Hydrological element integrated measurement method and system in high-salinity and high-sand environment

Abstract

The invention discloses an integrated measurement method and system for hydrological elements in a high-salinity and high-sand environment, and relates to the technical field of hydrological monitoring, and the method comprises the steps: correcting the theoretical signal receiving strength of each sonar through an established sound attenuation model, and then calculating an effective signal-to-noise ratio, thereby achieving the measurement of the underwater topography in the high-salinity and high-sand environment. According to the method, a dynamic weight fusion algorithm is adopted to perform data fusion on the water depth data of each sonar to obtain fused water depth data, and an inversion method based on residual error minimization is adopted to perform iterative optimization on model parameters, so that the accuracy is relatively high. When the water velocity and the flow rate are measured in the high-salt and high-sand environment, iterative decoupling is carried out based on the sediment particle motion comprehensive model to obtain the sediment particle velocity, then the water velocity is updated based on the measured apparent velocity and the difference value, and after the updated water velocity is converged, the flow rate is calculated. And the accurate water flow rate can be calculated by combining the flow cross section area. The measurement precision of the unmanned ship on the hydrological elements in the saline-alkali area can be improved.

Description

Technical Field

[0001] The present application relates to the field of hydrological monitoring technology, and in particular to an integrated measurement method and system for hydrological elements in a high-salt and high-sand environment. Background Art

[0002] At present, hydrological monitoring of inland rivers mainly relies on advanced equipment such as acoustic Doppler current profilers, echo sounders, and optical backscatter turbidity meters carried by unmanned boats. However, in saline-alkali and sandy rivers in Xinjiang, due to the salinity of the water (up to 5-15‰) and the sand content (more than 50kg / m during the flood season), the water quality is very high. 3 ) is significantly higher than that of conventional freshwater rivers, and the attenuation of acoustic signals is disturbed. First, the sound speed calculation deviation caused by salinity changes (the traditional algorithm defaults to a freshwater sound speed of 1500m / s) leads to systematic errors in water depth measurement; secondly, the penetration ability of traditional single-frequency sonar in high-sand water bodies is insufficient, and sediment particles cause increased sound wave scattering and absorption, resulting in virtual images or data loss in topographic measurements. In addition, relying only on the linear relationship between turbidity and sediment content, the sound wave scattering characteristics of each river vary significantly, and the failure to introduce sediment particle size distribution parameters leads to increased errors. At the same time, multi-parameter coupling interference is not decoupled, and the existing flow measurement algorithm does not consider the salinity-temperature-turbidity multi-physical field coupling effect: salt ions change the conductivity of the water body, affecting the accuracy of the electromagnetic flowmeter; in addition, high sediment concentrations cause the Doppler flow profiler to misjudge particle motion as water flow velocity.

[0003] Therefore, the low measurement accuracy seriously limits the realization of national strategic needs such as hydrological ecological monitoring in arid areas and water resource scheduling in irrigation areas. Faced with these challenges, how to quickly and accurately measure hydrological elements in a high-salt and high-sand environment has become a crucial issue. Summary of the invention

[0004] The purpose of this application is to provide an integrated measurement method and system for hydrological elements in a high-salt and high-sand environment, which can quickly and accurately measure hydrological elements in a high-salt and high-sand environment.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In the first aspect, the present application provides a method for integrated measurement of hydrological elements in a high-salt and high-sand environment, which is used to accurately measure underwater terrain in a high-salt and high-sand environment using an unmanned ship equipped with multiple measuring instruments; the method for integrated measurement of hydrological elements in a high-salt and high-sand environment comprises the following steps:

[0007] An acoustic attenuation model is established for the laser particle size analyzer and multi-frequency sonar group carried by the unmanned ship; the multi-frequency sonar group includes multiple sonars with different frequencies; the acoustic attenuation model is used to characterize the attenuation of sound waves emitted by sonars of different frequencies under different sand contents.

[0008] The theoretical signal receiving strength of each sonar is corrected by the sound attenuation model and the sound wave propagation distance to obtain the corrected theoretical signal receiving strength of each sonar.

[0009] The water depth data of each sonar are time synchronized and spatially aligned, and the effective signal-to-noise ratio of each sonar is calculated based on the corrected theoretical signal receiving strength of each sonar.

[0010] Based on the effective signal-to-noise ratio of each sonar, the dynamic weight fusion algorithm is used to fuse the water depth data of each sonar to obtain the fused water depth data; the water depth data is the water depth data measured after calibrating the sound wave speed emitted by the sonar with salinity, water temperature and turbidity.

[0011] According to the residual value between the actual signal reception strength of each sonar and the theoretical signal reception strength, the inversion method based on residual minimization is adopted to iteratively optimize the parameters of the acoustic attenuation model, so as to realize dynamic adjustment of parameters in a complex environment with dynamic changes in salinity and sediment content.

[0012] Optionally, the sound attenuation model is as follows:

[0013] α(f)=k·C m ·f n ·d p .

[0014] Among them, α(f) is the sound attenuation coefficient of the sonar with a sound wave frequency of f, k is the water body attenuation proportional coefficient, C is the sediment content, m is the sediment content index, f is the sound wave frequency, n is the frequency index, d is the sediment particle size, and p is the particle size index.

[0015] The theoretical signal reception strength of any sonar is corrected by the following formula:

[0016]

[0017] Among them, P signal (f) is the corrected theoretical signal receiving strength of the sonar with a sound wave frequency of f, P tx is the sonar transmission power, e is a natural constant, D is the water depth where the unmanned ship is located, A eff is the effective irradiation area of ​​the sonar beam, and R is the riverbed reflection coefficient where the unmanned boat is located.

[0018] Optionally, based on the effective signal-to-noise ratio of each sonar, a dynamic weight fusion algorithm is used to fuse the water depth data of each sonar to obtain fused water depth data, which specifically includes the following steps:

[0019] For the sonar whose effective signal-to-noise ratio is higher than the second threshold, the weight of the sonar is reset to zero.

[0020] For the sonar whose effective signal-to-noise ratio is higher than the second threshold, the weight of the sonar is reset to one.

[0021] For a sonar whose effective signal-to-noise ratio is higher than the first threshold and lower than the second threshold, the weight of the sonar is calculated according to the proportion of the effective signal-to-noise ratio of the sonar in the total effective signal-to-noise ratios of all the sonars.

[0022] According to the weight of each sonar and the water depth data of each sonar, the fused water depth data is calculated.

[0023] Optionally, calculate the effective signal-to-noise ratio of any sonar according to the following formula:

[0024]

[0025] Among them, SNR(f) is the effective signal-to-noise ratio of the sonar with a sound wave frequency of f, P signal (f) is the corrected theoretical signal receiving strength of the sonar with a sound wave frequency of f, P noise (f) is the background noise energy of the sonar with a sound wave frequency of f.

[0026] When the multi-frequency sonar group includes only one high-frequency sonar and one low-frequency sonar, the fused water depth data is calculated according to the following formula:

[0027] H fused =w low ·H low +w high ·H high .

[0028] Among them, H fused is the fused water depth data, w low is the weight of low-frequency sonar, H low is the water depth data of low-frequency sonar, w high is the weight of high-frequency sonar, H high It is the water depth data of high frequency sonar.

[0029] Optionally, according to the residual value between the actual signal receiving strength of each sonar and the theoretical signal receiving strength, an inversion method based on residual minimization is adopted to iteratively optimize the parameters of the sound attenuation model, which specifically includes the following steps:

[0030] For any sonar, the theoretical signal reception strength of the sonar is calculated according to the current sound attenuation coefficient.

[0031] The signal reception strength residual is calculated based on the actual sonar signal reception strength and the theoretical signal reception strength.

[0032] Based on the partial derivative of the square of the residual error of the signal reception intensity with respect to the acoustic attenuation coefficient, the gradient descent method is used to iteratively optimize the acoustic attenuation coefficient until the difference between the current acoustic attenuation coefficient and the acoustic attenuation coefficient of the previous round is less than the convergence threshold, and the target acoustic attenuation coefficient is obtained.

[0033] Based on the target sound attenuation coefficient, the parameters of the sound attenuation model are recalibrated to achieve dynamic updating of the sound attenuation model.

[0034] Optionally, the speed of sound waves emitted by the sonar is calibrated according to the following formula:

[0035] c=1449.2+4.6T-0.055T 2 +0.0003T 3 +(1.34-0.01T)(S-35)+0.016D.

[0036] Among them, c is the sound wave velocity of the calibrated sonar, T is the water temperature measured at the location of the unmanned ship, S is the salinity measured at the location of the unmanned ship, and D is the water depth at the location of the unmanned ship.

[0037] In the second aspect, the present application also provides a method for integrated measurement of hydrological elements in a high-salt and high-sand environment, which is used to accurately measure water flow velocity and flow in a high-salt and high-sand environment using an unmanned boat equipped with multiple measuring instruments; the method for integrated measurement of hydrological elements in a high-salt and high-sand environment comprises the following steps:

[0038] Align the timestamps and spatial positions of various measuring instruments carried by the unmanned ship to achieve time synchronization and spatial registration of various measurement data; the measuring instruments carried by the unmanned ship include electromagnetic flowmeter, radar flowmeter, laser particle size analyzer and corrosion-resistant acoustic Doppler flow profiler.

[0039] According to the water flow velocity measured by the electromagnetic flowmeter, the sediment particle velocity is iteratively decoupled based on the comprehensive sediment particle motion model to obtain the sediment particle velocity.

[0040] The updated water velocity is calculated based on the apparent velocity and sediment particle velocity measured by the corrosion-resistant acoustic Doppler current profiler.

[0041] Based on the comparison between the difference between the updated water flow velocity and the surface flow velocity measured by the radar flow meter and the water flow velocity error threshold, it is determined whether the updated water flow velocity converges.

[0042] If the updated water flow velocity has not converged, optimize the parameters in the comprehensive model of sediment particle motion and jump to the step "According to the water flow velocity measured by the electromagnetic flowmeter, the sediment particle velocity is iteratively decoupled based on the comprehensive model of sediment particle motion to obtain the sediment particle velocity".

[0043] If the updated water flow velocity converges, the water flow velocity is corrected according to the measured salinity, and the water flow rate is calculated based on the salinity-corrected water flow velocity and the water cross-sectional area.

[0044] Optionally, the comprehensive model of sediment particle movement is as follows:

[0045]

[0046] in, is the sediment particle velocity at the nth iteration decoupling, is the water velocity at the nth iteration decoupling, K is the concentration attenuation coefficient, ρ s and ρ w are the densities of sediment and water, d is the sediment particle size, I t is the turbulence intensity.

[0047] The updated water flow velocity is calculated according to the following formula:

[0048]

[0049] in, is the updated water velocity, v ADCP The superficial velocity measured by a corrosion-resistant acoustic Doppler current profiler.

[0050] Optionally, the water velocity is corrected according to the following formula:

[0051] v EM,corrected =v EM ·(1+β·(SS 0 )).

[0052] Among them, v EM,corrected is the salinity-corrected water velocity, v EM is the water flow velocity measured by the electromagnetic flowmeter, β is the salinity sensitivity coefficient, S is the measured salinity, S 0 To calibrate salinity.

[0053] On the third aspect, the present application provides an integrated measurement system for hydrological elements in a high-salt and high-sand environment, wherein the main body of the integrated measurement system for hydrological elements in a high-salt and high-sand environment is an unmanned ship with a sand-proof hull structure, and the integrated measurement system for hydrological elements in a high-salt and high-sand environment includes a platform adaptation module, an environmental perception module, a data processing module and an energy and communication module; the platform adaptation module is used to provide hardware support for the unmanned ship, and the platform adaptation module includes a titanium alloy sensor housing, a salt spray isolation electronic cabin, a propeller loaded with a protective net, and a positioning unit; the environmental perception module is a number of measuring instruments carried on the unmanned ship; the energy and communication module is used to provide energy and power for the unmanned ship and provide support for communication between modules; the data processing module is used to implement the integrated measurement method for hydrological elements in a high-salt and high-sand environment as mentioned above.

[0054] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0055] The present application provides an integrated measurement method and system for hydrological elements in a high-salt and high-sand environment. When measuring underwater terrain in a high-salt and high-sand environment, the scheme establishes an acoustic attenuation model for the multi-frequency sonar group carried by the unmanned ship, and calculates the effective signal-to-noise ratio after correcting the theoretical signal reception intensity of each sonar through the acoustic attenuation model. Based on the obtained effective signal-to-noise ratio, the water depth data of each sonar is fused using a dynamic weight fusion algorithm to obtain the fused water depth data with high accuracy. In practical applications, the residual between the actual signal reception intensity and the theoretical value is also provided, and the parameters of the acoustic attenuation model are iteratively optimized using an inversion method based on residual minimization. When measuring water flow velocity and flow in a high-salt and high-sand environment, the sediment particle velocity is iteratively decoupled based on the comprehensive model of sediment particle motion to obtain the sediment particle velocity, and then the water flow velocity is updated based on the difference between the apparent velocity and the sediment particle velocity measured by the corrosion-resistant acoustic Doppler current profiler. After the updated water flow velocity converges, combined with the water-passing cross-sectional area, the accurate water flow rate can be calculated. The above scheme of this application adopts multi-frequency sonar measurement, and uses the acoustic attenuation model to establish a dynamic sonar weight distribution mechanism to achieve the fusion of multi-frequency sonar data, solve the contradiction between penetration and resolution of highly turbid water bodies, and invert the acoustic attenuation coefficient through the residual inversion of the measured signal reception intensity and the theoretical value, so as to achieve dynamic adjustment of parameters and improve the measurement accuracy. In addition, through multi-sensor cross-validation to construct sediment velocity decoupling measurement technology, the flow rate of the river where the unmanned boat is located can be accurately measured, which can improve the measurement accuracy of underwater topography and sediment of unmanned boats in saline-alkali areas, and provide more accurate and practical solutions for hydrological and ecological monitoring in arid areas and water resource scheduling in irrigation areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0057] Figure 1 A flow chart of a method for integrated measurement of hydrological elements in a high-salt and high-sand environment provided in one embodiment of the present application.

[0058] Figure 2 A flow chart of a method for integrated measurement of hydrological elements in a high-salt and high-sand environment provided in another embodiment of the present application.

[0059] Figure 3 A schematic diagram of an unmanned boat measuring water velocity and flow in a high-salt and high-sand environment in an integrated measurement method of hydrological elements in a high-salt and high-sand environment provided in another embodiment of the present application.

[0060] Figure 4 A schematic diagram of the functional modules of an integrated measurement system for hydrological elements in a high-salt and high-sand environment provided in one embodiment of the present application. DETAILED DESCRIPTION

[0061] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0062] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0063] The present application provides an integrated measurement method for hydrological elements in a high-salt and high-sand environment. In an exemplary embodiment, the method is used to accurately measure underwater terrain in a high-salt and high-sand environment using an unmanned ship equipped with multiple measuring instruments, such as Figure 1 As shown, the following steps are included:

[0064] A1. A sound attenuation model is established for the laser particle size analyzer and multi-frequency sonar group carried by the unmanned ship; the multi-frequency sonar group includes multiple sonars with different frequencies; the sound attenuation model is used to characterize the attenuation of sound waves emitted by sonars of different frequencies under different sand contents.

[0065] For the laser particle size analyzer and multi-frequency sonar group carried by the unmanned ship, due to the high sediment concentration, the sound wave scattering attenuation and the decrease in echo signal strength, the turbidity sensor and laser particle size analyzer are installed to measure the suspended matter concentration in real time, and the sound attenuation model is established to quantify the attenuation of different frequencies under different sand contents in combination with the sediment concentration, so as to guide the selection of the best frequency combination to adjust the weight and adjust the sonar transmission frequency to compensate for the signal loss. Specifically in this embodiment, the sound attenuation model is shown as follows:

[0066] α(f)=k·C m ·f n ·d p .

[0067] Among them, α(f) is the acoustic attenuation coefficient of the sonar with an acoustic wave frequency of f, k is the water body attenuation proportional coefficient, which reflects the overall influence of water body characteristics on attenuation, C is the sediment content, m is the sediment content index, which describes the nonlinear influence of the sediment content C on attenuation, f is the acoustic wave frequency, n is the frequency index, which describes the nonlinear influence of the acoustic wave frequency f on attenuation, d is the sediment particle size, and p is the particle size index, which describes the nonlinear influence of the sediment particle size d on attenuation. Provide experimental calibration k, m, n, p to avoid relying solely on the linear relationship between turbidity and sediment content, and improve the measurement accuracy of the sediment model by introducing sediment particle size distribution parameters.

[0068] A2. The theoretical signal receiving strength of each sonar is corrected by the sound attenuation model and the sound wave propagation distance to obtain the corrected theoretical signal receiving strength of each sonar. In order to eliminate the energy loss of the sound wave on the propagation path, restore the true reflection intensity and signal receiving strength, and accurately calculate, the theoretical signal receiving strength P is corrected by the attenuation coefficient α(f) and the propagation distance D. signal (f) Specifically, the theoretical signal reception strength of any sonar is corrected by the following formula:

[0069]

[0070] Among them, P signal (f) is the corrected theoretical signal receiving strength of the sonar with a sound wave frequency of f, P tx is the sonar transmission power, e is a natural constant, D is the water depth where the unmanned ship is located, A eff is the effective irradiation area of ​​the sonar beam, and R is the riverbed reflection coefficient where the unmanned boat is located.

[0071] In order to be suitable for high-salt and high-sand environments, unmanned ships are equipped with turbidity sensors to obtain real-time turbidity data, time synchronization and spatial alignment of low-frequency and high-frequency sonar data are performed, the effective signal-to-noise ratio (SNR) of each frequency is calculated based on the sound attenuation model, and the fusion weight of low-frequency sonar and high-frequency sonar data is dynamically allocated to ensure that low-frequency data is mainly used in high-turbidity areas, while high-frequency details are retained in low-turbidity areas.

[0072] A3. Time synchronization and spatial registration are performed on the water depth data of each sonar, and the effective signal-to-noise ratio of each sonar is calculated based on the corrected theoretical signal receiving strength of each sonar. In this embodiment, the effective signal-to-noise ratio of any sonar is calculated according to the following formula:

[0073]

[0074] Among them, SNR(f) is the effective signal-to-noise ratio of the sonar with a sound wave frequency of f, P signal (f) is the corrected theoretical signal receiving strength of the sonar with a sound wave frequency of f, P noise (f) is the background noise energy of the sonar with a sound wave frequency of f, which interferes with the extraction of effective signals; further, the background noise energy calculation formula is as follows:

[0075] P noise (f) = P env (f)+P equip (f)+P scatter (f).

[0076] Among them, P env (f) is the environmental noise, P equip (f) is the equipment noise, P scatter (f) is the scattering noise.

[0077] A4. Based on the effective signal-to-noise ratio of each sonar, a dynamic weight fusion algorithm is used to fuse the water depth data of each sonar to obtain fused water depth data; the water depth data is the water depth data measured after the speed of the sound waves emitted by the sonar is calibrated by salinity, water temperature and turbidity. In this embodiment, step A4 includes the following steps:

[0078] A41. For sonars with effective signal-to-noise ratio higher than the second threshold, the weight of the sonar is reset to zero. Specifically, when SNR<10dB, the data is low-quality data and is directly discarded.

[0079] A42. For the sonar whose effective signal-to-noise ratio is higher than the second threshold, the weight of the sonar is reset to 1. When the SNR>20dB, the data is high-quality data and is directly used for fusion.

[0080] A43. For sonars with an effective signal-to-noise ratio higher than the first threshold and lower than the second threshold, the weight of the sonar is calculated according to the proportion of the effective signal-to-noise ratio of the sonar in the total effective signal-to-noise ratio of all sonars. Specifically, when the multi-frequency sonar group includes only one high-frequency sonar and one low-frequency sonar, and assuming that the effective signal-to-noise ratios of the two sonars are both between the first threshold and the second threshold, the weights of the two sonars are determined according to the following formula:

[0081]

[0082] Among them, w low is the percentage of the effective signal-to-noise ratio of low-frequency sonar to the total effective signal-to-noise ratio of low-frequency and high-frequency sonars, w high SNR(f low ) is the effective signal-to-noise ratio of low-frequency sonar, SNR(f high ) is the effective signal-to-noise ratio of high-frequency sonar.

[0083] A44. Calculate the fused water depth data based on the weight of each sonar and the water depth data of each sonar. Specifically, when the multi-frequency sonar group includes only one high-frequency sonar and one low-frequency sonar, calculate the fused water depth data based on the following formula:

[0084] H fused =w low ·H low +w high ·H high .

[0085] Among them, H fused is the fused water depth data, w low is the weight of low-frequency sonar, H low is the water depth data of low-frequency sonar, w high is the weight of high-frequency sonar, H high It is the water depth data of high-frequency sonar. By combining the penetration ability of low frequency and the detail resolution of high frequency, it generates high-precision and full-coverage underwater terrain data.

[0086] Since the speed of sound in salt water is higher than that in fresh water, the default speed of sound of about 1500m / s of traditional unmanned boat equipment needs to be dynamically adjusted. By measuring the salinity-temperature-turbidity integrated sensor and corrosion-resistant ADCP carried by the unmanned boat, the speed of sound in the environment where the unmanned boat is located can be calculated in real time to improve the depth measurement accuracy. Specifically, in this embodiment, the speed of sound waves emitted by the sonar is calibrated according to the following formula:

[0087] c=1449.2+4.6T-0.055T 2 +0.0003T 3 +(1.34-0.01T)(S-35)+0.016D.

[0088] Among them, c is the sound wave speed of the calibrated sonar, T is the water temperature measured at the location of the unmanned ship, S is the salinity measured at the location of the unmanned ship, and D is the water depth at the location of the unmanned ship. By feeding back the dynamic sound speed c to the depth-measuring sonar, the water depth calculation error is corrected, replacing the traditional fixed sound speed model.

[0089] A5. According to the residual value of the actual signal receiving strength of each sonar and the theoretical signal receiving strength, the inversion method based on residual minimization is used to iteratively optimize the parameters of the sound attenuation model; dynamic adjustment of parameters is achieved in a complex environment with dynamic changes in salinity and sand content. The specific objective function is as follows:

[0090]

[0091] Wherein, ε is the residual value between the actual signal receiving strength and the theoretical signal receiving strength.

[0092] The parameters of the sound attenuation model are recalibrated to dynamically adjust the parameters, thereby optimizing the model and improving the measurement accuracy. Specifically in this embodiment, step A5 includes the following steps:

[0093] First, assume that the initial attenuation coefficient α 0 (f), the calculation formula is as follows:

[0094]

[0095] A51. For any sonar, calculate the theoretical signal reception strength of the sonar based on the current sound attenuation coefficient.

[0096] According to the current α i (f) Calculate the theoretical signal reception strength P theory,i (f), the calculation formula is as follows:

[0097]

[0098] A52. Calculate the signal receiving strength residual according to the actual sonar signal receiving strength and the theoretical signal receiving strength. Specifically, the signal receiving strength residual ε is calculated as follows:

[0099] ε i =P meas (f)-P theory,i (f).

[0100] Among them, P means (f) is the actual signal receiving strength.

[0101] Furthermore, the partial derivative of the residual ε squared with respect to α is calculated as follows:

[0102]

[0103] in,

[0104] Further, Then we can further obtain,

[0105] A53. Based on the partial derivative of the square of the signal receiving strength residual to the acoustic attenuation coefficient, the acoustic attenuation coefficient is iteratively optimized using the gradient descent method until the difference between the current acoustic attenuation coefficient and the acoustic attenuation coefficient of the previous round is less than the convergence threshold, and the target acoustic attenuation coefficient is obtained. The calculation formula for the optimized acoustic attenuation coefficient is as follows:

[0106]

[0107] Where η is the learning rate (calibrated by experiments); until |α i+1 (f)-α i (f)|<threshold.

[0108] A54. Based on the target sound attenuation coefficient, the parameters of the sound attenuation model are recalibrated to achieve dynamic update of the sound attenuation model. Therefore, the target sound attenuation coefficient is substituted into the sound attenuation model, and the experimental parameters are recalibrated through multiple iterations to optimize the inversion sound attenuation coefficient α(f) and achieve dynamic parameter adjustment.

[0109] The above method of the present application, when conducting underwater topography measurement, dynamically corrects and calibrates the sound velocity through the data measured by the temperature-salinity-turbidity integrated sensor, adopts multi-frequency sonar measurement, and uses the sound attenuation model to establish a dynamic sonar weight distribution mechanism to achieve multi-frequency sonar fusion, solve the contradiction between the penetration and resolution of highly turbid water bodies, and invert the sound attenuation coefficient through the residual between the measured signal reception intensity and the theoretical value, recalibrates the parameters of the sound attenuation model, and realizes dynamic adjustment of parameters, thereby optimizing the acoustic model and improving the measurement accuracy.

[0110] In another exemplary embodiment of the present application, the present application provides an integrated measurement method of hydrological elements in a high-salt and high-sand environment, which is used to accurately measure water flow velocity and flow in a high-salt and high-sand environment using an unmanned boat equipped with multiple measuring instruments; Figure 2 As shown, the integrated measurement method of hydrological elements in a high-salt and high-sand environment includes the following steps:

[0111] B1. Align the timestamps and spatial positions of various measuring instruments carried by the unmanned ship to achieve time synchronization and spatial registration of various measurement data; the measuring instruments carried by the unmanned ship include electromagnetic flowmeter, radar flowmeter, laser particle size analyzer and corrosion-resistant acoustic Doppler flow profiler. First, obtain the data of each measuring instrument, such as the corrosion-resistant ADCP by measuring the Doppler frequency shift f d Get the apparent velocity v ADCP , to reflect the combined movement of particles and water flow; the electromagnetic flowmeter measures the conductivity output and only depends on the water flow velocity v wThe laser particle size analyzer measures the sediment content C and the sediment particle size d to provide sediment physical property parameters and optimize the model coefficients. The surface radar velocity meter measures the surface flow velocity v radar , constrain the surface water velocity and verify the decoupling results.

[0112] B2. According to the water velocity measured by the electromagnetic flowmeter, the sediment particle velocity is iteratively decoupled based on the comprehensive model of sediment particle motion to obtain the sediment particle velocity. Specifically, the schematic diagram of the unmanned boat measuring water velocity and flow in a high-salt and high-sand environment is as follows: Figure 3 As shown, the comprehensive model of sediment particle movement can comprehensively model the sediment movement velocity by combining the sedimentation velocity model and the turbulent diffusion effect. In this embodiment, the comprehensive model of sediment particle movement is shown as follows:

[0113]

[0114] Among them, v s is the sediment particle velocity, v w is the water velocity, K is the concentration attenuation coefficient, ρ s and ρ w are the densities of sediment and water respectively, and d is the sediment particle size.

[0115] Sediment settling velocity v settle The calculation formula is as follows:

[0116]

[0117] Among them, ρ s and ρ w are the densities of sediment and water, C d is the drag coefficient, g is the acceleration due to gravity, and d is the sediment particle size.

[0118] Furthermore, the particle velocity fluctuation v turb and turbulence intensity I t The calculation formula is as follows:

[0119]

[0120] Among them, I t is the turbulence intensity. Then the comprehensive model of sediment particle movement can be expressed by the following formula:

[0121]

[0122] in, is the sediment particle velocity at the nth iteration decoupling, is the water velocity at the nth iteration decoupling.

[0123] The initial water flow velocity is obtained by the electromagnetic flow meter The laser particle size analyzer is used to input the sediment content C and particle size d. Iterative decoupling is performed to obtain the sediment particle velocity.

[0124] B3. Based on the apparent velocity and sediment particle velocity measured by the corrosion-resistant acoustic Doppler current profiler, the updated water flow velocity is calculated. The updated water flow velocity is calculated according to the following formula:

[0125]

[0126] in, is the updated water velocity, v ADCP The superficial velocity measured by a corrosion-resistant acoustic Doppler current profiler.

[0127] B4. Based on the comparison between the difference between the updated water flow velocity and the surface flow velocity measured by the radar flow meter and the water flow velocity error threshold, determine whether the updated water flow velocity has converged. If the updated water flow velocity has not converged, execute step B5; if the updated water flow velocity has converged, execute step B6. Specifically, When , that is, the measurement accuracy reaches the centimeter level, the updated water flow velocity converges.

[0128] B5. Optimize the parameters in the comprehensive model of sediment particle movement and jump to step B2.

[0129] B6. Correct the water flow velocity according to the measured salinity, and calculate the water flow rate based on the salinity-corrected water flow velocity and the water flow cross-sectional area. Correct the water flow velocity according to the following formula:

[0130] v EM,corrected =v EM ·(1+β·(SS 0 )).

[0131] Among them, v EM,corrected is the salinity-corrected water velocity, v EM is the water flow velocity measured by the electromagnetic flowmeter, β is the salinity sensitivity coefficient, S is the measured salinity, S 0 To calibrate salinity.

[0132] The solution provided in this embodiment of the present application establishes a comprehensive model of sediment particle movement by simultaneously considering turbulent dynamics, sediment kinematics, and salinity electrochemical effects, covering all elements of a complex environment, verifies the decoupling results through radar current meters and acoustic Doppler current profilers, triggers anomaly detection and parameter recalibration, forms a closed-loop feedback mechanism, and improves the measurement accuracy of water velocity and flow rate.

[0133] Based on the same inventive concept, the embodiment of the present application also provides a system for implementing the above-mentioned method for integrated measurement of hydrological elements in a high-salt and high-sand environment. The solution provided by the system is similar to the solution described in the above method. In an exemplary embodiment, Figure 4 As shown, a system for integrated measurement of hydrological elements in a high-salt and high-sand environment is provided, the main body of which is an unmanned boat with an anti-sand hull structure, and the system for integrated measurement of hydrological elements in a high-salt and high-sand environment includes a platform adaptation module, an environmental perception module, a data processing module, and an energy and communication module; the platform adaptation module is used to provide hardware support for the unmanned boat, and the platform adaptation module includes a titanium alloy sensor housing, a salt spray isolation electronic cabin, a propeller loaded with a protective net, and a positioning unit; the environmental perception module is a plurality of measuring instruments carried on the unmanned boat; the energy and communication module is used to provide energy and power for the unmanned boat and to provide support for communication between modules; the data processing module is used to realize the integrated measurement method of hydrological elements in a high-salt and high-sand environment as mentioned above.

[0134] As an optional implementation, the environmental perception module is composed of a multi-frequency sonar group (28kHz+200kHz), a salinity-temperature-turbidity sensor, a laser particle size analyzer (LISST), and an anti-corrosion ADCP, which is used to collect environmental data based on the unmanned ship, provide precise data and send it to the data processing module.

[0135] The data processing module, which consists of a dynamic sound velocity calibration unit, a multi-frequency sonar fusion algorithm unit, and a sediment-velocity decoupling model, is used to accurately process the data and send the unified environmental data to the remote platform through the energy and communication module.

[0136] As an optional implementation, the energy and communication module is jointly composed of solar-lithium battery hybrid power supply and LoRa / 4G dual-link redundant communication.

[0137] certainly, Figure 4 The architecture shown is only exemplary and can be omitted according to actual needs when implementing different functions. Figure 4 One or at least two components of the system shown.

[0138] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0139] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for integrated measurement of hydrological elements in a high-salt and high-sand environment, characterized in that: The integrated measurement method of hydrological elements in a high-salt and high-sand environment is used to accurately measure underwater terrain in a high-salt and high-sand environment using an unmanned ship equipped with multiple measuring instruments; the integrated measurement method of hydrological elements in a high-salt and high-sand environment includes: A sound attenuation model is established for the laser particle size analyzer and the multi-frequency sonar group carried by the unmanned ship; the multi-frequency sonar group includes a plurality of sonars with different frequencies; the sound attenuation model is used to characterize the attenuation of the sound waves emitted by the sonars with different frequencies under different sand contents; The theoretical signal receiving strength of each sonar is corrected by using the sound attenuation model and the sound wave propagation distance to obtain the corrected theoretical signal receiving strength of each sonar; The water depth data of each sonar is synchronized in time and space, and the effective signal-to-noise ratio of each sonar is calculated based on the corrected theoretical signal receiving strength of each sonar; Based on the effective signal-to-noise ratio of each sonar, a dynamic weight fusion algorithm is used to fuse the water depth data of each sonar to obtain fused water depth data; the water depth data is the water depth data measured after the speed of the sound waves emitted by the sonar is calibrated by salinity, water temperature and turbidity; According to the residual value between the actual signal reception strength of each sonar and the theoretical signal reception strength, an inversion method based on residual minimization is adopted to iteratively optimize the parameters of the acoustic attenuation model, so as to realize dynamic adjustment of parameters in a complex environment with dynamic changes in salinity and sediment content.

2. The integrated measurement method of hydrological elements in a high-salt and high-sand environment according to claim 1 is characterized in that: The sound attenuation model is shown as follows: α(f)=k·C m ·f n ·d p ; Among them, α(f) is the sound attenuation coefficient of the sonar with a sound wave frequency of f, k is the water body attenuation proportional coefficient, C is the sediment content, m is the sediment content index, f is the sound wave frequency, n is the frequency index, d is the sediment particle size, and p is the particle size index; The theoretical signal reception strength of any sonar is corrected by the following formula: Among them, P signal (f) is the corrected theoretical signal receiving strength of the sonar with a sound wave frequency of f, P tx is the sonar transmission power, e is a natural constant, D is the water depth where the unmanned ship is located, A eff is the effective irradiation area of ​​the sonar beam, and R is the reflection coefficient of the riverbed where the unmanned boat is located.

3. The integrated measurement method of hydrological elements in a high-salt and high-sand environment according to claim 1 is characterized in that: Based on the effective signal-to-noise ratio of each sonar, the dynamic weight fusion algorithm is used to fuse the water depth data of each sonar to obtain the fused water depth data, which specifically includes: For a sonar whose effective signal-to-noise ratio is higher than a second threshold, resetting the weight of the sonar to zero; For a sonar whose effective signal-to-noise ratio is higher than a second threshold, resetting the weight of the sonar to one; For a sonar whose effective signal-to-noise ratio is higher than a first threshold and lower than a second threshold, a weight of the sonar is calculated according to a proportion of the effective signal-to-noise ratio of the sonar in the total effective signal-to-noise ratios of all the sonars; According to the weight of each sonar and the water depth data of each sonar, the fused water depth data is calculated.

4. The integrated measurement method of hydrological elements in a high-salt and high-sand environment according to claim 3 is characterized in that: Calculate the effective signal-to-noise ratio of any sonar according to the following formula: Among them, SNR(f) is the effective signal-to-noise ratio of the sonar with a sound wave frequency of f, P signal (f) is the corrected theoretical signal receiving strength of the sonar with a sound wave frequency of f, P noise (f) is the background noise energy of the sonar with a sound wave frequency of f; When the multi-frequency sonar group includes only one high-frequency sonar and one low-frequency sonar, the fused water depth data is calculated according to the following formula: H fused =w low ·H low +w high ·H high ; Among them, H fused is the fused water depth data, w low is the weight of low-frequency sonar, H low is the water depth data of low-frequency sonar, w high is the weight of high-frequency sonar, H high It is the water depth data of high frequency sonar.

5. The integrated measurement method of hydrological elements in a high-salt and high-sand environment according to claim 1 is characterized in that: According to the residual value between the actual signal receiving strength of each sonar and the theoretical signal receiving strength, an inversion method based on residual minimization is adopted to iteratively optimize the parameters of the acoustic attenuation model, specifically including: For any sonar, calculating the theoretical signal receiving strength of the sonar according to the current sound attenuation coefficient; Calculating a signal reception strength residual according to the actual signal reception strength of the sonar and the theoretical signal reception strength; Based on the partial derivative of the square of the signal reception strength residual with respect to the acoustic attenuation coefficient, the acoustic attenuation coefficient is iteratively optimized using a gradient descent method until the difference between the current acoustic attenuation coefficient and the acoustic attenuation coefficient of the previous round is less than a convergence threshold, thereby obtaining a target acoustic attenuation coefficient; Based on the target sound attenuation coefficient, the parameters of the sound attenuation model are recalibrated to achieve dynamic update of the sound attenuation model.

6. The integrated hydrological element measurement system in a high-salt and high-sand environment according to claim 1 is characterized in that: The speed of the sound waves emitted by the sonar is calibrated according to the following formula: c=1449.2+4.6T-0.055T 2 +0.0003T 3 +(1.34-0.01T)(S-35)+0.016D; Among them, c is the sound wave velocity of the calibrated sonar, T is the water temperature measured at the location of the unmanned ship, S is the salinity measured at the location of the unmanned ship, and D is the water depth at the location of the unmanned ship.

7. A method for integrated measurement of hydrological elements in a high-salt and high-sand environment, characterized in that: The integrated measurement method of hydrological elements in a high-salt and high-sand environment is used to accurately measure water flow velocity and flow in a high-salt and high-sand environment using an unmanned ship equipped with multiple measuring instruments; the integrated measurement method of hydrological elements in a high-salt and high-sand environment includes: Align the timestamps and spatial positions of various measuring instruments carried by the unmanned ship to achieve time synchronization and spatial registration of various measurement data; the measuring instruments carried by the unmanned ship include electromagnetic flowmeter, radar flowmeter, laser particle size analyzer and corrosion-resistant acoustic Doppler flow profiler; According to the water flow velocity measured by the electromagnetic flowmeter, the sediment particle velocity is iteratively decoupled based on the comprehensive sediment particle motion model to obtain the sediment particle velocity; The updated water velocity is calculated based on the apparent velocity and sediment particle velocity measured by the corrosion-resistant acoustic Doppler current profiler; Based on the comparison between the difference between the updated water flow velocity and the surface flow velocity measured by the radar flow meter and the water flow velocity error threshold, it is determined whether the updated water flow velocity converges; If the updated water flow velocity has not converged, optimize the parameters in the comprehensive model of sediment particle movement, and jump to the step of "according to the water flow velocity measured by the electromagnetic flowmeter, iteratively decouple the sediment particle velocity based on the comprehensive model of sediment particle movement to obtain the sediment particle velocity"; If the updated water flow velocity converges, the water flow velocity is corrected according to the measured salinity, and the water flow rate is calculated based on the salinity-corrected water flow velocity and the water cross-sectional area.

8. The integrated measurement method of hydrological elements in a high-salt and high-sand environment according to claim 7 is characterized in that: The comprehensive model of sediment particle movement is shown as follows: in, is the sediment particle velocity at the nth iteration decoupling, is the water velocity at the nth iteration decoupling, K is the concentration attenuation coefficient, ρ s and ρ w are the densities of sediment and water, d is the sediment particle size, I t is the turbulence intensity; The updated water flow velocity is calculated according to the following formula: in, is the updated water velocity, v ADCP The superficial velocity measured by a corrosion-resistant acoustic Doppler current profiler.

9. The integrated measurement method of hydrological elements in a high-salt and high-sand environment according to claim 7 is characterized in that: The water velocity is corrected according to the following formula: v EM,corrected =v EM ·(1+β·(S-S0)); Among them, v EM,corrected is the salinity-corrected water velocity, v EM is the water flow velocity measured by the electromagnetic flowmeter, β is the salinity sensitivity coefficient, S is the measured salinity, and S0 is the calibrated salinity.

10. An integrated hydrological element measurement system in a high-salt and high-sand environment, characterized in that: The main body of the integrated measurement system of hydrological elements in a high-salt and high-sand environment is an unmanned boat with an anti-sand hull structure, and the integrated measurement system of hydrological elements in a high-salt and high-sand environment includes a platform adaptation module, an environmental perception module, a data processing module and an energy and communication module; the platform adaptation module is used to provide hardware support for the unmanned boat, and the platform adaptation module includes a titanium alloy sensor housing, a salt spray isolation electronic cabin, a propeller loaded with a protective net, and a positioning unit; the environmental perception module is a plurality of measuring instruments carried on the unmanned boat; the energy and communication module is used to provide energy and power for the unmanned boat and to provide support for communication between modules; the data processing module is used to implement the integrated measurement method of hydrological elements in a high-salt and high-sand environment as described in any one of claims 1-9.

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