A method and system for evaluating the coverage capability of a drone nest operation

Abstract

The application discloses a kind of unmanned plane nest operation coverage capacity evaluation method and system, it is related to unmanned plane technical field, including: the three-axis acceleration of target nest base is collected;According to the dynamic slope sequence of target nest platform calculated according to three-axis acceleration, according to the candidate window set of target landing section generated according to dynamic slope sequence and preset safety threshold;Two narrow-band components of dynamic slope sequence are generated, the phase consistency determination of amplitude threshold and instantaneous change rate of narrow-band component is carried out, and phase encounter mark is obtained;According to dynamic slope sequence and phase encounter mark, generate violent jump instruction;Based on preset approach time and violent jump instruction, safety window determination is carried out to candidate window set, and the safety window of target nest platform is obtained.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) technology, and in particular to a method and system for assessing the coverage capability of UAV nest operations. Background Technology

[0002] In offshore infrastructure such as offshore wind farms, fixing drone pods to floating platforms or wind turbine nacelles is becoming a practical necessity for automated inspection and material delivery. However, these platforms experience multi-degree-of-freedom motion (heave, sway, pitch) under the combined influence of wind and waves, causing the actual force direction on the deck to constantly change relative to the direction of gravity. This leads to a sudden safety abrupt change on a timescale of seconds: the instantaneous tilting abrupt change of the platform. The mechanism lies in the fact that the platform's motion in the two main wave frequency bands intensifies in the same direction at certain moments, causing the tangential inertial component to momentarily dominate the normal support component, and the dynamic slope rapidly exceeds the pre-set safety threshold. This phenomenon fragments what would otherwise be a seemingly continuous and usable landing time into isolated segments, directly leading to forced go-arounds during the drone's terminal approach and landing, mission delays, overly optimistic capacity assessments, failure to fulfill scheduling and service commitments, and even increased operational risks.

[0003] Existing methods typically employ a coarse-grained assessment approach based on significant wave height, root mean square acceleration, or long-term average availability. These methods treat platform motion as a statistically relatively stable process, using a single threshold to simply judge the entire timeframe, or representing availability by statistically analyzing the percentage exceeding the threshold at a fixed time resolution. These methods ignore the decisive impact of second-level sudden dynamic slope jumps on the critical short time window of the final approach phase. They also lack the ability to identify and eliminate instances where two dominant frequency components simultaneously amplify, causing momentary exceedances. As a result, the obtained safe availability time is systematically amplified, failing to provide a sufficiently reliable basis for task scheduling, resource allocation, and service level management. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies that ignore the decisive influence of second-level sudden dynamic slope jumps on the critical short time window of terminal approach, and to propose a method and system for evaluating the coverage capability of UAV nest operations.

[0005] To address the problems existing in the prior art, the present invention adopts the following technical solution:

[0006] A method for assessing the coverage capability of drone nesting operations includes:

[0007] S1. Collect the triaxial acceleration of the target nest base;

[0008] S2. Calculate the dynamic slope sequence of the target aircraft nest platform based on the three-axis acceleration, and generate a candidate window set for the target landing section based on the dynamic slope sequence and the preset safety threshold.

[0009] S3. Generate two narrowband components of the dynamic slope sequence, and determine the phase consistency of the amplitude threshold and instantaneous rate of change of the narrowband components to obtain the phase encounter indicator.

[0010] S4. Generate a burst indicator based on the dynamic slope sequence and phase encounter identifier;

[0011] S5. Based on the preset approach duration and blast indication, determine the safe window of the candidate window set to obtain the safe window of the target machine nest platform;

[0012] S6. Calculate the total safe duration of all safe windows, and manage and predict the operational coverage capability of the target nest platform based on the total safe duration.

[0013] Preferably, the dynamic slope sequence of the target aircraft nest platform is calculated based on triaxial acceleration, and a candidate window set for the target landing section is generated based on the dynamic slope sequence and a preset safety threshold, including:

[0014] Calculate the dynamic slope sequence based on the triaxial acceleration and gravitational acceleration constant;

[0015] A threshold determination is performed on the dynamic slope sequence and the preset safety threshold to obtain a binary sequence;

[0016] Continuous interval extraction is performed on the binary sequence to obtain a set of candidate windows for the target landing zone.

[0017] Preferably, the two narrowband components that generate the dynamic slope sequence include:

[0018] Power spectrum analysis was performed on the dynamic slope sequence to obtain the frequency distribution;

[0019] The frequency distribution is subjected to main peak detection, resulting in two main peak frequencies;

[0020] By performing bandpass filtering on the two main peak frequencies, two narrowband components are obtained.

[0021] Preferably, phase consistency is determined for the amplitude threshold and instantaneous rate of change of the narrowband component to obtain a phase encounter indicator, including:

[0022] The root mean square of the two narrowband components is calculated separately to obtain two amplitude thresholds;

[0023] By taking the derivatives of the two narrowband components respectively, two instantaneous rates of change are obtained;

[0024] If the amplitude threshold is greater than the corresponding instantaneous rate of change, and the product of the two instantaneous rates of change is greater than 0, then the phase encounter flag is set to 1; otherwise, the phase encounter flag is set to 0.

[0025] Preferably, generating a burst indicator based on a dynamic slope sequence and a phase encounter identifier includes:

[0026] If the dynamic slope value in the dynamic slope sequence is greater than the preset safety threshold and the phase encounter flag is set to 1, then the burst indicator is set to 1; otherwise, the burst indicator is set to 0.

[0027] Preferably, a safe window is determined based on a preset approach duration and blast indicator to obtain the safe window for the target nest platform, including:

[0028] For each candidate window in the candidate window set, the start time of the candidate window is used as the starting point of the fixed verification time interval, and the difference between the end time of the candidate window and the preset approach time is used as the ending point of the fixed verification time interval.

[0029] The approach time window is determined based on the preset approach duration, which defines a fixed verification time interval.

[0030] If all blast indicators are 0 within the approach time window, and the duration of the candidate window is greater than or equal to the preset approach duration, then the candidate window is determined to be a safe window for the target hive platform.

[0031] Preferably, the total safe duration for all safe windows is calculated, including:

[0032] Calculate the safe duration of the safe window based on its start and end times;

[0033] The total safe duration is obtained by summing up all the safe durations.

[0034] Preferably, the operational coverage capability of the target nesting platform is managed and predicted based on the total safe operating time, including:

[0035] The ratio of the total safe duration to the total observation duration of the target nest platform is used as the time utilization coefficient of the target nest platform.

[0036] The effective sortie rate of the target aircraft nest platform is calculated as the product of the nominal sortie rate and the time utilization coefficient.

[0037] Based on the effective sortie rate, the coverage per sortie of the target aircraft nest platform, and the planned operation duration, calculate the operation coverage index of the target aircraft nest platform;

[0038] The operational coverage capability of the target hive platform is managed and predicted based on operational coverage indicators.

[0039] To address the above problems, the present invention also provides a drone nesting operation coverage capability assessment system, the system comprising:

[0040] The data acquisition module is used to collect the three-axis acceleration of the target nest base;

[0041] The candidate set generation module is used to calculate the dynamic slope sequence of the target aircraft nest platform based on the triaxial acceleration, and generate a candidate window set for the target landing section based on the dynamic slope sequence and the preset safety threshold.

[0042] The phase encounter identification module is used to generate two narrowband components of the dynamic slope sequence, and to determine the phase consistency of the amplitude threshold and instantaneous rate of change of the narrowband components to obtain the phase encounter identifier.

[0043] The burst detection module is used to generate burst indicators based on the dynamic slope sequence and phase encounter identifier;

[0044] The safety window determination module is used to determine the safety window of the candidate window set based on the preset approach duration and blast indication, so as to obtain the safety window of the target machine nest platform.

[0045] The job coverage management and prediction module is used to calculate the total safe duration of all safety windows and manage and predict the job coverage capability of the target nesting platform based on the total safe duration.

[0046] Compared with the prior art, the beneficial effects of the present invention are:

[0047] 1. In this invention, by collecting triaxial acceleration data of the target drone's base and calculating the dynamic slope sequence, high-resolution real-time monitoring of the platform's attitude is achieved, enabling precise capture of the platform's tilt changes within a short period. By setting a safety threshold and generating a candidate window set, this solution can identify the available initial landing zone for the drone, effectively solving the problem of insufficient response to instantaneous tilt changes in traditional methods, thereby avoiding the risk of drone approach and landing failures due to sudden platform tilt.

[0048] 2. This invention introduces a bandpass filtering and phase consistency analysis method based on the main peak frequency. By utilizing the amplitude and instantaneous rate of change of narrowband components, it can identify the instantaneous tilt enhancement phenomenon caused by the superposition of wind and waves, and mark high-risk periods through a burst indicator. This method overcomes the traditional evaluation method that relies solely on mean or long-term statistical indicators, improves the response capability to high-frequency short-term anomalies, ensures more accurate and reliable safety window screening, and thus reduces unnecessary misjudgments and redundant safety time.

[0049] 3. In this invention, the total safe time is calculated by accumulating the safety window, and then combined with the total observation time to calculate the time utilization coefficient and effective sortie rate, thereby completing the dynamic assessment and prediction of operational coverage capability. This scheme not only quantifies the actual operational capability of UAVs in complex sea conditions, but also provides data support for task scheduling and resource allocation, making up for the shortcomings of traditional methods in dynamically managing and predicting operational capabilities, and making the overall operational planning more scientific and efficient. Attached Figure Description

[0050] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0051] Figure 1 This is a flowchart illustrating a method for assessing the coverage capability of drone nest operations according to an embodiment of the present invention.

[0052] Figure 2 This is a functional block diagram of a drone nesting operation coverage capability assessment system provided in an embodiment of the present invention. Detailed Implementation

[0053] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0054] Example: This example provides a method for assessing the coverage capability of UAV nesting operations. See [link to example]. Figure 1 Specifically, including:

[0055] S1. Collect the triaxial acceleration of the target nest base;

[0056] Specifically, in the bimodal sea state scenario where the floating wind turbine nest is located, the nest base will experience complex six-degree-of-freedom motions due to the coupling effects of waves, wind, and currents, including sway, roll, pitch, pitch, and bow. Three-axis acceleration can reflect these changes in motion in real time. Using three-axis acceleration data, the velocity and displacement information of the nest base in different directions can be accurately calculated, thus obtaining the dynamic attitude of the nest, such as pitch and roll angles. This is crucial for subsequent calculations of the dynamic slope, as the dynamic slope is defined based on the arctangent of the ratio of in-plane acceleration to normal acceleration. Three-axis acceleration is the foundation for calculating these acceleration components. In bimodal sea states, when the wind peak and swell peak coincide, abnormal fluctuations in the nest base motion occur. Three-axis acceleration data can sensitively capture these fluctuations, thus helping us identify MSI burst events.

[0057] Specifically, a calibrated inertial measurement unit (IMU) is installed on the base of the target nest platform. This IMU has a built-in triaxial accelerometer that can acquire linear acceleration signals in the longitudinal, lateral, and vertical directions in real time. Secondly, the sampling frequency is set to meet the dynamic response requirements of the platform's motion characteristics, and the raw acceleration data output by the sensor is continuously recorded to the storage device through the data acquisition module. Then, the acquired raw acceleration data is subjected to necessary front-end processing, including zero-bias correction and noise filtering, to eliminate sensor drift and environmental interference, ensuring that the obtained acceleration signal can accurately reflect the motion state of the target nest platform in the actual operating environment.

[0058] S2. Calculate the dynamic slope sequence of the target aircraft nest platform based on the three-axis acceleration, and generate a candidate window set for the target landing section based on the dynamic slope sequence and the preset safety threshold.

[0059] Specifically, an MSI hop event refers to the phenomenon where the dynamic slope index suddenly exceeds the safety threshold in a short period of time due to violent platform movement. It occurs when a floating platform is subjected to the combined effects of a bimodal sea state phase encounter, causing an instantaneous imbalance between the platform's tangential inertial component and normal support component, resulting in a rapid increase in the dynamic slope and exceeding the preset safety limit in a short period of time. This hop often lasts for a very short time, but it can cause the terminal approach or landing window of the UAV to instantly lose its safety.

[0060] In an embodiment of the present invention, the dynamic slope sequence of the target aircraft nest platform is calculated based on triaxial acceleration, and a candidate window set for the target landing section is generated based on the dynamic slope sequence and a preset safety threshold, including:

[0061] Based on the triaxial acceleration and gravitational acceleration constant, the dynamic slope sequence is calculated. The formula for calculating the dynamic slope sequence is as follows:

[0062]

[0063] In the formula, At any moment The dynamic slope sequence, , and At any moment The acceleration components of the triaxial acceleration, It is the gravitational acceleration constant. It is the magnitude of the resultant acceleration in the tangential direction of the deck. It is the effective support acceleration in the normal direction of the deck. It is the arctangent function;

[0064] Specifically, the dynamic slope aims to quantify the degree of dynamic tilt of the deck plane caused by platform motion. The formula constructs the geometric relationship between tangential and normal acceleration based on the principles of physical kinematics: the composite tangential acceleration of the deck reflects the intensity of horizontal motion, while the effective normal support acceleration of the deck integrates gravity and vertical motion acceleration. The ratio of the two corresponds to the relationship between opposite and adjacent sides in a right triangle. The arctangent function can convert this ratio into an angle value, which directly corresponds to the dynamic tilt angle of the deck plane. Therefore, the sequence calculated by this formula can characterize the dynamic slope of the deck at different times, providing a key dynamic tilt quantification index for subsequent evaluation of UAV landing windows.

[0065] A threshold determination is performed on the dynamic slope sequence and the preset safety threshold to obtain a binary sequence;

[0066] Specifically, at each time sampling point, the dynamic slope value at that moment is compared with the safety threshold. When the dynamic slope value is less than or equal to the safety threshold, the corresponding time point is marked as a safe state and assigned a value of one. When the dynamic slope value is greater than the safety threshold, it is marked as an unsafe state and assigned a value of zero. Then, the safety state markings in the above time order are combined to form a complete binary sequence. This sequence can accurately reflect the changes in the platform's safety state throughout the entire observation period. Finally, the generated binary sequence is stored and output for use in subsequent candidate window extraction and safety window determination steps.

[0067] Continuous interval extraction is performed on the binary sequence to obtain a set of candidate windows for the target landing zone.

[0068] Specifically, continuous interval extraction refers to identifying and recording time periods in a binary state sequence arranged chronologically, thereby transforming the original discrete state points into continuous time intervals. For example, when a certain segment in a binary sequence is in a continuous safe state, the start and end points of this segment are extracted to form a continuous interval.

[0069] Specifically, the process begins by scanning point by point from the starting position of the binary sequence. When a continuous safety status is detected as 1, this time point is recorded as the start time of the candidate window. The process continues to scan until a safety status is detected as 0, at which point this time point is recorded as the end time of the candidate window, thus forming a complete candidate window interval. Then, the entire binary sequence is fully traversed, and all continuous safety intervals that meet the conditions are extracted in sequence. These intervals are then stored and summarized in chronological order to form a candidate window set for the target landing segment.

[0070] Specifically, the candidate window set refers to the set of all time intervals that meet the safety conditions obtained from the binary state sequence through continuous interval extraction. These intervals are considered as candidate landing periods for subsequent safety verification or mission scheduling. It is equivalent to the preliminary screening results of the original safety state, providing a data foundation for further analysis of the safety window.

[0071] S3. Generate two narrowband components of the dynamic slope sequence, and determine the phase consistency of the amplitude threshold and instantaneous rate of change of the narrowband components to obtain the phase encounter indicator.

[0072] In an embodiment of the present invention, generating two narrowband components of the dynamic slope sequence includes:

[0073] Power spectrum analysis was performed on the dynamic slope sequence to obtain the frequency distribution;

[0074] The frequency distribution is subjected to main peak detection, resulting in two main peak frequencies;

[0075] Specifically, the dynamic slope sequence is first preprocessed by using a linear detrending algorithm to eliminate the linear trend term. Then, a fast Fourier transform algorithm is used to perform spectral analysis on the windowed sequence to calculate the power spectral density function of the dynamic slope sequence. Next, peak detection is performed on the power spectral density function by traversing the frequency axis of the power spectrum to identify all local maxima and calculating the power spectral amplitude corresponding to each maxima. All maxima are then sorted from largest to smallest power spectral amplitude, and the frequencies corresponding to the top two maxima are selected as the two main peak frequencies. If fewer than two maxima are detected, the power spectrum is scanned a second time using the prior frequency range of the bimodal sea state, such as the typical frequency range of the wind peak and swell peak. The supplementary main peak frequencies are determined by amplitude weighting calculation within a preset frequency range, ultimately yielding two main peak frequencies that accurately reflect the energy distribution of the dynamic slope sequence.

[0076] By performing bandpass filtering on the two main peak frequencies, two narrowband components are obtained.

[0077] Specifically, the two main peak frequencies identified through power spectrum analysis are input to the filter design module. In this module, a corresponding bandpass filter is designed based on the center value of each main peak frequency and the required bandwidth range to ensure that the dynamic slope component within the corresponding frequency band can be effectively separated. The dynamic slope sequence is then input into the two bandpass filters to filter out low-frequency and high-frequency signals that are irrelevant to the target frequency band, retaining only the narrowband components around the two main peak frequencies. The filter output is then normalized in amplitude and corrected for boundary effects to ensure that the obtained narrowband components can accurately reflect the true change characteristics of the dynamic slope within the frequency range. The two narrowband components after bandpass filtering are output in time series form.

[0078] Specifically, the narrowband component refers to the portion of the signal separated from the original signal by a bandpass filter, containing only frequency components within a specific narrow frequency band. It reflects the energy and variation characteristics of the original signal near a specific dominant frequency, effectively removing irrelevant high-frequency noise and low-frequency trends. In the assessment of UAV nesting operation coverage capabilities, the narrowband component obtained after bandpass filtering of the dynamic slope sequence can clearly characterize the motion component corresponding to the dominant frequency in a bimodal sea state.

[0079] In an embodiment of the present invention, phase consistency determination is performed on the amplitude threshold and instantaneous rate of change of the narrowband component to obtain a phase encounter indicator, including:

[0080] The root mean square of the two narrowband components is calculated separately to obtain two amplitude thresholds;

[0081] By taking the derivatives of the two narrowband components respectively, two instantaneous rates of change are obtained;

[0082] Specifically, the two narrowband components are first preprocessed to eliminate DC offset by linear fitting, and then a sliding window smoothing algorithm is used for noise reduction. For the root mean square (RMS) calculation, the preprocessed first narrowband component is divided into continuous analysis segments according to the time series, with each segment lasting at least 30 seconds. The average of the squared values ​​of the signal within each segment is calculated, and the arithmetic square root is taken to obtain the RMS value of that component. This RMS value is then multiplied by a preset coefficient to obtain the first amplitude threshold. Similarly, the same operation is performed on the second narrowband component to obtain the second amplitude threshold. The preset coefficient is determined based on the significance level of historical data. For differentiation operations, the central difference method is used to numerically differentiate the first narrowband component after preprocessing. The difference step size is consistent with the signal sampling period. The ratio of the signal difference between adjacent sampling points before and after each moment to the sampling interval is calculated to obtain the instantaneous rate of change of the first narrowband component. For boundary moments, forward difference or backward difference methods are used for supplementary calculation. Similarly, the same operation is performed on the second narrowband component to obtain the second instantaneous rate of change. Finally, the validity of the calculation results is verified, and outliers with instantaneous rates of change exceeding the physical reasonable range are eliminated to ensure that the two amplitude thresholds and the two instantaneous rates of change can accurately reflect the amplitude characteristics and dynamic change trends of the narrowband components.

[0083] If the amplitude threshold is greater than the corresponding instantaneous rate of change, and the product of the two instantaneous rates of change is greater than 0, then the phase encounter flag is set to 1; otherwise, the phase encounter flag is set to 0.

[0084] Specifically, the amplitude threshold is a significance criterion determined based on the energy characteristics of the narrowband component. When the amplitude threshold is greater than the corresponding instantaneous rate of change, it indicates that the narrowband component is in a state of significant energy fluctuation, rather than noise or minor disturbance, and can truly reflect the motion characteristics of the corresponding frequency component. If the product of the two instantaneous rates of change is greater than 0, it indicates that the two change in the same direction, that is, they increase or decrease at the same time. This means that the two narrowband components are in phase at that moment and are in a state of in-phase superposition. The core characteristic of phase encounter is that the two main frequency components in the bimodal sea state have significant energy and are in phase. Only when both components are significant and in phase is it determined to be a phase encounter and the flag is assigned a value of 1; otherwise, it is assigned a value of 0.

[0085] S4. Generate a burst indicator based on the dynamic slope sequence and phase encounter identifier;

[0086] In an embodiment of the present invention, generating a burst indicator based on a dynamic slope sequence and a phase encounter identifier includes:

[0087] If the dynamic slope value in the dynamic slope sequence is greater than the preset safety threshold and the phase encounter flag is set to 1, then the burst indicator is set to 1; otherwise, the burst indicator is set to 0.

[0088] Specifically, a dynamic slope value exceeding the preset safety threshold indicates that the tilt of the drone's base has exceeded the basic limits for safe take-off and landing, posing a potential risk. A phase encounter indicator of 1 indicates that the two main frequency components, corresponding to wind and swell in a bimodal sea state, are in phase superposition. This phase coupling amplifies the dynamic tilt of the base, which is the core cause of the sudden increase in tilt. Only by combining these two indicators can the dangerous tilt caused by a phase encounter be accurately identified. A dynamic slope exceeding the limit alone may be a normal fluctuation, and a phase encounter alone may not necessarily lead to a dangerous tilt. Assigning a value of 1 to the surge indicator at this time accurately marks such high-risk moments; conversely, assigning a value of 0 indicates a surge. This provides targeted hazard indicators for subsequent safety window screening, avoiding overestimation or misjudgment of the drone's safe operational capabilities.

[0089] S5. Based on the preset approach duration and blast indication, determine the safe window of the candidate window set to obtain the safe window of the target machine nest platform;

[0090] In embodiments of the present invention, a safe window is determined based on a preset approach duration and a burst indication to obtain a safe window for the target nest platform, including:

[0091] For each candidate window in the candidate window set, the start time of the candidate window is used as the starting point of the fixed verification time interval, and the difference between the end time of the candidate window and the preset approach time is used as the ending point of the fixed verification time interval.

[0092] Specifically, the setting of the fixed verification time interval must strictly match the actual operational logic of the UAV's terminal approach. The start time of the candidate window is used as the starting point of the interval because the UAV can initiate the approach process from the earliest moment the window begins. The end time of the candidate window minus the preset approach duration is used as the end point of the interval to ensure that after the UAV initiates the approach at this end time, it can complete the approach process before the candidate window ends, avoiding approach interruption due to insufficient window duration. The fixed verification interval defined in this way can fully cover all possible safe approach start times, providing a precise time boundary for subsequent sliding checks within the interval to check for erratic events within the approach time window. This ensures that the selected safe windows not only meet the time requirements of the UAV approach but also strictly avoid potentially dangerous moments during the approach process, thus providing a reasonable and rigorous verification range for subsequent safe window determination.

[0093] The approach time window is determined based on the preset approach duration, which defines a fixed verification time interval.

[0094] Specifically, a preset approach duration is used as the shortest continuous time condition required to determine the safe landing of the UAV. Then, within the fixed verification time interval of each candidate window, a time window of equal length to the approach duration is slid sequentially from the start of the interval according to the time step. Each time the window is slid, the start and end points of the current time window are recorded to form a set of continuous approach time window sequences. Then, each generated approach time window is stored and sorted so that these time windows can completely cover the entire fixed verification time interval and achieve seamless connection within the interval.

[0095] If all blast indicators are 0 within the approach time window, and the duration of the candidate window is greater than or equal to the preset approach duration, then the candidate window is determined to be a safe window for the target hive platform.

[0096] Specifically, all hop indicators within the approach time window are 0, indicating that no dynamic slope hop caused by phase encounter occurred during this period, and the tilt of the nest base remains within the threshold range for safe UAV approach, meeting the dynamic stability requirements of the approach process. Furthermore, a candidate window duration greater than or equal to the preset approach duration ensures that the UAV has sufficient continuous time to complete the entire process from approach initiation to landing, preventing approach interruptions due to insufficient window duration. Dynamic stability ensures no dangerous interference during the approach process, and sufficient duration ensures the complete execution of the operational procedures. Only when both conditions are met simultaneously can the candidate window be considered a truly suitable effective range for safe UAV takeoff and landing, providing accurate data for the statistical analysis of safe available time in subsequent operational coverage capability assessments.

[0097] S6. Calculate the total safe duration of all safe windows, and manage and predict the operational coverage capability of the target nest platform based on the total safe duration.

[0098] In an embodiment of the present invention, calculating the total security duration of all security windows includes:

[0099] Calculate the safe duration of the safe window based on its start and end times;

[0100] The total safe duration is obtained by summing up all the safe durations.

[0101] In embodiments of the present invention, managing and predicting the operational coverage capability of the target nesting platform based on the total safe duration includes:

[0102] The ratio of the total safe duration to the total observation duration of the target nest platform is used as the time utilization coefficient of the target nest platform.

[0103] The effective sortie rate of the target aircraft nest platform is calculated as the product of the nominal sortie rate and the time utilization coefficient.

[0104] Specifically, the nominal sortie rate represents the number of sorties that the target aircraft carrier platform can complete per unit time under ideal, undisturbed conditions, and is the maximum operational potential based on the theoretical performance of the equipment. The time utilization factor, on the other hand, quantifies the proportion of actual time available for safe operation to the total observation time, reflecting the time loss caused by disturbances such as malfunctions. The effective sortie rate needs to reflect both the theoretical capacity of the equipment and the constraints of actual available time. Multiplying the nominal sortie rate by the time utilization factor is essentially converting the theoretical maximum number of sorties into the proportion of actual available time. This preserves the influence of the inherent performance of the equipment and incorporates the restrictions of safe time on operation, thus obtaining a sortie rate that truly reflects the stable completion rate of the target aircraft carrier platform under actual working conditions.

[0105] Based on the effective sortie rate, the coverage per sortie of the target aircraft nest platform, and the planned operation duration, calculate the operation coverage index of the target aircraft nest platform;

[0106] Specifically, the effective sortie rate, adjusted for the nominal sortie rate using a time utilization factor, reflects the number of sorties the target hive platform can safely complete per unit time under actual operating conditions; the coverage per sortie quantifies the effective coverage area or region achievable with each operation, serving as a fundamental measure of operational capability; and the planned operation duration clarifies the time boundary for the assessment. Multiplying the effective sorties per unit time by the coverage per sortie yields the actual coverage efficiency per unit time, which, in turn, is multiplied by the total planned operation duration to obtain the total coverage that the target hive platform can actually achieve within the planned period. This comprehensively incorporates equipment performance, safety constraints, and time limitations, fully reflecting the actual achievable operational coverage effect.

[0107] The operational coverage capability of the target hive platform is managed and predicted based on operational coverage indicators.

[0108] Specifically, firstly, a mapping relationship is established between operational coverage indicators and operational coverage capability levels. Multiple preset coverage indicator thresholds are used to classify operational coverage capabilities into different levels, such as excellent, good, satisfactory, and unsatisfactory. Next, a predictive model is built based on historical operational data and current environmental parameters. The predictive model takes operational coverage indicators as output and sea state parameters, meteorological data, and equipment status as input, and is trained using machine learning algorithms. Then, real-time collected environmental data and equipment status are input into the predictive model to obtain predicted values ​​for operational coverage indicators over a future period. Finally, by comparing the predicted operational coverage indicators with the current operational coverage indicators, trend warning information is generated. If the predicted value of the operation coverage index is lower than the current operation coverage index and the difference exceeds a preset threshold, a downgrade warning is triggered. Then, corresponding management strategies are formulated for different operation coverage capability levels. For example, when the capability level is excellent, the density of operation plans can be increased, and when it is unqualified, equipment maintenance or adjustment of the operation area can be initiated. At the same time, a feedback correction mechanism is established to dynamically adjust the prediction model parameters based on the deviation between the actual operation effect and the prediction result, thereby improving the prediction accuracy. Finally, a comprehensive report containing the current operation coverage capability level, future trend prediction, and management strategy recommendations is generated for decision-makers to refer to, so as to realize dynamic management and accurate prediction of the operation coverage capability of the target machine nest platform.

[0109] like Figure 2 The diagram shown is a functional block diagram of a drone nesting operation coverage capability assessment system provided in an embodiment of the present invention.

[0110] In this embodiment, the functions of each module / unit are as follows:

[0111] The data acquisition module is used to collect the three-axis acceleration of the target nest base;

[0112] The candidate set generation module is used to calculate the dynamic slope sequence of the target aircraft nest platform based on the triaxial acceleration, and generate a candidate window set for the target landing section based on the dynamic slope sequence and the preset safety threshold.

[0113] The phase encounter identification module is used to generate two narrowband components of the dynamic slope sequence, and to determine the phase consistency of the amplitude threshold and instantaneous rate of change of the narrowband components to obtain the phase encounter identifier.

[0114] The burst detection module is used to generate burst indicators based on the dynamic slope sequence and phase encounter identifier;

[0115] The safety window determination module is used to determine the safety window of the candidate window set based on the preset approach duration and blast indication, so as to obtain the safety window of the target machine nest platform.

[0116] The job coverage management and prediction module is used to calculate the total safe duration of all safety windows and manage and predict the job coverage capability of the target nesting platform based on the total safe duration.

[0117] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for evaluating the coverage capability of unmanned aerial vehicle (UAV) nest operations, characterized in that, Includes the following steps: S1. Collect the triaxial acceleration of the target nest base; S2. Calculate the dynamic slope sequence of the target aircraft nest platform based on the three-axis acceleration, and generate a candidate window set for the target landing section based on the dynamic slope sequence and the preset safety threshold. S3. Generate two narrowband components of the dynamic slope sequence, and determine the phase consistency of the amplitude threshold and instantaneous rate of change of the narrowband components to obtain a phase encounter indicator; wherein, generating two narrowband components of the dynamic slope sequence includes: performing power spectrum analysis on the dynamic slope sequence to obtain the frequency distribution; performing main peak detection on the frequency distribution to obtain two main peak frequencies; and performing bandpass filtering on the two main peak frequencies to obtain two narrowband components; S4. Generate a burst indicator based on the dynamic slope sequence and phase encounter identifier; S5. Based on the preset approach duration and blast indication, determine the safe window of the candidate window set to obtain the safe window of the target machine nest platform; S6. Calculate the total safe duration of all safe windows, and manage and predict the operational coverage capability of the target nest platform based on the total safe duration.

2. The method for evaluating the coverage capability of UAV nest operations according to claim 1, characterized in that, The dynamic slope sequence of the target aircraft nest platform is calculated based on triaxial acceleration. A candidate window set for the target landing zone is generated based on the dynamic slope sequence and a preset safety threshold, including: Calculate the dynamic slope sequence based on the triaxial acceleration and gravitational acceleration constant; A threshold determination is performed on the dynamic slope sequence and the preset safety threshold to obtain a binary sequence; Continuous interval extraction is performed on the binary sequence to obtain a set of candidate windows for the target landing zone.

3. The method for evaluating the coverage capability of UAV nest operations according to claim 1, characterized in that, Phase consistency is determined by assessing the amplitude threshold and instantaneous rate of change of the narrowband component to obtain a phase encounter indicator, including: The root mean square of the two narrowband components is calculated separately to obtain two amplitude thresholds; By taking the derivatives of the two narrowband components respectively, two instantaneous rates of change are obtained; If the amplitude threshold is greater than the corresponding instantaneous rate of change, and the product of the two instantaneous rates of change is greater than 0, then the phase encounter flag is set to 1; otherwise, the phase encounter flag is set to 0.

4. The method for evaluating the coverage capability of UAV nest operations according to claim 1, characterized in that, A burst indicator is generated based on the dynamic slope sequence and phase encounter identifier, including: If the dynamic slope value in the dynamic slope sequence is greater than the preset safety threshold and the phase encounter flag is set to 1, then the burst indicator is set to 1; otherwise, the burst indicator is set to 0.

5. The method for evaluating the coverage capability of UAV nest operations according to claim 1, characterized in that, Based on the preset approach duration and blitz indicator, a safe window is determined from the candidate window set to obtain the safe window for the target nest platform, including: For each candidate window in the candidate window set, the start time of the candidate window is used as the starting point of the fixed verification time interval, and the difference between the end time of the candidate window and the preset approach time is used as the ending point of the fixed verification time interval. The approach time window is determined based on the preset approach duration, which defines a fixed verification time interval. If all blast indicators are 0 within the approach time window, and the duration of the candidate window is greater than or equal to the preset approach duration, then the candidate window is determined to be a safe window for the target hive platform.

6. The method for evaluating the coverage capability of UAV nest operations according to claim 1, characterized in that, Calculate the total safe duration for all safe windows, including: Calculate the safe duration of the safe window based on its start and end times; The total safe duration is obtained by summing up all the safe durations.

7. The method for evaluating the coverage capability of UAV nest operations according to claim 1, characterized in that, The operational coverage capability of the target hive platform is managed and predicted based on the total safe operating time, including: The ratio of the total safe duration to the total observation duration of the target nest platform is used as the time utilization coefficient of the target nest platform. The effective sortie rate of the target aircraft nest platform is calculated as the product of the nominal sortie rate and the time utilization coefficient. Based on the effective sortie rate, the coverage per sortie of the target aircraft nest platform, and the planned operation duration, calculate the operation coverage index of the target aircraft nest platform; The operational coverage capability of the target hive platform is managed and predicted based on operational coverage indicators.

8. A system for assessing the coverage capability of unmanned aerial vehicle (UAV) nesting operations, characterized in that, The system includes: The data acquisition module is used to collect the three-axis acceleration of the target nest base; The candidate set generation module is used to calculate the dynamic slope sequence of the target aircraft nest platform based on the triaxial acceleration, and generate a candidate window set for the target landing section based on the dynamic slope sequence and the preset safety threshold. The phase encounter identification module is used to generate two narrowband components of the dynamic slope sequence, and to determine the phase consistency of the amplitude threshold and instantaneous rate of change of the narrowband components to obtain a phase encounter identifier. Generating the two narrowband components of the dynamic slope sequence includes: performing power spectrum analysis on the dynamic slope sequence to obtain the frequency distribution; detecting the main peaks in the frequency distribution to obtain two main peak frequencies; and performing bandpass filtering on the two main peak frequencies to obtain two narrowband components. The burst detection module is used to generate burst indicators based on the dynamic slope sequence and phase encounter identifier; The safety window determination module is used to determine the safety window of the candidate window set based on the preset approach duration and blast indication, so as to obtain the safety window of the target machine nest platform. The job coverage management and prediction module is used to calculate the total safe duration of all safety windows and manage and predict the job coverage capability of the target nesting platform based on the total safe duration.

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