Ship intelligent navigation scene processing method and system

Abstract

The invention discloses a processing method and system for a ship intelligent navigation scene, and relates to the technical field of ship intelligent navigation, and the method comprises the following steps: carrying out the continuous waveform analysis of an energy return path of a laser ranging link based on the sudden increase of the density of seawater suspended particles caused by the frontier of a storm, constructing a particle disturbance recognition zone, and carrying out the recognition of the particle disturbance; generating a disturbance identifier carrying an energy rebound mode characteristic according to an analysis result; and expanding the ranging pulse to a time domain by using the disturbance identifier, performing pulse form reconstruction on an energy abnormal section of the ranging pulse, so that the ranging pulse still keeps a stable main peak structure under the influence of energy rebound, and outputting a stable main peak waveform after pulse form reconstruction. According to the invention, through constructing a disturbance identification and form reconstruction mechanism, continuous restoration and smooth output of the distance measurement signals are realized, and the stability and anti-interference performance of distance sensing are improved; and through approaching artifact weakening and gradual change operation control, excessive operation is prevented, and stable course and avoidance safety of the ship in a storm environment are ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent navigation of ships, in particular to a processing method and system for intelligent navigation scenes of ships. BACKGROUND

[0002] Intelligent navigation scene processing of ships refers to real-time analysis, structured expression and dynamic response of the whole process of constantly changing external conditions in the navigation process by fusing multi-source sensing data, intelligent recognition algorithms and time sequence decision logic under complex sea environment. The core is to convert environmental information from channels such as radar, photoelectric imaging, satellite positioning, water depth mapping, weather broadcast, etc. into a calculable scene unit, and then combine the motion state of the ship itself, the characteristics of the power device and the navigation regulations to construct a dynamic scene model containing obstacle distribution, track evolution trend, risk level and passing window. On this basis, through intelligent planning, collision avoidance determination and maneuver coordination, the ship can autonomously identify sudden targets, predict the intentions of surrounding ships, assess the stability impact of extreme wind and waves, and generate optimal steering and speed instructions within the safety boundary to achieve high-reliability navigation, intelligent collision avoidance and stable operation under complex working conditions.

[0003] The prior art has the following disadvantages: In the stage of the rapid approach of the storm front, the concentration of suspended particles in seawater will rise sharply in a short time window, causing obvious nonlinear disturbance to the energy return path of the laser ranging link, and then causing abnormal energy return of the ranging pulse. This return will suddenly shorten the distance solution result in the time scale of milliseconds, destroy the continuity of distance change, and form a sudden drop type false proximity signal. When the intelligent navigation scene processing relies on this distance input to judge the proximity of the target, this kind of false signal will be mistaken for an external object approaching at high speed, thereby triggering unnecessary and excessive avoidance decisions, causing the ship to produce a steering response that exceeds the design threshold in severe sea conditions. This excessive steering not only may cause abnormal amplification of the roll amplitude of the ship body, but also may cause instantaneous water ingress on the side under the superimposed action of crosswind and cross sea, further increasing the risk of sliding, instability of stacking and even collapse of the entire cargo compartment, and ultimately significantly threatening the safety of navigation.

[0004] The above information disclosed in the background section is only used to enhance the understanding of the background of the present disclosure, and therefore it can include information that does not constitute prior art known to those of ordinary skill in the art. SUMMARY

[0005] The purpose of the present application is to provide a processing method and system for intelligent navigation scenes of ships to solve the problems in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for processing intelligent navigation scenarios of ships, characterized by comprising the following steps: Step 1: Based on the sudden increase in seawater suspended particle density caused by the storm front, continuous waveform analysis is performed on the energy return path of the laser ranging link to construct a particle disturbance identification band. Based on the analysis results, a disturbance label carrying the characteristics of the energy return mode is generated to characterize the nonlinear disturbance of the ranging pulse during the energy return process. Step 2: Using the disturbance flag, the ranging pulse is expanded to the time domain and pulse shape reconstruction is performed on its energy anomaly section. This ensures that the ranging pulse maintains a stable main peak structure under the influence of energy rebound, thereby outputting a stable main peak waveform after pulse shape reconstruction for subsequent distance calculation. Step 3: Based on the stable main peak waveform, a dynamic span mapping loop is constructed in the distance calculation link. By performing continuous mapping on the time span of the stable main peak waveform, a smooth distance sequence is formed, so that the distance calculation result has time continuity and eliminates the sudden drop in distance caused by energy bounce. Step 4: Based on the smooth distance sequence, construct an approach artifact reduction layer during the navigation scene processing. By utilizing the trend information of the smooth distance sequence, weaken the sudden drop in false approach signals caused by energy bounce, so that the target proximity judgment maintains continuous and stable input characteristics in scene modeling. Step 5: Based on the real proximity information output by the proximity artifact reduction layer, deploy a gradual amplitude control frame in the avoidance control link. By driving the gradual control curve with real proximity information, suppress excessive control caused by false proximity, enable the ship to maintain a stable course in complex sea conditions, and build a safety closed loop from disturbance identification to control output.

[0007] Preferably, the step of performing continuous waveform analysis on the energy return path of the laser ranging link based on the sudden increase in seawater suspended particle density caused by the storm front includes: By using the energy return signal acquisition unit of the laser ranging link in the storm front environment, the energy return between the sea surface and the target is continuously recorded with high temporal resolution, forming a time-expanded energy sequence. The obtained continuous waveforms are subjected to amplitude variation trend and waveform morphology analysis to identify energy anomaly segments affected by suspended particle disturbances, and particle disturbance identification bands are marked along the time axis. Based on the particle perturbation identification band, the energy distribution pattern within the identification band is analyzed, and the peak duration, peak width and energy attenuation characteristics of the energy bounce pattern are extracted to generate a set of perturbation parameters carrying the characteristics of the energy bounce pattern. By associating the time range of the particle perturbation identification band with the set of perturbation parameters, a perturbation identifier with temporal continuity and energy distribution correlation is generated. This identifier is used to characterize the nonlinear perturbation state of the energy return path and to provide input for subsequent pulse shape reconstruction.

[0008] Preferably, the step of unfolding the ranging pulse into the time domain using a perturbation flag and performing pulse shape reconstruction on its energy anomaly segment includes: Based on the generated disturbance markers, the ranging pulses are expanded along the time dimension to the complete time domain, and the start and end points of the disturbance markers are mapped to the time axis of the ranging pulses to form the time range of the energy anomaly segment. A stratified analysis of the energy distribution pattern in the energy anomaly section was conducted, and based on the energy bounce pattern characteristics carried in the disturbance marker, abnormal structures such as energy uplift, multi-peak superposition, and main peak fronting were identified. Based on the characteristic analysis results of the energy anomaly section, a morphological adjustment operation is performed on the ranging pulse to restore the symmetry and continuity of the main peak structure, and a stable main peak morphology is formed by smoothing the energy distribution and reducing the energy of the secondary peaks. The reconstructed ranging pulse is subjected to time continuity and energy stability verification to ensure that the output stable main peak waveform has smooth energy distribution and time-position consistency for subsequent distance calculation.

[0009] Preferably, the step of constructing a dynamic span mapping loop in the distance calculation link based on a stable main peak waveform includes: By continuously measuring the extension characteristics of the stable main peak waveform in the time dimension, the starting point, peak and ending point of the main peak energy distribution are determined, and the main peak time span curve is formed. Adjacent correlation analysis is performed on the main peak time span curve to identify the time extension differences between adjacent ranging pulses, and the gradient of the main peak time span change is extracted to represent the time continuity. A dynamic span mapping loop is constructed based on the main peak's time span variation trajectory, establishing a continuous mapping relationship between the main peak's time span and the distance calculation output, and maintaining the smoothness of time and distance changes through buffer mapping; Continuous smoothness verification is performed on the distance sequence output by the mapping ring. The mapping weights are adjusted according to the rate of change of adjacent distance points to form a smooth distance sequence with time continuity and disturbance resistance.

[0010] Preferably, when establishing the mapping relationship between the main peak time span and the distance calculation output, the dynamic span mapping loop performs weighted smoothing on the gradient of the main peak time span change, so that the change of abnormal time span is buffered and transitioned to the adjacent normal segment, thereby further improving the temporal continuity and output stability of the distance sequence in the storm front environment.

[0011] Preferably, the step of constructing a near-artifact attenuation layer based on a smooth distance sequence includes: The trend of distance change over time is extracted as a whole based on the smoothed distance sequence to form a trend curve reflecting the relative motion state between the target and the ship; The rate of change of distance within a local time window of the trend curve is continuously detected to identify abrupt drop-type abnormal signals between adjacent time points and to determine the abrupt drop-type artifact identification segment. Weakening operations are performed based on the degree of deviation between the artifact identification segment and the smooth distance trend. This is achieved by introducing a transition interval or extending the trend line between the artifact segment and the normal segment to restore the continuity of distance changes. The continuity of the distance change curve after the weakening process is verified and the proximity output is corrected to ensure that the proximity information output by the proximity artifact weakening layer has temporal continuity and numerical stability.

[0012] Preferably, when performing the weakening operation, the transition interval smoothing or trend line extension is used to correct the artifact recognition segment based on the duration and amplitude changes of the sudden drop artifact. When the sudden drop amplitude exceeds a preset threshold, the start and end points of the artifact segment are reconnected by extending and smoothing the overall trend line of the distance sequence to maintain the temporal continuity and smooth transition of the distance change curve.

[0013] Preferably, the step of deploying amplitude-gradient manipulation frames in the avoidance control link based on the true proximity information output by the proximity artifact attenuation layer includes: Based on the real proximity information output by the proximity artifact attenuation layer, the dynamic proximity relationship between the ship and surrounding targets is continuously collected and processed over time to form a proximity curve that reflects the trend of the target approaching or moving away. A gradual change reference is established based on the ship's maneuvering response characteristics, so that the changes in rudder angle and propulsion power gradually transition with the change in approach, while maintaining maneuvering continuity and stability. Based on the amplitude gradient reference, the real approach information is converted into a gradual control curve of rudder angle and thrust, so as to realize continuous control adjustment from slow to fast to form a smooth avoidance action. The control output results are dynamically verified and the safety closed-loop is confirmed. By monitoring the rudder angle response, thrust distribution and attitude change rate, the control amplitude is corrected to ensure the ship's course stability and control safety in complex sea conditions.

[0014] Preferably, in the process of establishing the amplitude gradual change benchmark, the actual approach change rate is correlated with the ship's speed, heading angle and inertial response characteristics, so that the rudder angle adjustment amplitude and propulsion power change rate change continuously within a preset safety threshold, thereby avoiding attitude changes caused by excessive manipulation and maintaining the stability of the avoidance process.

[0015] The intelligent navigation scenario processing system for ships includes a particle disturbance recognition module, a pulse pattern reconstruction module, a dynamic span mapping module, a proximity artifact reduction module, and an amplitude gradual control module. Particle disturbance identification module: Based on the sudden increase in seawater suspended particle density caused by the storm front, the energy return path of the laser ranging link is continuously analyzed to construct a particle disturbance identification band, and a disturbance label carrying the characteristics of energy bounce mode is generated based on the analysis results. Pulse shape reconstruction module: Using disturbance identifiers, the ranging pulse is expanded to the time domain and pulse shape reconstruction is performed on its energy anomaly section, so that the ranging pulse still maintains a stable main peak structure under the influence of energy rebound, and outputs a stable main peak waveform after pulse shape reconstruction. Dynamic span mapping module: Based on the stable main peak waveform, a dynamic span mapping loop is constructed in the distance calculation link, and a smooth distance sequence is formed by performing continuous mapping on the time span of the stable main peak waveform; Approach artifact reduction module: Based on the smooth distance sequence, an approach artifact reduction layer is constructed during the navigation scene processing. By utilizing the trend information of the smooth distance sequence, the sudden drop in false approach signals caused by energy bounce is reduced. Amplitude Gradual Manipulation Module: Based on the real proximity information output by the proximity artifact reduction layer, an amplitude gradual manipulation frame is deployed in the avoidance control link. By driving the gradual manipulation curve with real proximity information, excessive manipulation caused by false proximity is suppressed.

[0016] The technical effects and advantages provided by the present invention in the above technical solution are as follows: This invention introduces a particle disturbance identification band, pulse morphology reconstruction, and a dynamic span mapping loop into the ranging signal processing link. This achieves a continuous repair mechanism from energy return path disturbance identification to smooth distance sequence generation, ensuring the temporal continuity and physical stability of laser ranging output even in high-particle-density environments such as storm fronts. This effectively eliminates abrupt range jumps caused by energy return, providing realistic, smooth, and traceable distance input in navigation scenarios. It significantly improves the anti-interference capability and data reliability of distance measurement, providing a high-precision input foundation for environmental perception in intelligent navigation.

[0017] This invention achieves dynamic closed-loop control from true proximity recognition to maneuver response by constructing a proximity artifact attenuation layer and a gradually varying amplitude control frame. This allows the avoidance process to be gradually adjusted based on continuous and reliable proximity information, preventing excessive maneuvering induced by false proximity signals. This mechanism ensures the smoothness of the ship's course changes and the controllability of the maneuver amplitude under complex sea conditions, reduces the risk of amplified roll and sudden attitude changes, and enhances the ship's stable navigation capability under storm conditions. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0019] Figure 1 This is a flowchart of the method for processing intelligent navigation scenarios of ships according to the present invention.

[0020] Figure 2 This is a schematic diagram of the modules of the intelligent navigation scenario processing system for ships according to the present invention. Detailed Implementation

[0021] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0022] This invention provides, for example Figure 1 The method for handling intelligent navigation scenarios of ships, as shown, includes the following steps: Step 1: Based on the sudden increase in seawater suspended particle density caused by the storm front, continuous waveform analysis is performed on the energy return path of the laser ranging link to construct a particle disturbance identification band. Based on the analysis results, a disturbance label carrying the characteristics of the energy return mode is generated to characterize the nonlinear disturbance of the ranging pulse during the energy return process. The specific implementation method for this step is as follows: First, the energy return signal acquisition unit of the laser ranging link in the storm front environment continuously records the return energy from the sea surface to the target object with high temporal resolution, forming a time-expanded energy sequence. During the acquisition process, a short window is selected during the approach phase of the storm front, typically within a few seconds to tens of seconds. High-frequency sampling within this window captures the transient response of rapid changes in the density of suspended particles in the air and seawater driven by the storm. To ensure the continuity of the energy return path, the acquired energy data is arranged chronologically, including complete information on the return light intensity, rise time, main peak position, and waveform decay tail. By recording this information continuously, the energy response curve of the laser pulse from emission to return can be obtained. Under storm conditions, this curve exhibits abnormal shapes such as irregular amplitude fluctuations, overlapping double peaks, or waveform expansion. These changes are caused by scattering and reflection of suspended particles in the optical path. During this stage, to avoid transient noise interference, the acquired signal needs to undergo continuous time smoothing to ensure that subsequent analysis of the energy return path accurately reflects the actual disturbance behavior.

[0023] After obtaining the complete continuous waveform of the energy return path, a detailed analysis is performed on the amplitude variation trend, peak shape, and energy gradient between adjacent sampling points to identify abnormal segments in the energy return path affected by suspended particle disturbances. By comparing the energy rise and fall rates at each time point in the continuous waveform segment by segment, abrupt energy changes or waveform abrupt changes in local areas can be detected. For these significantly changing segments, further analysis is needed on their duration, energy change amplitude, and the smoothness of the waveform before and after them. If the energy change amplitude within a certain time slice exceeds the normal range of changes in adjacent time slices, and the main peak shape shows obvious stretching or distortion, then this segment can be identified as a region affected by particle scattering or reflection. These abnormal segments are marked along the time axis to form a series of interconnected time intervals, each interval corresponding to one particle disturbance. Arranging these time intervals sequentially constitutes the particle disturbance identification band. The particle disturbance identification band integrates the energy disturbance segments in the continuous waveform affected by suspended particles, reflecting the range of nonlinear energy disturbance experienced by the laser ranging link within a specific time range. At this point, each identification band contains a specific time start point, end point, and energy change trajectory, which can intuitively reflect the interference process of the storm front on the ranging link.

[0024] After constructing the particle disturbance identification bands, the energy distribution patterns within the bands are analyzed in detail to extract disturbance features carrying energy bounce patterns. Specifically, within each particle disturbance identification band, the rate of energy rise, peak duration, and morphology of the descent curve are analyzed chronologically. When the storm front causes suspended particles on the sea surface to rise densely in a very short time, the short-range energy of the laser return signal usually shows a significant enhancement, characterized by a sudden rise in energy at the beginning of the waveform followed by rapid decay. The energy curve at this time exhibits a steep structure with a high initial value followed by a low final value, which is a typical forward bounce pattern. When the superposition of wind and waves causes interference between the light energy of different reflective layers, the waveform may show multiple adjacent peaks with extremely short energy intervals, forming a multi-peak bounce pattern. For these different types of energy bounce patterns, key characteristic information such as peak energy, peak width, peak spacing, and recovery time are recorded and marked within the particle disturbance identification bands. By summarizing the energy change trends of all identification bands, a set containing multiple bounce pattern characteristics can be obtained. This set describes the nonlinear perturbation distribution characteristics of the energy return path in the laser ranging link under storm front conditions, and systematically expresses the manifestation of the energy bounce phenomenon.

[0025] Finally, based on the aforementioned energy bounce pattern feature set, disturbance markers carrying energy bounce pattern characteristics are generated to characterize the nonlinear disturbance state of the energy return path in the laser ranging link. During generation, the time range of the particle disturbance identification band is combined with various characteristic parameters of the energy bounce pattern to form a set of descriptive information with temporal continuity and energy distribution correlation. Each disturbance marker includes the corresponding time start and end points, peak energy ratio, energy fall rate, and waveform non-smoothness level. This information collectively characterizes the abnormal state of the energy return path within a specific disturbance segment. By arranging multiple disturbance markers consecutively, a complete disturbance distribution sequence can be obtained, corresponding to the entire energy disturbance process under the influence of the storm front. In use, the disturbance markers serve as standard evidence for identifying energy anomaly intervals. When reconstructing the ranging pulse shape subsequently, these disturbance markers can be used to determine the affected time segment, and the pulse shape can be adjusted accordingly based on the energy change characteristics in the disturbance markers. This not only ensures that the ranging pulse maintains its main peak stability after passing through the disturbance segment but also provides continuous energy input conditions for distance calculation. In this way, the disturbance identification achieves a complete closed loop from energy disturbance identification to feature representation, enabling the laser ranging link to effectively identify and track abnormal changes in the nonlinear energy return path in a storm-front environment with highly suspended particles, providing a real, stable and continuous basic input for subsequent pulse shape reconstruction and smooth distance calculation.

[0026] Step 2: Using the disturbance flag, the ranging pulse is expanded to the time domain and pulse shape reconstruction is performed on its energy anomaly section. This ensures that the ranging pulse maintains a stable main peak structure under the influence of energy rebound, thereby outputting a stable main peak waveform after pulse shape reconstruction for subsequent distance calculation. The specific implementation method for this step is as follows: Based on the generated disturbance markers, the ranging pulse is expanded along the time dimension to the complete time domain to obtain an energy distribution sequence covering the entire return cycle. By expanding the pulse signal along the time dimension, the energy evolution trajectory of the pulse from emission to return can be clearly presented. At this point, the disturbance markers are used as a time positioning reference to identify abnormal periods in the energy return path affected by storm-front suspended particles. Specifically, the start and end points of each disturbance marker are mapped onto the time axis of the ranging pulse, forming multiple time ranges of energy anomaly segments, thus accurately marking energy fluctuation areas in the time domain. Through this expansion and marking method, the entire ranging pulse forms a continuous structure in time distribution consisting of alternating stable and abnormal segments. During the expansion process, the time sampling interval of the pulse signal must be sufficiently fine to capture subtle differences in energy changes. This method ensures that the energy anomaly segments corresponding to the disturbance markers are fully expanded along the time axis, providing accurate energy segment boundaries for subsequent morphological reconstruction.

[0027] After the ranging pulse is fully expanded and the energy anomaly sections are identified, the energy distribution pattern of each anomaly section is analyzed in layers to clarify the specific characteristics of energy rebound in the pulse waveform. Energy anomaly sections typically exhibit phenomena such as sudden waveform rise, local multi-peak superposition, or the leading edge of the main peak. These anomalies directly lead to the accumulation of distance calculation errors. Therefore, it is necessary to analyze the energy change process within each anomaly section in detail based on the energy rebound pattern characteristics contained in the disturbance marker. For example, when the disturbance marker represents a forward rebound pattern, it indicates that there is an early energy enhancement phenomenon in this section, and the focus should be on the energy surge pattern at the leading edge of the pulse main peak; when the disturbance marker represents a multi-peak rebound pattern, the focus should be on observing the energy ratio of multiple sub-peaks within the waveform and their relative spacing on the time axis. By analyzing and recording these energy change details one by one, the specific energy distribution curve characteristics of each energy anomaly section can be obtained. The core of this process is to capture the spatial distribution and temporal duration characteristics of energy anomalies through the feature information provided by the disturbance marker, thereby providing a targeted reference for subsequent pattern reconstruction.

[0028] Subsequently, based on the characteristic analysis results of the energy anomaly segments, the morphology of the ranging pulse is reconstructed so that it can still recover a waveform structure with a stable main peak under the influence of energy bounce interference. In this process, the pulse time-domain expanded signal is associated with the energy segments corresponding to the disturbance markers, and morphological adjustment operations are performed on each energy anomaly segment. The goal of morphological adjustment is to restore the symmetry and continuity of the main peak structure, restoring the position, amplitude, and width of the main peak of the energy waveform to a state similar to that before interference. Specifically, within each anomaly segment, by extending the smooth trend of the energy distribution, the leading and trailing ends of the waveform are made to form a transitional connection with adjacent stable segments, thereby eliminating the discontinuity caused by energy abrupt changes. For segments with multiple peaks superimposed, based on the peak spacing information marked in the disturbance markers, while keeping the main peak energy unchanged, the relative energy of the secondary peaks is gradually reduced to form a single and stable main peak shape. When the leading edge of the main peak rises prematurely, the energy rise time is extended to alleviate the excessively rapid energy growth rate, allowing the main peak position to return to the normal range. After this multi-layered morphological restoration, the energy curve of the entire ranging pulse will exhibit characteristics of a clear main peak, smooth waveform, and continuous amplitude. At this point, the energy anomaly section has been transformed into a structurally stable main peak transition region, providing a highly continuous input waveform for distance calculation.

[0029] After pulse morphology reconstruction, the reconstructed ranging pulse is verified for temporal continuity and energy stability to ensure that the output waveform meets the accuracy requirements of subsequent distance calculations. The effect of morphology reconstruction on pulse waveform restoration can be evaluated by comparing the position, width, and rise slope of the pulse peak before and after reconstruction. If the reconstructed peak maintains the same relative position as the original pulse in time, and the peak energy distribution is smooth and without abrupt changes, then the morphology reconstruction has achieved its intended goal. In this case, the reconstructed ranging pulse can be defined as a stable peak waveform. The output of a stable peak waveform not only avoids misjudgments caused by energy bounce but also ensures that the distance calculation link can perform time-span mapping based on continuous peak input, thus obtaining a smooth distance sequence without sudden drops. In this way, the morphology reconstruction process physically eliminates the nonlinear interference caused by the sudden increase in suspended particle density at the storm front to the laser ranging pulse, ensuring that the ranging results retain temporal continuity and numerical stability even under extreme sea conditions. The reconstructed stable peak waveform can be directly input into the subsequent distance calculation stage to form a reliable distance baseline, providing real distance information support for dynamic perception and safety decision-making in intelligent ship navigation scenarios.

[0030] Through the above steps, the ranging pulse can still recover to a waveform structure with a stable main peak even when disturbed by suspended particles at the storm front. The entire process makes full use of the time and energy characteristics contained in the disturbance marker, realizing an orderly transition from anomaly identification to main peak restoration, ensuring the controllability of the energy return path and the high reliability of the ranging results in complex marine environments.

[0031] Step 3: Based on the stable main peak waveform, a dynamic span mapping loop is constructed in the distance calculation link. By performing continuous mapping on the time span of the stable main peak waveform, a smooth distance sequence is formed, so that the distance calculation result has time continuity and eliminates the sudden drop in distance caused by energy bounce. The specific implementation method for this step is as follows: Based on the stable main peak waveform output from the previous process, its extension characteristics in the time dimension are continuously measured to determine the starting point, peak, and ending point of the main peak energy distribution on the time axis. By precisely measuring the time span of the stable main peak waveform, the relative position and duration of the main peak in the entire return waveform can be clearly defined. During measurement, the rising edge, peak apex, and falling edge of the main peak need to be segmented and identified separately to ensure that the temporal structure characteristics of the main peak can be completely captured. Since the morphological reconstruction in the previous stage has eliminated the local anomalies caused by energy rebound, its time distribution has good continuity and traceability. By recording the start and end time points of the main peak under continuous time sampling, a main peak time span curve can be formed, which describes the extension and change of the stable main peak at different sampling times. At this time, the main peak time span becomes the core variable for subsequent mapping, which directly reflects the dynamic change law of the ranging pulse propagation and return process, and provides a time reference for establishing a dynamic span mapping loop.

[0032] After obtaining the main peak time span curve, an adjacency correlation analysis is performed on the temporal variation trend of the stable main peak waveform within a continuous sampling period to identify the time delay differences between adjacent ranging pulses. Because seawater refractive index, aerosol density, and particle concentration fluctuate rapidly in the storm front environment, causing slight disturbances in laser propagation time, it is necessary to continuously observe the time span difference between adjacent main peaks to determine the trend of time delay variation. If the main peak time span difference between adjacent pulses exhibits a smooth, gradually increasing or decreasing characteristic, it indicates that the ranging link is in a stable state; however, if the difference suddenly drops or increases at a certain time point, it indicates that the energy return path is affected by external disturbances. By continuously correlating these time differences, a trajectory of the main peak time span variation can be established, reflecting the degree of temporal continuity in the distance calculation process. Based on this, the gradient of the main peak time span variation is extracted to represent the stability of variation between adjacent time points in the distance calculation link. This process ensures that the distance calculation input at each moment is based on interconnected time bases, avoiding discontinuities in the calculation due to local energy mutations.

[0033] After obtaining the trajectory of the main peak's time span, a dynamic span mapping loop is constructed based on this trajectory, establishing a correspondence between each main peak time span and the distance calculation output. The core function of the dynamic span mapping loop is to map the extension and variation of the stable main peak waveform in the time domain to the distance output space, forming a continuous mapping channel between time and distance. Specifically, the main peak time span curve is used as input, and each time span value is mapped to a distance calculation point in the ranging calculation link according to the time sequence, thus forming a continuous mapping relationship in the time dimension. In this process, the key is to maintain the monotonicity and smoothness of the mapping relationship, ensuring that small changes in the time span can cause gradual adjustments in the distance output, rather than sudden jumps. When there are abnormal changes in the main peak time span between adjacent time points, the dynamic span mapping loop performs a buffer mapping, so that the change in the time span is reflected in the distance output result in a continuous transition. In this way, even in extreme environments where the energy return path is briefly disturbed, the distance calculation result will not produce a significant drop, but will maintain an overall smooth change through the continuous mapping of the mapping loop. The establishment of this mapping process means that the distance calculation output no longer directly depends on the single-point distance measurement value, but forms a mapping response based on the overall continuous trend of the main peak time span, thereby significantly improving the stability and time consistency of the distance measurement results.

[0034] After establishing the dynamic span mapping loop, its output distance sequence is continuously smoothed to ensure the consistency and continuity of the distance calculation results over the entire time series. Specifically, the distance sequence output by the mapping loop is checked point by point, and the rate of change of the difference between adjacent distance points is analyzed. If the difference between adjacent distances shows a smooth transition in the time series, it indicates that the continuous mapping effect of the mapping loop is good. If a sudden drop or rise in the distance output is found within a certain time period, it is necessary to trace back the corresponding main peak time span change and adjust its mapping weight in the mapping loop to bring the mapping relationship back to a smooth state. In this way, the dynamic span mapping loop can adaptively correct local fluctuations, allowing the distance sequence to maintain a smooth extension over a long time scale. The final smooth distance sequence is a mapping result of the continuous change of the stable main peak waveform time span. This sequence has high time continuity and anti-disturbance capability. Compared with the distance calculation result without the mapping loop processing, the smooth distance sequence effectively eliminates the sudden drop phenomenon caused by energy bounce, ensuring that the distance change conforms to the actual physical propagation law. Therefore, under storm front and complex sea conditions, the distance calculation link can maintain stable output under the action of dynamic span mapping loop, providing reliable continuous distance input for subsequent navigation scenario modeling and proximity judgment.

[0035] Through the above steps, a time-continuous mapping mechanism from the stable main peak waveform to the smooth distance output was established. Based on the time distribution of the stable main peak waveform and with the dynamic span mapping loop as the core, this mechanism realizes the continuous smoothing of the distance calculation results under energy disturbance conditions, eliminates the sudden drop in distance caused by energy rebound, and provides a highly stable and reliable distance input basis for subsequent risk judgment and avoidance control in intelligent navigation scenarios.

[0036] Step 4: Based on the smooth distance sequence, construct an approach artifact reduction layer during the navigation scene processing. By utilizing the trend information of the smooth distance sequence, weaken the sudden drop in false approach signals caused by energy bounce, so that the target proximity judgment maintains continuous and stable input characteristics in scene modeling. The specific implementation method for this step is as follows: Based on the generated smoothed distance sequence, the overall trend of distance change over time is extracted to identify the continuous distribution characteristics of distance changes during navigation. The smoothed distance sequence is obtained by continuously mapping a stable main peak waveform through a dynamic span mapping loop, possessing temporal consistency and energy continuity. This smoothed distance sequence is then unfolded chronologically to obtain a curve representing the relative distance between the target and the ship over time. Continuous observation of the curve reveals the main trends in distance change, such as continuous approach, stable maintenance, or gradual departure. After the abrupt change in suspended particle concentration caused by the storm front subsides, the smoothed distance sequence still maintains stable change characteristics. However, if the residual energy rebound effect causes minor disturbances within a local time window, the distance curve may show a slight drop or sudden descent. These sudden drops do not represent the actual target approach but rather false distance contraction caused by external environmental disturbances. Therefore, at this stage, a detailed analysis of the overall trend of the smoothed distance sequence is needed to clarify its dominant change direction in the time dimension and extract the smoothed trend line related to the motion of the real object, providing a benchmark for subsequent artifact identification and attenuation.

[0037] After clarifying the overall trend of the smoothed distance sequence, the rate of distance change within local time windows is continuously monitored to capture the location and duration of sudden drop-type anomaly signals. Because laser ranging links in storm environments may still be affected by residual effects of particle disturbances in a short period, even after processing with a dynamic span mapping loop, the rate of distance change at individual moments may still exhibit sudden drops or discontinuous shifts within extremely small time scales. Therefore, it is necessary to analyze the time-oriented change characteristics of the smoothed distance sequence segment by segment, continuously comparing the distance differences between adjacent time points. If the rate of distance change within a certain time slice exceeds the normal range of the average rate of change in its neighboring time slices, it can be preliminarily identified as a potential sudden drop-type artifact signal. To avoid mistaking real rapid approach events for artifact signals, the overall direction of the smoothed trend line should also be considered during analysis: if the overall trend is stable or a slow approach, but a local segment suddenly experiences a sharp drop exceeding the normal rate of change, then that segment can be confirmed as the region corresponding to a false sudden drop signal. In this way, the temporal location and influence range of sudden drop artifact signals can be accurately marked on the time axis, forming a set of artifact recognition segments. These segments will serve as the main processing targets for the artifact attenuation layer.

[0038] After identifying the artifact segment with a sudden drop in distance, a weakening operation is performed based on its deviation from the smoothed distance trend, gradually suppressing the false drop signal within the overall distance change sequence. Specifically, the identified artifact segment is first matched with its adjacent normal segment for continuity, comparing their temporal extension and differences in distance change amplitude. If the artifact with a sudden drop is short in duration and large in amplitude, while the adjacent segment changes gradually, a transition interval is introduced between them to mitigate the abrupt difference caused by the drop, restoring the distance curve to a continuous state. When the artifact with a sudden drop is long in duration, the start and end points of the drop segment are connected to the trend line according to the overall direction of the smoothed trend line, re-establishing a continuous transition relationship by extending the trend line. During this process, the trend information of the smoothed distance sequence is used as a correction reference, ensuring that the weakening operation not only eliminates local drops but also maintains consistency with the overall trend after weakening. In this way, the false approach signal with a sudden drop is weakened into a gradually changing curve that matches the true approach trend, and the distance sequence regains its temporal continuity and stability. The distance change trajectory after weakening processing can truly reflect the movement trend of external targets, without being affected by short-term environmental disturbances.

[0039] After weakening false approach signals, the corrected distance change curve is validated for continuity and the proximity output is corrected to ensure that the proximity judgment input in the navigation scenario remains stable and consistent. By comparing the distance curves before and after weakening on a time series, if the overall shape of the curve remains smooth and no new discontinuities appear, it indicates that the approach artifact weakening layer has a good processing effect. At this time, the relative proximity of the target can be calculated based on the corrected distance curve, and this proximity information can be used as the basic input for subsequent navigation scenario modeling. In actual navigation scenarios, the output distance change curve of the proximity artifact weakening layer not only eliminates false approach signals caused by energy bounce, but also maintains a stable response under complex conditions such as wind and wave disturbances or sea fog diffusion, making the proximity judgment in the navigation scenario have temporal continuity and numerical reliability. When the ship enters the edge zone of the storm front or a high particle concentration area, the proximity artifact weakening layer can still rely on the trend information of the smooth distance sequence to dynamically suppress sudden distance disturbances, preventing the scenario judgment system from mistakenly interpreting environmental changes as false images of external objects approaching at high speed. Therefore, during the navigation scenario modeling stage, the input proximity information always maintains smooth, stable, and traceable characteristics, ensuring the safety and rationality of the avoidance decision-making process.

[0040] Through the above process, this step realizes the approach artifact reduction process based on smooth distance sequences. This process makes full use of the temporal continuity and trend information of smooth distance sequences, which can effectively identify and weaken the sudden drop in false approach signals caused by energy bounce. This ensures that the target proximity judgment in the navigation scenario maintains stable input characteristics in complex environments. Through this multi-level trend tracking and continuity repair mechanism, the robustness of ship intelligent navigation scenario modeling in storm environments is guaranteed.

[0041] Step 5: Based on the real proximity information output by the proximity artifact reduction layer, deploy a gradual amplitude control frame in the avoidance control link. By driving the gradual control curve with real proximity information, suppress excessive control caused by false proximity, enable the ship to maintain a stable course in complex sea conditions and build a safety closed loop from disturbance identification to control output. The specific implementation method for this step is as follows: Based on the true proximity information output by the proximity artifact attenuation layer, the dynamic proximity relationship between the ship and surrounding targets is continuously acquired and processed in a time series. The true proximity information is stable data obtained after attenuating sudden-descent artifact signals, accurately reflecting the target object's motion trend and distance change rate relative to the ship. By unfolding the true proximity information on the time axis, a proximity curve reflecting the target's approach or departure trend can be formed. The temporal continuity of this proximity curve ensures the stability of the data input, while its amplitude and direction of change reflect the potential impact of the external environment on the ship's motion safety. At this stage, it is necessary to correlate the temporal rate of change of the true proximity with the ship's current speed, heading angle, and inertial response characteristics to establish a mapping basis between proximity changes and the ship's dynamic response. For example, when the proximity curve continuously decreases within a certain time window and the rate of decrease accelerates, it indicates that the external target is approaching at a high speed, and the maneuvering readiness should be gradually increased; conversely, when the proximity curve remains stable or changes slowly, the current maneuvering amplitude should be maintained to avoid unnecessary attitude adjustments. Through this process, a continuous correspondence between proximity and maneuvering requirements is established, laying the foundation for subsequent gradual amplitude control.

[0042] After obtaining the time-series-based true approach curve, a gradual amplitude reference is constructed based on the ship's maneuvering response characteristics to guide the dynamic changes in rudder angle and propulsion power. Because ships are subjected to the combined effects of wind, waves, and currents at sea, their maneuvering system response exhibits inertial lag. Directly adjusting instantaneously based on approach changes can easily lead to over-maneuvering or oscillations. Therefore, a gradual amplitude reference needs to be established based on the true approach, allowing the amplitude of maneuvering commands to transition gradually with changes in approach. Specifically, when approach changes are slow, the gradual amplitude reference maintains a low gradient, keeping the rudder angle adjustment and thrust change rate within a smooth range. When approach changes accelerate, the gradient of the gradual amplitude reference increases accordingly, but still maintains continuity to avoid sudden amplification of the maneuvering amplitude. In this way, the formation of the gradual amplitude reference not only considers the rate of approach change but also integrates the ship's structural dimensions, radius of inertia, and the response limits of the maneuvering devices, ensuring that the maneuvering output maintains a balanced and stable state while meeting safety avoidance requirements. In this way, the gradual amplitude reference forms a smooth transition curve in the time dimension, enabling maneuvering commands to achieve a controlled response under continuously changing approach inputs.

[0043] After the amplitude gradual change reference is established, the actual approach information is gradually transformed into specific maneuvering command outputs based on this reference, so that the maneuvering curve exhibits a gradual change characteristic from slow to rapid and from small to large. Specifically, by continuously monitoring the trend of approach change, the change direction of rudder angle adjustment rate and propulsion power is determined in real time. When the approach indicator shows that the target is gradually approaching, the amplitude gradual change control box makes a small pre-adjustment of the rudder angle according to the gradual change reference curve, so that the ship's course deviates slightly in advance to form a preventive avoidance. When the approach shortens further and the approach speed continues to increase, the adjustment amplitude and frequency of the rudder angle gradually increase, and the thrust distribution between the main engine and the side thrusters achieves a smooth transition to ensure the smoothness of the ship's avoidance maneuvers. When the approach curve tends to stabilize or begins to rise, the amplitude gradual change control box automatically reduces the rudder angle deflection amplitude, so that the ship's course returns to a stable state. Throughout the process, the adjustment of rudder angle and thrust changes continuously according to the gradual change law, avoiding sudden large-scale maneuvers caused by misjudgment in traditional control methods. In this way, the amplitude-gradient control frame achieves a flexible response to real proximity information, enabling the ship to maintain a stable and smooth control trajectory during dynamic avoidance.

[0044] After real-time adjustment of the amplitude-gradient maneuvering curve, the maneuvering output is dynamically verified and a safety closed-loop is confirmed to ensure that the output actions of the entire avoidance control link are consistent with the actual navigation state. The verification process includes real-time monitoring of rudder angle response delay, thrust distribution effect, and ship attitude change rate. If the rudder angle response is detected to be consistent with the approach change direction, and the thrust output does not exceed the preset safety threshold, it indicates that the maneuvering response is within a safe range. If the rudder angle adjustment results in overshoot or uneven thrust distribution, amplitude correction is required based on the actual approach change trend to bring the maneuvering curve back to the gradual state. Through this closed-loop verification, the amplitude-gradient maneuvering frame can correct the maneuvering response in real time according to the approach change, ensuring that the control actions remain stable and coordinated under environmental changes. This safety closed loop not only suppresses the risk of excessive maneuvering caused by false approach signals but also ensures that the maneuvering output is always consistent with the actual approach through a continuous dynamic verification mechanism. When the storm front causes increased external disturbances, the safety closed loop automatically delays the change in maneuvering amplitude to avoid amplified roll amplitude or course instability caused by frequent adjustments to the hull. Ultimately, the amplitude-gradient control frame achieves a complete closed loop in the avoidance control link, from the actual proximity input to the stable control output, enabling the ship to maintain course balance, reduce attitude disturbances, and ensure the safety and continuity of control actions in complex sea conditions.

[0045] Through the above steps, a gradual maneuvering process based on real proximity information is achieved. This process establishes a continuous mapping relationship between proximity changes and maneuvering amplitude at the physical level, enabling the ship's rudder angle adjustment and thrust control to respond flexibly to changes in proximity, thereby suppressing excessive maneuvering caused by false proximity signals. Through this gradual control mechanism, the ship can maintain a stable course even under storm, wave, and multi-target interference conditions, forming a complete safety closed loop from disturbance identification and signal attenuation to maneuvering output, providing a highly secure course control guarantee for intelligent navigation.

[0046] This invention introduces a particle disturbance identification band, pulse morphology reconstruction, and a dynamic span mapping loop into the ranging signal processing link. This achieves a continuous repair mechanism from energy return path disturbance identification to smooth distance sequence generation, ensuring the temporal continuity and physical stability of laser ranging output even in high-particle-density environments such as storm fronts. This effectively eliminates abrupt range jumps caused by energy return, providing realistic, smooth, and traceable distance input in navigation scenarios. It significantly improves the anti-interference capability and data reliability of distance measurement, providing a high-precision input foundation for environmental perception in intelligent navigation.

[0047] This invention achieves dynamic closed-loop control from true proximity recognition to maneuver response by constructing a proximity artifact attenuation layer and a gradually varying amplitude control frame. This allows the avoidance process to be gradually adjusted based on continuous and reliable proximity information, preventing excessive maneuvering induced by false proximity signals. This mechanism ensures the smoothness of the ship's course changes and the controllability of the maneuver amplitude under complex sea conditions, reduces the risk of amplified roll and sudden attitude changes, and enhances the ship's stable navigation capability under storm conditions.

[0048] This invention provides, for example Figure 2 The intelligent navigation scenario processing system shown includes a particle disturbance recognition module, a pulse pattern reconstruction module, a dynamic span mapping module, a proximity artifact reduction module, and an amplitude gradual control module. Particle disturbance identification module: Based on the sudden increase in seawater suspended particle density caused by the storm front, the energy return path of the laser ranging link is continuously analyzed to construct a particle disturbance identification band, and a disturbance label carrying the characteristics of energy bounce mode is generated based on the analysis results. Pulse shape reconstruction module: Using disturbance identifiers, the ranging pulse is expanded to the time domain and pulse shape reconstruction is performed on its energy anomaly section, so that the ranging pulse still maintains a stable main peak structure under the influence of energy rebound, and outputs a stable main peak waveform after pulse shape reconstruction. Dynamic span mapping module: Based on the stable main peak waveform, a dynamic span mapping loop is constructed in the distance calculation link, and a smooth distance sequence is formed by performing continuous mapping on the time span of the stable main peak waveform; Approach artifact reduction module: Based on the smooth distance sequence, an approach artifact reduction layer is constructed during the navigation scene processing. By utilizing the trend information of the smooth distance sequence, the sudden drop in false approach signals caused by energy bounce is reduced. Amplitude Gradual Manipulation Module: Based on the real proximity information output by the proximity artifact reduction layer, an amplitude gradual manipulation frame is deployed in the avoidance control link. By driving the gradual manipulation curve with real proximity information, excessive manipulation caused by false proximity is suppressed.

[0049] The method for processing intelligent navigation scenarios of ships provided in this embodiment of the invention is implemented through the above-mentioned intelligent navigation scenario processing system. For details of the specific methods and processes of the intelligent navigation scenario processing system, please refer to the embodiments of the above-mentioned intelligent navigation scenario processing method, which will not be repeated here.

[0050] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A method of processing for a ship intelligent navigation scenario, characterized in that, The method comprises the following steps: Step 1: Based on the sudden increase of seawater suspended particle density caused by the storm front, the energy return path of the laser ranging link is analyzed by continuous waveform, the particle disturbance identification band is constructed, and the disturbance identification carrying the energy return jump mode characteristics is generated according to the analysis result; Step 2: Using the disturbance identification, the ranging pulse is unfolded to the time domain, and the pulse shape reconstruction is performed on the energy abnormal section, so that the ranging pulse still maintains the stable main peak structure under the influence of energy return jump, and the stable main peak waveform after pulse shape reconstruction is output; Step 3: Based on the stable main peak waveform, a dynamic span mapping ring is constructed in the distance calculation link, and a smooth distance sequence is formed by continuously mapping the time span of the stable main peak waveform; Step 4: According to the smooth distance sequence, a proximity artifact weakening layer is constructed in the navigation scene processing process, and the sudden false proximity signal caused by energy return jump is weakened by using the trend information of the smooth distance sequence; Step 5: Based on the real proximity information output by the proximity artifact weakening layer, a gradual change steering frame is deployed in the avoidance control link, and the gradual change steering curve is driven by the real proximity information to suppress the excessive steering caused by false proximity.

2. The method of claim 1, wherein, The step of analyzing the energy return path of the laser ranging link based on the sudden increase of seawater suspended particle density caused by the storm front comprises: Through the energy return signal acquisition unit of the laser ranging link in the storm front environment, the return energy between the sea surface and the target object is continuously recorded with high time resolution to form a time-unfolded energy sequence; Performing amplitude variation trend and waveform shape analysis on the obtained continuous waveform, identifying the energy abnormal section affected by the suspended particle disturbance, and marking along the time axis to form a particle disturbance identification band; Based on the particle disturbance identification band, the energy distribution mode in the identification band is analyzed, the peak duration, peak width and energy attenuation characteristics of the energy return shape are extracted, and a disturbance parameter set carrying the energy return mode characteristics is generated; The time range of the particle disturbance identification band is associated with the disturbance parameter set to generate a disturbance identification with time continuity and energy distribution correlation.

3. The method of claim 1, wherein, The step of unfolding the ranging pulse to the time domain and performing pulse shape reconstruction on the energy abnormal section by using the disturbance identification comprises: According to the generated disturbance identification, the ranging pulse is unfolded to the complete time domain along the time dimension, and the time start and end points of the disturbance identification are mapped to the time axis of the ranging pulse to form the time range of the energy abnormal section; Performing hierarchical analysis on the energy distribution shape of the energy abnormal section, identifying the abnormal structure according to the energy return mode characteristics carried in the disturbance identification; Based on the feature analysis result of the energy abnormal section, performing shape adjustment operation on the ranging pulse to repair the symmetry and continuity of the main peak structure, and forming a stable main peak shape by smoothing the energy distribution and reducing the secondary peak energy; Performing time continuity and energy stability verification on the reconstructed ranging pulse to ensure that the output stable main peak waveform has smooth energy distribution and time position consistency.

4. The method of claim 3, wherein, The step of constructing a dynamic span mapping ring in the distance calculation link based on the stable main peak waveform comprises: The time span curve of the main peak is obtained by continuously measuring the time span of the main peak based on the stable main peak waveform, determining the start point, peak top and end point of the main peak energy distribution, and forming the main peak time span curve; The time span curve of the main peak is analyzed by adjacent correlation analysis, the time span difference between adjacent ranging pulses is identified, and the time span change gradient of the main peak is extracted to represent the time continuity; Based on the main peak time span change trajectory, a dynamic span mapping ring is constructed to establish a continuous mapping relationship between the main peak time span and the distance calculation output, and the time and distance change is kept smooth through buffer mapping; The distance sequence output by the mapping ring is continuously smoothed, the mapping weight is adjusted according to the change rate of adjacent distance points, and a smooth distance sequence is formed.

5. The method of claim 4, wherein, In the process of establishing the mapping relationship between the main peak time span and the distance calculation output, the abnormal time span change is transitioned to the adjacent normal section in a buffer manner through weighted smoothing processing of the main peak time span change gradient.

6. The method of claim 4, wherein, The steps of constructing the proximity artifact weakening layer based on the smooth distance sequence include: Based on the smooth distance sequence, the overall distance change trend with time is extracted to form a trend curve reflecting the relative motion state of the target and the ship; The distance change rate in the local time window of the trend curve is continuously detected to identify the sudden drop type abnormal signal between adjacent time points and determine the sudden drop type artifact identification section; According to the deviation between the sudden drop type artifact identification section and the smooth distance trend, the weakening operation is performed, the transition interval or the extension trend line is introduced between the artifact section and the normal section to restore the continuity of the distance change; The distance change curve after the weakening processing is continuously verified and the proximity output is corrected.

7. The method of claim 6, wherein, In the process of performing the weakening operation, the transition interval smoothing or the trend line extension is used for correction according to the duration and amplitude change of the sudden drop type artifact identification section, and when the sudden drop amplitude exceeds the preset threshold, the overall trend line of the smooth distance sequence is used to reconnect the start and end points of the artifact section.

8. The method of claim 6, wherein, The steps of deploying the amplitude gradual manipulation frame in the avoidance control link based on the real proximity information output by the proximity artifact weakening layer include: According to the real proximity information output by the proximity artifact weakening layer, the dynamic proximity relationship between the ship and the surrounding target is continuously collected and time sequenced to form a proximity curve reflecting the trend of the target approaching or moving away; According to the steering response characteristics of the ship, the amplitude gradual reference is established to gradually transition the change amplitude of the rudder angle and the propulsion power with the change of proximity; The real proximity information is converted into a gradual steering curve of the rudder angle and the thrust according to the amplitude gradual reference; The steering output result is dynamically verified and safety closed loop confirmed, and the steering amplitude is corrected by monitoring the rudder angle response, thrust distribution and attitude change rate.

9. The method of claim 8, wherein, In the process of establishing the amplitude gradual reference, the real proximity change rate is associated with the ship speed, heading angle and inertial response characteristics, so that the rudder angle adjustment amplitude and the propulsion power change rate continuously change within the preset safety threshold.

10. A processing system for a ship intelligent navigation scenario, configured to implement the method for processing a ship intelligent navigation scenario according to any one of claims 1-9, characterized in that, The system includes a particle disturbance identification module, a pulse shape reconstruction module, a dynamic span mapping module, a proximity artifact weakening module, and an amplitude gradual manipulation module. The particle disturbance identification module: based on the sudden increase of seawater suspended particle density caused by the storm front, the energy return path of the laser ranging link is analyzed continuously, the particle disturbance identification band is constructed, and the disturbance identification is generated according to the analysis result, which carries the energy return mode characteristics; The pulse shape reconstruction module: using the disturbance identification, the ranging pulse is expanded to the time domain and the energy abnormal section is executed, so that the ranging pulse still maintains the stable main peak structure under the influence of energy return, and the stable main peak waveform after pulse shape reconstruction is output; The dynamic span mapping module: based on the stable main peak waveform, a dynamic span mapping ring is constructed in the distance solving link, and a smooth distance sequence is formed by continuously mapping the time span of the stable main peak waveform; The proximity artifact weakening module: according to the smooth distance sequence, a proximity artifact weakening layer is constructed in the navigation scene processing process, and the trend information of the smooth distance sequence is used to weaken the sudden false proximity signal caused by energy return; The amplitude gradual change manipulation module: based on the real proximity information output by the proximity artifact weakening layer, an amplitude gradual change manipulation frame is deployed in the avoidance control link, and the gradual change manipulation curve is driven by the real proximity information to suppress the excessive manipulation caused by false proximity.

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