Flight path optimization method based on flow field adaptive discrimination and compensation

Abstract

The invention provides a flight path optimization method based on flow field adaptive discrimination and compensation, and the method comprises the steps: collecting environment information through a sensor, and obtaining flow velocity and flow direction data; acquiring data of the tide database, and performing validity judgment on the tide database; when the tidal data is judged to be effective, judging and fusing the tidal data with flow velocity and flow direction data to obtain real-time flow field estimation; on the basis of real-time flow field estimation, a cross flow danger angle threshold value is obtained through nonlinear function mapping, abnormal flow field measurement can be effectively recognized and corrected by introducing a fusion judgment mechanism of sensor data and tide database prior, the stability and reliability of ocean current estimation are guaranteed, and on the basis, a cross flow danger degree judgment method is combined, so that the ocean current estimation accuracy is improved. The included angle between the flight path and the flow direction is converted into a risk index, and multi-objective optimization is introduced, so that smooth and safe flight path planning is realized; and finally, downstream / countercurrent compensation control is implemented on the control layer according to the flow velocity component, and the tracking precision and stability of the aircraft are ensured.

Description

A trajectory optimization method based on adaptive flow field discrimination and compensation Technical Field

[0001] This invention relates to the field of path planning, and in particular to a path optimization method based on adaptive flow field discrimination and compensation. Background Technology

[0002] In underwater or surface environments, ocean currents have a significant impact on the stability, safety, and energy consumption of vehicles, especially cross currents, which can easily cause track deviations and attitude instability, and even lead to control system failures, posing a serious threat to mission execution. However, existing research still has significant shortcomings in the following aspects: existing track planning algorithms are mostly based on fast search random tree algorithms, artificial potential field methods, particle swarm optimization, etc., with the shortest path or obstacle avoidance as the main objectives. Even if some methods consider the influence of the flow field, they are mostly limited to simple modeling based on potential field functions, lacking systematic consideration of the effects of adverse flow fields such as cross currents, and even more so lacking modeling of the safety risks that cross currents may cause.

[0003] Current flow field information acquisition usually relies on sensors on board the vehicle or pre-built tidal databases. However, existing methods often use processing techniques such as direct filtering or interpolation, which makes it difficult to guarantee the real-time performance and reliability of the data. At the same time, in complex environments, flow field data often contains noise or failures, and the lack of dynamic validity discrimination and reliable fusion mechanisms leads to limited estimation accuracy.

[0004] Due to the lack of sufficient modeling of the effects of crossflow, existing studies have failed to provide a mechanism for identifying "dangerous angles" or determining safe angle boundaries. In strong crossflow environments, vehicles are prone to deviating from their expected trajectories, posing a high safety risk.

[0005] Existing methods (such as PID and MPC) mostly treat the flow field simply as an external disturbance, lacking differentiated treatment and targeted compensation strategies for different flow directions (co-current, counter-current, etc.), making it difficult to balance energy consumption and task efficiency while ensuring control stability. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to overcome the defects of the existing technology. The present invention proposes a trajectory optimization method based on adaptive flow field discrimination and compensation.

[0007] To address the aforementioned technical problems, this invention provides the following technical solution: Firstly, this invention provides a trajectory optimization method based on adaptive flow field discrimination and compensation, comprising: collecting environmental information through sensors to obtain flow velocity and flow direction data; acquiring data from a tidal database and performing validity discrimination on the tidal database; when the tidal database is deemed valid, performing discrimination and fusion with the flow velocity and flow direction data to obtain a real-time flow field estimate; based on the real-time flow field estimate, obtaining a crossflow hazard angle threshold through nonlinear function mapping to determine whether the angle between the path and the flow direction enters a hazard zone; under the constraint of the crossflow hazard angle threshold data, constructing a multi-objective cost function including crossflow penalty, curvature penalty, and path length penalty, and using an intelligent optimization algorithm to generate the optimal reference trajectory.

[0008] Preferably, when the tidal database is determined to be invalid, the real-time flow field estimation is obtained solely from the flow velocity and direction data collected by the sensors.

[0009] Preferably, the direction and magnitude of the flow field in the target sea area are estimated based on the real-time flow field. When the angle between the direction of the track segment and the direction of the flow field segment is 90°, the crossflow risk angle range is adaptively determined by the magnitude of the flow velocity.

[0010] Preferably, the method further includes determining the downstream or upstream state based on the projection component of the real-time flow field estimate in the heading direction, and superimposing the corresponding longitudinal thrust compensation into the control input.

[0011] Preferably, the method further includes triggering replanning to form a closed-loop operation mechanism when the flow field conditions change or the trajectory deviation exceeds the limit.

[0012] Preferably, when the tidal data is determined to be valid, the original flow velocity and flow direction data are preprocessed, including deduplication and interpolation correction, to reduce errors caused by differences in sampling frequency and data loss.

[0013] Preferably, the validity of the tidal database is determined as valid, questionable, or invalid.

[0014] In a second aspect, the present invention provides an electronic device, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, wherein when the computer-executable instructions are executed by the processor, the steps of the trajectory optimization method based on flow field adaptive discrimination and compensation are implemented.

[0015] Thirdly, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the aforementioned trajectory optimization method based on flow field adaptive discrimination and compensation.

[0016] Compared with existing technologies, the beneficial effects of this invention include: by introducing a fusion discrimination mechanism of sensor data and tidal database priors, it can effectively identify and correct abnormal flow field measurements, ensuring the stability and reliability of ocean current estimation. On this basis, combined with the cross-current hazard discrimination method, the angle between the track and the flow direction is transformed into a risk index and introduced into multi-objective optimization, achieving smooth and safe track planning. Finally, at the control layer, downstream / counter-current compensation control is implemented according to the velocity component to ensure the tracking accuracy and stability of the vehicle. Compared with traditional methods, this invention focuses on the flow field characteristics in every step, forming a multi-layer closed-loop technical framework of "data-evaluation-planning-control". It realizes a comprehensive response to the influence of the flow field throughout the entire process from information acquisition to route optimization to compensation control. This not only improves the safety and adaptability of the system in complex flow fields, but also takes into account execution efficiency and energy consumption optimization, and has outstanding engineering application value. Attached Figure Description

[0017] The disclosure of this invention is illustrated with reference to the accompanying drawings. It should be understood that the drawings are for illustrative purposes only and are not intended to limit the scope of protection of this invention. In the drawings, the same reference numerals are used to refer to the same parts. Specifically: Figure 1 schematically shows a flowchart of a trajectory optimization method based on adaptive flow field discrimination and compensation according to an embodiment of the present invention.

[0018] Figure 2 schematically shows a flow field data estimation flowchart proposed according to an embodiment of the present invention.

[0019] Figure 3 schematically shows the results of ocean current data estimation proposed according to one embodiment of the present invention.

[0020] Figure 4 schematically shows the relationship between the crossflow danger angle zone and the flow velocity according to an embodiment of the present invention.

[0021] Figure 5 schematically illustrates the influence of the waypoint connection line and the angle of current velocity on trajectory planning according to an embodiment of the present invention.

[0022] Figure 6 schematically shows the effect of a discrimination-based compensation control proposed according to an embodiment of the present invention. Detailed Implementation

[0023] It is readily understood that, based on the technical solution of this invention, those skilled in the art can propose various interchangeable structural methods and implementations without altering the essential spirit of the invention. Therefore, the following detailed embodiments and accompanying drawings are merely illustrative examples of the technical solution of this invention and should not be considered as the entirety of the invention or as limitations or restrictions on the technical solution of this invention.

[0024] Example 1, referring to Figure 1, is an embodiment of the present invention, providing a trajectory optimization method based on adaptive flow field discrimination and compensation, including: S100: Flow field measurement and discrimination fusion: collecting sensor flow velocity / direction data, combining it with tidal reservoir information for processing and analysis, completing the adaptive discrimination of "valid / doubtful / invalid", and performing fusion estimation according to the discrimination results to obtain real-time flow field information estimation.

[0025] The sensors include acoustic Doppler flow profilers, Doppler logs, electromagnetic flow meters, and pressure / Pitot tube flow meters, etc.

[0026] S200: Crossflow danger angle determination: Based on the estimated flow velocity magnitude and direction, the crossflow danger angle threshold is obtained by mapping the flow velocity-related nonlinear function, and it is determined whether the angle between the path and the flow direction enters the danger zone.

[0027] S300: Track Planning and Optimization: Under the constraint of crossflow hazard angle discrimination results, a multi-objective cost function including crossflow penalty, curvature penalty, and distance penalty is established, and intelligent optimization methods (such as PSO, GA, MPP, etc.) are used to generate a smooth and safe optimal reference trajectory.

[0028] S400: Discrimination-based compensation control: During track tracking, the system identifies downstream and upstream states based on the discrimination results and injects longitudinal thrust compensation accordingly to optimize track tracking accuracy and energy efficiency.

[0029] S500: Update and Replanning: When a sudden change occurs, the crossflow danger angle exceeds the threshold, or the compensation approaches the limit, the re-entry steps S100-S400 are triggered to form an online closed-loop operation mechanism, enabling dynamic adaptation to complex ocean current environments.

[0030] Example 2, referring to Figure 2, is an embodiment of the present invention. Based on the above embodiment, a trajectory optimization method based on flow field adaptive discrimination and compensation is provided.

[0031] Flow field measurement and discrimination fusion: When the vehicle is in different flow field environments, the flow direction data collected by the sensor shows different characteristics in the 0-360° range: when there is no obvious ocean current, the flow direction data is approximately uniformly distributed and it is difficult to form a stable direction; when there is an effective ocean current, the flow direction data is concentrated near a certain dominant direction. Based on this, this invention performs data preprocessing on the collected data and combines prior information from an effective tidal database to achieve hierarchical processing of the flow field through standard deviation discrimination and residual discrimination. Specifically, it includes: S101: Data preprocessing: interpolating, filtering, or smoothing the original collected flow direction / velocity data to obtain the processed velocity / flow direction data sequence. The prior prediction results provided by the tidal database are denoted as .

[0032] S102: Data Validity Assessment: Data stability is assessed based on statistical characteristics (such as standard deviation and variance), using a sliding window approach. Mean of ocean current data within Calculate the standard deviation: (1) The angular standard deviation represents the direction of the ocean current, and W represents the number of ocean current data samples used in the calculation within the sliding window. This represents the raw flow direction data collected by the sensor at time i. (t) represents the average value of all ocean current direction data within the sliding window at time t.

[0033] Calculate the residuals: (2) Based on the standard deviation and residual size, the data are divided into three categories: invalid data, valid data and questionable data.

[0034] The data is deemed invalid when both the standard deviation and residual exceed the set thresholds; it is deemed valid when the standard deviation is less than the threshold; and it is deemed questionable when the result falls within the critical range.

[0035] Based on this, different fusion strategies are adopted according to the discrimination results: for valid data, weighted fusion is performed with the tidal database; for invalid data, database predictions are used as substitutes; for questionable data, adaptive weights calculated based on variance and residuals are used to fuse the data with the tidal database.

[0036] Taking measurement data as an example, as shown in Figure 3, flow direction data obtained before 10:38:00 is deemed invalid or questionable, and the system mainly relies on the database correction results. Data collected after this time is defined as valid or questionable, and a standard or adaptive fusion strategy is used for flow field estimation. The results show that the flow field estimates processed by the above discrimination and fusion methods are basically consistent with the actual flow field values, significantly improving the accuracy and stability of the estimation.

[0037] The validity of the collected flow direction is then determined by the standard deviation. If the standard deviation and residuals are greater than a certain range, an invalid value is returned, and the residuals indicate that there is no obvious ocean current in the current water area. If the standard deviation is less than 15°, smoothing is performed to obtain an estimated flow direction to replace the actual flow direction. As shown in Figure 3, before approximately 10:38:00, the flow field discrimination mechanism judges the flow field measurement as invalid based on the standard deviation and residuals. At this time, tidal database data is used to estimate the flow field data. After 10:38:00, the data is judged as valid or questionable. It can be seen that after discrimination and fusion, the result is better than the flow field results obtained from the database or direct measurement. The flow field estimate obtained after discrimination and fusion is closest to the true value of the flow field.

[0038] S103: Fusion and Processing Strategy: Different data fusion methods are used to estimate the flow field based on the different discrimination results. (3) Among them, Thresholds designed based on experience or historical data, when When the measured data is deemed valid, it is then fused with the tidal database using standard data fusion methods, with weights assigned. For fixed or covariance matrix-based fusion weights; when When the measured data is deemed questionable, it is then adaptively weighted and fused with the tidal database, with the weights adjusted accordingly. The residuals and standard deviation are dynamically adjusted. When the data fluctuates or the residuals are too large, Rapidly reduce, thereby increasing the database's weight; when If the measured data is deemed invalid, the predicted value from the database will be used directly.

[0039] S104: Tidal Database Validity Determination: Before the prior information of the tidal database is incorporated into the fusion, its validity needs to be determined. This scheme determines whether the database should participate in the fusion based on the database validity determination result. If the database is deemed valid, the database prediction value is used as an auxiliary reference; if it is deemed invalid, it will not participate in the fusion. When the tidal database is invalid, a conservative determination is made on the measurement data to determine whether the ocean current measurement data can be used directly.

[0040] S105: Output: The final flow field estimation result The data is output to the trajectory planning and control module; when the proportion of questionable or invalid data continues to rise, a conservative control strategy can be triggered to ensure the safety of the aircraft.

[0041] The simulation results are shown in Figure 5. The results show that under strong crossflow (2 m / s), as shown in Figure 5 (a), the trajectory shows obvious bending and detouring, but only one side change is made, which reduces the influence of pure crossflow.

[0042] During moderate crossflow (1 m / s), as shown in Figure 5(b), the track exhibits a moderate degree of curvature, which is less pronounced than the track curvature during strong crossflow, thus avoiding the stronger crossflow angle range.

[0043] When there is a strong current but weak crossflow (2 m / s), as shown in Figure 5(c), although the current velocity is high, the line connecting the starting point and the target point is not subjected to pure crossflow disturbance. Therefore, the planned trajectory, after passing through the PSO algorithm, does not bear a large risk of crossflow and will choose an approximately straight trajectory that follows the current.

[0044] Example 3, referring to Figures 1-5, is an embodiment of the present invention. Based on the above embodiment, a trajectory optimization method based on flow field adaptive discrimination and compensation is provided.

[0045] During the navigation of a vehicle, crossflow can cause track deviation and attitude instability. To address this, this invention proposes a method for determining the dangerous angle of crossflow based on adaptive adjustment of flow velocity, which is used to identify dangerous angle zones at different flow velocity levels.

[0046] S201: First, set the basic safety angle. This angle can be understood as the minimum allowable difference angle between the vehicle's heading and its actual direction of motion under weak current conditions, used to characterize the minimum safe angle requirement under crossflow; then, the maximum dangerous angle is set. The maximum avoidance range under strong current conditions is represented by a nonlinear function, and the transition between the two is described by the following: (4) Among them, Indicates the magnitude of the flow velocity. Indicates the flow rate The dangerous angle threshold below, To adjust the parameters and control the steepness of the transition curve, this function satisfies the following properties: ① When At this time, the flight path is almost unaffected by cross currents; ② when When increased, the danger angle The resulting increase in nonlinearity causes the system to automatically expand the crossflow avoidance angle band in moderate or strong current environments; ③ By adjusting parameters It allows for flexible setting of the sensitivity between flow velocity and danger angle zone, thereby adapting to different vehicle dynamics characteristics and operating environments.

[0047] S202: In practical applications, the trajectory planning module will, based on the judgment results, select paths with an angle greater than [missing information] with respect to the flow direction. The situation is identified as having crossflow risk. The path hazard is calculated based on the degree to which it exceeds the angle. This hazard is introduced as a cost factor, which constitutes an important part of the trajectory planning objective in the flow field environment. It is also weighed in conjunction with other indicators to avoid high-risk areas of crossflow in the overall optimization process.

[0048] Example 4, referring to Figures 1-6, is an embodiment of the present invention. Based on the above embodiment, a trajectory optimization method based on flow field adaptive discrimination and compensation is provided.

[0049] In the trajectory planning stage, this invention, based on the traditional path length and curvature smoothness constraints, further introduces the aforementioned crossflow hazard angle discrimination results to construct a multi-objective optimization cost function, thereby achieving the safety and smoothness of the trajectory under complex and smooth conditions.

[0050] S301: Construction of the Comprehensive Cost Function: This invention constructs the following cost function: (5) Among them, As a penalty term for track length, the total length of the constrained path is used to avoid excessively long tracks: J ang The crossflow hazard angle penalty term quantifies the risk level of the trajectory in a crossflow environment. Based on the angle between the path heading and the flow direction, the closer the angle is to 90°, the greater the penalty, thereby guiding the planning algorithm to avoid areas with strong crossflow. curv The curvature penalty term constrains the smoothness of the trajectory. It adopts a power function form with dead zone, and the penalty increases rapidly with the power when the angle difference between adjacent segments exceeds a threshold.

[0051] (6) Among them, This indicates the number of segments after the path is discretized. Indicates the first The segment path vector (composed of a horizontal increment and a vertical step size) is used to constrain the overall length of the path and avoid excessively long paths.

[0052] This is a penalty item for dangerous angles in crossflow: (7) Among them, For the first The angle between the path direction and the flow field direction. For the magnitude of the flow velocity, Let be the velocity vector of the flow field. Crossflow penalty function. Defined as: (8) Among them, The aforementioned critical angle bandwidth, which adapts to the flow velocity. For the first The heading angle of the path segment. For based on The defined penalty function applies a penalty based on the degree to which the course and crossflow angle are 90° when the path segment enters the danger zone. The closer the angle is to 90°, the greater the danger and the greater the penalty. This mechanism can dynamically distinguish crossflow risks in weak, medium, and strong current environments, enabling quantitative avoidance of dangerous crossflow angle zones.

[0053] It is a curvature penalty term: (9) Among them, and The first The heading angle of adjacent flight segments, For the first Length of the path segment This uses a power function form with a dead zone: (10) Among them, The dead zone threshold for the planned heading angle is exceeded. The purpose of this design, using power-law parameters, is to: avoid excessive suppression of seat adjustments when the trajectory changes smoothly, without imposing penalties; and increase the penalty rapidly with power when the angle difference exceeds a certain threshold, with stronger constraints for larger angle changes, thus effectively suppressing sharp inflection points and sudden large turns. Furthermore, this is achieved by introducing path length... Ensuring that curvature abrupt changes occur on longer segments incurs a higher cost allows for a balance between smoothness and feasibility in the overall trajectory.

[0054] S302: In addition These are weighted coefficients for path length, crossflow penalty, and curvature, used to balance the impact of different objectives on the trajectory planning results.

[0055] Path geometric constraint mechanism: To avoid excessive oscillations or unreasonable swings in the trajectory, this invention introduces several constraints during the path generation process to correct the angle sequence of candidate paths. Projection corrections are performed to ensure the following constraints are met: Single yaw constraint: Only one yaw is allowed across the entire line to avoid zigzag oscillations and potential risks; Absolute angle limit: The yaw angle of any segment does not exceed the upper limit yaw angle. .

[0056] Adjacent rate limiting: The angle difference between adjacent segments does not exceed the threshold. This ensures a smooth transition.

[0057] This geometric constraint, as a hard constraint, is independent of the cost function, ensuring the rationality of the path geometry and the controllability of the engineering.

[0058] Example 5, referring to Figures 1-6, is an embodiment of the present invention. Based on the above embodiment, a trajectory optimization method based on adaptive flow field discrimination and compensation is provided.

[0059] Between the path tracking controller and the actuator, the present invention sets up an adaptive discrimination compensation module. This module determines the downstream or upstream state in real time by the geometric relationship between flow velocity and heading, and superimposes directional compensation on the original control input to achieve adaptive correction of the influence on the flow field.

[0060] S401: Discrimination mechanism: Input is the filtered flow field vector Heading unit vector The output consists of the parallel and vertical projection components of the flow field in the heading direction. and This can be expressed as: (11) (12) Among them, For the flow field vector Heading unit vector The angle between the projected components is used to adaptively determine the operating condition of the vehicle based on the magnitude of the projected components. It is determined to be in a downstream state; when It is determined to be in a countercurrent state.

[0061] S402: Compensation Control Law: Based on the output of the path tracking controller, the compensation amount corresponding to the discrimination result is superimposed to obtain the final longitudinal thrust compensation control input. (13) Among them, The longitudinal thrust command output by the original path tracking control. This is an adaptive longitudinal compensation gain based on upstream / counterstream discrimination. When When sailing downstream, longitudinal compensation is positive, with small-scale thrust, to avoid excessive speed and track overshoot caused by the downstream flow; when When flowing against the current, the longitudinal compensation is negative, which is equivalent to increasing the thrust to offset the velocity loss caused by the countercurrent.

[0062] By introducing adaptive compensation based on the co-current / countercurrent discrimination in the longitudinal thrust, this invention can maintain course stability while effectively offsetting flow field interference, thereby improving path tracking and system robustness.

[0063] S500: During mission execution, if the flow field conditions change and cause the original trajectory to enter the dangerous angle range of crossflow, or if the actual trajectory deviation continues to exceed the threshold, the path update and replanning will be triggered. The system will take the current position as the new starting point, call the trajectory planning module to regenerate an optimized path that meets the crossflow discrimination constraints to replace the original reference trajectory, thereby ensuring the continuity and stability of the closed-loop operation of planning-control-compensation in the crossflow environment.

[0064] A simulation model containing ocean current disturbances was built using a simulation platform, with the ocean current velocity set to 0.5 m / s. The result is shown in Figure 6. Taking the track planned in the previous example as a reference, including the first half of the upstream section and the second half of the downstream section, the track passed through each reference track point evenly and smoothly after adopting this method, verifying the effectiveness and robustness of the invention.

[0065] This embodiment also provides an electronic device, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, wherein when the computer-executable instructions are executed by the processor, the steps of a trajectory optimization method based on flow field adaptive discrimination and compensation are implemented.

[0066] This embodiment also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of a trajectory optimization method based on adaptive flow field discrimination and compensation.

[0067] The storage medium proposed in this embodiment and the trajectory optimization method based on flow field adaptive discrimination and compensation proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0068] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0069] The technical scope of this invention is not limited to the content described above. Those skilled in the art can make various modifications and variations to the above embodiments without departing from the technical concept of this invention, and all such modifications and variations should fall within the protection scope of this invention.

Claims

1. A trajectory optimization method based on adaptive flow field discrimination and compensation, characterized in that, include: By collecting environmental information through sensors, flow velocity and flow direction data can be obtained. Obtain data from the tidal database and determine its validity. When the tidal database is deemed valid, it is fused with the velocity and direction data to obtain a real-time flow field estimate. Based on the real-time flow field estimate, the crossflow danger angle threshold is obtained through nonlinear function mapping to determine whether the angle between the path and the direction of flow enters the danger zone. Under the constraint of crossflow hazard angle threshold data, a multi-objective cost function including crossflow penalty, curvature penalty and path length penalty is constructed, and an intelligent optimization algorithm is used to generate the optimal reference trajectory.

2. The trajectory optimization method based on adaptive flow field discrimination and compensation according to claim 1, characterized in that, When the tidal database is deemed invalid, real-time flow field estimation is obtained solely from the flow velocity and direction data collected by sensors.

3. The trajectory optimization method based on adaptive flow field discrimination and compensation according to claim 2, characterized in that, The direction and magnitude of the flow field in the target sea area are estimated based on the real-time flow field. When the angle between the direction of the track segment and the direction of the flow field segment is 90°, the crossflow risk angle range is adaptively determined by the magnitude of the flow velocity.

4. The trajectory optimization method based on adaptive flow field discrimination and compensation according to claim 1, characterized in that, The method further includes determining the downstream or upstream state based on the projection component of the real-time flow field estimate in the heading direction, and superimposing the corresponding longitudinal thrust compensation into the control input.

5. The trajectory optimization method based on adaptive flow field discrimination and compensation according to claim 1, characterized in that, The method also includes triggering replanning to form a closed-loop operation mechanism when the flow field conditions change or the trajectory deviation exceeds the limit.

6. The trajectory optimization method based on adaptive flow field discrimination and compensation according to claim 1, characterized in that, When the tidal data is determined to be valid, the original flow velocity and direction data are preprocessed, including deduplication and interpolation correction, to reduce errors caused by differences in sampling frequency and data loss.

7. The trajectory optimization method based on adaptive flow field discrimination and compensation according to claim 6, characterized in that, The validity of the tidal database is categorized into valid, questionable, and invalid.

8. An electronic device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, they implement the steps of the trajectory optimization method based on flow field adaptive discrimination and compensation as described in any one of claims 1 to 7.

9. A computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the trajectory optimization method based on flow field adaptive discrimination and compensation as described in any one of claims 1 to 7.

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