A method and apparatus for measuring ocean current field

Abstract

The application relates to the technical field of flow field measurement, in particular to a marine flow field measurement method and device, and the following scheme is provided: a vertical sensor is used to collect marine environment parameters, an optimal algorithm is used to determine the horizontal angle of a tilt sensor, a virtual Doppler measurement array is generated by using micro-scan, and the virtual Doppler measurement array is input into a chaotic oscillator network to identify the maximum direction of Lyapunov exponent and determine the horizontal angle of a second tilt sensor; then, a frequency shift signal is collected, and the flow velocity and flow direction are calculated by combining adjustment parameters, so that high-precision and high-adaptability flow field measurement is realized; and the application has dynamic response capability and is suitable for direction structure identification and real-time flow direction calculation under complex sea conditions.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of flow field measurement, in particular to a marine flow field measurement method and device. BACKGROUND

[0002] Most of the existing marine flow field measurement techniques rely on a fixed-angle Doppler sensor array, which is difficult to adapt to the complex and frequently changing direction of the ocean environment. In the face of strong disturbance, non-uniform flow direction or dynamic angle requirement scenarios, the existing scheme often cannot provide stable and continuous flow direction recognition results, resulting in a decrease in measurement accuracy and a deviation in direction calculation, making it difficult to meet the requirements of real-time and direction resolution in engineering applications. To solve the above problems, the present application designs a marine flow field measurement method and device. SUMMARY

[0003] The technical problem to be solved by the present application is to provide a marine flow field measurement method and device to solve the problems of the prior art. The method collects marine environmental parameters through a vertical sensor and determines the viewing angle of a tilted sensor based on an optimization algorithm. A virtual Doppler measurement array is generated using a small amplitude scan and input into a chaotic oscillator network to identify the maximum direction of the Lyapunov exponent to determine the horizontal angle of the second tilted sensor. Then, the frequency shift signal is collected and combined with the adjustment parameter to calculate the flow rate and flow direction, achieving high-precision and high-adaptive flow field measurement. The method has dynamic response capability and is suitable for direction structure identification and real-time flow direction calculation under complex sea conditions.

[0004] To achieve the above-mentioned purpose, the present application provides the following technical scheme:

[0005] A marine flow field measurement method applied to a measurement device, the measurement device comprising a sensor assembly, a movement structure and a processing module, the sensor assembly comprising a vertical sensor, a first tilted sensor and a second tilted sensor, the movement structure being used to adjust the viewing angle and horizontal angle of the first and second tilted sensors, the processing module being used to determine the viewing angle and horizontal angle and calculate the flow rate and flow direction in the target flow field, the method comprising:

[0006] Obtaining marine environmental parameters of the target flow field through the vertical sensor, wherein the marine environmental parameters include wave height and wave direction;

[0007] Determining the adjustment parameters of the first and second tilted sensors according to the marine environmental parameters to collect the frequency shift signal, wherein the adjustment parameters include the viewing angle and the horizontal angle, and the horizontal angle is determined by the response result of the first tilted sensor;

[0008] Calculating the flow rate and flow direction of the target flow field according to the frequency shift signal combined with the adjustment parameters.

[0009] determining an adjustment parameter of the first and second tilt sensors, comprising:

[0010] calculating a look-up angle of the first and second tilt sensors by an optimization algorithm according to the marine environment parameter;

[0011] driving the first tilt sensor to perform a horizontal micro-scan at the look-up angle, generating a virtual Doppler measurement array;

[0012] inputting the virtual Doppler measurement array into a preset chaotic oscillator network, responding to the virtual Doppler measurement array by the chaotic oscillator network, obtaining a spatial direction with a maximum Lyapunov exponent, and outputting a horizontal angle between the spatial direction and the second tilt sensor as a horizontal included angle.

[0013] the calculating a look-up angle of the first and second tilt sensors by an optimization algorithm, comprising:

[0014] matching a plurality of candidate look-up angles from a preset look-up angle set according to a current marine state, wherein the current marine state is determined according to the marine environment parameter;

[0015] calculating a measurement response of each candidate look-up angle according to a current attitude state of the first and second tilt sensors;

[0016] inputting the candidate look-up angle, taking expected intensity, direction separation degree and signal continuity as objective functions, and constructing an optimization solving model, wherein a constraint condition of the optimization solving model is the measurement response;

[0017] the optimization solving model iteratively calculates the candidate look-up angle, and outputs a pitch angle, wherein the iterative calculation calculates a function value of the objective function at each candidate look-up angle by a particle swarm algorithm under the condition of satisfying the constraint condition, compares the function value with a preset convergence condition, retains a current angle as a candidate optimal solution if the convergence condition is satisfied, and updates the convergence condition until there is only one candidate optimal solution, and the candidate optimal solution is output as the pitch angle.

[0018] responding to the virtual Doppler measurement array by the chaotic oscillator network to obtain a spatial direction with a maximum Lyapunov exponent, comprising:

[0019] inputting each channel signal in the virtual Doppler measurement array into an initial space of the chaotic oscillator network, nonlinearly interacting the each channel signal according to the initial space, exciting the initial space of the chaotic oscillator network, and obtaining a state space;

[0020] The state space is transformed into a chaotic resonance state, so that the attractor orbits of the chaotic oscillator network are distributed and changed.

[0021] By monitoring the state evolution of the chaotic oscillator network, the chaotic attraction response intensity of each channel signal is obtained;

[0022] The Lyapunov exponent of each channel signal is calculated based on the chaotic attraction response intensity, and the location with the largest Lyapunov exponent is determined as the sensitive region. The spatial direction of the sensitive region is then output.

[0023] Before inputting the virtual Doppler measurement array of the first tilt sensor into the chaotic oscillator network, the method further includes:

[0024] Acquire the Doppler measurement array signal of the second tilt sensor in the previous downward angle state;

[0025] Based on the frequency structure of the Doppler measurement array signal, a perturbation signal is constructed, wherein the perturbation signal includes the frequency domain distribution characteristics of the Doppler measurement array signal;

[0026] The disturbance signal is input as a feedforward signal to the chaotic oscillator network to update the modulation response characteristics of the initial space of the chaotic oscillator network.

[0027] The initial space of the chaotic oscillator network is excited by nonlinear interaction of the signals from each channel according to the initial space, including:

[0028] Each channel signal is mapped to the multidimensional state variable domain of the initial space;

[0029] Based on the modulation response characteristics, the channel signals in the multidimensional state variable domain are coupled to each other during the mapping process to generate an unsteady oscillation response;

[0030] The initial space is excited based on the unsteady oscillation response to obtain the state space.

[0031] Updating the modulation response characteristics of the initial space of the chaotic oscillator network includes:

[0032] The frequency domain coupling factor is obtained by coupling the dominant frequency of the disturbance signal with the state variables of the initial space.

[0033] The frequency domain coupling factor is mapped to a parameter set used for initialization in the chaotic oscillator network, wherein the parameter set includes the orbital starting position, coupling strength coefficient, and boundary response threshold.

[0034] Adjust the initial values ​​of the parameter group according to the mapped distribution characteristics;

[0035] According to the adjusted initial value, the modulation response characteristics of the initial space are updated.

[0036] According to the frequency shift signal, the flow velocity and flow direction of the target flow field are calculated in combination with the adjustment parameter, including:

[0037] The Doppler frequency shift signals collected by the first and second tilt sensors are obtained to obtain corresponding first and second signals.

[0038] The frequency shift difference value of the first and second signals is calculated to obtain a velocity component.

[0039] According to the velocity component and the adjustment parameter, in combination with the geometric relationship, the flow direction angle in the horizontal plane of the target flow field is calculated.

[0040] A sea flow field measurement device, the measurement device comprising a sensor assembly, a mobilization structure and a processing module, wherein:

[0041] The sensor assembly comprises a vertical sensor, a first tilt sensor and a second tilt sensor, the vertical sensor being used to obtain a sea environment parameter of a target flow field, and the first and second tilt sensors being used to collect frequency shift signals.

[0042] The mobilization structure is used to adjust the overhead angle and horizontal included angle of the first and second tilt sensors.

[0043] The processing module is used to determine the overhead angle and horizontal included angle and calculate the flow velocity and flow direction in the target flow field.

[0044] The processing module comprises:

[0045] An angle decision unit is used to determine the overhead angle and horizontal included angle of the first and second tilt sensors according to the sea environment parameter and output angle adjustment instructions to the mobilization structure.

[0046] A signal processing unit is used to receive the Doppler frequency shift signals collected by the two tilt sensors and calculate the flow velocity and flow direction in the target flow field.

[0047] Compared with the prior art, the beneficial effects of the present application are:

[0048] The application realizes dynamic adjustment of the angle parameters of the tilt sensor and adaptive construction of the included angle structure by introducing the top view angle calculation based on the optimization algorithm and the direction response identification mechanism of the chaotic oscillator network, does not rely on fixed array layout, and can stably obtain frequency shift data with direction sensitivity in complex sea conditions. Through the nonlinear interaction and Lyapunov index identification method, the accuracy and robustness of the flow direction judgment are significantly improved, and the adaptability of the system to non-uniform flow field and dynamic disturbance conditions is enhanced, thereby realizing high-resolution and high-continuity flow field measurement effect. BRIEF DESCRIPTION OF DRAWINGS

[0049] Other features, objects, and advantages of the application will become more apparent from the following detailed description of non-limiting embodiments, made with reference to the accompanying drawings:

[0050] Figure 1 An exemplary application scenario of an embodiment of the application is shown in the figure;

[0051] Figure 2 A flowchart of a flow field measurement method of an embodiment of the application is shown in the figure;

[0052] Figure 3 A calculation flowchart of the adjustment parameters of an embodiment of the application is shown in the figure;

[0053] Figure 4 A flowchart of an initial space update method of a chaotic oscillator network of an embodiment of the application is shown in the figure;

[0054] Figure 5 A flowchart of a chaotic oscillator network response method of an embodiment of the application is shown in the figure;

[0055] Figure 6 A principle diagram of initial space excitation of an embodiment of the application is shown in the figure;

[0056] Figure 7 A principle diagram of post-orbital change of chaotic resonance state of an embodiment of the application is shown in the figure. DETAILED DESCRIPTION

[0057] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application.

[0058] In this document, "embodiments" means that the specific features, structures, or characteristics described in connection with the embodiments can be included in at least one embodiment of the application. The appearance of this phrase in various places in the specification does not necessarily all refer to the same embodiment, nor is it necessarily mutually exclusive or alternative embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0059] The present application is suitable for deployment in an open flow field environment, especially for ocean current velocity and direction measurement scenarios with non-uniform direction flow velocity distribution, strong surface disturbance, limited observation angle, etc.

[0060] Existing measurement systems rely on fixed overhead angle and single frequency shift signal angle solving method, which leads to interference from surface waves, incident direction changes or observation blind area in dynamic sea conditions, especially when there are sudden changes in flow field direction or nonlinear disturbance structures, the stability and resolution of the measurement results are severely reduced.

[0061] The application scenarios of the present application include but are not limited to:

[0062] Periodic monitoring of irregular deep water flow field by offshore buoys, platforms or floating detectors;

[0063] Real-time flow measurement operation in ocean wind field, tidal channel or nearshore multi-direction flow field convergence area;

[0064] Need to overcome the problem of uneven observation direction and asymmetric frequency shift signal in high disturbance and low reflection area;

[0065] Based on the multi-sensor array to construct a virtual space observation path and carry out multi-angle optimization inversion scenario.

[0066] It should be noted that the measurement method of the present application does not rely on fixed sea area structure model or known flow direction field reference system, and can adapt to complex flow field environment with multiple path interference, multiple source disturbance, angle shielding and inversion uncertainty characteristics.

[0067] It is worth mentioning that the angle adjustment and direction inversion logic described in the present application is not limited to a specific platform installation structure or marine engineering series, and can also be applied to:

[0068] Underwater detection system with asymmetric observation path;

[0069] Natural scene where the measured fluid presents dynamic disturbance direction migration;

[0070] Measurement system that needs to realize angle optimization and precision enhancement in low signal strength, high reflection background or measurement direction imbalance state.

[0071] Please refer to Figure 1 , which is a schematic diagram of an exemplary application scenario provided by an embodiment of the present application.

[0072] Figure 1The measurement device for ocean flow field measurement is deployed on the surface of the target sea area, and the Doppler frequency shift signal is collected by multiple tilt sensors at a specific overhead angle and horizontal angle, and the dynamic measurement of the water flow velocity and direction is realized by combining the vertical direction environmental perception. Its application scene is suitable for complex sea state environment with unstable wave height, variable flow direction and limited observation angle, and can realize high-precision real-time measurement of ocean flow velocity and flow direction.

[0073] Next, a kind of ocean flow field measurement method provided by the embodiment of the application is introduced, Figure 2 The method shown is applied to a measurement device, the measurement device includes a sensor assembly, a mobilization structure and a processing module, the sensor assembly includes a vertical sensor, a first tilt sensor and a second tilt sensor, the mobilization structure is used to adjust the overhead angle and horizontal angle of the first tilt sensor and the second tilt sensor, and the processing module is used to determine the overhead angle and horizontal angle and calculate the flow velocity and flow direction in the target flow field, and the method includes:

[0074] S1: obtain the ocean environmental parameters of the target flow field by the vertical sensor;

[0075] In this embodiment, the vertical sensor is used to collect the wave height and wave direction of the target sea area, and the wave height and wave direction can be obtained by a microwave altimeter or a vertically arranged Doppler ranging device. The type of sensor selected can be adjusted according to the actual deployment accuracy requirement and power consumption budget, as long as it can form continuous output of surface wave parameter, which is not limited in the present application.

[0076] S2: according to the ocean environmental parameters, determine the adjustment parameters of the first tilt sensor and the second tilt sensor to collect the frequency shift signal;

[0077] In this embodiment, the adjustment parameters include two dimensions of overhead angle and horizontal angle, wherein the overhead angle is selected from a set of preset angles by an optimization algorithm, and is calculated in real time in combination with the current posture state of the sensor and the environmental disturbance characteristics; the horizontal angle is determined by the response direction of the chaotic oscillator network driven by the virtual Doppler measurement array constructed by the first tilt sensor, and the direction corresponds to the position with the largest Lyapunov exponent. The horizontal angle adjustment process does not depend on historical heading data or fixed flow field model, but evolves in real time based on the nonlinear resonance behavior of the frequency shift response, avoiding the amplification of projection error and the decoupling distortion of flow direction caused by fixed angle in the traditional single-channel scanning structure, and improving the physical rationality and spatial response stability of angle matching.

[0078] S3: calculate the flow velocity and flow direction of the target flow field according to the frequency shift signal in combination with the adjustment parameters;

[0079] In the embodiment, the first and second tilt sensors respectively collect Doppler frequency shifts under the current set of overhead angle and horizontal angle conditions. The frequency shift signals are filtered, differentially analyzed, and weighted and fused to calculate the projection components of the target flow rate. A velocity inversion model is constructed based on the spatial angle between the sensors to inversely calculate the flow rate and flow direction information in the two-dimensional coordinate system of the target sea area. The entire inversion process does not require the introduction of additional structural boundary information or reference coordinate systems, but relies on the geometric mapping relationship between multi-angle signals to complete the calculation, thereby maintaining the robustness and direction consistency of the calculation results in irregular flow and frequent flow direction disturbances in actual marine environments, and having higher anti-drift capability and engineering adaptability.

[0080] Before expanding the specific technical content of the embodiment, the present application further describes a basic measurement difficulty commonly existing in the ocean flow field, that is, in the scene where multi-directional water flow intersects, disturbance fluctuates violently, or reflection interference is strong, any tilt sensor arranged at a fixed angle is difficult to obtain directionally decoupled frequency shift information at a single viewing angle.

[0081] It can be understood that in such a scene, the local non-uniformity of the flow rate and the short-period disturbance of the flow direction often result in minimal projection differences of the Doppler signal in different directions, especially when the sensor angle is not sufficiently spatially separated from the main flow direction, both tilt sensors may fall into the signal ambiguity band at the same time, and finally produce a high correlation solution or direction drift problem when inverting the velocity direction.

[0082] The measurement logic adopted in the embodiment is not to directly improve the signal strength or improve the projection quality, but to dynamically adjust the horizontal angle between the two tilt sensors to reconstruct the observation direction from the input structure, so that it always has controllable direction discrimination ability.

[0083] Further, the adjustment process of the horizontal angle is not through a preset rule or static table, but based on the response sensitivity of the chaotic oscillator network to the virtual measurement array signal collected by the first tilt sensor, a directional attractive shift is formed in the chaotic system, and the sensitive direction is determined accordingly. The spatial direction obtained in this way is used as the target for adjusting the angle, so that the second tilt sensor is biased in this direction to form an effective spatial projection difference.

[0084] It is easy to understand that the present application does not tolerate poor signals by optimizing the calculation model, but controls the input path to naturally have strong observation difference between the two tilt directions, thereby fundamentally reducing the loss of calculation accuracy caused by too small frequency shift amplitude difference.

[0085] Further, in the mixed disturbance flow field, this kind of adjustment based on the included angle not only improves the spatial stability of the solution, but also effectively suppresses the false velocity component caused by the ambiguity of the incident direction. Especially in the irregular water area structure, the directional separation effect brought by the included angle control can significantly improve the directional confidence of the flow direction result. Even if the flow velocity fluctuates greatly in multiple acquisition periods, the main direction corresponding to it can be continuously observed under the included angle adjustment structure, so that the subsequent direction time series has stronger continuity and physical consistency.

[0086] Compared with the traditional fixed angle arrangement scheme or the direction decision method based on single channel frequency shift threshold judgment, the included angle adjustment logic provided by the embodiment establishes a coupling path between the dynamic angle regulation capability and the signal response characteristic matching capability, providing structural level input guarantee for subsequent high frequency disturbance solution and multi-direction flow field reconstruction, especially suitable for complex sea condition scenes with strict structural constraints, unpredictable measurement direction or significant reflection interference.

[0087] Next, the part of the method of the application related to the vertical sensor is further expanded.

[0088] In the embodiment, the wave height and wave direction are obtained by the vertical sensor. The wave height reflects the amplitude information of the water surface disturbance, and the wave direction indicates the propagation direction of the water surface fluctuation, which together constitute the basic expression of the hydrodynamic characteristics in the current measurement scene.

[0089] It can be understood that the vertical sensor in the application is not limited to a specific form of distance measuring component, as long as it can form a vertical measurement response to the target water surface to meet the application requirements. For example, the vertical sensor can be a non-contact distance measuring module based on millimeter wave radar or laser radar, which realizes high frequency sea surface height sampling by transmitting and receiving electromagnetic waves, and is suitable for platform installation and layout environment with small wave response; it can also be a hydroacoustic depth sounder or an ultrasonic distance measuring device with attitude compensation, which is used in nearshore or deep sea floating structures with strong tidal response and severe sea conditions.

[0090] In an optional embodiment, the vertical sensor can also have multi-point scanning capability or array measurement function, which establishes a local water surface distribution model while obtaining the wave height and wave direction, to assist in judging the main propagation direction of the wave, the reflection interference area or the hydrodynamic structure boundary condition. In such a design, the wave direction calculation is no longer dependent on single point phase difference calculation, but can obtain higher resolution direction estimation results based on multi-point wave peak tracking.

[0091] It can be understood by those skilled in the art that the above different types of vertical sensors differ in integration, sampling frequency and response sensitivity, and the actual selection can be determined comprehensively according to the structural characteristics, environmental stability and power supply capacity of the deployment platform, as long as the real-time measurement ability of the current sea state wave height and wave direction is met to the minimum extent, which does not constitute a substantial limitation on the subsequent scheme of the application.

[0092] Next, the part of the method of the application related to the tilt sensor is further expanded.

[0093] In this embodiment, the tilt sensor is used to collect Doppler frequency shift signals with directional characteristics in the target sea area under the condition of a set overhead angle and horizontal angle, and the Doppler frequency shift signals are the basic physical quantities for subsequent calculation of water flow velocity and direction. In the implementation process, the two tilt sensors are distributed at a certain angle, and the directional layout feature enables the system to have the ability to separately observe the projection of the velocity vector, so as to restore the velocity and direction distribution of the real flow field through differential calculation and angle inversion model.

[0094] It can be understood that the tilt sensor in the application is used to detect the Doppler reflection echo formed by the movement of seawater surface, which can essentially include but is not limited to underwater acoustic Doppler sensor, laser speed measurement module or microwave Doppler speed measurement device, and the key is to be able to output frequency shift information related to the relative speed of the target under oblique layout condition. The sensor mounting structure can be a rigid support or a dynamic adjustment mechanism driven by a servo motor, as long as it can respond to the adjustment instructions of the overhead angle and the horizontal angle and keep the posture stable.

[0095] In an alternative embodiment, the tilt sensor is provided as a bidirectional structure that can be independently driven and adjusted, and the angle linkage constraint exists between the signal acquisition channels. The spatial distribution relationship can be set through the angle optimization mechanism before measurement, and the obtained frequency shift signals are uploaded to the processing module for fusion calculation after acquisition. The dual sensors can differ in angle control accuracy and frequency shift response bandwidth, and can also be heterogeneously configured to achieve optimal sensing capability for different frequency intervals or signal directions.

[0096] In actual deployment, the type and number of tilt sensors can be selected and configured according to the flow velocity level, background noise level and transmission path stability of the target water area, and the application does not limit the type or number, as long as the oblique acquisition ability of the Doppler frequency shift information is met, and the effective observation of the target velocity direction component can be realized by cooperating with the overhead angle and horizontal angle adjustment strategy, which can meet the application requirements.

[0097] It is easy to understand that the number of tilt sensors is at least two, and in the case of more than or equal to three tilt sensors, a spatial redundant observation network can be further constructed on the basis of the original two-channel structure. The spatial redundant observation network can group and combine the observation directions of multiple tilt sensors through a pre-set angle layout strategy to form different included angle pairs to achieve independent acquisition of multiple sets of frequency shift differences. The multiple sets of frequency shift differences can be comprehensively utilized through weighted averaging, main direction extraction, residual suppression and other methods when performing velocity inversion, thereby improving the stability of the velocity vector solution result.

[0098] It should be noted that in the embodiment in which the number of tilt sensors is three or more, the device does not necessarily dynamically adjust all tilt sensors at the same time in terms of structure, and only at least two groups need to form a spatial angle combination that meets the included angle optimization path requirement. The remaining tilt sensors can be in auxiliary acquisition or verification paths for redundant verification, comparison calibration or real-time compensation.

[0099] Reference can be made to Figure 3 , Figure 3 The calculation flowchart of the adjustment parameter of the embodiment of the application is shown in the figure.

[0100] In one example, determining the adjustment parameters of the first tilt sensor and the second tilt sensor comprises:

[0101] S2.1: calculating the tilt-down angle of the first tilt sensor and the second tilt sensor through an optimization algorithm according to the marine environment parameters;

[0102] Specifically, by obtaining the wave height and wave direction information of the target flow field in real time, the main direction of the current sea state disturbance and the amplitude of the water surface disturbance can be inferred. In different sea states, if the tilt-down angle is not set reasonably, the tilt sensor may have a signal acquisition dead zone, resulting in distorted frequency shift response or an observation blind area. In order to avoid the problem of response drift caused by fixed angle layout, the tilt-down angle needs to be dynamically calculated before measurement according to the current environmental state, so that the tilt direction has strong incident response ability and is in a balanced position of signal strength and direction discrimination ability.

[0103] In the embodiment, the calculation of the tilt-down angle uses a multi-candidate angle combination to construct a candidate set, iteratively scores the expected intensity, direction separation and signal continuity under the candidate angle through a particle swarm optimization algorithm, and selects a group of angles with the best comprehensive score as the final tilt-down angle output. The scoring function nonlinearly combines multiple observation quality indicators to avoid angle imbalance caused by single indicator bias, and introduces historical data as a disturbance term in the algorithm to improve the global convergence of the solution.

[0104] In one example, the calculation of the tilt-down angle of the first tilt sensor and the second tilt sensor through an optimization algorithm comprises:

[0105] S2.1.1: matching a plurality of candidate overhead angles from a preset overhead angle set according to a current sea state, wherein the current sea state is determined according to the marine environment parameters;

[0106] Specifically, to avoid that the tilt sensor cannot be adjusted to the optimal observation posture in time when the sea state changes dramatically, it is necessary to determine which type of sea state is currently in before determining the overhead angle, so as to dynamically match the overhead angle in the appropriate candidate range. The current sea state is a classification parameter obtained based on the combination of wave height and wave direction.

[0107] In the embodiment, the sea state is calculated by marine environment parameters, the marine environment parameters are normalized to form a feature vector, and the feature vector is input into a preset state discrimination model. The state discrimination model is classified by a rule threshold and a fitting result of historical data, and the classification output is a discrete state label.

[0108] Further, after the discrete state label is determined, a subset matched with the current state is extracted according to a preset overhead angle candidate set structure. Each overhead angle value in the candidate set is segmented and configured in combination with empirical observation and measured data at the initial stage, and each state label corresponds to a candidate subset containing a plurality of overhead angle values. The matching process does not directly output the overhead angle, but sets a reasonable and better convergent input range for the subsequent optimization algorithm, thereby improving the stability of the subsequent iteration.

[0109] S2.1.2: calculating a measurement response of each candidate overhead angle according to the current posture state of the first tilt sensor and the second tilt sensor;

[0110] Specifically, simply extracting the candidate angle from the sea state cannot reflect the observation ability of the device under the current posture condition. To ensure that the subsequently selected overhead angle can work effectively within the hardware feasible range, the observation performance of each candidate angle under the current device posture needs to be calculated to determine whether it can collect effective frequency shift signals under the current configuration.

[0111] It can be understood that the measurement response in the embodiment is a comprehensive evaluation of the direction sensitivity of the sensor to the incident signal, the echo intensity simulation value and the structure blocking risk under the change of the overhead angle.

[0112] In the embodiment, the measurement response adopts a data-driven modeling method to pre-establish a nonlinear fitting curve between the direction angle and the response intensity, which is realized by fast table lookup in actual operation. It reflects the quality of the Doppler frequency shift signal that can be obtained under a specific overhead angle, and the angle between the incident direction and the wave direction is added as an auxiliary factor to correct the direction interference effect caused by the change of the sea state.

[0113] S2.1.3: An optimization solving model is constructed by taking the expected intensity, direction separation degree and signal continuity as the objective function and taking the candidate look-down angle as the input, wherein a constraint condition of the optimization solving model is the measurement response;

[0114] Specifically, after obtaining the candidate look-down angle and the corresponding measurement response, an optimization solving model with clear target orientation needs to be constructed to ensure that the selected angle has good signal response and analysis ability for the flow direction in actual use. The optimization solving model does not take single signal intensity as the target, but considers three aspects comprehensively:

[0115] The first aspect is the frequency shift signal intensity, that is, whether the target incident direction can effectively receive a high signal-to-noise ratio echo signal;

[0116] The second aspect is the direction separation degree, that is, whether the frequency shift signals collected by the two inclined sensors under this angle condition have sufficient angle distinguishing ability for subsequent direction solving;

[0117] The third aspect is the signal continuity, that is, whether the current angle has the response characteristics of a stable time sequence to avoid the jump or drift of the measurement value caused by the extremely extreme look-down angle setting.

[0118] In this embodiment, the three objective function items are evaluated in a linear combination manner, and different weight parameters are given to express the engineering priority. Among them, the signal intensity item is derived from the angle response curve given by the simulation or preset model, the direction separation degree is calculated by the angle projection with another inclined sensor, and the signal continuity is constructed based on the response coefficient of variation in the historical window. The measurement response value is used as a constraint term to eliminate angle items with response values below the stable working threshold, ensuring that the final optimal solution comes from the physically available angle range.

[0119] S2.1.4: The optimization solving model iteratively calculates the candidate look-down angle and outputs the pitch angle, wherein the iterative calculation calculates the function value of the objective function at each candidate look-down angle by a particle swarm algorithm under the condition of meeting the constraint condition, compares the function value with a preset convergence condition, retains the current angle as a candidate optimal solution if the convergence condition is met, updates the convergence condition, and outputs the candidate optimal solution as the pitch angle until there is only one candidate optimal solution.

[0120] Specifically, the angle optimization problem constructed based on the above objective function and constraint condition is iteratively solved by a particle swarm algorithm.

[0121] It can be understood that the particle swarm algorithm has better global convergence ability and calculation efficiency in solving discrete variables and low-dimensional space.

[0122] In the embodiment, each candidate view angle is taken as a particle position, and the fitness thereof is calculated by a corresponding target function value. In the initial stage, the algorithm uniformly distributes a plurality of particles in the candidate set, and randomly initializes the speed and direction.

[0123] Further, in each iteration process, the particles update the position according to the information interaction and the inertia term, and the system selects the position with the highest fitness after each round as the current global optimum. The updating path of the particles is limited by the measurement response constraint, that is, if the angle response value corresponding to the new position is lower than the threshold value, the particle is directly frozen and no longer updated.

[0124] Further, to prevent falling into local optimum, the model also includes convergence dynamic threshold updating logic, that is, if the optimal fitness improvement amplitude in continuous multiple iterations is lower than the set threshold, the convergence standard is automatically tightened, the acceptable solution interval is narrowed, and the stop condition is finally triggered. Finally, the angle that meets all the convergence conditions and is located in the response interval is selected as the final pitch angle output, which is used to drive the tilt sensor to perform physical adjustment.

[0125] S2.2: driving the first tilt sensor to perform horizontal micro-scan at the view angle to generate a virtual Doppler measurement array;

[0126] Specifically, under the premise that the view angle has been determined, the fixed tilt sensor can only provide single direction frequency shift information, which is insufficient to support direction recognition and angle determination. To solve this limitation, the application uses the concept of virtual array, that is, by performing micro-angle scanning of the tilt sensor at the updated view angle, synchronous measurement in multiple adjacent directions is simulated, so that multi-channel frequency shift data is obtained without increasing the number of hardware.

[0127] In the embodiment, the horizontal scanning action is realized by a servo mechanism, the scanning angle range is limited within a preset angle range, the scanning step is set according to the sensor angle resolution, the Doppler frequency shift data is collected once for each scanning step, and finally a one-dimensional measurement array composed of N direction frequency shift values is formed. The array takes the current view angle of the sensor as the main axis direction, records the spatial distribution characteristics of the signal response, and thus maps the response change of the current sea current to different incident directions.

[0128] It can be understood that the adjustment structure first drives the first tilt sensor to reach the corresponding view angle according to the calculated view angle, and then performs micro-angle scanning operation in the horizontal direction under the condition that the angle is fixed.

[0129] It should be noted that the main direction obtained at this stage is not the final flow direction result, but is used to guide the second tilt sensor to complete the orientation setting in the subsequent attitude adjustment. Since the observation direction of the first tilt sensor is a fixed value after the setting of the elevation angle, and the virtual array is generated with it as the main axis, by comparing the angular deviation between the main response direction and the original observation direction of the first tilt sensor, the target orientation of the second tilt sensor can be accurately obtained, and the horizontal angle to be constructed between the two can be derived.

[0130] S2.3: input the virtual Doppler measurement array into a preset chaotic oscillator network, respond to the virtual Doppler measurement array through the chaotic oscillator network, obtain a spatial direction with the largest Lyapunov exponent, and output a horizontal angle between the spatial direction and the second tilt sensor as a horizontal included angle;

[0131] Specifically, the virtual array itself does not directly provide spatial included angle information, so it needs to be converted into a direction recognition result through a nonlinear processing mechanism. The traditional direction discrimination method relies on frequency shift intensity sorting or angle difference judgment, which is easy to fail under weak signal conditions. To break this limitation, the chaotic oscillator network is used as the response structure of the virtual measurement array, the sensitivity of the chaotic system to small perturbations is used to realize nonlinear amplification of the direction response behavior, and the main response direction of the signal in the array is judged through the system orbit evolution behavior.

[0132] In this embodiment, the chaotic oscillator network is constructed in a feedback model based on state variable coupling, each data point of the virtual array is taken as one of the input dimensions of the system, and an input perturbation item related to the signal strength and frequency information is constructed. The system generates multiple attractor orbits in the active state, and a group of responses with the largest Lyapunov exponent in the attractor orbits is determined as the spatial direction with the strongest directionality of the current frequency shift signal. This direction represents the direction in which the current flow velocity responds most concentratedly to multiple observation directions, thereby deducing the spatial angle at which the second tilt sensor should be deployed.

[0133] In an optional embodiment, the construction process of the chaotic oscillator network includes the following three dimensions:

[0134] In the first dimension, the topology configuration of the oscillator network is completed. The topology configuration is composed of a plurality of nonlinear oscillator units, each unit is a state evolution unit, has a self-feedback loop and a coupling input interface, and the units are connected through a connection relationship with a direction weight to form a directed coupled graph structure. The number of oscillator units, connection sparsity, coupling mode and feedback function type are determined in the preset stage. The topology configuration is configured according to the dimension characteristics of the typical input frequency shift array when it is constructed, so that it has the ability to propagate the direction perturbation in the spatial structure, and can amplify and differentiate the input perturbation through multiple node channels.

[0135] In the second dimension, the variable range of the initial state space and the perturbation sensitivity parameters are set. Each oscillator unit is assigned a specific state vector containing position variables, velocity variables, and feedback function initial values at network initialization. The state vector is generated by a pseudo-random seed generator, ensuring that the network has sufficient perturbation but is reproducible before each run. The sensitivity parameters include Lyapunov response thresholds, state transition delay factors, and amplitude control coefficients, which are used to limit the network from entering the divergence or saturation region, thereby maintaining a controllable balance between chaos and convergence within the applicable range.

[0136] In the third dimension, the mapping rule definition of the input is completed. The input signal is a virtual Doppler array constructed by the first tilt sensor during scanning, usually a one-dimensional frequency shift value sequence sorted by angle. In the preset stage, the system assigns an input channel to each array element and maps it to the external perturbation port of the corresponding oscillator unit, with the perturbation mode being frequency shift value modulation of state variables or activation function offset. By defining a static mapping table in the preset stage, it ensures that the array input does not need dynamic connection reasoning during actual operation, improving the running efficiency.

[0137] Please refer to Figure 4 , Figure 4 is a flowchart of the initial space updating method of the chaotic oscillator network embodiment of the present application.

[0138] In one example, before inputting the virtual Doppler measurement array of the first tilt sensor into the chaotic oscillator network, the method further comprises:

[0139] S2.3.1: Obtain the Doppler measurement array signal of the second tilt sensor in the previous overhead angle state;

[0140] Specifically, to improve the direction sensitivity of the chaotic oscillator network to the current input frequency shift structure, the initial response structure of the network needs to be processed with targeted modulation before input. Since the attractor evolution path of the chaotic oscillator network under different initial spaces is dependent, a feedforward perturbation signal with structural significance can be added to change the direction response trajectory of the network to subsequent input, forming a bias enhancement to the main direction. Therefore, the embodiment selects the complete Doppler measurement array collected by the second tilt sensor in its previous overhead angle state before the current overhead angle action as the basic signal input for constructing the perturbation source.

[0141] In the embodiment, the Doppler measurement array signal has structural consistency with the virtual measurement array constructed by the first tilt sensor. The direction of the second tilt sensor is opposite to that of the first tilt sensor, and therefore the frequency shift data collected in the previous posture of the second tilt sensor generally reflects the main direction response distribution of the target flow field in the previous state. Using the main direction response distribution as a disturbance source for subsequent chaos network input can guide the network to produce behavioral bias in the response path of the current virtual array in the direction identification stage, thereby amplifying or screening the structural direction consistency.

[0142] S2.3.2: constructing a disturbance signal according to the frequency structure of the Doppler measurement array signal, wherein the disturbance signal comprises the frequency domain distribution characteristics of the Doppler measurement array signal;

[0143] Specifically, the Doppler measurement array signal collected by the second tilt sensor is essentially a set of directional frequency shift response data, which contains the frequency behavior of the current flow field to multiple directional disturbances. When the Doppler measurement array signal is used to construct the disturbance signal, it is not directly input into the chaos network as the original input, but its frequency domain structure is extracted and compressed first.

[0144] It can be understood that the chaos network is more sensitive to the frequency distribution, amplitude variation trend, etc. of the disturbance source when receiving the disturbance. If the time domain amplitude sequence is directly injected, the nonlinear response path of the network may not be fully excited. Therefore, the embodiment first performs discrete Fourier transform on the frequency shift array data to extract the main frequency component and its amplitude distribution characteristics, and establishes a disturbance energy spectrum in the frequency axis direction.

[0145] In the embodiment, the disturbance signal does not use the complete disturbance energy spectrum, but selects the frequency section with directional aggregation characteristics in the main frequency range of the disturbance energy spectrum for weighted compression to generate a one-dimensional disturbance vector. The one-dimensional disturbance vector contains the main frequency position, power spectral density and frequency distribution slope, which are used to reflect the frequency response concentration of the current flow field in the previous direction configuration.

[0146] Further, the disturbance signal does not participate in the direction judgment process as the main input signal after being constructed, but acts as a feedforward disturbance item in the initial state space of the chaos network. The goal is to reconstruct the network's attraction bias to the upcoming direction structure, so that the network is more inclined to identify the main response track with higher resonance to the historical direction structure, thereby improving the consistency and spatial sensitivity of direction judgment.

[0147] S2.3.3: inputting the disturbance signal as a feedforward signal into the chaos oscillator network to update the modulation response characteristics of the initial space of the chaos oscillator network;

[0148] Specifically, the state response path of the chaotic oscillator network is extremely dependent on the initial state space setting, and the variable setting of the initial space will determine the attractor behavior distribution of the subsequent input.

[0149] In the present embodiment, the aforementioned constructed perturbation signal is injected into the network through the feedforward channel, and the initial state variable of the chaotic network is modulated and adjusted before the activation of the chaotic network.

[0150] Further, each characteristic parameter in the perturbation signal is mapped to the initial value of the state variable of the chaotic network, the local feedback offset, and the connection weight perturbation factor, which is used to form an initial trajectory arrangement with perturbation memory when the network is activated.

[0151] It can be understood that the modulation process is implemented by using a perturbation injection matrix in specific implementation, and each perturbation dimension acts on the state variable of a number of key oscillator nodes through a mapping function. The influence of the perturbation signal on the initial space is manifested as displacement, turning or deformation of the orbit attractor, so as to guide the network to form a resonance path along the historical main direction after receiving a new virtual array, thereby improving the stability and direction sensitivity of the direction recognition result.

[0152] In one example, the modulation response characteristics of the initial space of the chaotic oscillator network are updated, including the following four processes:

[0153] In the first process, the main frequency of the perturbation signal is extracted from the spectrum structure and is structurally coupled with the current set of state variables of the chaotic network, including the initial phase of the core oscillator node, the local feedback item offset, and the starting gain factor of the cross-node coupling path. The coupling form is a nonlinear function modulation based on the frequency domain main frequency position, which is used to control the response strength of the coupling strength slope with the main frequency amplitude or frequency, and finally form a frequency domain coupling factor set;

[0154] In the second process, the frequency domain coupling factor is mapped to the parameter group used for initialization in the chaotic oscillator network, including the orbit starting position (i.e. the spatial coordinate value of the initial state vector of the oscillator node), the coupling strength coefficient (reflecting the perturbation diffusion ability between nodes), and the boundary response threshold (a boundary condition for controlling whether the attractor triggers a phase jump behavior). The mapping process adopts an exponential or piecewise linear function to map the main frequency amplitude to the coupling gain factor and map the main frequency position to the starting orbit interval index, thereby constructing a set of initialization configurations with direction-sensitive pre-adjustment;

[0155] In the third process, the initial values in the above parameter group are adjusted according to the mapped distribution characteristics, wherein the track starting position is offset according to the projection position of the main peak in the frequency domain in the array, so that the vibrator network is more easily evolved along the historical disturbance direction in the directional response stage, the coupling strength coefficient determines the activation degree of the node coupling chain according to whether the frequency domain structure is continuous, and the boundary response threshold is adjusted to a high or low trigger state according to the main frequency aggregation degree;

[0156] In the fourth process, the initial spatial state variable set of the chaotic vibrator network is reconstructed using the above adjusted initial values, and the update of the modulation response characteristics is completed, so that when the network receives the virtual Doppler measurement array collected by the current first tilt sensor, the attractor orbit of the network can be actively biased to the direction channel consistent with the historical disturbance, a directional bias is introduced in the original response structure, thereby improving the identification accuracy, response stability and main direction retention capability of the network output direction structure.

[0157] Please refer to Figure 5 , Figure 5 The flowchart of the chaotic vibrator network response method of the embodiment is shown in the figure.

[0158] In one example, the chaotic vibrator network is used to respond to the virtual Doppler measurement array to obtain a spatial direction with the largest Lyapunov exponent, including:

[0159] S2.3.4: inputting each channel signal in the virtual Doppler measurement array into the initial space of the chaotic vibrator network, and performing nonlinear interaction on the each channel signal according to the initial space to excite the initial space of the chaotic vibrator network, and obtaining a state space;

[0160] Specifically, each channel frequency shift value in the virtual Doppler measurement array is input into the chaotic vibrator network to establish an initial driving condition that can amplify the directional information into a nonlinear dynamic behavior difference.

[0161] It can be understood that each array channel essentially represents a velocity projection at a small direction angle, and the corresponding frequency shift value implies the momentum response of the current fluid motion in that direction. In the traditional linear processing mechanism, these signals are often averaged or filtered, and cannot reflect the microstructure difference, making it difficult to identify the truly directional space channel. Therefore, the frequency shift value of each channel needs to be injected into the state variable space of the chaotic vibrator network as an independent disturbance factor, so that these differences can form track disturbance at the early stage of system evolution and guide the network state into the resonant path of structural differentiation.

[0162] In this embodiment, the initial space of the chaotic oscillator network has been modulated by the foregoing, with a certain directional response bias. After inputting the virtual Doppler measurement array, the phase starting point of each oscillator node and the feedback item activation amount are mapped to the channel frequency shift value, triggering local orbit deviation and propagating to the entire state space through the coupling path. The nonlinear interaction process is not handled independently point by point, but the resonance frequency or phase relationship between multiple channels triggers orbit competition and synchronization breakdown, so that the network as a whole exhibits response concentration to partial direction structures. Finally, the input excitation triggers the chaotic network to enter the direction response state from the static initialization state, generating a state space structure that can be used for subsequent judgment.

[0163] S2.3.5: convert the state space into a chaotic resonance state to cause the attractor orbit of the chaotic oscillator network to change in distribution;

[0164] Specifically, the response capability of the chaotic network depends on the evolution process of its attractor orbit, which represents the stable running path formed by the network state variables in the multidimensional space. Different direction structures will trigger orbit deviation or aggregation, and then form distinguishable response states.

[0165] It can be understood that only the state space does not have judgment ability, and it is necessary to guide the network into a chaotic resonance state to expand the disturbance amplitude between state variables, so that the difference between the orbits forms a clear distribution pattern.

[0166] It is easy to understand that the chaotic resonance state refers to the fact that some nodes in the network undergo sustained periodic oscillation under the disturbance of a specific frequency structure, while other nodes form a response inert zone. The overall state variable distribution is transformed from a mean value symmetric state to a skewed structure, so that the attractor is transformed from a uniform spherical orbit to a biased vortex orbit or a double-peak aggregation state.

[0167] In this embodiment, the state space transformation process to the resonance state is automatically controlled by the network structure parameters, including the coupling strength, local damping, and node phase delay. After reaching a certain input threshold, the attractor orbit bias mechanism is activated, triggering a part of the nodes to form the main orbit, while the other part of the nodes deviates to the discrete orbit, and finally presents the orbit distortion behavior of the direction response in the attractor space, forming the resonance aggregation channel that can be tracked.

[0168] In one example, the initial space of the chaotic oscillator network is excited by nonlinearly interacting the channel signals according to the initial space, including:

[0169] mapping the channel signals to the multidimensional state variable domain of the initial space, respectively;

[0170] According to the modulation response characteristics, each channel signal in the multi-dimensional state variable domain is coupled with each other in a mapping process to generate a non-steady oscillation response;

[0171] According to the non-steady oscillation response, the initial space is excited to obtain a state space;

[0172] For example, the initial space excitation can be understood with reference to Figure 6 Figure 6 The principle diagram of the initial space excitation of the embodiment of the application is shown in FIG. 1, wherein the dotted line represents a coupling process.

[0173] Figure 6 Each signal is mapped into the initial space shown by the circular region through the solid arrow; the circular region is a multi-dimensional state variable domain, and the inside includes four symbolic state variable nodes, which are connected by the dotted lines, and the dotted lines represent non-linear coupling paths established according to the modulation response characteristics. After the coupling is triggered, the phases and amplitudes of the nodes are mutually driven to form local non-steady oscillation, and the oscillation energy is output at the circular boundary along the solid arrow. The output oscillation generates a state space in the form of a continuous wave.

[0174] S2.3.6: The state evolution of the chaotic oscillator network is monitored to obtain the chaotic attractor response strength of each channel signal;

[0175] Specifically, the basis of the direction recognition lies in determining the response strength relationship of each channel input signal in the chaotic network. The frequency shift disturbance of different channels will activate the orbit response of different regions of the network, and the influence range, activation duration and state change amplitude will be embodied as the strength of the channel direction. The chaotic attractor response strength is used to measure the strength of the disturbance ability of the network state space after the input of each channel signal, and reflects whether the direction has the main response ability in the current flow field.

[0176] In the embodiment, the chaotic attractor response strength is extracted by statistically extracting the orbit density change, phase transition frequency and dynamic change degree of the coupling coefficient between oscillators to form a one-dimensional response degree vector. During the monitoring process, the state variables of each node are sampled and compared with the initial state space to obtain the state offset and the degree of coordination between adjacent nodes in a unit of time. Finally, each channel signal input is corresponded to a response strength value.

[0177] S2.3.7: The Lyapunov exponent of each channel signal is calculated according to the chaotic attractor response strength, and the position with the largest Lyapunov exponent is determined as a sensitive region, and the spatial direction of the sensitive region is output;

[0178] ​Specifically, the Lyapunov exponent reflects the exponential amplification rate of the state space evolving over time, and is a core indicator for measuring the sensitivity of the chaotic system to the input signal. In the direction identification process, by calculating the orbit divergence rate of the state change caused by each channel signal, it can be judged whether the channel is the main response direction in the current frequency shift structure. The larger the Lyapunov exponent, the more intense the response of the system to the input direction of the channel, the faster the orbit divergence, the more sufficient the information amplification, and the more likely it corresponds to the main velocity direction in the flow field.

[0179] In this embodiment, the calculation of the Lyapunov exponent adopts the perturbation orbit double-branch method, by synchronously monitoring the orbit distance change between the response orbit and its perturbation orbit corresponding to each channel, the average divergence rate is extracted within a fixed sampling period, and the relative sensitivity between it and other channels is corrected, and finally the exponential matrix is output. In the exponential matrix, the numerical maximum position is the position most sensitive to the input direction of the network, which corresponds to a certain spatial direction angle in the array. The direction angle is used as the output result of the main direction of the response of the chaotic oscillator network this time, which is used for subsequent second inclined sensor angle construction operation.

[0180] For example, in a chaotic resonance state, reference can be made to Figure 7 for understanding, Figure 7 which is a schematic diagram of the principle of the change of the post-orbit of the chaotic resonance state of the embodiment of the present application.

[0181] Figure 7 The butterfly-shaped trajectory is a typical attractor trajectory of the chaotic oscillator network under resonance conditions, and the left and right wings of the trajectory correspond to the alternative aggregation of positive and negative phase channel energy in the network. The difference in thickness of the trajectory line represents the difference in duration and amplitude of the non-steady-state oscillation. The bias degree of the trajectory form is determined by the feedforward perturbation and the virtual measurement array, and the dense area of the trajectory is the energy aggregation area of the direction response, and the sparse area of the trajectory corresponds to the direction suppression area.

[0182] In one example, the specific steps of S3 are as follows:

[0183] S3.1: Obtain the Doppler frequency shift signals collected by the first inclined sensor and the second inclined sensor to obtain corresponding first signals and second signals;

[0184] Specifically, in order to realize the inversion of flow velocity and flow direction based on the two-way view angle construction, two groups of velocity response signals in space directions need to be collected respectively. After determining the overhead angle, the horizontal angle between the first inclined sensor and the second inclined sensor forms an angle pair, which is used for multi-channel observation for angle separation of the target area in the water body. Due to the directionality of the ocean fluid velocity field, there is an intrinsic difference in the frequency shift value in different incident directions, so by collecting the frequency shift signals of the two sensors respectively, the directional component of the velocity can be extracted therefrom.

[0185] In the present embodiment, the acquisition mode of the frequency shift signal is real-time sampling of the Doppler echo signal, and the sampling rate is controlled by the processing module. The sampling window covers the complete scanning period, avoiding time asynchrony caused by angle changes. After the first tilt sensor and the second tilt sensor complete the current attitude adjustment, synchronous sampling is started respectively, and the echo signal is input to the pre-filter module for background noise suppression and high-pass filtering processing. After time-frequency conversion, the main frequency component is locked by the peak value extraction algorithm, and the frequency shift value is recorded as the input basis of the first signal and the second signal.

[0186] S3.2: Calculate the frequency shift difference value of the first signal and the second signal to obtain a velocity component;

[0187] Specifically, the difference between the two frequency shift signals reflects the difference in the velocity projection of the flow field in the respective directions. Since the sensor attitude angle is known, the frequency shift difference can be interpreted as the velocity gradient in the direction of the observation angle. By inversely calculating the difference, the actual velocity component can be obtained based on the known projection direction.

[0188] In the present embodiment, the processing module inputs the first signal and the second signal into the velocity inversion unit, and establishes a frequency shift and velocity mapping relationship through the known sound speed, transmission frequency and current attitude angle of the two sensors. After the frequency shift difference value is calculated by the projection conversion model, the velocity difference component of the target water body in the angle direction is obtained, and then the horizontal and vertical component values are calculated through inverse geometric conversion.

[0189] S3.3: According to the velocity component and the adjustment parameter, the flow direction angle in the horizontal plane of the target flow field is calculated combined with the geometric relationship;

[0190] Specifically, to determine the flow direction angle of the target water body, the calculated velocity component needs to be projected back to the horizontal plane, and a geometric model is established combined with the angle direction of the two sensors, the height of the overhead angle and the observation direction vectors of each sensor. The model converts the directional difference of the velocity into the projection angle of the main direction of the flow field in the horizontal plane through spatial geometric inversion, thereby obtaining the flow direction.

[0191] In the present embodiment, the processing module constructs a two-dimensional vector inversion framework, uses the angle direction formed by the first tilt sensor and the second tilt sensor as the basis of the coordinate system, and projects the velocity values calculated by the respective frequency shifts to the basis coordinate system. Then, the angle between the two projected vectors is calculated through the triangular relationship, combined with the overhead angle accuracy correction factor recorded in the adjustment parameter and the projection deviation model, and an angle value that can be positioned in the horizontal plane, i.e. the flow direction angle, is output.

[0192] In one example, the present embodiment provides a marine flow field measurement device, the measurement device comprising a sensor assembly, a tuning structure and a processing module, wherein:

[0193] The sensor assembly comprises a vertical sensor for acquiring the ocean environmental parameter of the target flow field, and first and second tilt sensors for collecting frequency shift signals;

[0194] The mobilization structure is used to adjust the elevation angle and horizontal included angle of the first and second tilt sensors;

[0195] The processing module is used to determine the elevation angle and horizontal included angle, and calculate the flow velocity and flow direction in the target flow field.

[0196] The processing module comprises:

[0197] An angle decision unit is used to determine the elevation angle and horizontal included angle of the first and second tilt sensors according to the ocean environmental parameter, and output angle adjustment instructions to the mobilization structure;

[0198] A signal processing unit is used to receive the Doppler frequency shift signals collected by the two tilt sensors, and calculate the flow velocity and flow direction in the target flow field.

[0199] Although the embodiments of the present application have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limiting the present application, and those of ordinary skill in the art can make changes, modifications, replacements and variations to the above embodiments within the scope of the present application.

Claims

1. A method for measuring ocean current fields, applied to a measuring device, the measuring device comprising a sensor assembly, an adjustment structure, and a processing module, the sensor assembly comprising a vertical sensor, a first tilt sensor, and a second tilt sensor, the adjustment structure being used to adjust the top angle and horizontal angle of the first tilt sensor and the second tilt sensor, the processing module being used to determine the top angle and horizontal angle, and to calculate the flow velocity and direction in the target flow field, characterized in that, The method includes: The marine environmental parameters of the target flow field are acquired through the vertical sensor, wherein the marine environmental parameters include wave height and wave direction; Based on the marine environmental parameters, adjustment parameters for the first and second tilt sensors are determined to acquire frequency shift signals. These adjustment parameters include a tilt angle and a horizontal angle. The horizontal angle is determined by analyzing the response of the first tilt sensor, specifically including: Based on the aforementioned marine environmental parameters, the tilt angles of the first and second tilt sensors are calculated using an optimization algorithm, including: Multiple candidate overhead views are matched from a preset set of overhead views based on the current ocean conditions, wherein the current ocean conditions are determined based on the ocean environment parameters; Calculate the measurement response of each candidate tilt angle based on the current attitude state of the first tilt sensor and the second tilt sensor. Using the candidate top viewpoint as input, and taking the desired intensity, direction separation, and signal continuity as objective functions, an optimization solution model is constructed, wherein the constraint condition of the optimization solution model is the measurement response; The optimization solution model iteratively calculates the candidate top angles and outputs the pitch angle. The iterative calculation uses a particle swarm optimization algorithm to calculate the function value of the objective function under each candidate top angle while satisfying the constraints. The function value is compared with the preset convergence condition. If the convergence condition is satisfied, the current angle is retained as a candidate optimal solution, and the convergence condition is updated until there is only one candidate optimal solution. The candidate optimal solution is then output as the pitch angle. The first tilt sensor is driven to perform a horizontal micro-scan under the top-down angle to generate a virtual Doppler measurement array; The virtual Doppler measurement array is input into a preset chaotic oscillator network. The chaotic oscillator network responds to the virtual Doppler measurement array to obtain the spatial direction with the largest Lyapunov exponent. The horizontal angle between the spatial direction and the second tilt sensor is output as the horizontal angle. Based on the frequency shift signal and the adjustment parameters, the velocity and direction of the target flow field are calculated.

2. The ocean current field measurement method according to claim 1, characterized in that, By responding to the virtual Doppler measurement array through the chaotic oscillator network, the spatial direction with the largest Lyapunov exponent is obtained, including: The signals from each channel in the virtual Doppler measurement array are input into the initial space of the chaotic oscillator network. The signals from each channel are nonlinearly interacted according to the initial space to excite the initial space of the chaotic oscillator network and obtain the state space. The state space is transformed into a chaotic resonance state, so that the attractor orbits of the chaotic oscillator network are distributed and changed. By monitoring the state evolution of the chaotic oscillator network, the chaotic attraction response intensity of each channel signal is obtained; The Lyapunov exponent of each channel signal is calculated based on the chaotic attraction response intensity, and the location with the largest Lyapunov exponent is determined as the sensitive region. The spatial direction of the sensitive region is then output.

3. The ocean current field measurement method according to claim 2, characterized in that, Before inputting the virtual Doppler measurement array of the first tilt sensor into the chaotic oscillator network, the method further includes: Acquire the Doppler measurement array signal of the second tilt sensor in the previous downward angle state; Based on the frequency structure of the Doppler measurement array signal, a perturbation signal is constructed, wherein the perturbation signal includes the frequency domain distribution characteristics of the Doppler measurement array signal; The disturbance signal is input as a feedforward signal to the chaotic oscillator network to update the modulation response characteristics of the initial space of the chaotic oscillator network.

4. The ocean current field measurement method according to claim 3, characterized in that, The initial space of the chaotic oscillator network is excited by nonlinear interaction of the signals from each channel according to the initial space, including: Each channel signal is mapped to the multidimensional state variable domain of the initial space; Based on the modulation response characteristics, the channel signals in the multidimensional state variable domain are coupled to each other during the mapping process to generate an unsteady oscillation response; The initial space is excited based on the unsteady oscillation response to obtain the state space.

5. The ocean current field measurement method according to claim 3, characterized in that, Updating the modulation response characteristics of the initial space of the chaotic oscillator network includes: The frequency domain coupling factor is obtained by coupling the dominant frequency of the disturbance signal with the state variables of the initial space. The frequency domain coupling factor is mapped to a parameter set used for initialization in the chaotic oscillator network, wherein the parameter set includes the orbital starting position, coupling strength coefficient, and boundary response threshold. Adjust the initial values ​​of the parameter group according to the mapped distribution characteristics; The modulation response characteristics of the initial space are updated based on the adjusted initial values.

6. The ocean current field measurement method according to claim 1, characterized in that, Based on the frequency shift signal and the adjustment parameters, the flow velocity and direction of the target flow field are calculated, including: Acquire the Doppler frequency shift signals collected by the first tilt sensor and the second tilt sensor to obtain the corresponding first signal and second signal; Calculate the frequency shift difference between the first signal and the second signal to obtain the velocity component; Based on the velocity components and the adjustment parameters, combined with geometric relationships, the flow direction angle in the horizontal plane of the target flow field is calculated.

7. A marine current field measuring device, used to implement the marine current field measuring method as described in any one of claims 1-6, characterized in that, The measuring device includes a sensor assembly, an adjustment structure, and a processing module, wherein: The sensor assembly includes a vertical sensor, a first tilt sensor, and a second tilt sensor. The vertical sensor is used to acquire marine environmental parameters of the target flow field, and the first tilt sensor and the second tilt sensor are used to acquire frequency shift signals. The adjustment structure is used to adjust the top angle and horizontal angle of the first tilt sensor and the second tilt sensor. The processing module is used to determine the angle between the top view and the horizontal angle, and to calculate the flow velocity and direction in the target flow field.

8. The ocean current field measuring device according to claim 7, characterized in that, The processing module includes: Angle decision unit is used to determine the top angle and horizontal angle of the first tilt sensor and the second tilt sensor based on marine environmental parameters, and output angle adjustment commands to the adjustment structure. The signal processing unit is used to receive the Doppler frequency shift signals collected by the two tilt sensors and calculate the flow velocity and direction in the target flow field.

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