Ultra-large bulk carrier anchoring state evaluation system based on intelligent monitoring

Abstract

The invention relates to the technical field of ship equipment monitoring, and discloses an ultra-large bulk carrier anchoring state evaluation system based on intelligent monitoring, which comprises an integrated sensing hawse pipe module, a perturbation active exciter module and a data acquisition and processing unit module in which a dynamic reference calibration module and a fusion diagnosis decision module are operated. According to the system, firstly, an exciter applies excitation and generates a dynamic reference spectrum changing along with the current working condition through an active calibration mode; and then in a passive monitoring mode, a fusion diagnosis decision module carries out quantitative comparison and cooperative logic judgment on the passively acquired multi-dimensional sensing signal characteristics and the reference spectrum so as to evaluate the anchoring state. Through combination of active calibration and passive monitoring and fusion of high-frequency, low-frequency, quasi-static and other multi-source information for collaborative decision making, high-reliability precursor early warning of the'dragging 'risk is achieved, the method has the capability of accurately positioning structural damage of anchoring equipment, and the safety of anchoring operation is remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of ship equipment monitoring, in particular to an ultra-large bulk carrier anchoring state evaluation system based on intelligent monitoring. BACKGROUND

[0002] Anchoring operation is a key link to ensure the safety of ultra-large bulk carriers when they are loading and unloading, waiting or avoiding danger in the port, anchorage or offshore waters. Due to the huge tonnage of such ships, they are significantly affected by environmental loads such as wind, wave and current. Once the anchor is moved, it may not only cause major maritime accidents such as ship grounding and collision, resulting in huge economic losses, but also may cause serious environmental pollution. Therefore, it is crucial to monitor the anchoring state in real time and reliably.

[0003] Currently, the anchoring state monitoring method commonly used on ships mainly relies on the anchor position monitoring function of the global positioning system (GPS). This method sets an electronic fence with the anchor throwing point as the center, and triggers an alarm when the ship's position exceeds this range. However, the essence of this method is a post-alarm mechanism, and the alarm trigger often lags behind the initial movement of the anchoring state, that is, it can only be detected after the ship has moved significantly, and cannot play an effective early warning role.

[0004] In addition, the judgment method based only on position information has inherent technical limitations. It is difficult to effectively distinguish between the ship's position swing within the safe range caused by normal wind and wave, and the dangerous slow displacement caused by the gradual failure of the anchor's holding force, which leads to a high false alarm rate or missed report risk. At the same time, the evaluation criteria of these methods are usually fixed and cannot be adaptively adjusted according to the dynamic changes of the specific working conditions when the anchor is thrown, such as the length of the chain, water depth, seabed bottom, and real-time sea conditions, which further reduces its reliability in complex and variable marine environments.

[0005] More importantly, existing monitoring technologies almost completely ignore the monitoring of the structural health state of the anchoring system itself. Key load-bearing components such as the anchor chain cylinder and chain stopper will bear huge impact loads and alternating stresses during long-term service, and there is a risk of fatigue crack initiation and propagation. These structural damages are the blind spot of existing monitoring methods, and once a sudden rupture occurs, it will directly lead to disastrous consequences. Therefore, there is an urgent need in the art for an intelligent anchoring state evaluation technology for ultra-large bulk carriers that can overcome the above-mentioned defects, realize the transition from post-alarm to early warning, and take into account both the functional state and the structural health. SUMMARY

[0006] In view of the deficiencies of the prior art, the present application provides an ultra-large bulk carrier anchoring state evaluation system based on intelligent monitoring, which solves the problems of the prior art that the anchoring state monitoring technology of the existing ultra-large bulk carrier can only perform post-alarm, the single monitoring parameter leads to insufficient reliability, and cannot adapt to variable working conditions, and cannot realize effective precursor warning of the "anchor walking" risk and accurate positioning of the anchoring equipment structure damage.

[0007] To achieve the above object, the present application is implemented by the following technical solutions: an ultra-large bulk carrier anchoring state evaluation system based on intelligent monitoring, comprising:

[0008] An integrated sensing hawse pipe module is physically integrated on the hawse pipe structure of the ship, and is used for real-time acquisition of dynamic response signals of the anchoring system;

[0009] A perturbation active exciter module is mechanically coupled with the anchor chain, and is used for applying a mechanical excitation with a known characteristic to the anchor chain;

[0010] A data acquisition and processing unit module is connected with the integrated sensing hawse pipe module and the perturbation active exciter module;

[0011] The data acquisition and processing unit module is configured to run:

[0012] A dynamics reference calibration module is used for controlling the perturbation active exciter module to apply a mechanical excitation, and generating a dynamics reference spectrum representing the current working condition of the anchoring system based on the excitation response signals collected by the integrated sensing hawse pipe module;

[0013] A fusion diagnosis decision module is used for, when there is no active excitation, quantitatively comparing and logically judging the real-time characteristics of the passive response signals collected by the integrated sensing hawse pipe module under the passive environmental load with the dynamics reference spectrum, to output an evaluation result.

[0014] In a possible implementation, the integrated sensing hawse pipe module internally integrates an array of fiber Bragg grating sensors for collecting low-frequency vibration and quasi-static strain signals, and an array of piezoelectric acoustic emission sensors for collecting high-frequency stress wave signals.

[0015] In a possible implementation, the array of piezoelectric acoustic emission sensors is composed of at least four sensors, and the at least four sensors are arranged in a spatial geometric array with known three-dimensional coordinates on the hawse pipe, to provide geometric conditions for subsequent acoustic source positioning.

[0016] In a possible implementation, the perturbation active exciter module comprises an electromagnetic driving unit and an impact unit; the electromagnetic driving unit is configured to receive control instructions from the data acquisition and processing unit module and drive the impact unit to exert a transient mechanical impact force on the anchor chain.

[0017] In a possible implementation, the data acquisition and processing unit module comprises a fiber Bragg grating demodulator, a multi-channel acoustic emission signal acquisition card and a synchronous control unit; the synchronous control unit is configured to provide a unified clock reference signal for the fiber Bragg grating demodulator and the multi-channel acoustic emission signal acquisition card, so as to ensure accurate synchronization in time of multi-source heterogeneous data acquisition.

[0018] In a possible implementation, the dynamic reference calibration module determines the dynamic transfer function of the mooring system by calculating the cross-power spectral density between the excitation signal and the excitation response signal and the self-power spectral density of the excitation signal, and takes the dynamic transfer function as the dynamic reference spectrum. The calculation process is specifically as follows:

[0019] First, the cross-power spectral density S xy (f) between the excitation signal x(t) and the response signal y(t) is calculated. xx Then, the dynamic transfer function H(f) is estimated as:

[0020]

[0021] Wherein:

[0022] H(f) is the dynamic transfer function of the mooring system; S xy (f) is the calculated cross-power spectral density; S xx (f) is the calculated self-power spectral density.

[0023] In a possible implementation, the fusion diagnosis decision module is configured to execute the “walk-off-anchor” precursor cooperative early warning logic, and the judgment conditions of the logic include combined judgment on the following three independent conditions: condition C1, high-frequency microscopic anomaly: when the root mean square value V RMS (t) of the high-frequency signal collected by the piezoelectric acoustic emission sensor array continuously exceeds a dynamic threshold, the condition is met.

[0024] Condition C2, quasi-static trend anomaly: when the time first-order derivative of the quasi-static strain ∈ qs (t) collected by the fiber Bragg grating sensor array continuously exceeds a preset strain growth rate threshold δ ∈ , the condition is met.

[0025] Condition C3, system modal shift: the condition is satisfied when a set of main resonance peak positions of a passive vibration power spectrum collected by the fiber Bragg grating sensor array is significantly shifted from a set of resonance peak positions of the dynamic benchmark spectrum.

[0026] The early warning logic is specifically: when condition C1 is satisfied, and at least one of condition C2 or condition C3 is satisfied, a "walk anchor" precursor warning signal is generated.

[0027] In a possible implementation, the fusion diagnosis decision module is further configured to execute structural damage positioning logic, which determines the position coordinates (x s ,y s ,z s ) of the damage source by calculating the time difference of arrival of burst signals received by each sensor in the piezoelectric acoustic emission sensor array and solving a nonlinear equation set composed of multiple hyperbolic equations based on the spatial geometry array.

[0028] The application provides an ultra-large bulk carrier anchoring state evaluation system based on intelligent monitoring.

[0029] Has the following beneficial effects:

[0030] 1、The present application realizes the precursor warning of "walk anchor" risk through the collaborative analysis of multi-source sensing signals by the fusion diagnosis decision module. Specifically, the system uses the piezoelectric acoustic emission sensor array to capture high-frequency stress wave signals generated when the friction state between the anchor chain and the seabed changes, and uses the fiber Bragg grating sensor array to monitor the quasi-static strain growth trend of the anchor chain tension. By logically combining these two characteristics that reflect micro-instability and macro-stress changes respectively, the present application can identify the instability precursor before the anchor system undergoes significant macro-displacement, thereby transforming the traditional "post-event warning" into "pre-event warning", providing a valuable time window for the crew to take preventive measures.

[0031] 2、The present application significantly improves the reliability of the evaluation results by introducing a dual-mode working mechanism combining active calibration and passive monitoring. The system uses the perturbation active exciter module and the dynamic benchmark calibration module to generate a unique dynamic benchmark spectrum for the current specific working condition (including chain length, water depth, seabed bottom, etc.) after each anchoring. In subsequent passive monitoring, the fusion diagnosis decision module compares the real-time signal with this dynamically updated benchmark, rather than relying on fixed and universal thresholds. This adaptive benchmark establishment method effectively overcomes the evaluation standard mismatch problem caused by changes in working conditions, thereby significantly reducing the false alarm rate and improving the accuracy of evaluation.

[0032] 3、The application expands the monitoring dimension of the mooring system, and realizes the leap from the functional state evaluation to the structural health evaluation. With the aid of the piezoelectric acoustic emission sensor array arranged in the spatial geometric array, the system can not only perceive the high-frequency vibration, but also process the burst stress wave signals generated by the structural damage events such as fatigue crack propagation through the structural damage positioning logic. The logic is based on the time difference of signal arrival at different sensors, and accurately calculates the three-dimensional physical coordinates of the damage source, so as to realize the damage positioning of the key load-bearing components such as the anchor barrel, and provide direct decision basis for equipment maintenance and safety management. BRIEF DESCRIPTION OF DRAWINGS

[0033] Figure 1 The system framework diagram of the application;

[0034] Figure 2 The hardware structure implementation diagram of the application;

[0035] Figure 3 The core algorithm flow implementation diagram of the application;

[0036] Figure 4 The state evaluation and output mechanism implementation diagram of the application.

[0037] Among them, 100, integrated sensing anchor barrel module; 200, perturbation active exciter module; 300, data acquisition and processing unit module; 400, dynamic reference calibration module; 500, fusion diagnosis decision module. DETAILED DESCRIPTION

[0038] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the specification of the application. Obviously, the described embodiments are only part of the embodiments of the application, not all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the application.

[0039] Referring to the drawings Figure 1 , Figure 1 The overall structure diagram of the mooring state evaluation system of the super large bulk carrier based on intelligent monitoring according to an embodiment of the application is shown in FIG. 1. The system constructs a technical architecture working cooperatively by the physical hardware layer and the functional algorithm layer, which is used to realize the intelligent evaluation of the mooring system state.

[0040] The physical hardware layer of the system constitutes the basis of data acquisition, signal excitation and local processing. The physical hardware layer specifically includes the integrated sensing anchor barrel module 100 and the perturbation active exciter module 200 deployed at the field end of the mooring equipment, and the data acquisition and processing unit module 300 as the local computing core.

[0041] The integrated sensing hawse pipe module 100, as the core sensing component of the system, is physically integrated on the hawse pipe structure of the ship, and is used for acquiring dynamic response signals of the mooring system under various loads (including active excitation loads and passive environmental loads) in real time. The integrated sensing hawse pipe module 100 sends the original sensing data collected by it to the data acquisition and processing unit module 300 through an optical fiber or an electrical signal link.

[0042] The perturbation active exciter module 200, as the signal excitation execution component of the system, is physically installed near the stopper or the anchor engine base in the hawse pipe, and is mechanically coupled with the hawse pipe. The perturbation active exciter module 200 is used for receiving control instructions from the data acquisition and processing unit module 300, and applying a mechanical excitation with known time domain and frequency domain characteristics to the hawse pipe according to the instructions.

[0043] The data acquisition and processing unit module 300 is the hub connecting the physical hardware layer and the functional algorithm layer. The unit module 300 is connected with the integrated sensing hawse pipe module 100 through a data interface to receive sensing data, and is connected with the perturbation active exciter module 200 through a control interface to send excitation instructions. The data acquisition and processing unit module 300 internally integrates a processor, a memory, and an analog-digital conversion circuit and an optical-electric demodulation circuit for data acquisition.

[0044] The functional algorithm layer of the system is the logical core of the evaluation method of the application. The functional algorithm layer specifically includes a dynamic benchmark calibration module 400 and a fusion diagnosis decision module 500. In a specific embodiment, the dynamic benchmark calibration module 400 and the fusion diagnosis decision module 500 are stored in the memory of the data acquisition and processing unit module 300 as a set of software instruction sets (i.e., program codes), and are loaded and executed by the internal processor. In another embodiment, the two modules can also be hardware logic circuits fixed in a programmable logic device (such as an FPGA), and are also integrated in the internal data acquisition and processing unit module 300.

[0045] The overall information flow and working principle of the system embodies the dual-mode cooperative mechanism of active calibration and passive monitoring.

[0046] In the active calibration mode (i.e. the system calibration stage), the information flow path is as follows: the processor in the data acquisition and processing unit module 300 executes the logic of the dynamic reference calibration module 400, generates the excitation instruction and sends it to the perturbation active exciter module 200. The perturbation active exciter module 200 applies the excitation. The integrated sensing anchor chain cylinder module 100 collects the response signal corresponding to the excitation and transmits the signal to the data acquisition and processing unit module 300. The data acquisition and processing unit module 300 provides the response signal to the dynamic reference calibration module 400 together with the parameters of the excitation signal. The dynamic reference calibration module 400 processes the above-mentioned signals, generates a dynamic reference spectrum representing the current working condition of the system, and stores the reference spectrum in the memory of the data acquisition and processing unit module 300 for subsequent calling.

[0047] In the passive monitoring mode (i.e. the system monitoring stage), the information flow path is as follows: the perturbation active exciter module 200 is in an inactive state. The integrated sensing anchor chain cylinder module 100 continuously collects passive response signals caused by environmental loads such as wind, waves, and flow, and transmits the signals to the data acquisition and processing unit module 300 in real time.

[0048] In the fusion diagnosis stage, the data acquisition and processing unit module 300 imports the real-time passive response signal into the fusion diagnosis decision module 500. At the same time, the fusion diagnosis decision module 500 retrieves the dynamic reference spectrum generated by the dynamic reference calibration module 400 from the memory of the data acquisition and processing unit module 300. The fusion diagnosis decision module 500 compares and judges the real-time characteristics (such as high-frequency stick-slip vibration characteristics and low-frequency modal characteristics) of the passive response signal with the dynamic reference spectrum through its internal collaborative decision algorithm, and finally outputs the evaluation results representing the "anchor walking" risk or structural damage state of the mooring system.

[0049] Referring to the accompanying drawings Figure 2 , Figure 2 is a structural schematic diagram of a system hardware module according to an embodiment of the present application. The system hardware module described in the present application is a functional integration and expansion of the existing mooring equipment of a ship, to form a physical basis for data acquisition and signal excitation required to implement the evaluation method described in the present application.

[0050] The integrated sensing mooring hawse pipe module 100 is a composite structure that is retrofitted or redesigned from a standard mooring hawse pipe of a ship. In one embodiment, the barrel of the integrated sensing mooring hawse pipe module 100 is pre-processed with internal grooves for accommodating sensors along specific stress paths during its manufacturing process. After the sensors are installed in the grooves, the sensors are encapsulated with a water-resistant and high-strength wear-resistant epoxy resin. This integration ensures a rigid connection between the sensors and the barrel structure, allowing the sensing signals to accurately reflect the small deformation of the barrel, while also providing long-term physical protection for the sensors.

[0051] The integrated sensing mooring hawse pipe module 100 internally integrates an array of fiber Bragg grating sensors and an array of piezoelectric acoustic emission sensors. The array of fiber Bragg grating sensors is composed of multiple fiber grating sensors, with a spatial layout designed as follows: a portion of the sensors are arranged at equal intervals along the axial direction of the hawse pipe, which makes them most sensitive to the overall stretching, compression of the barrel caused by the tension of the anchor chain, and the overall bending deformation caused by the ship's oscillation; another portion of the sensors are arranged in the hoop direction along the bell mouth and the middle section of the barrel, which allows them to accurately capture the local stress concentration and radial deformation caused by the contact between the anchor chain and the barrel wall.

[0052] The array of piezoelectric acoustic emission sensors is composed of at least four wideband piezoelectric acoustic emission sensors. To realize the subsequent acoustic emission source positioning function, the sensors of the array are arranged in a spatial geometric array with known three-dimensional coordinates on the outer wall of the hawse pipe, such as a tetrahedron or other non-coplanar array. This spatial array layout maximizes the path differences of signals received by different sensors, providing optimal geometric conditions for subsequent time difference of arrival-based positioning algorithms, thereby improving the accuracy of damage positioning.

[0053] The perturbation active exciter module 200 is the actuator for active calibration of the system. In one embodiment, the perturbation active exciter module 200 includes an electromagnetic drive unit composed of a high-power electromagnetic coil and a core, and an impact unit made of high-hardness alloy in contact with the anchor chain. The perturbation active exciter module 200 is fixedly installed on the stopper base of the hawse pipe or the adjacent ship's hull stiffener, and its installation position ensures that when the anchor chain is locked by the stopper and is in a tensioned state, the impact unit can exert a nearly vertical, instantaneous mechanical impact force on a certain link. The electromagnetic drive unit generates a transient strong magnetic field by receiving a pulsed current signal from the data acquisition and processing unit module 300, thereby driving the impact unit to complete an excitation action with a preset energy and impact waveform.

[0054] The data acquisition and processing unit module 300 is a core hardware unit connecting field sensing, excitation components and background algorithm processing. The physical entity of the data acquisition and processing unit module 300 is an electronic equipment cabinet integrating multiple functions. The data acquisition and processing unit module 300 specifically includes a fiber grating demodulator, a multi-channel acoustic emission signal acquisition card, a synchronous control unit and a central processing unit.

[0055] The fiber grating demodulator is connected to the fiber Bragg grating sensor array of the integrated sensing anchor module 100 through an optical fiber. The fiber grating demodulator includes a broadband light source and a high-speed spectrum analysis module inside, can demodulate the drift amount of the central concave reflection wavelength of each grating sensor in real time at a sampling frequency of kilohertz (kHz) level, and convert the optical signal into a digital data stream.

[0056] The multi-channel acoustic emission signal acquisition card is connected to the piezoelectric acoustic emission sensor array of the integrated sensing anchor module 100 through a low-noise coaxial cable. The acquisition card has multiple parallel, high-speed analog-to-digital conversion (ADC) channels, can synchronously collect the weak, transient voltage signals output by each piezoelectric sensor at a sampling frequency of megahertz (MHz) level. The high sampling rate ensures that the waveform characteristics of the acoustic emission signal, especially its steep rising edge, can be recorded without distortion.

[0057] The synchronous control unit is the key to ensuring the accurate alignment of multi-source data in time. The unit provides a unified high-precision clock reference signal to the fiber grating demodulator and the multi-channel acoustic emission signal acquisition card, and controls the sampling trigger time of the two. Through the synchronization mechanism, the low-frequency vibration data and high-frequency acoustic emission data collected by the system are both provided with strictly synchronized time stamps, which provides the necessary conditions for subsequent cross-band data fusion analysis.

[0058] The central processing unit is the operation core of the evaluation method described in the application. It is responsible for coordinating the work of the above-mentioned hardware components, executing the program instructions of the dynamic reference calibration module 400 and the fusion diagnosis decision module 500 stored in its internal, performing data processing and calculation, and outputting the final evaluation results to the human-computer interaction interface.

[0059] Referring to the accompanying drawings Figure 3 , Figure 3 is a schematic diagram of the core algorithm module implementation process according to an embodiment of the application. The dynamic reference calibration module 400 and the fusion diagnosis decision module 500 described in the application are a group of executable computer program instructions, stored in the non-volatile memory of the data acquisition and processing unit module 300, and called and executed by the central processing unit inside.

[0060] The implementation flow of the dynamic reference calibration module 400, i.e. the active calibration mode, specifically includes the following steps:

[0061] S301: The dynamic reference calibration module 400 automatically triggers the active calibration flow after receiving the signal of the completion of the anchoring operation, or when it is determined that the current environmental load is lower than the preset threshold during passive monitoring. It sends an excitation instruction to the perturbation active exciter module 200 through the control interface.

[0062] S302: The excitation instruction contains specific parameter definitions for the excitation signal x(t). In one embodiment, the excitation signal x(t) is a linear frequency modulation signal, and its mathematical expression is:

[0063]

[0064] where A is the preset amplitude of the signal, f start is the starting frequency of the sweep, T is the duration of the signal, and k is the sweep rate defined as k = (f end -f start ) / T, f end is the termination frequency of the sweep.

[0065] S303: The dynamic reference calibration module 400 receives the vibration response signal y(t) collected by the fiber Bragg grating sensor array of the integrated sensing anchor barrel module 100 from the data acquisition and processing unit module 300. The module 400 uses the cross-power spectral density-based method to operate on the excitation signal x(t) and the response signal y(t) to calculate the dynamic transfer function H(t) of the anchoring system.

[0066] S304: The calculation process first calculates the cross-correlation function R xy (τ) of the excitation signal x(t) and the response signal y(t), and the autocorrelation function R xx (τ) of the excitation signal x(t):

[0067] R xy (τ) = E[x(t)y(t+τ)];

[0068] R xx (τ) = E[x(t)x(t+τ)];

[0069] where R xy (τ) is the cross-correlation function, R xx (τ) is the autocorrelation function, E[·] represents the operation of mathematical expectation over time, x(t) is the excitation signal, y(t) is the response signal, and τ is the time delay variable.

[0070] S305: Fourier transform is performed on the correlation function to obtain the cross-power spectral density Sxy (f) and the auto-power spectral density S xx (f) :

[0071]

[0072] where S xy (f) is the cross-power spectral density function; S xx (f) is the auto-power spectral density function; f is the frequency variable; j is the imaginary unit.

[0073] S306: Finally, the dynamic transfer function H(f) is estimated as:

[0074]

[0075] where:

[0076] H(f) is the dynamic transfer function of the mooring system; S xy (f) is the calculated cross-power spectral density; S xx (f) is the calculated auto-power spectral density. The dynamic reference calibration module 400 saves the calculated complex function H(f) in the memory of the data acquisition and processing unit module 300 as the dynamic reference spectrum for subsequent fusion diagnostic decision-making.

[0077] The implementation process of the fusion diagnostic decision-making module 500, i.e., the passive monitoring and diagnosis mode, specifically includes the following steps: S307: In the passive monitoring mode, the fusion diagnostic decision-making module 500 continuously acquires sensing signals from the data acquisition and processing unit module 300 and performs parallel multi-dimensional feature extraction. For the low-frequency signal of the fiber Bragg grating sensor, a digital low-pass filter is used to extract the quasi-static strain component ∈ qs (t) of the signal; at the same time, the time-varying power spectrum P p (f, t) of the signal is calculated by short-time Fourier transform (STFT). For the high-frequency signal of the piezoelectric acoustic emission sensor, a series of feature parameters such as the root mean square value V RMS , ring count, duration, etc. are calculated.

[0078] S308: The fusion diagnostic decision-making module 500 executes the "walk the anchor" precursor cooperative warning logic. This logic is realized by "and / or" combination judgment on three independent judgment conditions.

[0079] Condition C1 (high-frequency microscopic anomaly): When the root mean square value V RMS (t) of the acoustic emission signal continuously exceeds a dynamic threshold, this condition is satisfied. The threshold is defined as:

[0080] V RMS (t) > μ RMS + n · σRMS ;

[0081] wherein:

[0082] V RMS (t) is the root mean square value of the acoustic emission signal at time t; μ RMS is the mean value of the root mean square value of the signal statistically obtained from historical safe anchoring data; σ RMS is the standard deviation of the root mean square value of the signal statistically obtained from historical safe anchoring data; n is a preset coefficient for adjusting the early warning sensitivity. Condition C2 (quasi-static trend anomaly): when the quasi-static strain ∈ qs (t) has a positive time first-order derivative that lasts for a long time and exceeds a preset strain growth rate threshold δ ∈ , the condition is met:

[0083]

[0084] wherein:

[0085] ∈ qs (t) is the quasi-static strain component at time t; d / dt is the differential operator for time derivation; δ ∈ is the preset strain growth rate threshold.

[0086] Condition C3 (system modal shift): when the main resonance peak position set Ω p of the passive vibration power spectrum P P (f, t) is significantly deviated from the resonance peak position set Ω H of the dynamic reference spectrum |H(f)|, the condition is met. The deviation is quantified by a normalized modal shift index D(Ω H ,Ω P ), and the condition is met when D(Ω H ,Ω P ) exceeds a preset modal shift threshold δ D . The early warning decision logic of the fusion diagnostic decision module 500 is: when (condition C1 is true) and (condition C2 is true or condition C3 is true), a “walk anchor” precursor early warning signal is generated.

[0087] S309: The fusion diagnostic decision module 500 executes the structural damage positioning logic. When it receives a burst-type signal with high energy and short rise time characteristics from the piezoelectric acoustic emission sensor array, the logic is activated. The logic is activated. The logic accurately extracts the time t i ,t j , etc. of the signal arriving at each sensor from the waveform of each channel signal, and calculates the arrival time difference Δt ij = t i -t j. The logic determines the position coordinates (x s ,y s ,z s ) of the damage source by solving a nonlinear equation set composed of multiple hyperbolic surface equations:

[0088]

[0089] wherein:

[0090] (x s ,y s ,z s ) is the three-dimensional coordinate of the damage source to be solved; (x i ,y i ,z i ) is the known three-dimensional coordinate of the i th sensor; (x j ,y j ,z j ) is the known three-dimensional coordinate of the j th sensor; v is the propagation speed of the elastic wave in the chain cylinder material, which is a known physical constant; Δt ij is the time-of-flight difference of the acoustic emission signal to the i th and j th sensors.

[0091] The fusion diagnostic decision module 500 solves the equation set by using a Gauss-Newton or other nonlinear iterative algorithm to obtain the damage source coordinates (x s ,y s ,z s ), and generates a structure damage warning signal.

[0092] Referring to the accompanying Figure 4 , Figure 4 , the figure is a schematic diagram of a human-computer interaction interface according to an embodiment of the present application. The overall working process of the anchoring state evaluation system described in the present application embodies the organic combination of the two modes of active calibration and passive monitoring, and through a hierarchical information output mechanism, the complex diagnostic conclusion is converted into an intuitive evaluation result that can be used for decision-making by the crew.

[0093] The specific implementation of the overall working process of the system is as follows:

[0094] S401: After the ship completes the anchoring operation and the chain stopper locks the anchor chain, the system automatically enters the active calibration mode. The dynamic reference calibration module 400 is activated and performs the steps of S301 to S306 described above, that is, the excitation is applied through the perturbation active exciter module 200, and the response is collected through the integrated sensing chain cylinder module 100, and finally the dynamic reference spectrum H(f) under the current working condition (including the specific chain length, draft depth, seabed bottom material, etc.) is calculated and stored.

[0095] S402: After the generation and storage of the baseline spectrum, the system automatically switches to the passive monitoring mode seamlessly. In this mode, the perturbation active exciter module 200 is in standby state, while the integrated sensing mooring canister module 100 is switched to continuously and uninterruptedly collect passive vibration and stress wave signals caused by environmental loads (such as wind, wave, current) and system self-action (such as friction)

[0096] S403: During the passive monitoring mode, the fusion diagnostic decision module 500 is continuously invoked. This module 500 receives passive sensing signals in real time, and performs the steps of S307 to S309 described above. That is, on the one hand, the module 500 extracts multi-dimensional features of passive signals in parallel, and on the other hand, it retrieves the baseline spectrum generated by the dynamic baseline calibration module 400 from the memory, and continuously quantitatively compares and logically judges the real-time features of the passive signals with the baseline spectrum.

[0097] S404: The fusion diagnostic decision module 500 summarizes its diagnostic conclusions, i.e. whether the synergistic warning logic of the "walk-off anchor" precursor is met, or whether the structural damage source is located, and generates an evaluation result containing specific state information.

[0098] The specific implementation of the evaluation result grading and output of the system is as follows:

[0099] S405: The fusion diagnostic decision module 500 contains a state grading logic inside. This logic maps the original evaluation result generated by S404 to four explicit, progressively higher state levels.

[0100] State level 1 (safe): The basis for this level of judgment is that the fusion diagnostic decision module 500 does not trigger the "walk-off anchor" precursor warning logic, and does not trigger the structural damage positioning logic. All passive monitoring feature parameters fluctuate within the normal baseline range set based on historical data.

[0101] State level 2 (attention): The basis for this level of judgment is that the fusion diagnostic decision module 500 monitors any single condition (such as conditions C1, C2 or C3) that constitutes the "walk-off anchor" precursor warning, which is intermittently or slightly met, but has not yet met the complete synergistic decision logic of (C1) and (C2 or C3).

[0102] State level 3 (warning): The basis for this level of judgment is that the "walk-off anchor" precursor synergistic warning logic of the fusion diagnostic decision module 500 is completely triggered, i.e. (condition C1) and (condition C2 or condition C3) are met at the same time.

[0103] State level 4 (alarm): The basis for this level of judgment is that the structural damage positioning logic of the fusion diagnostic decision module 500 is triggered and successfully calculates the damage source coordinates (x s ,ys ,z s )。

[0104] S406: The fusion diagnosis decision module 500 sends the evaluated state level and related supporting data to the decision output layer module 300. The decision output layer module 300 visualizes the evaluation results through its human-computer interaction display unit.

[0105] For the "safe" level, the interface displays a green normal state identifier.

[0106] For the "attention" level, the interface displays a yellow prompt identifier and highlights the trend graph of the specific parameter that currently appears slightly abnormal, for example, the real-time curve of the acoustic emission signal root mean square value V RMS (t).

[0107] For the "early warning" level, the interface triggers a red visual early warning signal and a medium-priority audible early warning signal. The interface simultaneously displays the complete evidence chain constituting the early warning in a graphical manner, for example, the acoustic emission signal energy graph that continuously exceeds the threshold value, the quasi-static strain growth rate curve that continuously takes positive values, and the contrast graph of the significantly deviated modal resonance peaks.

[0108] For the "alarm" level, the interface triggers a flashing red visual alarm signal and a highest-priority audible alarm signal. The interface automatically calls up the three-dimensional model graph of the integrated sensing anchor chain cylinder module 100 and accurately superimposes the damage source coordinates (x s ,y s ,z s ) calculated by the fusion diagnosis decision module 500 on the corresponding physical position of the three-dimensional model in the form of a highlighted flashing marker point.

Claims

1. A system for evaluating the anchoring state of a very large ore carrier based on intelligent monitoring, characterized by, The application relates to a mooring system dynamic response monitoring and early warning system. The application comprises: an integrated sensing mooring pipe module physically integrated on the mooring pipe structure of a ship, used for acquiring dynamic response signals of a mooring system in real time; a perturbation active exciter module mechanically coupled with the mooring chain, used for applying mechanical excitation with known characteristics to the mooring chain; a data acquisition and processing unit module connected with the integrated sensing mooring pipe module and the perturbation active exciter module respectively; wherein the data acquisition and processing unit module is configured to run: a dynamic reference calibration module for controlling the perturbation active exciter module to apply mechanical excitation and generating a dynamic reference spectrum representing the current working condition of the mooring system based on the excitation response signals collected by the integrated sensing mooring pipe module; 2. The intelligent monitoring based very large ore carrier mooring condition assessment system according to claim 1, wherein, a fusion diagnosis decision module for, when there is no active excitation, quantitatively comparing and logically judging the real-time features of passive response signals collected by the integrated sensing mooring pipe module under passive environmental load with the dynamic reference spectrum to output an evaluation result. 3.The smart monitoring based very large crude carrier mooring condition assessment system according to claim 2, wherein, The integrated sensing mooring pipe module internally integrates an array of fiber Bragg grating sensors for collecting low-frequency vibration and quasi-static strain signals and an array of piezoelectric acoustic emission sensors for collecting high-frequency stress wave signals. 4.The intelligent monitoring based very large crude carrier mooring condition evaluation system according to claim 1, wherein, The array of piezoelectric acoustic emission sensors is composed of at least four sensors arranged in a spatial geometric array with known three-dimensional coordinates on the mooring pipe. 5.The smart monitoring based very large crude carrier mooring condition assessment system according to claim 2, wherein, The perturbation active exciter module comprises an electromagnetic drive unit and an impact unit; the electromagnetic drive unit is used for receiving control instructions from the data acquisition and processing unit module and driving the impact unit to apply transient mechanical impact force to the mooring chain. 6.The intelligent monitoring based very large crude carrier mooring condition evaluation system according to claim 1, wherein, The data acquisition and processing unit module comprises a fiber grating demodulator for demodulating signals of the array of fiber Bragg grating sensors, a multi-channel acoustic emission signal acquisition card for collecting signals of the array of piezoelectric acoustic emission sensors, and a synchronous control unit for providing a unified clock reference signal to the fiber grating demodulator and the multi-channel acoustic emission signal acquisition card to ensure time synchronization of data acquisition. 7.The smart monitoring based very large crude carrier mooring condition assessment system according to claim 2, wherein, The dynamic reference calibration module determines the dynamic transfer function of the mooring system by calculating the cross-power spectral density between the excitation signal and the excitation response signal and the self-power spectral density of the excitation signal and takes the dynamic transfer function as the dynamic reference spectrum. 8.The smart monitoring based very large crude carrier mooring condition assessment system according to claim 7, wherein, The fusion diagnosis decision module is configured to execute an anchor walking precursor cooperative early warning logic, and the judgment conditions of the logic include whether the high-frequency signal features collected by the array of piezoelectric acoustic emission sensors are abnormal, whether there is a sustained growth trend in the quasi-static strain collected by the array of fiber Bragg grating sensors, and whether the modal features of passive vibration signals collected by the array of fiber Bragg grating sensors are deviated from the dynamic reference spectrum. The judgment of whether the modal features of passive vibration signals are deviated from the dynamic reference spectrum is realized by comparing the main resonance peak position set of the passive vibration power spectrum with the resonance peak position set of the dynamic reference spectrum. 9.The smart monitoring based very large crude carrier mooring condition assessment system according to claim 3, wherein, The fusion diagnostic decision module is further configured to execute structural damage location logic to determine the location coordinates of the damage source by calculating the time difference of arrival of the burst signals received by each sensor in the array of piezoelectric acoustic emission sensors and solving a nonlinear equation set based on the spatial geometric array pattern.

10. The smart monitoring based very large crude carrier anchoring state evaluation system according to claim 1, characterized in that, characterized in that, The fusion diagnostic decision module further comprises state grading logic for mapping the evaluation results into at least two state grades of safety, attention, early warning and alarm, and visualizing the output through a human-machine interaction interface.

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