Marine ecological restoration effect evaluation system based on unmanned aerial vehicle remote sensing quantification

Abstract

The invention discloses an unmanned aerial vehicle remote sensing quantitative marine ecological restoration effect evaluation system, and relates to the technical field of marine ecological monitoring and evaluation, and the system comprises a rhythm resonance excitation module, a resonance coupling inversion module, an error autophagy verification module, and a resonance steady state adjustment module. According to the invention, a three-in-one framework of a rhythm resonance coupling mechanism, an error autophagy verification mechanism and a rhythm phase latch-cross-scale energy anchoring stator mechanism is utilized, and four core modules of rhythm resonance excitation, resonance coupling inversion, error autophagy verification and resonance steady state adjustment are combined. The method solves the problems of low evaluation precision, no self-verification capability and large errors caused by marine environment interference due to manual sampling recheck and rhythm splitting in the prior art, realizes quantitative evaluation and self-verification of the marine ecological restoration effect without manual sampling, remarkably improves evaluation efficiency, credibility and precision, and is suitable for large-scale popularization and application. And micro-scale power data can be synchronously output, the endurance of the unmanned aerial vehicle is prolonged, and the restoration trend is pre-judged.

Description

Technical Field

[0001] This invention relates to the field of marine ecological monitoring and assessment technology, specifically a marine ecological restoration effect assessment system based on UAV remote sensing. Background Technology

[0002] Marine ecological restoration is a key means of addressing marine pollution and habitat degradation, and its quantitative assessment directly determines the optimization of restoration plans and the determination of effectiveness. Currently, UAV remote sensing, with its advantages of flexibility, efficiency, and large-scale monitoring, has become a core technical means for the quantitative assessment of marine ecological restoration effects, and is widely used in parameter collection and analysis in restoration areas such as coral reefs, seagrass beds, and mangroves.

[0003] However, existing UAV remote sensing quantitative assessment systems suffer from significant technical bottlenecks, making it difficult to meet practical application needs. First, the assessment and verification processes are separated, requiring manual sampling and verification of remote sensing quantitative results. This is not only time-consuming, labor-intensive, and costly, but the inherent dispersion of manual sampling can also lead to discrepancies between remote sensing and field data, severely impacting the reliability of the assessment. Second, current technologies treat the biological metabolic rhythms of marine ecosystems, the transmission attenuation rhythms of UAV remote sensing signals, and the dynamic rhythms of the marine environment as independent variables, failing to recognize their intrinsic connections. Quantification is achieved solely through single-dimensional signal acquisition and model inversion, ignoring the potential impact of rhythmic characteristics on assessment accuracy.

[0004] Furthermore, the industry generally uses rhythmic information only for time correction, failing to explore its core value. This results in a lack of self-verification capability in quantitative assessments, making it impossible to determine the authenticity of results without external standard references. Simultaneously, the complex and variable marine environment makes remote sensing signals susceptible to interference, further exacerbating errors in quantitative assessments. These issues render existing UAV remote sensing quantitative assessment systems inadequate in terms of accuracy, efficiency, and reliability, making it difficult to fully support the scientific implementation and effectiveness assessment of marine ecological restoration work. Therefore, this paper proposes a UAV remote sensing quantitative marine ecological restoration effectiveness assessment system to overcome these problems. Summary of the Invention

[0005] The purpose of this invention is to provide a quantitative evaluation system for marine ecological restoration effects using unmanned aerial vehicle (UAV) remote sensing, in order to solve the problems mentioned in the background art.

[0006] To address the aforementioned technical problems, this invention provides a quantitative marine ecological restoration effect evaluation system based on UAV remote sensing, comprising a rhythmic resonance excitation module, a resonance coupling inversion module, an error autophagy verification module, and a resonance steady-state adjustment module.

[0007] The rhythmic resonance excitation module integrates a rhythmic phase latch-cross-scale energy anchoring mechanism. Through the rhythmic resonance coupling mechanism, the biological metabolic rhythm, the attenuation rhythm of UAV remote sensing signals, and the marine dynamic rhythm form a frequency doubling and phase synchronization relationship. Combined with the error autophagy verification mechanism, an error source tracing-phagocytosis-correction closed loop is constructed. Then, the rhythmic phase latch-cross-scale energy anchoring mechanism fixes the three types of rhythmic phase relationships and anchors the resonance energy, completing the quantitative assessment and self-verification of the marine ecological restoration effect without the need for manual sampling and verification.

[0008] Furthermore, the rhythmic resonance excitation module includes a rhythmic sensing component, a mechanical-signal dual-phase latching unit, and a cross-scale energy anchoring unit. The rhythmic sensing component embeds a phase sensing microchip in the biological rhythm detector, the signal attenuation rhythm sensor, and the dynamic rhythm monitor to collect biological metabolic phase, signal attenuation phase, and dynamic rhythm phase in real time, with the sampling delay controlled within 10μs.

[0009] Furthermore, the mechanical-signal dual-phase latch unit includes a mechanical phase latch structure and a signal phase latch logic. The mechanical phase latch structure consists of a ring slide rail, an elastic damping block, and a phase follower. The slide rail is elastically connected to the UAV body. The damping block is made of silicone. The phase follower is driven by a stepper motor. The stepper motor drives the phase follower to move along the slide rail, and the elastic deformation of the damping block generates a phase compensation torque. In the signal phase latch logic, the UAV projects modulated light signals with wavelengths of 450nm and 660nm. The signals are superimposed and a 1μs phase marker pulse synchronized with the biological metabolic phase is emitted every 10ms. The phase difference is controlled within 0.5°. The biological rhythm detector has a built-in pulse phase recognition circuit. When the phase offset exceeds 1°, pulse phase offset compensation is triggered.

[0010] Furthermore, the cross-scale energy anchoring unit includes an energy scale filter composed of a capacitor array and a threshold comparator. The capacitor array is divided into three groups corresponding to low, medium, and high energy scales. According to the preset energy thresholds of 10-100mJ for coral reef restoration and 5-50mJ for seagrass bed restoration, when the resonant energy exceeds the threshold, the threshold comparator triggers the capacitor array to switch. The signal attenuation coefficient is adjusted by charging and discharging the capacitors to anchor the energy back to the sensitive scale range. The invalid scale energy is converted into the driving energy of the phase follower stepper motor through the energy conversion circuit.

[0011] Furthermore, the resonance coupling inversion module includes a resonance energy extraction unit and a resonance coupling inversion model. The resonance energy extraction unit uses the anchoring energy ratio, resonance peak energy, resonance harmonic order, and resonance attenuation half-life as inversion input parameters. The resonance coupling inversion model constructs a coral coverage inversion formula for coral reef restoration scenarios and a seagrass density inversion formula for seagrass bed restoration scenarios, and automatically calculates the calibration coefficients in the inversion formulas through the energy anchoring conservation equation.

[0012] Furthermore, the error self-phagocytosis verification module performs error source tracing and elimination based on the anchoring resonance conservation equation. The anchoring resonance conservation equation contains resonance conservation constants, which are determined and stored by the initial ecological state of the restoration area. When the difference between the inversion results and the equation exceeds 1%, the error source is located based on the deviation direction of the parameters of the resonance energy contributed by the biological metabolic rhythm, the resonance energy contributed by the signal attenuation rhythm, and the resonance energy contributed by the marine dynamic rhythm. The mechanical-signal dual-phase latching unit and the cross-scale energy anchoring unit are linked to initiate targeted adjustments until the equation difference is ≤1%.

[0013] Furthermore, the resonance steady-state adjustment module includes a resonance drift monitoring unit and a steady-state adjustment unit; the resonance drift monitoring unit monitors the rate of change of the resonance coefficient and the rate of change of the latching coefficient in real time, with the monitoring time interval set to 1 hour. or When resonance drift occurs, the steady-state adjustment unit adjusts the UAV imaging frame rate, modulation optical signal frequency, flight trajectory, phase follower damping block preload, or energy scale filter threshold range according to the cause of the drift to maintain the ultra-stable resonance state.

[0014] Furthermore, the system workflow includes an initialization phase, a resonance excitation and latching phase, a resonance inversion phase, an error self-digestion phase, and a steady-state maintenance phase. In the initialization phase, three types of initial rhythm data are collected and initial parameters are calculated using underwater biodegradable buoys. In the resonance excitation and latching phase, the UAV parameters are adjusted to trigger rhythm synchronization, and dual-phase latching and energy anchoring are initiated. When the resonance coefficient R ≥ 0.85, the latching coefficient L ≤ 0.1, and the anchoring coefficient A ≥ 0.9, the system enters an ultra-stable resonance state. In the steady-state maintenance phase, parameters are recorded every hour and the resonance repair index is calculated.

[0015] Compared with the prior art, the beneficial effects of the present invention are:

[0016] 1. By constructing an endogenous self-verification mechanism through the anchored resonance conservation law, no manual sampling and verification is required. This completely eliminates the deviation between remote sensing data and field data caused by the dispersion of manual sampling, significantly improves the evaluation efficiency, and ensures that the reliability of the results is at an extremely high level.

[0017] 2. By using a three-in-one mechanism of rhythmic resonance coupling, error autophagy verification, and rhythmic phase locking-cross-scale energy anchoring, the coupling degree of biological metabolic rhythms, UAV remote sensing signal attenuation rhythms, and ocean dynamic rhythms is significantly improved, completely solving the problem of insufficient quantitative assessment accuracy caused by rhythm fragmentation in traditional technologies and significantly reducing inversion errors.

[0018] 3. Under ultra-stable resonance conditions, the system can assimilate interference signals in the marine environment into resonance energy, breaking through the limitation that the stronger the interference, the worse the accuracy of traditional technology. Even under complex sea conditions, it can still maintain a high level of assessment accuracy.

[0019] 4. Relying on the anchored resonance conservation system, the system achieves a high level of accuracy in determining the authenticity of evaluation results without external standard references, effectively solving the core pain point of existing technologies lacking self-verification capabilities.

[0020] 5. In the mechanical phase-locking structure, the deformation degree of the elastic damping block is linearly related to the surface microflow velocity. The system can synchronously output microscale dynamic environmental data, providing ecological effect and environmental dynamic dual-dimensional data support for the optimization of remediation schemes.

[0021] 6. During the cross-scale energy anchoring process, the energy of the invalid scale can be converted into the driving energy of the phase follower stepper motor through the energy conversion circuit, which significantly extends the UAV's endurance and achieves synergistic optimization of evaluation function and endurance capability, exceeding the functional boundaries of traditional evaluation systems.

[0022] 7. The phase latching coefficient can directly reflect the ecosystem's ability to adapt to environmental fluctuations, becoming a quantitative indicator of ecological resilience in addition to traditional assessment parameters, and providing a new dimension for the long-term stability assessment of restoration effects.

[0023] 8. By calculating the resonance repair index, the repair process trend can be reflected in real time. That is, a high growth rate indicates efficient repair and a low growth rate indicates inefficient repair. It can accurately locate weak links in the repair and provide a targeted basis for adjusting the repair plan, making up for the shortcomings of existing technologies that can only assess the current effect and cannot predict the trend. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of the marine ecological restoration effect evaluation system based on UAV remote sensing of the present invention. Detailed Implementation

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] Please see Figure 1 The present invention provides a technical solution:

[0027] See Figure 1 The following is an example of an implementation of a UAV remote sensing quantitative marine ecological restoration effect assessment system:

[0028] I. Core System Mechanism:

[0029] The core mechanism of this system is a three-in-one architecture consisting of rhythmic resonance coupling mechanism, error autophagy verification mechanism, and rhythmic phase latch-cross-scale energy anchoring mechanism.

[0030] 1. Rhythmic Resonance Coupling Mechanism: By adjusting the remote sensing parameters of the UAV, the biological metabolic rhythm, the remote sensing signal attenuation rhythm, and the ocean dynamic rhythm are made to form a frequency doubling and phase synchronization relationship, amplifying the effective signal and assimilating the interference signal;

[0031] 2. Error self-devouring verification mechanism: Utilizing the energy conservation relationship generated by rhythmic resonance, an error source tracing-devouring-correction closed loop is constructed, which can complete the verification of evaluation results without the need for external reference;

[0032] 3. Rhythmic Phase Latching-Cross-Scale Energy Anchoring Sub-Mechanism: The phase relationship of three types of rhythms is fixed through a mechanical-signal dual-phase latching structure, and the resonant energy is accurately anchored through energy scale screening, solving the problems of easy drift of resonant state and easy dispersion of energy. The three are deeply integrated to form a complete technical solution.

[0033] II. System Overall Architecture:

[0034] The system comprises four core units: a rhythmic resonance excitation module, a resonance coupling inversion module, an error autophagy verification module, and a resonance steady-state adjustment module. The rhythmic phase latch-cross-scale energy anchoring sub-mechanism is deeply integrated into the rhythmic resonance excitation module, and the other modules are adapted and optimized based on this sub-mechanism.

[0035] (I) Rhythmic Resonance Excitation Module:

[0036] 1. Core design logic:

[0037] The original active resonance only achieved frequency doubling and phase synchronization of the rhythm, but microscale fluctuations in the marine environment can lead to phase shifts and dispersion of resonance energy. This module uses a technical solution of locking the phase with mechanical structures and anchoring the energy with signal logic to physically lock the phase relationship of the three types of rhythms, while anchoring the resonance energy within a scale range sensitive to ecological parameters. This not only enhances resonance stability but also improves the correlation between energy and restoration effects.

[0038] 2. Implementation of key technologies:

[0039] 2.1 Rhythm Sense Component:

[0040] Phase-sensing microchips are embedded in the biorhythm detector, signal attenuation rhythm sensor, and dynamic rhythm monitor to collect the instantaneous phases of the three types of rhythms in real time, which are defined as: biological metabolic phase. Signal attenuation phase Dynamic rhythm phase The sampling delay is controlled within 10μs.

[0041] 2.2 Mechanical-Signal Dual-Phase Latch Unit:

[0042] Mechanical phase latch structure:

[0043] The remote sensing payload mounted on the UAV has an externally fixed phase synchronization adjustment ring. This adjustment ring consists of a ring slide rail, an elastic damping block, and a phase follower. The slide rail is elastically connected to the UAV body, the damping block is filled with silicone material, and the phase follower is driven by a common civilian stepper motor.

[0044] Working process: The dynamic rhythm monitor outputs data in real time. Stepper motor according to The change in the quantity drives the phase follower to move along the slide rail, generating a phase compensation torque through the elastic deformation of the damping block, physically offsetting the phase shift caused by the surge. For example, when When the signal suddenly advances by 5°, the follower moves 2mm to the left, and the deformation of the damping block generates a reverse torque, which finely adjusts the signal transmission direction of the remote sensing payload by 0.5° to ensure... and The phase difference stabilizes at the preset value;

[0045] This structure achieves phase locking through mechanical deformation, while the deformation process of the damping block simultaneously absorbs some of the environmental vibration interference, thus realizing the dual functions of phase locking and vibration absorption.

[0046] Signal phase latch logic:

[0047] The modulated light signal projected by the drone has wavelengths of 450nm and 660nm. A phase-marked pulse is superimposed on this signal. The pulse parameters are set as follows: one 1μs narrow pulse is emitted every 10ms. The pulse phase is correlated with the signals collected by the biorhythm detector. Strict synchronization, with phase difference controlled within 0.5°;

[0048] The biological rhythm detector has a built-in pulse phase recognition circuit, which is constructed from ordinary logic gate chips. When the offset exceeds 1°, the identification circuit immediately triggers pulse phase offset compensation of the modulated optical signal, so that... It maintains a constant phase latch with the light signal, thus solving the problem that biological rhythms are easily affected by light fluctuations.

[0049] 2.3. Cross-scale energy anchoring unit:

[0050] Resonant energy needs to be concentrated within the ecological parameter sensitive scale range. The energy scale corresponding to coral coverage is 10-100 mJ, and the energy scale corresponding to seagrass density is 5-50 mJ. Energy should be avoided from being dispersed to ineffective scales: <5 mJ or >100 mJ. An energy scale filter is embedded in the signal attenuation rhythm sensor. This filter consists of a capacitor array and a threshold comparator.

[0051] The capacitor array is divided into three groups, corresponding to low, medium and high energy scales respectively. The energy threshold is preset according to the repair scenario, such as the threshold for coral reef scenarios is set to 10-100mJ. When the resonant energy exceeds the preset scale, the threshold comparator immediately triggers the capacitor array to switch, and adjusts the signal attenuation coefficient by charging and discharging the capacitors to anchor the energy back to the sensitive scale range.

[0052] For example, during seagrass bed restoration, if the dynamic rhythm fluctuation causes the resonant energy to suddenly rise to 60mJ, that is, exceeding the 5-50mJ threshold, the filter automatically switches to the low capacitance group to increase the signal attenuation, so that the energy is reduced to 45mJ and anchored at the sensitive scale.

[0053] During the energy anchoring process, the energy of the invalid scale is converted into driving energy of the phase latch structure through the energy conversion circuit, which powers the stepper motor and realizes the coordinated operation of energy recovery and phase latch.

[0054] 2.4 Criteria for determining resonance state:

[0055] To quantify the stability and effectiveness of the resonance state, three core decision parameters are defined: resonance coefficient R, latching coefficient L, and anchoring coefficient A. ：

[0056] Resonance coefficient R ： The degree of coordination among the three types of rhythms is reflected by a cross-correlation function, calculated using the following formula:

[0057]

[0058] Where T is the sampling duration, These represent the biological metabolic phase, signal decay phase, and dynamic rhythm phase at time t, respectively.

[0059] Latch coefficient L ： The effectiveness of phase latching is reflected by the ratio of the actual phase difference to the preset phase difference, as shown in the following formula:

[0060]

[0061] in, To monitor the phase difference between two types of rhythms in real time, This is a preset phase difference threshold;

[0062] Anchoring coefficient A： The effectiveness of energy anchoring is reflected in the ratio of sensitive scale energy to total resonant energy, as shown in the following formula:

[0063]

[0064] Among them, E sen E represents the resonance energy within the sensitive scale range. i The resonant energies are defined in three energy scale ranges, where i = 1, 2, and 3 correspond to low, medium, and high energy scales, respectively.

[0065] When the three conditions of R≥0.85, L≤0.1, and A≥0.9 are met, the system is determined to have reached a super-stable resonance state. At this time, the effective signal strength is improved, the assimilation efficiency of the interference signal is improved, and the problem of environmental interference exacerbating the error is completely solved.

[0066] (II) Resonance Coupled Inversion Module:

[0067] 1. Resonance Energy Extraction Unit:

[0068] Based on a cross-scale energy anchoring technique, the resonance energy parameter adds an anchored energy percentage A, which, along with the original resonance main peak energy E, is used to... res The resonance harmonic order n and the resonance decay half-life τ together constitute the inversion input parameters. The quantitative inversion of the repair effect parameters is achieved by establishing an energy-effect mapping model.

[0069] 2. Resonance Coupled Inversion Model:

[0070] To clarify the relationships between the parameters in the model, inversion formulas were established for two typical restoration scenarios: coral reefs and seagrass beds.

[0071] Coral reef restoration scenario: The core assessment parameter is coral coverage C, and the inversion formula is as follows:

[0072] C = a·E res ·A+b·n;

[0073] Where a and b are resonance calibration coefficients, which are automatically calculated based on the energy anchoring conservation relationship at resonance steady state;

[0074] Seagrass bed restoration scenario: The core evaluation parameter is seagrass density D, and the inversion formula is as follows:

[0075]

[0076] Where c is the density calibration coefficient, which is also automatically calculated through the energy anchoring conservation relationship;

[0077] To achieve automatic acquisition of calibration coefficients, the energy anchoring conservation equation is defined as follows:

[0078] E res A = E bio ·k1+E atten ·k2+E dyn ·k3;

[0079] Among them, E bio The resonant energy contributed to biological metabolic rhythms, E atten The resonant energy E that contributes to the signal decay rhythm dyn The resonant energy contributed to the ocean dynamic rhythm; k1, k2, and k3 are anchoring weight coefficients, the values ​​of which are determined by the latching coefficient L, satisfying... That is, the smaller L is, the larger the weighting coefficient is, ensuring better phase latching and more accurate energy allocation. The values ​​of a, b, and c can be directly calculated using this equation without manual calibration.

[0080] (III) Error Autophagy Verification Module:

[0081] 1. Error traceability unit:

[0082] Based on the rhythmic phase latch-cross-scale energy anchor mechanism, the original resonance conservation law is upgraded to the anchored resonance conservation law. Error sources are traced through the degree to which this law is satisfied. The anchored resonance conservation equation is as follows:

[0083]

[0084] Wherein, K is the resonance conservation constant, which is determined by the initial ecological state of the restoration area. During system initialization, data is collected and calculated and stored through the rhythm sensing component.

[0085] When the inversion result is substituted into the equation, if the difference between the left and right sides of the equation exceeds 1%, an error is determined to exist. The source of the error is precisely located by the deviation direction of the parameters.

[0086] If E res If the value of A is too high and the value of n is too low, the error originates from a biological rhythm perception bias.

[0087] If the value of τ is too short and E res If the value of A is normal, then the error originates from the prediction deviation of the dynamic rhythm.

[0088] If E res If the value of A is normal but the values ​​of n and τ are abnormal, then the error originates from the deviation in the acquisition of the signal attenuation rhythm.

[0089] 2. Error swallowing unit:

[0090] Based on the error source tracing results, a targeted error adjustment process is initiated. During the adjustment process, the mechanical-signal dual-phase latch unit and the cross-scale energy anchoring unit are synchronously linked to ensure that the resonance state does not fail.

[0091] When the error originates from the biological rhythm perception deviation, the intensity of the modulated light signal is reduced, and the flight altitude of the UAV is adjusted to compensate for the energy contribution of the signal attenuation rhythm. The capacitor bank of the energy scale filter is switched synchronously to ensure that the anchoring coefficient A is maintained above 0.9.

[0092] When the error originates from the deviation in dynamic rhythm prediction, the UAV flight trajectory is adjusted to reduce the impact of dynamic rhythm fluctuations. At the same time, the preload of the damping block of the phase synchronization adjustment ring is adjusted to ensure that the latching coefficient L is kept below 0.1.

[0093] After adjustment, the anchoring resonance conservation equation is re-substituted for verification until the difference between the left and right sides of the equation is ≤1%, thus completing the error elimination.

[0094] (iv) Resonance Steady-State Regulation Module:

[0095] 1. Resonance drift monitoring unit:

[0096] Real-time monitoring of the rate of change of the resonance coefficient R Rate of change of latch coefficient L Where Δt is the monitoring time interval, set to 1 hour, when the condition is met... or When this occurs, the system is determined to have experienced resonance drift.

[0097] 2. Steady-state regulation unit:

[0098] When the fundamental frequency of biological rhythm increases, causing L to increase, the imaging frame rate of the UAV and the frequency of the modulated light signal are increased to keep the signal phase synchronized with the biological metabolic phase. At the same time, the damping block preload of the phase follower is adjusted by a micro electromagnetic valve to increase the phase compensation torque of the mechanical latch, ensuring that L is kept below 0.1.

[0099] When dynamic rhythm fluctuations cause A to decrease, adjust the drone's flight trajectory to reduce the amplitude of dynamic rhythm fluctuations, and at the same time switch the threshold range of the energy scale filter to ensure that A remains above 0.9.

[0100] - When the resonance coefficient R decreases, readjust the UAV remote sensing parameters: flight altitude, imaging frame rate, and band combination to restore the frequency harmonic relationship of the three types of rhythms and ensure that R is maintained above 0.85.

[0101] III. System Workflow:

[0102] 1. Initialization phase:

[0103] After the drone is deployed over the restoration area, it releases underwater biodegradable buoys. The rhythm sensing components on the buoys collect the initial fundamental frequency and phase data of three types of rhythms, and calculate the initial resonance conservation constant K and the resonance reference value (E).res0 (n0, τ0), and simultaneously calibrate the initial position of the phase follower: make and The initial phase difference is 0°, and the threshold range of the energy scale filter is set.

[0104] 2. Resonance Excitation and Latching Stage:

[0105] Step 1: Adjust the UAV remote sensing parameters (flight altitude 50-100m, imaging frame rate 10-20fps, band combination 450nm / 660nm) and modulated light signal intensity (100-200lux) to trigger the frequency doubling relationship of three types of rhythms (such as dynamic rhythm frequency 0.1Hz, signal attenuation rhythm frequency 0.2Hz, biological metabolic rhythm frequency 0.3Hz) and phase synchronization;

[0106] Step 2: Activate the mechanical-signal dual-phase latch unit; the stepper motor then... Real-time data drives the phase follower to move, and the pulse phase recognition circuit provides synchronous compensation. The offset makes the latch coefficient L ≤ 0.1;

[0107] Step 3: Activate the cross-scale energy anchoring unit. The energy scale filter switches the capacitor bank according to the real-time value of the resonant energy to make the anchoring coefficient A≥0.9.

[0108] Step 4: The three parameters R, L, and A are collected in real time through the rhythm sensing component. When the three parameters simultaneously satisfy R≥0.85, L≤0.1, and A≥0.9, the system enters the ultra-stable resonance state and executes the next stage of the process.

[0109] 3. Resonance Inversion Stage:

[0110] The rhythm sensing component continuously collects E res The system generates four parameters: , n, τ, and A. A set of valid data is output every 5 minutes. These are then substituted into the inversion formula corresponding to the repair scenario.

[0111] The coral reef scene is C = a·E res ·A+b·n,

[0112] The seaweed bed scene is Preliminary assessment results of the repair effect were obtained through calculation.

[0113] 4. Error self-devouring stage:

[0114] Substitute the parameters corresponding to the preliminary assessment results into the anchoring resonance conservation equation:

[0115]

[0116] Calculate the difference between the left and right sides of the equation:

[0117] If the difference is ≤1%, the evaluation result is considered valid and output directly;

[0118] If the difference is greater than 1%, the error is traced back to its source based on the direction of parameter deviation, and the error absorbing process is initiated. After adjustment, the evaluation result is recalculated and substituted into the equation again for verification until the difference is less than or equal to 1%.

[0119] 5. Steady-state maintenance phase:

[0120] Real-time monitoring of the rates of change of R, L, and A; when resonance drift occurs, the corresponding steady-state adjustment process is initiated to maintain the ultra-stable resonance state; simultaneously, E is recorded hourly. res The resonance restoration index RI is calculated based on the numerical changes of , n, τ, and A, using the following formula:

[0121]

[0122] Among them, E res,t E is the resonance peak energy at time t. res,0 n is the initial resonance peak energy. t Let τ be the resonance harmonic order at time t. t Let A be the resonance decay half-life at time t. t RI is the anchoring coefficient at time t; when RI>0.3, the repair effect is considered significant, and the resonance repair index and core evaluation parameters are output simultaneously to provide data support for the optimization of the repair scheme.

[0123] IV. Summary:

[0124] Integrated evaluation and verification: Endogenous self-verification is achieved by anchoring the resonance conservation law, eliminating the need for manual sampling and verification, significantly improving evaluation efficiency, completely eliminating discrete bias, and ensuring extremely high reliability of results;

[0125] Full utilization of rhythm correlation: The three-in-one mechanism greatly improves the coupling degree of biological metabolic rhythm, signal attenuation rhythm and ocean dynamic rhythm, significantly reduces inversion error, and completely solves the problem of insufficient accuracy caused by rhythm fragmentation;

[0126] Environmental interference resource utilization: Under ultra-stable resonance state, the interference signal is assimilated into resonance energy. Even under complex sea conditions, the assessment accuracy can still maintain a high level, breaking through the limitation of traditional technology where the stronger the interference, the worse the accuracy.

[0127] Enhanced self-verification capability: The anchored resonance conservation system ensures a high level of accuracy in determining the authenticity of results when there are no external references, thus addressing the core pain point of lacking self-verification capability.

[0128] Synchronous monitoring of microscale dynamic environment: The deformation degree of the damping block in the mechanical phase-locking structure is linearly related to the surface microflow velocity. The system can synchronously output microscale dynamic environment data, providing ecological effect and environmental dynamic dual-dimensional support for the optimization of remediation schemes. Existing technologies have not achieved this function.

[0129] Drone endurance synergistic enhancement: During the cross-scale energy anchoring process, ineffective energy is converted into driving energy for the phase follower stepper motor, which significantly extends the drone's endurance time, achieving synergistic optimization of assessment functions and endurance capabilities, exceeding the functional boundaries of traditional assessment systems;

[0130] Ecological resilience quantitative assessment: The phase latching coefficient L can directly reflect the ecosystem's ability to adapt to environmental fluctuations: when L is at a low level, the ecological resilience is strong; when L is at a medium level, the ecological resilience is moderate. It has become a quantitative indicator of ecological resilience in addition to traditional assessment parameters, providing a new dimension for the long-term stability assessment of restoration effects.

[0131] Targeted optimization of repair schemes: The Resonance Repair Index (RI) can reflect the repair process trend in real time: a high RI growth rate indicates efficient repair, while a low RI growth rate indicates inefficient repair. It can accurately locate weak links in the repair process and provide a targeted basis for scheme adjustment. Existing technologies can only assess the current effect and cannot predict the trend.

Claims

1. A quantitative evaluation system for marine ecological restoration effects using unmanned aerial vehicle (UAV) remote sensing, characterized in that: It includes a rhythmic resonance excitation module, a resonance coupling inversion module, an error autophagy verification module, and a resonance steady-state adjustment module. The rhythmic resonance excitation module integrates a rhythmic phase latch-cross-scale energy anchoring mechanism. Through the rhythmic resonance coupling mechanism, the biological metabolic rhythm, the attenuation rhythm of UAV remote sensing signals, and the marine dynamic rhythm form a frequency doubling and phase synchronization relationship. Combined with the error autophagy verification mechanism, an error source tracing-phagocytosis-correction closed loop is constructed. Then, the rhythmic phase latch-cross-scale energy anchoring mechanism fixes the three types of rhythmic phase relationships and anchors the resonance energy, completing the quantitative assessment and self-verification of the marine ecological restoration effect without the need for manual sampling and verification.

2. The UAV remote sensing quantitative marine ecological restoration effect evaluation system as described in claim 1, characterized in that: The rhythmic resonance excitation module includes a rhythmic sensing component, a mechanical-signal dual-phase latching unit, and a cross-scale energy anchoring unit. The rhythmic sensing component embeds a phase sensing microchip in the biological rhythm detector, the signal attenuation rhythm sensor, and the dynamic rhythm monitor to collect biological metabolic phase, signal attenuation phase, and dynamic rhythm phase in real time, with the sampling delay controlled within 10μs.

3. The UAV remote sensing quantitative marine ecological restoration effect evaluation system as described in claim 2, characterized in that: The mechanical-signal dual-phase latch unit includes a mechanical phase latch structure and a signal phase latch logic. The mechanical phase latch structure consists of a ring slide rail, an elastic damping block, and a phase follower. The slide rail is elastically connected to the UAV body. The damping block is made of silicone. The phase follower is driven by a stepper motor. The stepper motor drives the phase follower to move along the slide rail, and the elastic deformation of the damping block generates a phase compensation torque. In the signal phase latch logic, the UAV projects modulated light signals with wavelengths of 450nm and 660nm. The signals are superimposed and a 1μs phase marker pulse synchronized with the biological metabolic phase is emitted every 10ms. The phase difference is controlled within 0.5°. The biological rhythm detector has a built-in pulse phase recognition circuit. When the phase offset exceeds 1°, pulse phase offset compensation is triggered.

4. The UAV remote sensing quantitative marine ecological restoration effect evaluation system as described in claim 2, characterized in that: The cross-scale energy anchoring unit includes an energy scale filter composed of a capacitor array and a threshold comparator. The capacitor array is divided into three groups corresponding to low, medium and high energy scales. Based on the preset energy thresholds of 10-100mJ for coral reef restoration and 5-50mJ for seagrass bed restoration, when the resonant energy exceeds the threshold, the threshold comparator triggers the capacitor array to switch. The signal attenuation coefficient is adjusted by the charging and discharging of the capacitors to anchor the energy back to the sensitive scale range. The invalid scale energy is converted into the driving energy of the phase follower stepper motor through the energy conversion circuit.

5. The UAV remote sensing quantitative marine ecological restoration effect evaluation system as described in claim 1, characterized in that: The resonance coupling inversion module includes a resonance energy extraction unit and a resonance coupling inversion model; the resonance energy extraction unit uses anchor energy ratio, resonance peak energy, resonance harmonic order, and resonance decay half-life as inversion input parameters. The resonance coupling inversion model constructs a coral coverage inversion formula for coral reef restoration scenarios and a seagrass density inversion formula for seagrass bed restoration scenarios. The calibration coefficients in the inversion formulas are automatically calculated through the energy anchoring conservation equation.

6. The UAV remote sensing quantitative marine ecological restoration effect evaluation system as described in claim 1, characterized in that: The error self-phagocytosis verification module performs error source tracing and self-phagocytosis based on the anchoring resonance conservation equation. The anchoring resonance conservation equation contains resonance conservation constants, which are determined and stored by the initial ecological state of the restoration area. When the difference between the inversion results and the equation exceeds 1%, the error source is located based on the deviation direction of the parameters of the resonance energy contributed by the biological metabolic rhythm, the resonance energy contributed by the signal attenuation rhythm, and the resonance energy contributed by the marine dynamic rhythm. The module then initiates targeted adjustments in conjunction with the mechanical-signal dual-phase latching unit and the cross-scale energy anchoring unit until the equation difference is ≤1%.

7. The UAV remote sensing quantitative marine ecological restoration effect evaluation system as described in claim 1, characterized in that: The resonance steady-state adjustment module includes a resonance drift monitoring unit and a steady-state adjustment unit; the resonance drift monitoring unit monitors the rate of change of the resonance coefficient and the rate of change of the latch coefficient in real time, with the monitoring time interval set to 1 hour. or When resonance drift occurs, the steady-state adjustment unit adjusts the UAV imaging frame rate, modulation optical signal frequency, flight trajectory, phase follower damping block preload, or energy scale filter threshold range according to the cause of the drift to maintain the ultra-stable resonance state.

8. The UAV remote sensing quantitative marine ecological restoration effect evaluation system as described in claim 1, characterized in that: The system workflow includes an initialization phase, a resonance excitation and latching phase, a resonance inversion phase, an error self-digestion phase, and a steady-state maintenance phase. In the initialization phase, three types of initial rhythm data are collected and initial parameters are calculated using underwater biodegradable buoys. In the resonance excitation and latching phase, the UAV parameters are adjusted to trigger rhythm synchronization, and dual-phase latching and energy anchoring are initiated. When the resonance coefficient R ≥ 0.85, the latching coefficient L ≤ 0.1, and the anchoring coefficient A ≥ 0.9, the system enters a super-stable resonance state. In the steady-state maintenance phase, parameters are recorded every hour and the resonance repair index is calculated.

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