrated as ρ W and the acceleration due to gravity, calibrated as g W The pressure is converted into depth. The water pressure sensor measures the water pressure value in real time, and obtains the draft depth data through the water pressure measurement formula and the water depth conversion formula. That is, the water pressure measurement formula is P W =ρ W ×g W ×H W +P g0 , and P g0 ≈101325Pa, ρ W ≈1000kg / m 3 and g W ≈9.81m / s 2 , where P W It is the water pressure value measured by the water pressure sensor, in Pa, ρ W It is expressed as the density of the water to be tested, in kilograms per cubic meter, g WExpressed as the acceleration due to gravity in meters per second squared, H W It is expressed as the draft depth in meters; the water depth conversion formula is: The water pressure value measured in real time by the water pressure sensor can obtain the draft depth data H W ;

[0073] Ultrasonic data is obtained through ultrasonic sensors. The water depth is calculated by emitting ultrasonic pulses and measuring the time it takes for the ultrasonic pulses to be reflected from the bottom of the water. The underwater measurement time of ultrasonic emission and reception is calibrated as t W , to convert the water depth data and calibrate it to h W , to obtain the actual measured ultrasonic data, that is, the ultrasonic measurement time formula is t W =2h W / v W , and v W ≈1500m / s, where t W It is expressed as the measurement time of the ultrasonic wave emitted by the ultrasonic sensor being transmitted and received underwater, in seconds, h W Represents the water depth data measured by the ultrasonic sensor in meters, v W It is expressed as the propagation speed of the ultrasonic wave emitted by the ultrasonic sensor in the water, in meters per second; the conversion formula for water depth data is: Through the real-time measurement time of the ultrasonic sensor, the water depth data can be obtained. W ;

[0074] The actual depth data measured underwater is the sum of the draft depth data obtained by the water pressure sensor and the water depth data obtained by the ultrasonic sensor. The actual underwater depth data is calibrated as H WT , H WT =H W +h W ;

[0075] The acquisition of river channel topography and silt distribution information is done through sonar sensors, which emit sound waves and measure the signal time and signal strength of the sound waves reflected from the river bottom and silt. The distance to the river bottom and silt layer can be measured and calibrated as d W , based on the distance data acquired in real time, a three-dimensional topographic map and silt distribution map are generated using a three-dimensional reconstruction algorithm. The calculation formula for the distance data is: And V W ≈1500m / s, where d W It is expressed as the distance that the sonar sensor sends sound waves to detect the river bottom and silt layer, in meters, T W It is the measurement time of the sound waves emitted by the sonar sensor being transmitted and received underwater, in seconds, V WIt is expressed as the propagation speed of the ultrasonic wave emitted by the sonar sensor in water, in meters per second. Then the sensor data SD = {H W ,h W , d W};

[0076] The 3D reconstruction algorithm converts the 2D sampling data into a 3D model, which is used to extract isosurfaces from the 3D scalar field and generate polygonal meshes. The sonar sensor transmits sound waves and measures the signal time and signal strength of the sound waves reflected from the river bottom and silt, which are organized into a 3D scalar field to represent the sound wave reflection intensity values ​​at different underwater locations.

[0077] Based on the intensity of the signal reflected from the river bottom and silt by the sonar sensor, the Marching Cubes algorithm is used to set the threshold range that can be used to distinguish the river bottom, silt layer and water body;

[0078] Divide the three-dimensional scalar field into several small cubes, and each cube has 8 vertices, and each vertex has a scalar value;

[0079] Determine whether the scalar values ​​of the eight vertices of each cube are greater than a threshold range, determine whether the eight vertices of each cube are within the isosurface, and generate an 8-bit binary index to represent the state of the eight vertices of the cube;

[0080] According to the generated binary index, the edge intersection of each cube is searched in a lookup table containing all 256 possible vertex state combinations and corresponding edge intersection points;

[0081] The linear interpolation method is used to calculate the exact position of each edge intersection in the cube, and the corresponding triangles are generated using the triangulation table containing the triangle edges corresponding to each binary index combination, and each cube can generate 0-5 triangles;

[0082] The triangles generated by all the cubes are gathered together to construct a complete three-dimensional polygonal grid structure model of the riverbed and silt, thus obtaining the river topography and silt distribution information.

[0083] Sensor automatic orientation module: The sensor data collected in real time by the ultrasonic sensor and sonar sensor detection module are automatically oriented using the Kalman filter algorithm that fuses the gyroscope and magnetometer data. The underwater stable direction and positioning of the underwater survey device are further adjusted through real-time feedback to maintain the best working state.

[0084] Specifically, the steps for automatic sensor orientation are as follows:

[0085] A1. By reading the corresponding data of the gyroscope and magnetometer in real time in the sensor data and performing denoising, including the angular velocity data in the gyroscope and the geomagnetic field data in the magnetometer, the rotational motion information and azimuth information of the underwater survey device integrating the water pressure sensor, the ultrasonic sensor and the sonar sensor are obtained. The angular velocity data of the gyroscope is read and calibrated as ω0=[ω x ,ω y ,ω z ], read the geomagnetic field data of the magnetometer, calibrated as B0 = [B x , B y , B z ];

[0086] A2. Then use the Kalman filter algorithm for data fusion, including predicting the current attitude angle state of the underwater survey device based on the angular velocity data of the gyroscope, and correcting the predicted state using the geomagnetic field data of the magnetometer, and updating the attitude information of the geomagnetic field data. The calculation formula for the initial state estimation is: and Where f(x0) is represented by a discrete state transfer matrix composed of angular velocity data ω0 and geomagnetic field data B0. They represent the three initial degrees of freedom of position, velocity and acceleration respectively, T represents the transposition, x0 represents the initial quantity of the angular velocity data ω0 and the geomagnetic field data B0 input, ω0 represents the angular velocity data of the gyroscope, and B0 represents the geomagnetic field data of the magnetometer;

[0087] The calculation formula for the predicted state is h(x0) k =f(x0)×h(x0) k-1 +b(x0) k ×u(x0) k , where h(x0) k It is expressed as the prior state estimate of the initial quantity x0 at time k, h(x0) k-1 It is expressed as the posterior state estimate of the initial quantity x0 at time k-1, b(x0) k It is represented as the control input matrix of the initial quantity x0 at time k, u(x0) k It is expressed as the control input of the initial quantity x0 at time k, and the prediction formula of the error covariance is P(x0) k =f(x0)×P(x0) k-1 ×f(x0) T +Q(x0) k , where P(x0) k It is expressed as the prior error covariance matrix of the initial quantity x0 at time k, P(x0) k-1 It is expressed as the posterior error covariance matrix of the initial quantity x0 at time k-1, Q(x0)k It is expressed as the process noise covariance matrix of the initial quantity x0 at time k;

[0088] The calculation formula for updating the state is h′(x0) k =h(x0) k-1 +K(x0) k ×(z(x0) k -H(x0) k ×h(x0) k-1 ), and K(x0) k =P(x0) k ×H(x0) k T ×H(x0) k ×P(x0) k-1 ×H(x0) k T +R(x0) k -1 , where h′(x0) k Expressed as the posterior state estimate of the initial quantity x0 at time k, K(x0) k Expressed as the Kalman gain of the initial quantity x0 at time k, z(x0) k It is expressed as the measured value of the initial quantity x0 at time k, H(x0) k It is expressed as the measurement matrix of the initial quantity x0 at time k, P(x0) k 、P(x0) k-1 They are respectively expressed as the prior error covariance matrix of the initial quantity x0 at time k and the posterior error covariance matrix of the initial quantity x0 at time k-1, R(x0) k It is expressed as the measurement noise covariance matrix of the initial quantity x0 at time k, and the update formula of the error covariance is P′(x0) k =(1-K(x0) k ×H(x0) k )×P(x0) k , where P′(x0) k It is represented as the posterior error covariance matrix of the initial quantity x0 updated at time k;

[0089] A3. Based on the predicted data after fusion by Kalman filter algorithm, the roll angle of the underwater survey device is calculated using the quaternion method and calibrated as p′ x0 , pitch angle, calibrated as and yaw angle, calibrated as attitude angle changes, then according to the predicted data results of the Kalman filter algorithm, the expression of the quaternion method is q0 = [h(x0)1, h(x0)2, h(x0)3, h(x0)4], where q0 is represented by a quaternion, h(x0)1, h(x0)2, h(x0)3, and h(x0)4 are respectively represented by the four components of the quaternion, and correspond to the angle and direction of rotation, that is, h(x0)1 is the first component of the rotation size around the positive direction of the x-axis, h(x0)2 is the second component of the rotation size around the positive direction of the y-axis, h(x0)3 is the third component of the rotation size around the positive direction of the z-axis, and h(x0)4 is the fourth component of the rotation size around the orthogonal axis. The calculation formula for the roll angle is The formula for calculating the pitch angle is: The calculation formula for the yaw angle is:

[0090] A4. Implement feedback mechanism in real time to monitor the attitude changes of underwater survey equipment and Pitch Angle and yaw angle The attitude angle changes, the PID controller is used to adjust the working state of the propeller and the rudder, and the attitude and direction of the underwater survey device are further automatically controlled to ensure the stable function and optimal working state of the sensor's automatic orientation. Among them, the control formula of the PID controller for the attitude angle change is: and as well as In the formula, They are respectively represented by the PID controller for the roll angle Pitch Angle and yaw angle The control output, They are respectively expressed as the roll angle p′ x0 , Pitch angle and yaw angle The predicted value of Compared with the actual measured value The error value between Respectively expressed as PID to roll angle Pitch Angle and yaw angle The proportional gain, Respectively expressed as PID to roll angle Pitch Angle and yaw angle The integral gain, Respectively expressed as PID to roll angle Pitch Angle and yaw angle The differential gain.

[0091] Automatic surface keeping module: The sensor data collected in real time by the ultrasonic sensor and sonar sensor detection module are used to automatically keep the sensor on the water surface using the PID control algorithm. The buoyancy and posture of the underwater survey device are adjusted underwater through real-time feedback to ensure the stability of the underwater survey device on the water surface.

[0092] Specifically, the steps for the sensor to automatically maintain the water surface are as follows:

[0093] B1. Obtain the real underwater depth data H through the draft depth data obtained by the water pressure sensor and the water depth data obtained by the ultrasonic sensor WT , as the current water surface height value;

[0094] B2. Obtain the roll angle based on the real-time monitoring of the attitude changes of the underwater survey device and pitch angle As a method of providing the current attitude value of the underwater survey device;

[0095] B3. Set the target height value of the standard water level in the water area to be measured and calibrate it as H th By collecting sensor data in real time, the PID controller calculates the error value and updates the control output to adjust the buoyancy and posture of the underwater survey device underwater, and further control the sensor to automatically maintain a stable working state on the water surface. The error calculation formula is: In the formula, e(H) k It is expressed as the target height value of the standard water level of the water area to be measured at time k and the actual underwater depth data H WT The difference, It is expressed as the target height value of the standard water level of the water area to be measured at time k. It is represented by the actual underwater depth data actually measured by the water pressure sensor and ultrasonic sensor at time k. The calculation formula of the PID control algorithm is: In the formula, U(H) k It is represented as the control output of PID controller for water depth, Kp(H), Ki(H), Kp(H) respectively represent the proportional gain, integral gain and differential gain for water depth regulation.

[0096] Cloud data interaction processing module: Use the real-time data transmission http protocol on the cloud platform to receive the sensor data obtained from the ultrasonic sensor and sonar sensor detection module, and perform data storage, denoising and reading analysis to generate denoised data. The denoising data includes using signal processing algorithms to filter out noise and calculate the ultrasonic data obtained by the ultrasonic sensor to obtain water depth data, and using the convolutional neural network CNN algorithm to denoise the river topography and silt distribution information obtained by the sonar sensor, and using a three-dimensional reconstruction algorithm to generate a 3D model of the river topography and silt distribution to improve the image quality of the 3D model;

[0097] Specifically, the steps for obtaining denoising data are as follows:

[0098] Receive sensor data from water pressure, ultrasonic sensors, and sonar sensors using HTTP protocol, store them in the cloud platform database, and then perform denoising on the data;

[0099] The signal processing algorithm uses a low-pass filter to process the data collected by the water pressure sensor and the ultrasonic sensor. WT To remove high-frequency noise, the filtering calculation formula of the low-pass filter is: In the formula, H′ WTn Expressed as the input signal H WTn After filtering, the signal H WTn Represents the input signal collected by n water pressure sensors and ultrasonic sensors, α i Expressed as the corresponding input signal H WTn-i The filter coefficients, β j Expressed as the corresponding filtered signal H′ WTn-j The filter coefficients, H′ WTn-j Represented as the njth input signal H WTn The filtered signal, i and n represent the i-th input signal H WTn and input signal H WTn The total number of, j, m represent the jth input signal H WTn The filtered signal H′ WTn H′ WTn and input signal H WTn The filtered signal H′ WTn The total number of

[0100] The convolutional neural network (CNN) algorithm is a method for analyzing the data collected by the sonar sensor. W Denoising is performed, including convolutional layer feature extraction and pooling layer downsampling, and then the denoised data is output after integration through the fully connected layer. The calculation formula for convolutional layer feature extraction is: And the calculation formula for downsampling in the pooling layer is d′ W(x,y)=Max{i,j}×d W (i+x,j+y), where d′ W (x,y) represents the value of the output feature map at position (x,y), d W (i+x,j+y) represents all input data d within the range of position (i+x,j+y) W , W(i,j) represents the convolution kernel weight, is represented as the bias term, k is the convolution kernel size, i and j are the distances that can be translated at the position (x, y), respectively. The training data is used to train the CNN model, and the loss function is optimized to adjust the model parameters. Then, the new sonar sensor data can be input into the trained CNN model to obtain the denoised data.

[0101] The denoised data obtained by denoising and storing in the cloud is calibrated as Dpd, then

[0102] GPS satellite positioning module: Install a GPS satellite locator on the underwater survey device, use differential GPS / DGPS technology for high-precision positioning, and obtain the positioning signal of the measured position. The positioning signal includes the measurement time and the longitude and latitude data of the measurement position, and then synchronously transmit the positioning signal to the data fusion analysis and calculation module;

[0103] Specifically, the steps for obtaining the positioning signal are as follows:

[0104] A GPS receiver is used to receive satellite signals containing satellite positions and timestamps, and differential GPS / DGPS technology is applied to calculate the error between the actual measured position and the known position, and obtain the correction error information, which is transmitted to a mobile GPS receiver in the underwater survey device, so that the mobile GPS receiver in the underwater survey device can perform high-precision position calculation in combination with the satellite signal and the correction error information, and obtain the calculated measurement time, longitude and latitude data, which are calibrated as Mst, Lng, Lat, that is, the positioning signal, and calibrated as GPS, then GPS = {Mst, Lng, Lat}.

[0105] Natural factors impact correction and compensation module: establish an environmental error compensation model, combine the impact of natural factors such as tidal changes and swimming organisms on sensor measurement data, compare the measured sensor data with the environmental error compensation model, perform correction calculations, generate environmental correction data, and then pass the environmental correction data to the data fusion analysis and calculation module to eliminate the impact of natural factors in the environment on the initial measurement results;

[0106] Specifically, the steps for establishing the environmental error compensation model are as follows:

[0107] C1. Use the acoustic wave sensor and sonar sensor to detect sensor data affected by natural factors under different environmental conditions, and collect and record the corresponding environmental condition data, including tidal height, ocean current speed and environmental conditions of swimming organisms;

[0108] C2. Clean and filter the collected data to remove outliers and noise, match and synchronize the sensor measurement data with the corresponding environmental condition data, analyze the error between the sensor measurement data and the true value, find out the influence of environmental factors on the measurement data, and generate error analysis results;

[0109] C3. Based on the error analysis results, the random forest algorithm is used to establish an environmental error compensation model to fit the relationship between the sensor measurement data and the environmental condition data;

[0110] C4. Divide the measurement data under different environmental conditions into a training set and a validation set, wherein the training set data is used to train the environmental error compensation model, adjust the parameters of the environmental error compensation model, and optimize the performance of the environmental error compensation model. At the same time, the validation set is used to verify the accuracy and generalization ability of the environmental error compensation model to evaluate the error compensation effect of the environmental error compensation model;

[0111] C5. Optimize and adjust the environmental error compensation model based on the verification results, and update the model regularly as environmental conditions in natural factors change and new data are input.

[0112] Specifically, the steps for obtaining environmental correction data are as follows:

[0113] The sensor collects and measures K kinds of sensor data affected by natural factors such as tide height, ocean current velocity and swimming biological activities, and calibrates them as SD K , and collect and record the corresponding environmental condition data, calibrated as ED K ;

[0114] The sensor data and environmental condition data are matched and synchronized by time alignment and interpolation to obtain a measurement data set, calibrated as Y, and Y = {(t 1 , S.D. 1 , E.D. 1 ), (t 2 , S.D. 2 , E.D. 2 ),…,(t K , S.D. K , E.D. K )}, and the denoised data Dpd calculated by the sensor after denoising without the influence of natural factors is taken as the true value;

[0115] The error analysis result is calculated based on the measured data set Y and the true value Dpd. The calculation formula for the error analysis result is: And Y-Dpd=(SD 1 -Dpd)+(SD 2 -Dpd)+…+(SD K -D), where e SD It represents the error analysis result of natural factors, K represents the K data affected by natural factors collected by the sensor, Y represents the measurement data set collected by the sensor under the environmental conditions affected by K natural factors, and Dpd represents the true value calculated by the sensor after denoising without the influence of natural factors;

[0116] Use linear regression analysis algorithm to describe environmental condition data ED K The error analysis results SD The degree of influence of is calculated and the regression coefficient is obtained. The calculation formula of the regression coefficient is e SD =η0+η1×ED 1 +η2×ED 2 +…+η K ×ED K +E0, and η′=((ED K ) T ×ED K ) -1 ×(ED K ) T ×E0, where, ED 1 , ED 2 ,…,ED K They are respectively represented as environmental condition data affected by K kinds of natural factors, η0 is represented as a constant of the regression coefficient vector, η1, η2, …, η K Respectively expressed as the corresponding environmental condition data ED K The regression coefficient vector, E0 represents the residual vector of linear regression, η′ represents the predicted regression coefficient in the linear regression analysis algorithm, and T represents the transpose;

[0117] By evaluating the fitting effect of the linear regression model, the environmental correction data is calculated and calibrated as Ecd to identify and reduce the impact of environmental factors on the error. The calculation formula for the environmental correction data is: Where Ecd represents the environmental correction data, E0 represents the residual vector of linear regression, η′ represents the predicted regression coefficient in the linear regression analysis algorithm, and e SD It is expressed as the error analysis result.

[0118] Data fusion analysis and calculation module: receives the denoising data in the cloud data interaction processing module, the positioning signal in the GPS satellite positioning module, and the environmental correction data in the natural factor impact correction and compensation module, uses the multi-source data fusion algorithm and the numerical integration algorithm based on volume calculation to calculate the earthwork volume value, generates the desilting earthwork volume report according to the earthwork volume value, and transmits it to the cloud data interaction processing module and the cloud data interaction processing module for interactive processing;

[0119] Specifically, the data processing steps of the multi-source data fusion algorithm are as follows:

[0120] The denoised data Dpd, positioning signal GPS and environmental correction data Ecd are collected and standardized respectively to facilitate comparison and fusion on the same scale. The calculation formula for data standardization is: and In the formula, They respectively represent the normalized data of the denoised data Dpd, the positioning signal Gps and the environmental correction data Ecd after data normalization processing, and Dpd, Gps and Ecd respectively represent the original data of the denoised data Dpd, the positioning signal Gps and the environmental correction data Ecd, They are respectively represented as the average values ​​of the original data of the denoised data Dpd, the positioning signal Gps and the environmental correction data Ecd, σ Dpd , σ Gps , σ Ecd They are respectively represented as the standard deviation of the original data of the denoised data Dpd, the positioning signal Gps and the environmental correction data Ecd;

[0121] Use the weighted average method to standardize data Dpd from different sources 0 、GPS 0 、Ecd 0 Fusion is performed to generate fused data. The calculation formula for fused data is FD = W1 × Dpd 0 +W2×Gps 0 +W3×Ecd 0 , and W1+W2+W3=1, where FD represents the standardized data Dpd from different sources 0 、GPS 0 、Ecd 0 The fusion data W1, W2, and W3 are represented as corresponding to the standardized data Dpd 0 、GPS 0 、Ecd 0 The weight value of

[0122] Then use Kalman filtering to dynamically update and correct the fusion data FD, including prediction calculation and update calculation. The prediction calculation formula is H(FD) t =A FD ×H(FD) t-1 +B FD ×U(FD) t , and P(FD) t =A FD ×P(FD) t-1 ×A FD T +Q(FD) t , where H(FD) t It is represented as the state vector predicted by the fusion data FD at the current time step t, A FD It is represented as the state transfer matrix of the fused data FD, H(FD) t-1 It is represented as the state vector predicted by the fusion data FD at the previous time step t-1, B FD It is represented as the control matrix of the fused data FD, U(FD) t It is represented as the control input of the fusion data FD at the current time step t, P(FD) t It is represented as the covariance matrix of the fusion data FD prediction at the current time step t, P(FD) t-1 It is represented as the covariance matrix of the fusion data FD prediction at the previous time step t-1, Q(FD) t It is represented as the process noise covariance matrix of the fused data FD at the current time step t, T is represented as the transpose, and t and t-1 represent the current time step and the previous time step respectively;

[0123] The updated calculation formula is K(FD) t =P(FD) t ×L(FD) t T ×(L(FD) t ×P(FD) t ×L(FD) t T +R(FD) t ) -1 , and H(FD) t+1 =H(FD) t +K(FD) t ×(Z(FD) t -L(FD) t ×H(FD) t ), and P(FD) t+1 =(1-K(FD) t ×L(FD) t )×P(FD) t, where K(FD) t It is represented as the Kalman gain of the fused data FD at the current time step t, L(FD) t Represented as the observation matrix of the fused data FD at the current time step t, R(FD) t It is represented as the observation noise covariance matrix of the fused data FD at the current time step t, H(FD) t+1 It is represented as the state vector predicted by the fusion data FD at the next time step t+1 after the update, Z(FD) t It is represented as the observation vector of the fused data FD at the current time step t, P(FD) t+1 It is represented as the covariance matrix of the prediction of the fused data FD at the next time step t+1 after the update.

[0124] Specifically, the steps for calculating the earthwork volume using the numerical integration algorithm are as follows:

[0125] The river topography and silt distribution information obtained by the sonar sensor is represented in the form of discrete point cloud data, and the discrete point area is divided into a number of unit grids, and the side length of each unit grid is represented by a and b;

[0126] Set the height of each grid's base surface to h th , calculate the height difference of each mesh vertex, then the height difference calculation formula is Δhd(u,v)=hd(u,v)-h th , where Δhd(u,v) represents the height difference, hd(u,v) represents the height of the (u,v)th grid vertex, and (u,v) represents the variable values ​​corresponding to the horizontal and vertical axes in the grid area;

[0127] The volume of each grid is calculated using the numerical integration method. The volume calculation formula is: Where Vg(u,v) represents the volume of the (u,v)th grid, hd(u,v), hd(u+1,v), hd(u,v+1), hd(u+1,v+1) represent the heights of the four vertices of a single grid, and a and b represent the side lengths of each unit grid respectively;

[0128] The total earthwork volume is obtained by summing up the volumes of several grids. The calculation formula for the total earthwork volume is: In the formula, Vte represents the total earthwork volume, Vg(u,v) represents the volume of the (u,v)th grid, rows and cols represent the number of rows and columns of the grid respectively;

[0129] The numerical calculation results of the total earthwork volume are collated to generate reports with visual topographic maps and volume change diagrams.

[0130] Human-computer interaction interface module: Receives the desilting volume report from the data fusion analysis and calculation module through the communication interface, uses a graphical user interface GUI design, provides a visual display of measurement data and an operation interface, monitors the measurement process in real time, and enables operators to view measurement data and analysis reports on mobile phones or computers.

[0131] Specifically, the interaction process on the mobile phone or computer is as follows:

[0132] Data reception and display on mobile or computer terminals: Receive visual desilting volume reports from the data fusion analysis and calculation module and upload them to the cloud database for storage;

[0133] Design GUI: Display the report list on the mobile phone or computer, provide viewing and selection operation functions for data acquisition, underwater survey device status, communication transmission and measurement process, so as to display the data changes in the measurement process in real time, and provide visualization charts of data flow diagrams, topographic maps, volume change diagrams, and provide data query, filtering and export functions for staff;

[0134] Human-computer interaction: By setting user identity authentication, users can log in to the mobile phone or computer to operate the entire underwater survey process in real time, and obtain desilting earthwork volume reports with detailed data and visual charts. Users can enter the real-time monitoring interface to view the data in the current measurement process, and update the latest measurement data and calculation results in real time. Users can make targeted comments and feedback based on the results, and adjust the system to make corresponding adjustments and improvements;

[0135] System implementation technology: Software development on mobile phones or computers, including front-end development, back-end development, cloud communication and integration of visualization tools to realize data collection, transmission, processing and interaction functions, and establish a 3D model to display the river topography and silt distribution status in real time.

[0136] An embodiment of the present invention provides an underwater 3D visualization intelligent survey system. The specific methods and processes are detailed in the above-mentioned embodiment of an underwater 3D visualization intelligent survey system, which will not be repeated here.

[0137] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. An underwater 3D visualization intelligent survey system, characterized in that: Including ultrasonic sensor and sonar sensor detection module: Integrate water pressure sensor, ultrasonic sensor and sonar sensor on the underwater survey device to obtain sensor data of underwater detection in real time, and then transmit the real-time collected sensor data to the cloud data interaction processing module through the communication protocol for further denoising and data storage; Sensor automatic orientation module: The sensor data collected in real time by the ultrasonic sensor and sonar sensor detection module are automatically oriented using the Kalman filter algorithm that fuses the gyroscope and magnetometer data, and the stable direction and positioning of the underwater survey device are further adjusted through real-time feedback; Automatic surface keeping module: The sensor data collected in real time by the ultrasonic sensor and sonar sensor detection module are used to automatically keep the sensor on the surface using the PID control algorithm, and the buoyancy and posture of the underwater survey device are adjusted underwater through real-time feedback; Cloud data interaction processing module: uses the real-time data transmission http protocol on the cloud platform to receive the sensor data obtained from the ultrasonic sensor and sonar sensor detection modules, and performs data storage, denoising and reading analysis to generate denoising data; GPS satellite positioning module: Install a GPS satellite locator on the underwater survey device, use differential GPS / DGPS technology for high-precision positioning, and obtain the positioning signal of the measured position, and then synchronously transmit the positioning signal to the data fusion analysis and calculation module; Natural factors impact correction and compensation module: establish an environmental error compensation model, combine the impact of natural factors on sensor measurement data, compare the measured sensor data with the environmental error compensation model, perform correction calculations, generate environmental correction data, and then pass the environmental correction data to the data fusion analysis and calculation module; Data fusion analysis and calculation module: receives the denoising data from the cloud data interaction processing module, the positioning signal from the GPS satellite positioning module, and the environmental correction data from the natural factor impact correction and compensation module, calculates the earthwork volume using a multi-source data fusion algorithm and a numerical integration algorithm based on volume calculation, generates a desilting earthwork volume report based on the earthwork volume, and transmits it to the cloud data interaction processing module and the cloud data interaction processing module for interactive processing; Human-computer interaction interface module: Receives the desilting volume report from the data fusion analysis and calculation module through the communication interface, uses a graphical user interface GUI design, provides a visual display of measurement data and an operation interface, monitors the measurement process in real time, and enables operators to view measurement data and analysis reports on mobile phones or computers.

2. The underwater 3D visualization intelligent survey system according to claim 1, characterized in that: The steps for acquiring the sensor data are as follows: The sensor data includes the water pressure sensor measuring the draft data, the ultrasonic sensor measuring underwater to obtain the depth data in real time, and the sonar sensor obtaining the river topography and silt distribution information in real time. The sensor data is calibrated as SD. The draft depth data is obtained by measuring the water pressure value through the water pressure sensor and calibrating it as P W , to calculate the water depth, calibrated as H W , using the density of water, calibrated as ρ W and the acceleration due to gravity, calibrated as g W The pressure is converted into depth. The water pressure sensor measures the water pressure value in real time, and obtains the draft depth data through the water pressure measurement formula and the water depth conversion formula. That is, the water pressure measurement formula is P W =ρ W ×g W ×H W +P g0 , and P g0 ≈101325Pa, ρ W ≈1000kg / m 3 and g W ≈9.81m / s 2 , where P W It is the water pressure value measured by the water pressure sensor, in Pa, ρ W It is expressed as the density of the water to be tested, in kilograms per cubic meter, g W Expressed as the acceleration due to gravity in meters per second squared, H W It is expressed as the draft depth in meters; the water depth conversion formula is: The water pressure value measured in real time by the water pressure sensor can obtain the draft depth data H W ; Ultrasonic data is obtained through ultrasonic sensors. The water depth is calculated by emitting ultrasonic pulses and measuring the time it takes for the ultrasonic pulses to be reflected from the bottom of the water. The underwater measurement time of ultrasonic emission and reception is calibrated as t W , to convert the water depth data and calibrate it to h W , to obtain the actual measured ultrasonic data, that is, the ultrasonic measurement time formula is t W =2h W / v W , and v W ≈1500m / s, where t W It is expressed as the measurement time of the ultrasonic wave emitted by the ultrasonic sensor being transmitted and received underwater, in seconds, h W Represents the water depth data measured by the ultrasonic sensor in meters, v W It is expressed as the propagation speed of the ultrasonic wave emitted by the ultrasonic sensor in the water, in meters per second; the conversion formula for water depth data is: Through the real-time measurement time of the ultrasonic sensor, the water depth data can be obtained. W ; The actual depth data measured underwater is the sum of the draft depth data obtained by the water pressure sensor and the water depth data obtained by the ultrasonic sensor. The actual underwater depth data is calibrated as H WT , H WT =H W +h W ; The acquisition of river channel topography and silt distribution information is done through sonar sensors, which emit sound waves and measure the signal time and signal strength of the sound waves reflected from the river bottom and silt. The distance to the river bottom and silt layer can be measured and calibrated as d W , based on the distance data acquired in real time, a three-dimensional topographic map and silt distribution map are generated using a three-dimensional reconstruction algorithm. The calculation formula for the distance data is: And V W ≈1500m / s, where d W It is expressed as the distance that the sonar sensor sends sound waves to detect the river bottom and silt layer, in meters, T W It is the measurement time of the sound waves emitted by the sonar sensor being transmitted and received underwater, in seconds, V W It is expressed as the propagation speed of the ultrasonic wave emitted by the sonar sensor in water, in meters per second. Then the sensor data SD = {H W ,h W , d W }.

3. The underwater 3D visualization intelligent survey system according to claim 2, characterized in that: The steps of automatic sensor orientation are as follows: A1. By reading the corresponding data of the gyroscope and magnetometer in real time in the sensor data and performing denoising, including the angular velocity data in the gyroscope and the geomagnetic field data in the magnetometer, the rotational motion information and azimuth information of the underwater survey device integrating the water pressure sensor, the ultrasonic sensor and the sonar sensor are obtained. The angular velocity data of the gyroscope is read and calibrated as ω0=[ω x ,ω y ,ω z ], read the geomagnetic field data of the magnetometer, calibrated as B0 = [B x , B y , B z ]; A2. Then use the Kalman filter algorithm for data fusion, including predicting the current attitude angle state of the underwater survey device based on the angular velocity data of the gyroscope, and correcting the predicted state using the geomagnetic field data of the magnetometer, and updating the attitude information of the geomagnetic field data. The calculation formula for the initial state estimation is: and Where f(x0) is represented by a discrete state transfer matrix composed of angular velocity data ω0 and geomagnetic field data B0. They represent the three initial degrees of freedom of position, velocity and acceleration respectively, T represents the transposition, x0 represents the initial quantity of the angular velocity data ω0 and the geomagnetic field data B0 input, ω0 represents the angular velocity data of the gyroscope, and B0 represents the geomagnetic field data of the magnetometer; The calculation formula for the predicted state is h(x0) k =f(x0)×h(x0) k-1 +b(x0) k ×u(x0) k , where h(x0) k It is expressed as the prior state estimate of the initial quantity x0 at time k, h(x0) k-1 It is expressed as the posterior state estimate of the initial quantity x0 at time k-1, b(x0) k It is represented as the control input matrix of the initial quantity x0 at time k, u(x0) k It is expressed as the control input of the initial quantity x0 at time k, and the prediction formula of the error covariance is P(x0) k =f(x0)×P(x0) k-1 ×f(x0) T +Q(x0) k , where P(x0) k It is expressed as the prior error covariance matrix of the initial quantity x0 at time k, P(x0) k-1 It is expressed as the posterior error covariance matrix of the initial quantity x0 at time k-1, Q(x0) k It is expressed as the process noise covariance matrix of the initial quantity x0 at time k; The calculation formula for updating the state is h′(x0) k =h(x0) k-1 +K(x0) k ×(z(x0) k -H(x0) k ×h(x0) k-1 ), and K(x0) k =P(x0) k ×H(x0) k T ×H(x0) k ×P(x0) k-1 ×H(x0) k T +R(x0) k -1 , where h′(x0) k Expressed as the posterior state estimate of the initial quantity x0 at time k, K(x0) k Expressed as the Kalman gain of the initial quantity x0 at time k, z(x0) k It is expressed as the measured value of the initial quantity x0 at time k, H(x0) k It is expressed as the measurement matrix of the initial quantity x0 at time k, P(x0) k 、P(x0) k-1 They are respectively expressed as the prior error covariance matrix of the initial quantity x0 at time k and the posterior error covariance matrix of the initial quantity x0 at time k-1, R(x0) k It is expressed as the measurement noise covariance matrix of the initial quantity x0 at time k, and the update formula of the error covariance is P′(x0) k =(1-K(x0) k ×H(x0) k )×P(x0) k , where P′(x0) k It is represented as the posterior error covariance matrix of the initial quantity x0 updated at time k; A3. Based on the predicted data after fusion of Kalman filter algorithm, the roll angle of underwater survey device is calculated and calibrated using quaternion method. Pitch angle, calibrated as and yaw angle, calibrated as attitude angle changes, then according to the predicted data results of the Kalman filter algorithm, the expression of the quaternion method is q0 = [h(x0)1, h(x0)2, h(x0)3, h(x0)4], where q0 is represented by a quaternion, h(x0)1, h(x0)2, h(x0)3, and h(x0)4 are respectively represented by the four components of the quaternion, and correspond to the angle and direction of rotation, that is, h(x0)1 is the first component of the rotation size around the positive direction of the x-axis, h(x0)2 is the second component of the rotation size around the positive direction of the y-axis, h(x0)3 is the third component of the rotation size around the positive direction of the z-axis, and h(x0)4 is the fourth component of the rotation size around the orthogonal axis. The calculation formula for the roll angle is The formula for calculating the pitch angle is: The calculation formula for the yaw angle is: A4. Implement feedback mechanism in real time to monitor the attitude changes of underwater survey equipment and Pitch Angle and yaw angle The attitude angle changes, the PID controller is used to adjust the working state of the propeller and the rudder, and the attitude and direction of the underwater survey device are further automatically controlled to ensure the stable function and optimal working state of the sensor's automatic orientation. Among them, the control formula of the PID controller for the attitude angle change is: and as well as In the formula, They are respectively represented by the PID controller for the roll angle Pitch Angle and yaw angle The control output, Respectively expressed as the roll angle Pitch Angle and yaw angle The predicted value of Compared with the actual measured value The error value between Respectively expressed as PID to roll angle Pitch Angle and yaw angle The proportional gain, Respectively expressed as PID to roll angle Pitch Angle and yaw angle The integral gain, Respectively expressed as PID to roll angle Pitch Angle and yaw angle The differential gain.

4. The underwater 3D visualization intelligent survey system according to claim 3, characterized in that: The steps of automatic water surface maintenance by the sensor are as follows: B1. Obtain the real underwater depth data H through the draft depth data obtained by the water pressure sensor and the water depth data obtained by the ultrasonic sensor WT , as the current water surface height value; B2. Obtain the roll angle based on the real-time monitoring of the attitude changes of the underwater survey device and pitch angle As a method of providing the current attitude value of the underwater survey device; B3. Set the target height value of the standard water level in the water area to be measured and calibrate it as H th By collecting sensor data in real time, the PID controller calculates the error value and updates the control output to adjust the buoyancy and posture of the underwater survey device underwater, and further control the sensor to automatically maintain a stable working state on the water surface. The error calculation formula is: In the formula, e(H) k It is expressed as the target height value of the standard water level of the water area to be measured at time k and the actual underwater depth data H WT The difference, It is expressed as the target height value of the standard water level of the water area to be measured at time k. It is represented by the actual underwater depth data actually measured by the water pressure sensor and ultrasonic sensor at time k. The calculation formula of the PID control algorithm is: In the formula, U(H) k It is represented as the control output of PID controller for water depth, Kp(H), Ki(H), Kp(H) respectively represent the proportional gain, integral gain and differential gain for water depth regulation.

5. The underwater 3D visualization intelligent survey system according to claim 4, characterized in that: The steps for obtaining the denoising data are as follows: Receive sensor data from water pressure, ultrasonic sensors, and sonar sensors using HTTP protocol, store them in the cloud platform database, and then perform denoising on the data; The signal processing algorithm uses a low-pass filter to process the data collected by the water pressure sensor and the ultrasonic sensor. WT To remove high-frequency noise, the filtering calculation formula of the low-pass filter is: In the formula, Represented as the input signal After filtering, the signal Represents the input signal collected by n water pressure sensors and ultrasonic sensors, α i Represented as the corresponding input signal The filter coefficients, β j Represented as the corresponding filtered signal The filter coefficients are Represented as the njth input signal The filtered signal, i and n represent the i-th input signal H WTn and input signal The total number of, j, m represent the jth input signal The filtered signal and input signal The filtered signal The total number of The convolutional neural network (CNN) algorithm is a method for analyzing the data collected by the sonar sensor. W Denoising is performed, including convolutional layer feature extraction and pooling layer downsampling, and then the denoised data is output after integration through the fully connected layer. The calculation formula for convolutional layer feature extraction is: And the calculation formula for downsampling in the pooling layer is d′ W (x,y)=Max{i,j}×d W (i+x,j+y), where d′ W (x,y) represents the value of the output feature map at position (x,y), d W (i+x,j+y) represents all input data d within the range of position (i+x,j+y) W , W(i,j) represents the convolution kernel weight, is represented as the bias term, k is the convolution kernel size, i and j are the distances that can be translated at the position (x, y), respectively. The training data is used to train the CNN model, and the loss function is optimized to adjust the model parameters. Then, the new sonar sensor data can be input into the trained CNN model to obtain the denoised data. The denoised data obtained by denoising and storing in the cloud is calibrated as Dpd, then 6. The underwater 3D visualization intelligent survey system according to claim 5, characterized in that: The steps of acquiring the positioning signal are as follows: A GPS receiver is used to receive satellite signals containing satellite positions and timestamps, and differential GPS / DGPS technology is applied to calculate the error between the actual measured position and the known position, and obtain the correction error information, which is transmitted to a mobile GPS receiver in an underwater survey device, so that the mobile GPS receiver in the underwater survey device can perform high-precision position calculation in combination with the satellite signal and the correction error information, and obtain the calculated measurement time, longitude and latitude data, which are calibrated as Mst, Lng, Lat, that is, the positioning signal, and calibrated as GPS, then GPS = {Mst, Lng, Lat}.

7. The underwater 3D visualization intelligent survey system according to claim 6, characterized in that: The steps for establishing the environmental error compensation model are as follows: C1. Use the acoustic wave sensor and sonar sensor to detect sensor data affected by natural factors under different environmental conditions, and collect and record the corresponding environmental condition data, including tidal height, ocean current speed and environmental conditions of swimming organisms; C2. Clean and filter the collected data to remove outliers and noise, match and synchronize the sensor measurement data with the corresponding environmental condition data, analyze the error between the sensor measurement data and the true value, find out the influence of environmental factors on the measurement data, and generate error analysis results; C3. Based on the error analysis results, the random forest algorithm is used to establish an environmental error compensation model to fit the relationship between the sensor measurement data and the environmental condition data; C4. Divide the measurement data under different environmental conditions into a training set and a validation set, wherein the training set data is used to train the environmental error compensation model, adjust the parameters of the environmental error compensation model, and optimize the performance of the environmental error compensation model. At the same time, the validation set is used to verify the accuracy and generalization ability of the environmental error compensation model to evaluate the error compensation effect of the environmental error compensation model; C5. Optimize and adjust the environmental error compensation model based on the verification results, and update the model regularly as environmental conditions in natural factors change and new data are input.

8. The underwater 3D visualization intelligent survey system according to claim 7, characterized in that: The steps for obtaining the environmental correction data are as follows: The sensor collects and measures K kinds of sensor data affected by natural factors such as tide height, ocean current velocity and swimming biological activities, and calibrates them as SD K , and collect and record the corresponding environmental condition data, calibrated as ED K ; The sensor data and environmental condition data are matched and synchronized by time alignment and interpolation to obtain a measurement data set, calibrated as Y, and Y = {(t 1 , S.D. 1 , E.D. 1 ), (t 2 , S.D. 2 , E.D. 2 ),…,(t K , S.D. K , E.D. K )}, and the denoised data Dpd calculated by the sensor after denoising without the influence of natural factors is taken as the true value; The error analysis result is calculated based on the measured data set Y and the true value Dpd. The calculation formula for the error analysis result is: And Y-Dpd=(SD 1 -Dpd)+(SD 2 -Dpd)+…+(SD K -D), where e SD It represents the error analysis result of natural factors, K represents the K data affected by natural factors collected by the sensor, Y represents the measurement data set collected by the sensor under the environmental conditions affected by K natural factors, and Dpd represents the true value calculated by the sensor after denoising without the influence of natural factors; Use linear regression analysis algorithm to describe environmental condition data ED K The error analysis results SD The degree of influence of is calculated and the regression coefficient is obtained. The calculation formula of the regression coefficient is e SD =η0+η1×ED 1 +η2×ED 2 +...+η K ×ED K +E0, and η′=((ED K ) T ×ED K ) -1 ×(ED K ) T ×E0, where, ED 1 , ED 2 ,…,ED K They are respectively represented as environmental condition data affected by K kinds of natural factors, η0 is represented as a constant of the regression coefficient vector, η1, η2, …, η K Respectively represented as the corresponding environmental condition data ED K The regression coefficient vector, E0 represents the residual vector of linear regression, η′ represents the predicted regression coefficient in the linear regression analysis algorithm, and T represents the transpose; By evaluating the fitting effect of the linear regression model, the environmental correction data is calculated and calibrated as Ecd to identify and reduce the impact of environmental factors on the error. The calculation formula for the environmental correction data is: Where Ecd represents the environmental correction data, E0 represents the residual vector of linear regression, η′ represents the predicted regression coefficient in the linear regression analysis algorithm, and e SD It is expressed as the error analysis result.

9. The underwater 3D visualization intelligent survey system according to claim 8, characterized in that: The data processing steps of the multi-source data fusion algorithm are as follows: Collect the denoising data Dpd, positioning signal GPS and environmental correction data Ecd, and perform data standardization respectively. The calculation formula for data standardization is: and Where Dpd 0 、GPS 0 、Ecd 0 They respectively represent the normalized data of the denoised data Dpd, the positioning signal Gps and the environmental correction data Ecd after data normalization processing, and Dpd, Gps and Ecd respectively represent the original data of the denoised data Dpd, the positioning signal Gps and the environmental correction data Ecd, They are respectively represented as the average values ​​of the original data of the denoised data Dpd, the positioning signal Gps and the environmental correction data Ecd, σ Dpd , σ Gps , σ Ecd They are respectively represented as the standard deviation of the original data of the denoised data Dpd, the positioning signal Gps and the environmental correction data Ecd; Use the weighted average method to standardize data Dpd from different sources 0 、GPS 0 、Ecd 0 Fusion is performed to generate fused data. The calculation formula for fused data is FD = W1 × Dpd 0 +W2×Gps 0 +W3×Ecd 0 , and W1+W2+W3=1, where FD represents the standardized data Dpd from different sources 0 、GPS 0 、Ecd 0 The fusion data W1, W2, and W3 are represented as corresponding to the standardized data Dpd 0 、GPS 0 、Ecd 0 The weight value of Then use Kalman filtering to dynamically update and correct the fusion data FD, including prediction calculation and update calculation. The prediction calculation formula is H(FD) t =A FD ×H(FD) t-1 +B FD ×U(FD) t , and P(FD) t =A FD ×P(FD) t-1 ×A FD T +Q(FD) t , where H(FD) t It is represented as the state vector predicted by the fusion data FD at the current time step t, A FD It is represented as the state transfer matrix of the fused data FD, H(FD) t-1 It is represented as the state vector predicted by the fusion data FD at the previous time step t-1, B FD It is represented as the control matrix of the fused data FD, U(FD) t It is represented as the control input of the fusion data FD at the current time step t, P(FD) t It is represented as the covariance matrix of the fusion data FD prediction at the current time step t, P(FD) t-1 It is represented as the covariance matrix of the fusion data FD prediction at the previous time step t-1, Q(FD) t It is represented as the process noise covariance matrix of the fused data FD at the current time step t, T is represented as the transpose, and t and t-1 represent the current time step and the previous time step respectively; The updated calculation formula is K(FD) t =P(FD) t ×L(FD) t T ×(L(FD) t ×P(FD) t ×L(FD) t T +R(FD) t ) -1 , and H(FD) t+1 =H(FD) t +K(FD) t ×(Z(FD) t -L(FD) t ×H(FD) t ), and P(FD) t+1 =(1-K(FD) t ×L(FD) t )×P(FD) t , where K(FD) t It is represented as the Kalman gain of the fused data FD at the current time step t, L(FD) t Represented as the observation matrix of the fused data FD at the current time step t, R(FD) t It is represented as the observation noise covariance matrix of the fused data FD at the current time step t, H(FD) t+1 It is represented as the state vector predicted by the fusion data FD at the next time step t+1 after the update, Z(FD) t It is represented as the observation vector of the fused data FD at the current time step t, P(FD) t+1 It is represented as the covariance matrix of the prediction of the fused data FD at the next time step t+1 after the update.

10. The underwater 3D visualization intelligent survey system according to claim 9, characterized in that: The steps of calculating the earthwork volume value using the numerical integration algorithm are as follows: The river topography and silt distribution information obtained by the sonar sensor is represented in the form of discrete point cloud data, and the discrete point area is divided into a number of unit grids, and the side length of each unit grid is represented by a and b; Set the height of each grid's base surface to h th , calculate the height difference of each mesh vertex, then the height difference calculation formula is Δhd(u,v)=hd(u,v)-h th , where Δhd(u,v) represents the height difference, hd(u,v) represents the height of the (u,v)th grid vertex, and (u,v) represents the variable values ​​corresponding to the horizontal and vertical axes in the grid area; The volume of each grid is calculated using the numerical integration method. The volume calculation formula is: Where Vg(u,v) represents the volume of the (u,v)th grid, hd(u,v), hd(u+1,v), hd(u,v+1), hd(u+1,v+1) represent the heights of the four vertices of a single grid, and a and b represent the side lengths of each unit grid respectively; The total earthwork volume is obtained by summing up the volumes of several grids. The calculation formula for the total earthwork volume is: In the formula, Vte represents the total earthwork volume, Vg(u,v) represents the volume of the (u,v)th grid, rows and cols represent the number of rows and columns of the grid respectively; The numerical calculation results of the total earthwork volume are collated to generate reports with visual topographic maps and volume change diagrams.

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