Optical fiber communication link system for unmanned equipment and application method thereof

Abstract

The invention belongs to the technical field of physical layers of mobile communication, and discloses an optical fiber communication link system for unmanned equipment and an application method of the optical fiber communication link system. The system comprises an innovative optical fiber storage device, an inner winding mode is adopted to avoid physical hooking risks, real-time visibility of optical fiber allowance is achieved through a transparent shell, rapid field repair is supported by means of a detachable structure, and on the basis of hardware, the optical fiber storage device is convenient to use. The invention further provides a diagnosis method which comprises the following steps: identifying a potential risk by correlation analysis of the maneuvering state of the unmanned equipment and physical layer characteristics of an endogenous reference signal, actively driving the equipment to execute diagnostic perturbation maneuvering after identification, and performing final confirmation by analyzing the physical response of the equipment. According to the method, a reliable physical protection structure and an intelligent active diagnosis mechanism are combined, traditional catastrophe type communication link failure is converted into a management process capable of being perceived online in real time, and deterministic information support is provided for risk early warning and decision making of links.

Description

TECHNICAL FIELD

[0001] The present application relates to a kind of optical fiber communication link system for unmanned equipment and its application method, belong to the physical layer field of mobile communication. BACKGROUND

[0002] In the current unmanned intelligent equipment technology development, to meet the demand of high bandwidth data real-time backhaul and anti-electromagnetic interference, using optical fiber as communication medium has become a widely recognized and applied technical path, with high fidelity and stability in digital information transmission level, for remote precise control and high value data recovery provides basic guarantee, however, when this physical link bearing key digital information is deployed in complex dynamic and unpredictable real environment, a kind of cognitive limitation from the industry generally considers optical fiber link as ideal digital pipeline emerges, its core lies in, the focus of the system is completely fixed in whether the final demodulated digital signal is correct, and the subtle state change experienced by physical light wave carrying these signals in medium propagation is completely ignored, this cognitive mode brings a kind of technical risk that is not easy to detect in communication data level when unmanned equipment, especially underwater submersible, approaches or navigates in unknown seabed.

[0003] In the above operation scene, the slight and persistent physical interaction between optical fiber and underwater rock, abandoned fishing net or other obstacles, such as scratching or sticking, will not immediately cause physical fracture enough to interrupt communication in the initial stage, and its more common consequence is to exert a continuous and cumulative stress on a small point of optical fiber, which will cause micro deformation of optical fiber core and cause slight jitter of light wave polarization state phase, but this physical layer disturbance is far from reaching the degree of triggering the upper limit of forward error correction mechanism caused by large-scale bit error, therefore, for the existing system that relies on communication protocol stack macroscopic index to judge link state, the backhaul data stream has no any abnormality in quality, and presents the false appearance of everything normal, and between this everything normal appearance and the reality of continuous deterioration of physical link health status, a silent risk window is formed.

[0004] To improve the physical survivability of the link, the most direct improvement idea is, for example, simply using a special optical fiber with higher mechanical strength or increasing redundant backup optical fibers. The former increases the cost and deployment difficulty of the system, and cannot avoid the final failure under stress accumulation. The latter is not feasible in long-distance single-cable application scenarios. These methods do not touch the core of the problem, that is, there is a lack of an internal mechanism that can effectively perceive and warn the abnormal physical state of the link in the early stage of physical damage accumulation. The fundamental contradiction is: 1. Lack of diagnostic information. The system cannot obtain any early warning information about the abnormal physical interaction that the link is undergoing from the unchanged digital communication quality. 2. Confusion of disturbance sources. Even if the jitter of some physical layer signals can be monitored, the system cannot effectively distinguish it from the jitter generated by the unmanned equipment itself during normal maneuvering, which may result in a high false alarm rate. Therefore, how to construct a diagnostic method based on physical layer characteristics without increasing hardware costs and system complexity, so that it can effectively distinguish between the changes in the state of the optical fiber link caused by the normal maneuvering of the equipment itself and the abnormal external physical interaction, and then convert the catastrophic communication interruption into a risk management process that can be predicted in advance, has become a technical problem to be solved by the present application. SUMMARY

[0005] The present application provides an optical fiber communication link system for unmanned equipment and an application method thereof, which mainly aims to solve the problem of silent failure risk caused by the inability to effectively distinguish between normal operating stress and abnormal external interaction, which leads to changes in the physical state of the optical fiber link in the prior art.

[0006] To achieve the above-mentioned purpose, the present application provides an optical fiber communication link system for unmanned equipment, which comprises:

[0007] An optical fiber link for transmitting signals between the unmanned equipment and a remote control end, and an optical fiber storage device provided on the unmanned equipment for storing and releasing the optical fiber link during the maneuvering of the unmanned equipment;

[0008] An endogenous reference signal generation module provided at one end of the optical fiber link and configured to generate an endogenous reference signal and inject the endogenous reference signal into the optical fiber link;

[0009] A signal receiving and separating module provided at the other end of the optical fiber link and configured to separate the endogenous reference signal from the optical fiber link; and one or more processors executing an associated diagnostic module, which is configured to:

[0010] analyzing the physical layer features of the endogenous reference signal in real time to identify potential health risks that deviate from the reference correlation pattern; and upon identifying the potential health risks, actively driving the unmanned equipment to perform a diagnostic perturbation maneuver, and confirming the health status of the fiber link by analyzing the response features of one or more physical layer features of the endogenous reference signal to the diagnostic perturbation maneuver.

[0011] Preferably, the system further comprises: wherein the fiber storage device comprises:

[0012] a stationary outer shell that remains stationary during fiber release;

[0013] a rotatable inner core wrapped in the stationary shell for winding the fiber link;

[0014] a fiber guide outlet fixed to the central axis region of the stationary shell; wherein the fiber released by the rotatable inner core is guided out through the central guide outlet, thereby forming a constant outlet point and protecting the rotatable inner core from the stationary shell;

[0015] The shell is at least partially transparent to enable real-time visual monitoring of the remaining amount of fiber link on the inner core; and the inner core and the shell are connected by a detachable structure.

[0016] Preferably, the system further comprises:

[0017] an endogenous reference signal generation module arranged at one end of the fiber link and configured to generate an endogenous reference signal and inject the endogenous reference signal into the fiber link;

[0018] a signal receiving and separating module arranged at the other end of the fiber link and configured to receive signals from the fiber link and separate the endogenous reference signal; and one or more processors executing a correlation diagnosis module configured to:

[0019] Step a, obtaining and characterizing one or more physical layer features of the separated endogenous reference signal to generate link state information;

[0020] Step b, obtaining one or more self-maneuvering state data of the unmanned equipment;

[0021] Step c, continuously analyzing the real-time acquired link status information based on a baseline correlation pattern between the link status information and the self-motion status data established when the unmanned equipment is normally maneuvering, and identifying any link status information deviating from the baseline correlation pattern as a potential and uncertain health risk;

[0022] Step d, upon identifying the potential and uncertain health risk, actively sending an instruction to the control system of the unmanned equipment to drive the unmanned equipment to perform a diagnostic perturbation maneuver with a predetermined frequency and amplitude;

[0023] Step e, generating a confirmation signal representing the final health status of the fiber link by analyzing the response features of one or more physical layer characteristics of the endogenous reference signal to the diagnostic perturbation maneuver.

[0024] Preferably, the correlation diagnosis module, when performing step e, is further configured to analyze one or more spectral features of the response features; and identify and classify the physical nature of the health risk based on the one or more spectral features.

[0025] Preferably, the correlation diagnosis module, when identifying and classifying the physical nature of the health risk, is further configured to calculate a harmonic distortion index of the response features , wherein , wherein, is a reference frequency of the diagnostic perturbation maneuver, is a power of the response features at the reference frequency, is a power of the response features at the th harmonic frequency of the reference frequency; and identify the physical nature of the health risk as a rigid point contact when the calculated harmonic distortion index exceeds a rigid contact threshold value stored in the memory.

[0026] Preferably, the system further comprises a dynamic latency calibration mechanism configured to: at the unmanned equipment end, synchronously embed a corresponding time beacon in the endogenous reference signal at the same time as sending a specific data packet containing the self-motion status data; at the end of the signal receiving and separating module, measure the time difference between the arrival time of the time beacon and the arrival time of the specific data packet to determine a dynamic transmission latency; and the correlation diagnosis module uses the determined dynamic transmission latency to time-align the link status information and the self-motion status data before performing step c.

[0027] Preferably, the correlation diagnosis module is further configured to perform long-term statistical analysis on the one or more physical layer features of the endogenous reference signal across multiple task cycles to obtain one or more statistical features characterizing long-term drift trend thereof, and diagnose the performance degradation status of the one or more optoelectronic transceiver components in the fiber-optic communication link system based on whether the one or more statistical features exceed their initial reference ranges.

[0028] Preferably, the physical layer features include one or more of optical power, polarization state and phase of the endogenous reference signal.

[0029] Preferably, the endogenous reference signal generation module is configured to inject the endogenous reference signal into the fiber-optic link by multiplexing the endogenous reference signal with the main data optical signal in a wavelength-division multiplexing manner.

[0030] Preferably, the correlation diagnosis module, when performing step c, uses a technical solution to establish the reference correlation pattern, which includes recording the link state information of the unmanned equipment in a state of uniform linear motion and the self-motion state data of the unmanned equipment to form an initial cancellation baseline.

[0031] Preferably, when identifying and classifying the physical properties of the health risk, the correlation diagnosis module is further configured to identify the physical properties of the health risk as flexible surface drag when the fundamental frequency signal energy in the frequency spectrum of the response feature is clearly attenuated and the harmonic component is weak, and identify the physical properties of the health risk as elastic body winding when the phase of the fundamental frequency signal in the frequency spectrum of the response feature is clearly lagged or there is a specific resonance peak, and the correlation diagnosis module is further configured to retrieve and output the disposal action instruction corresponding to the identified physical properties from a strategy library storing the corresponding relationship between the risk properties and disposal actions.

[0032] An application method of a fiber-optic communication link system for unmanned equipment, comprising:

[0033] generating an endogenous reference signal and injecting the endogenous reference signal into the fiber-optic link;

[0034] receiving a signal from the fiber-optic link and separating the endogenous reference signal;

[0035] obtaining and characterizing one or more physical layer features of the separated endogenous reference signal to generate link state information, obtaining self-motion state data of the unmanned equipment, and analyzing the link state information based on a reference correlation pattern established when the unmanned equipment is normally maneuvered to identify a potential and uncertain health risk;

[0036] when the potential and uncertain health risk is identified, sending an instruction to a control system of the unmanned equipment to drive the unmanned equipment to perform a diagnostic perturbation maneuver with a predetermined frequency and amplitude;

[0037] By analyzing one or more physical layer characteristics of the endogenous reference signal in response to the diagnostic perturbation maneuver, a confirmation signal is generated that characterizes the ultimate health state of the fiber link.

[0038] Compared with the prior art, the beneficial effects of the present application are:

[0039] 1. By introducing an independent stable endogenous reference signal in the fiber link, and continuously correlating the real-time monitoring of the signal's physical layer characteristics with the maneuver state data originating from the unmanned equipment itself, a link state awareness method different from the prior art is established; in this way, the expected fiber physical stress caused by the normal maneuver of the equipment and its reaction on the signal are taken as the dynamic reference, and any signal feature jitter that deviates from this reference and is unrelated to the equipment maneuver state directly points to the physical interaction between the fiber and the unknown external environment, which separates the normal operation disturbance and the abnormal risk disturbance that were mixed together in the past, and thus transforms the potential risk of the link from a lagging state that must wait for the deterioration of the communication quality macroscopic indicators to be discovered into a real-time perceptible process.

[0040] 2. When the above-mentioned correlation diagnosis mechanism identifies that there is a potential physical risk with unclear characteristics in the link, instead of continuously passive observation, the control system issues instructions to the unmanned equipment to drive it to perform a preset characteristic diagnostic perturbation maneuver, and further analyzes the response spectrum of the endogenous reference signal's physical layer characteristics to this active perturbation to confirm the existence of the risk and identify its physical properties; this process temporarily includes the unmanned equipment and its control system in the diagnosis loop, uses the response analysis of a known physical excitation to replace the passive inference of unknown disturbances, not only avoids the uncertainty in the diagnosis process, but also provides objective evidence for distinguishing whether the physical constraints encountered by the link are caused by rigid point contact flexible surface dragging or elastic body winding by identifying the harmonic attenuation or phase characteristics in the response signal, so that the subsequent disposal decision obtains information support.

[0041] 3、The application utilizes the same internal reference signal, without increasing the complexity of physical hardware and communication protocol, solves the problems of external risk perception, internal component health assessment and data synchronization in three different dimensions by analyzing the physical layer characteristics in different time scales in parallel; in the short time domain of milliseconds, the system realizes instant diagnosis of external physical risks through correlation analysis, while in the long time domain of hours or days, the system monitors the performance drift of the optical transceiver component caused by long-term service through trend analysis of the mean, baseline noise and other statistical quantities of the same signal, at the same time, by embedding a time beacon in the reference signal when a key data packet is sent, the dynamic transmission delay between the two data streams required for diagnosis is measured and calibrated, and finally a trinity of external perception, internal self-reflection and time self-consistency is formed. BRIEF DESCRIPTION OF DRAWINGS

[0042] Fig. 1 The overall workflow diagram of the system diagnosis and risk confirmation of the application;

[0043] Fig. 2 The spectrum fingerprint diagram of a typical physical interaction event of the application;

[0044] Fig. 3 The core functional unit and data flow diagram of the correlation diagnosis module of the application. DETAILED DESCRIPTION

[0045] In order to make the technical solutions and advantages of the application clearer, the technical solutions of the application will be described in detail below. Obviously, the described embodiments are only some 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.

[0046] The application provides an optical fiber communication link system for unmanned equipment and an application method thereof. The system comprises: an optical fiber link for transmitting signals between the unmanned equipment and a remote control end; and an optical fiber storage device arranged on the unmanned equipment for receiving and releasing the optical fiber link during movement of the unmanned equipment. The optical fiber storage device comprises: an inner core for winding the optical fiber link; and a shell covering the inner core. The optical fiber link is received in the space defined by the inner core and the shell in an inner winding manner to reduce the risk of being hooked or entangled by external obstacles in a complex environment. The shell is at least partially transparent to realize visual real-time monitoring of the remaining amount of the optical fiber link on the inner core. The inner core and the shell are connected through a detachable structure to support quick replacement of the inner core or on-site fusion repair of the optical fiber after the optical fiber link is broken.

[0047] The system mainly comprises an optical fiber link, an endogenous reference signal generating module arranged at one end of the optical fiber link for generating and injecting an endogenous reference signal, a signal receiving and separating module arranged at the other end of the optical fiber link for receiving and separating the signal, and one or more processors executing an associated diagnosis module, wherein the diagnosis procedure is constructed as two interrelated stages, the first stage is to acquire the link state information and the unmanned equipment itself maneuvering state data in real time through correlation analysis to identify any potential uncertainty risk deviating from the reference correlation mode, and the second stage is to finally confirm and identify the nature of the risk when the risk is identified by actively driving the equipment to perform a diagnostic perturbation maneuver and analyzing the physical response, so that the early warning and management of the potential physical risk of the link obtain certainty and decision information support.

[0048] In an application scenario of an underwater pipeline inspection unmanned underwater vehicle, to deal with the risk of link interruption caused by the scratching and jamming of the optical fiber and underwater obstacles, the system is configured to execute a set of diagnosis procedures; since the standard digital communication quality index is not sensitive to the slight jitter of the optical signal at the initial stage of physical stress accumulation, there is a delay window for risk perception, and in the optical transmitting module of the unmanned equipment, an endogenous reference signal with stable power and polarization state and different wavelength from the main data channel is generated by an additional low-power continuous wave laser, and the ERS signal and the main data optical signal are combined by a wavelength division multiplexer and injected into the same optical fiber link, so that the ERS signal serves as a reference sensitive to the external stress, bending, vibration and other physical disturbances of the optical fiber, and carries the real-time physical state information of the link with the main data; at the remote control end, the signal receiving and separating module also separates the main data signal and the ERS signal by a WDM, the main data signal is sent to its standard receiver for demodulation, and the separated ERS signal enters a dedicated physical layer feature monitoring unit, which quantitatively samples the slight fluctuations of one or more physical layer features of the ERS signal such as optical power, polarization state and phase by an optical detector and an analog-to-digital conversion circuit to generate link state information representing the physical disturbance of the link, and this procedure enables the system to obtain an information dimension independent of the digital communication quality and reflecting the physical health state of the link.

[0049] Furthermore, to address the high false alarm rate caused by the confusion between link jitter generated by the unmanned equipment's normal maneuvering and jitter generated by abnormal external physical interactions, the correlation diagnosis module is configured to execute a disturbance source separation algorithm based on correlation analysis. Before executing step c, it first performs a baseline correlation mode establishment process. This process is defined as follows: recording the link status information output by the physical layer feature monitoring unit and the synchronously acquired maneuvering status data of the unmanned equipment under baseline motion states such as uniform linear motion, to form an initial cancellation baseline for canceling the equipment's inherent vibration. During the mission, the system continuously correlates and compares real-time link status information with the maneuver status data of the unmanned equipment. When the signal jitter characteristics indicated by the link status information match the maneuver behavior indicated by its own maneuver status data in terms of pattern and amplitude, the link health status is determined to be normal. However, when the link status information indicates a jitter that does not match the stable navigation state indicated by its own maneuver status data, the event is identified as a potential uncertain health risk. Through this pattern matching based on the correlation between equipment maneuver and signal jitter, the system can effectively separate normal operational disturbances from abnormal risk disturbances.

[0050] To standardize the establishment process of the baseline correlation model, the process begins by instructing the unmanned equipment to perform a uniform linear motion for 120 seconds at a representative cruise speed in the mission profile. During this period, link status information and its own maneuver status data are recorded simultaneously at a sampling rate no less than ten times the diagnostic perturbation reference frequency. Subsequently, Fourier transform is applied to the recorded link status information time-domain signal to obtain its power spectral density, and all power spectral density results within the 120 seconds are processed by time averaging to generate an average power spectrum representing the inherent vibration response of the equipment under this cruise state. This is the initial cancellation baseline. Simultaneously, the harmonic distortion index is... upper limit of summation in Its value is determined through analysis during the offline calibration phase, that is, a value is selected that makes the first... subharmonic power Harmonic orders that are stable below the system background noise power spectral density are used as The value can be set to, for example, as follows: This value is then embedded in the correlation diagnosis module. When the system identifies an abnormal signal with unclear characteristics, and the unmanned equipment happens to be in a state of weak undercurrent disturbance, making it difficult for passive correlation analysis to accurately determine the source of risk, in order to resolve this uncertainty, the correlation diagnosis module is configured to execute step d upon identifying this uncertain health risk. This involves actively sending a command to the unmanned equipment's control system, which drives the unmanned equipment to execute a function with a predetermined frequency. and amplitude, accordingly, when performing step e, the correlation diagnosis module will analyze the response characteristics of the physical layer features of the endogenous reference signal to the known excitation, by extracting the response signal component in the ERS signal that is in the same frequency and phase as the perturbation action; if a clear and normally-amplitude synchronous response signal can be detected, it proves that the optical fiber is in a free state, and if no synchronous response signal can be detected or its response characteristics are severely suppressed, it constitutes objective evidence that the optical fiber is constrained somewhere, and the system generates a confirmation signal representing the final health status of the link, which from passive inference to active evidence diagnosis mechanism provides objective decision-making basis for risk management.

[0051] To further provide structured information about the nature of the risk, the correlation diagnosis module, after confirming the existence of the risk, is further configured to analyze one or more spectral features of the response characteristics to identify and classify the physical nature of the health risk. Specifically, it calculates the frequency spectrum of the response characteristics and identifies it according to a set of mapping rules: Rule 1, calculate a harmonic distortion index of the response characteristics , whose calculation formula is , where is the reference frequency of the diagnostic perturbation maneuver, is the power of the response characteristics at the reference frequency, and is the power at the th harmonic frequency. When the calculated index exceeds a pre-marked rigid contact threshold, the nature of the risk is identified as rigid point contact, Rule 2, when the fundamental signal energy in the frequency spectrum of the response characteristics is clearly attenuated and the harmonic component is weak, it is identified as flexible surface drag, Rule 3, when the phase of the fundamental signal in the frequency spectrum of the response characteristics is clearly lagged or there is a specific resonance peak, it is identified as elastic body winding. The system can further retrieve and output the disposal action instructions corresponding to the identified physical nature from a strategy library that stores the correspondence between risk nature and disposal action, thereby providing specific decision-making information support for subsequent avoidance actions; the mapping relationship between risk nature and response spectrum characteristics is based on the difference in linearity of physical interaction. Specifically, when the optical fiber has rigid point contact with a hard object, its force deformation relationship exhibits a nonlinear constraint. When a nonlinear system is excited by a single frequency of reference frequency , its output signal will inevitably contain the second and higher harmonic components of the reference frequency, so a higher harmonic distortion index value constitutes the objective fingerprint of this type of interaction; in contrast, when the optical fiber is dragged by a flexible object over a large area, its physical process is close to a linear viscous damping system, which will not produce new harmonic components, and its response only exhibits a fundamental frequency The energy is dissipated and attenuated; when the fiber is wound by the elastomer, the combination forms a vibration system with its own inherent frequency, so when the frequency of the diagnostic excitation is coupled with the inherent frequency of the system, one or more resonance peaks will appear at the specific position of the response spectrum.

[0052] To ensure the accuracy of all the above-mentioned correlation diagnosis algorithms, the system further includes a dynamic time delay calibration mechanism to cope with the dynamic change of time delay introduced by the transmission of unmanned equipment mobility state data through the main data channel, and the influence of the accuracy of the correlation analysis time alignment; the calibration procedure is determined as follows: at the unmanned equipment end, while sending a specific data packet containing its own mobility state data, a corresponding time beacon is synchronously embedded in the endogenous reference signal; at the end of the signal receiving and separating module, the system measures the time difference between the arrival time of the time beacon and the arrival time of the specific data packet to determine a dynamic transmission time delay; finally, the correlation diagnosis module uses the determined dynamic transmission time delay to calibrate the time alignment of the link state information and its own mobility state data before performing correlation analysis, which ensures the running accuracy of the correlation diagnosis core principle in real engineering environment; in addition, to realize the management of the health status of the components of the link system, the correlation diagnosis module is further configured to perform parallel analysis on the same endogenous reference signal at different time scales; while performing short-term domain analysis to diagnose external risks, it also performs long-term domain statistical analysis on one or more physical layer characteristics of the endogenous reference signal spanning multiple task cycles to obtain one or more statistical characteristics representing its long-term drift trend, which includes recording the statistical benchmark values such as mean and variance of the physical layer characteristics of the ERS signal in previous tasks in a database, and performing trend calculation on these data; when the drift trend of the statistical characteristics exceeds its initial benchmark range, for example, the average received optical power continuously decreases, or the baseline noise level continuously rises, the performance degradation state of one or more optoelectronic transceiver components in the fiber communication link system is diagnosed accordingly, thereby realizing the diagnosis of external physical risks and the monitoring of internal component performance degradation in the same system.

[0053] In a preferred embodiment, the core structure of the optical fiber storage device includes an inner core for winding the optical fiber and an outer protective shell, the optical fiber is received in the inner core and the shell in a inner winding manner, and the release port is specially designed to ensure that the optical fiber is always close to the equipment body during deployment, thereby greatly reducing the probability of physical interaction with external obstacles. In order to enable the user to monitor the link margin in real time and avoid communication interruption due to depletion of the optical fiber during the task, the shell of the device is at least partially made of transparent material or provided with an observation window to realize visual monitoring of the optical fiber margin. Further, considering that the optical fiber may still break in extreme cases, the inner core and the shell of the device are connected through a quick detachable buckle or threaded structure. Once the optical fiber breaks, the operator does not need to replace the entire expensive device, but only needs to quickly detach the inner core for on-site fusion repair or directly replace the spare inner core, thereby significantly improving the maintainability and task continuity of the system. All of the above are extension embodiments known to those skilled in the art.

[0054] Embodiment 1: In an unmanned underwater vehicle task of autonomous inspection of a logarithmic kilometer long submarine oil and gas pipeline, the vehicle is connected to the mother ship through an optical fiber for data transmission. When the vehicle approaches a complex seabed structure, the optical fiber behind the vehicle is stuck by a rock protrusion. This event does not cause damage to the optical fiber in the initial stage, so the communication quality does not change and the video data stream does not have any abnormalities for the system which relies on forward error correction mechanism and error rate indicators to judge the link state. In this working condition, the correlation diagnosis module deployed by the system monitors the change of the link state information in the continuous passive correlation analysis process. The polarization state of the endogenous reference signal collected by the module shows a weak amplitude but irregular frequency continuous jitter. At the same time, the self-motion state data of the unmanned equipment after time alignment calibration by the dynamic time delay calibration mechanism shows that the vehicle is in a uniform straight line navigation state. Since the jitter characteristics of the endogenous reference signal and the stable motion state of the vehicle do not match the established reference correlation mode, this deviation event is identified by the correlation diagnosis module as a potential uncertain health risk, which triggers the secondary response procedure.

[0055] After identifying the uncertain risk, the correlation diagnosis module performs the active diagnosis procedure to confirm it. It sends instructions to the control system of the vehicle through the main data link to drive it to perform a diagnostic perturbation maneuver with a predetermined frequency When the rudder of the vehicle executes this perturbation maneuver, the correlation diagnosis module analyzes the response characteristics of the endogenous reference signal and finds that the physical layer characteristics do not appear the perturbation maneuver frequency The synchronous response signal, whose energy was severely suppressed, constituted objective evidence that the optical fiber was physically constrained at some point. This confirmed that the previously identified uncertain risk was a real external physical jamming event. This diagnostic method, through active physical interaction, provided confirmation of the signal anomalies identified by passive monitoring. After confirming the existence of the jamming risk, the operator received a confirmation signal indicating a deterministic physical constraint risk in the link and stopped the forward command of the submersible, performing a backward and lateral movement to release the optical fiber from the rock. Subsequently, the intrinsic reference signal jitter monitored by the correlation diagnostic module returned to a normal level matching the submersible's own vibration baseline, and the risk state was resolved. This system, through a diagnostic procedure combining passive correlation analysis and active physical interaction, transformed a sudden communication interruption event into an online management process with deterministic evidence for risk intervention.

[0056] Example 2: To quantitatively verify the effectiveness of the fiber optic communication link system of the present invention in distinguishing between normal operating stress and external abnormal physical interactions, and to confirm its proactive diagnostic capabilities, a hardware-in-the-loop simulation test platform was built, comprising an unmanned equipment simulator and a 5km standard single-mode fiber optic link. The unmanned equipment simulator generates its own maneuvering state data based on a predetermined script, while a programmable mechanical perturbation device composed of a piezoelectric ceramic actuator is installed on the fiber optic link to apply repeatable physical stress at a specified location on the fiber, thereby simulating different external physical interaction events. In the experiment, the reference frequency for diagnostic perturbation maneuvers was... The setting aims to balance the avoidance of low-frequency environmental noise with the responsiveness of physical actuators such as the unmanned equipment's servo motors. This frequency must be higher than the main vibration frequency range generated by the interaction between the aircraft and fluids during normal navigation, while remaining within the frequency range that the servo motors and other control surfaces can execute. Given that the main vibration energy spectrum in the unmanned equipment simulator's output maneuvering data is concentrated below 0.5Hz, this experiment selected... As the reference frequency for diagnostic perturbation maneuvers, this setting allows the diagnostic signal to be separated from the main noise source in the frequency domain. During the test, the active diagnostic verification procedure was disabled in a control group configuration, while the full procedure was enabled in an experimental group configuration. Under simulated constant speed straight navigation and predetermined route turning conditions, the correlation diagnostic modules of both configurations output normal status. This is because the intrinsic reference signal jitter baseline is stable during constant speed navigation, while during turning maneuvers, the high amplitude jitter of the ERS signal and the self-maneuvering state data output by the unmanned equipment simulator are highly consistent in time and pattern, which conforms to the established reference correlation pattern. When the programmable mechanical disturbance device applies an initial scraping signal simulating an external rock, both configurations output potential risks due to the detection of signal jitter inconsistent with the stable navigation state, demonstrating the basic identification capability of the passive correlation analysis mechanism.

[0057] To further define the system's performance under diagnostic ambiguity conditions, the test platform simulated a working condition in which a programmable mechanical disturbance device applied weak, continuous vibration, while the unmanned equipment simulator output a weak body vibration signal. Under this condition, the diagnostic module configured in the control group continuously output potential risks but could not provide a final confirmation. In contrast, the diagnostic module configured in the test group, after identifying a potential risk, automatically triggered an active diagnostic procedure, driving the unmanned equipment simulator to execute a frequency of [reference frequency missing]. The system employs a diagnostic perturbation maneuver. When the fiber optic link is actually in a free state, the physical layer feature monitoring unit detects a clear 2.0Hz response component in the ERS signal, and the system immediately removes the risk warning, determining the state to be normal. When the fiber optic link is subjected to a jamming constraint by a perturbation device, this step is repeated, and the 2.0Hz response component in the ERS signal is suppressed. Based on this, the system updates the risk state to a deterministic risk and further classifies the physical nature of the risk by analyzing the spectral characteristics of the response signal. Experimental results show that by correlating the physical layer characteristics of the endogenous reference signal with the maneuvering state data of the unmanned equipment, the system can identify potential risks caused by external physical interactions and avoid false alarms during normal maneuvers. Its active diagnostic perturbation mechanism, in situations where passive analysis cannot yield deterministic conclusions, can confirm the existence of a risk and classify its physical nature through an active physical interaction and response analysis. This demonstrates that the technical solution can transform an invisible link physical health state into a measurable and verifiable engineering event.

[0058] Example 3: This example combines Figs. 1 to 3 This section describes the fiber optic communication link system used in unmanned equipment and its application methods, such as... Fig. 1As shown, this procedure begins with two parallel information inputs: the endogenous reference signal ERS carrying real-time physical state information of the fiber optic link and the unmanned equipment's own maneuvering state data describing the equipment's real-time motion state and behavior. To ensure the accuracy of subsequent correlation analysis, a dynamic delay calibration mechanism first performs precise time alignment of these two data streams using a time beacon. The aligned data then enters the passive correlation analysis stage, which compares the real-time link state with a pre-established reference correlation pattern library to separate normal maneuvering disturbances from abnormal disturbances. If no abnormal physical interaction is observed, the link state is determined to be normal and monitoring continues. If a potential uncertainty risk deviating from the reference pattern is identified... This triggers an active diagnostic perturbation. In this stage, the unmanned equipment control system sends a perturbation command to the equipment, driving it to perform an active physical stimulus action. After the equipment performs the maneuver, its physical response is fed back to the response characteristic analysis and confirmation stage in real time through the ERS signal. This stage generates a final health status confirmation signal by analyzing the response characteristics of the ERS to the perturbation. After confirming the existence of a risk, the system further enters the risk nature identification and classification stage. The physical nature of the risk is identified through spectral feature analysis, such as rigid contact or flexible dragging. Finally, based on the identified nature, the system retrieves and outputs the optimal avoidance action that matches the risk nature from a disposal action command library.

[0059] like Fig. 2 As shown in the figure, the horizontal axis represents frequency in Hertz (Hz), and the vertical axis represents normalized power spectral density. The figure illustrates the typical spectral characteristics of the response signal under four different physical states when performing a diagnostic perturbation maneuver with a reference frequency of f0 (2.0Hz in this example): the solid line representing the normal state shows that the signal energy is mainly concentrated at the fundamental frequency of 2.0Hz; the dotted line representing flexible surface dragging shows that its energy at the fundamental frequency is significantly suppressed and there are no obvious harmonic components; the dashed line representing rigid point contact exhibits significant nonlinear characteristics, that is, in addition to the fundamental frequency of 2.0Hz, it shows clear energy peaks at the second harmonic of 4.0Hz and the third harmonic of 6.0Hz, corresponding to a high harmonic distortion index (DHD); and the dotted line representing elastic body winding shows unique resonance peaks at specific frequencies outside the fundamental frequency, such as 6.0Hz. These diverse spectral fingerprints provide an objective quantitative basis for the system to accurately identify and classify the physical properties of risks.

[0060] like Fig. 3As shown, the core of the module is composed of multiple engineized functional units: the data synchronization and preprocessing unit is responsible for performing dynamic time delay calibration, and distributing the aligned multi-channel data to the subsequent units, the passive correlation analysis engine is responsible for comparing with the reference pattern, and activating the active diagnostic controller to generate perturbation instructions when potential risks are triggered, the response features generated by the active diagnosis are sent to the perturbation response analysis engine for processing, the analysis results are used to generate the final diagnostic results on the one hand, and are sent to the risk qualification and classifier on the other hand, which identifies the physical properties of the risks according to the spectral features, at the same time, a long-term performance monitor receives long-term statistical data from the preprocessing unit in parallel to diagnose the performance degradation state of the optoelectronic components of the system, finally, all diagnostic information including confirmation signals, classified risk information and component degradation state are collected to the interface and instruction generation unit, which is responsible for outputting confirmation signals and disposal instructions to the outside.

[0061] Embodiment 4: In order to make the judgment threshold and response logic of the internal correlation diagnosis module of the system of the present application have verifiable setting basis before being deployed in actual operation, a set of offline system calibration and verification procedures need to be performed; this procedure is performed in a pre-deployment laboratory environment equipped with a hardware-in-the-loop simulation test platform composed of an unmanned equipment simulator long optical fiber link and a programmable mechanical perturbation device; the procedure first calibrates the judgment threshold of the harmonic distortion index used to identify rigid point contact; on the test platform, the programmable mechanical perturbation device is configured to use a hard alloy knife edge to apply a series of discrete rigid point contact events to the optical fiber with a controllable pressure gradient from 0.1N to 2.0N, stepping by 0.1N; at each pressure level, the system is instructed to trigger a diagnostic perturbation maneuver with a reference frequency of , and the calculated value of the response feature is recorded; then, the perturbation device is replaced with flexible silicone tape and elastic nylon net to simulate two working conditions of flexible surface dragging and elastic body winding, and the above perturbation and measurement process is repeated under the same pressure gradient; by comparing the data sets under the three working conditions, a threshold is determined, which is higher than the maximum value recorded in all flexible and elastic tests, and lower than the minimum value recorded in all rigid point contact tests, which is fixed in the memory of the correlation diagnosis module as the basis for subsequent risk property judgment.

[0062] Further, to establish a policy library mapping the nature of risks to the corresponding evasive actions, the procedure utilizes the test platform to verify the pre-set evasive actions; in the process of calibrating the evasive action for the risk of elastomer entanglement, after the disturbance device applies a sustained elastic constraint to the optical fiber through the nylon net, the system first identifies its nature as elastomer entanglement through the active diagnosis procedure; then, the associated diagnosis module is instructed to call a set of instructions stored locally in sequence, which includes the candidate evasive actions of sustained retreat, increasing power forward, and high-frequency small-amplitude oscillation of the tail rudder, and drives the unmanned equipment simulator to execute; in this process, the physical layer feature monitoring unit continuously monitors the jitter level of the endogenous reference signal, which is quantified as a real-time stress index; by comparing the decline rate of the stress index under different evasive actions, it is determined that the high-frequency small-amplitude oscillation of the tail rudder is the action that makes the stress index decline rate the fastest, and the control instruction sequence of this action is associated with the risk nature label of elastomer entanglement, as a key-value pair stored in the policy library; the execution of this calibration and verification procedure converts the key decision thresholds and decision logic in the diagnosis module into engineering parameters with traceability based on pre-deployment experimental data; when the system is put into practical application, its identification and classification of risks and subsequent disposal recommendations are all based on a set of quantitative causal relationships determined by the aforementioned experimental procedures, providing a technical foundation for its reliability in the environment.

[0063] In embodiment 5, after the unmanned equipment replaces the optoelectronic transceiver assembly or makes minor adjustments to its own mechanical structure, in order for the associated diagnosis module to adapt to the possible changes in the reference association mode between the equipment's own mobile state and the physical layer features of the endogenous reference signal, a set of pre-deployment calibration procedures need to be run before performing a new task; the procedure is carried out in a controlled test water area, first, the system continuously collects the endogenous reference signal for a period of 10 minutes while the unmanned equipment is powered on and stationary, and calculates the mean optical power and baseline noise variance, which are stored as the initial performance reference of the optoelectronic transceiver assembly in the current task cycle, serving as a reference for subsequent long-term statistical analysis and performance degradation diagnosis.

[0064] Subsequently, the unmanned equipment is instructed to perform a set of standardized maneuvering actions covering its main motion envelope, including straight-line motion at different speeds, turning maneuvers at different rudder angles, and vertical plane lifting maneuvers at different rates; during each specific maneuvering action, the associated diagnostic module synchronously records the self-motion state data of the unmanned equipment and the link state information output by the physical layer feature monitoring unit, and by performing synchronous power spectral density analysis on the two data streams, a baseline correlation pattern is established that maps specific motion states and their corresponding link state information spectral characteristics; in this pattern, a right rudder of 10 degrees at a speed of 5 knots is mapped to a spectral characteristic that has a specific energy peak in the 0.2-0.4 Hz frequency band, and this procedure generates an updated diagnostic model that matches the current state of the unmanned equipment, which is used for comparison and analysis in subsequent task execution.

[0065] In the scenario of an unmanned equipment performing a close-range observation task in a high sea state turbulent environment, to address the risk of diagnostic logic failure due to instantaneous fluctuations in the signal transmission channel, the correlation diagnostic module is configured to perform a set of online diagnostic integrity and fault-tolerant procedures, which aim to quantify the uncertainty in the diagnostic process and provide coping strategies for the instantaneous failure of key sub-functions; in this procedure, instead of qualitatively comparing link state information and self-motion state data, the correlation diagnostic module continuously calculates a quantitative correlation index through a sliding time window, which is determined as the normalized cross-correlation coefficient value of the energy envelope of the two data streams in the main frequency band; when the correlation index continuously exceeds a preset normal threshold of 0.8, the system remains in a normal state; when the index falls into an uncertain intermediate region of 0.3-0.8 within a 2-second duration, the system enters a potentially uncertain state of health risk and triggers active diagnostic perturbation maneuvers; when the index is definitely lower than a hard disengagement threshold of 0.3, it is determined that there is external physical interaction, and this approach provides a clear numerical basis for the transition of diagnostic states.

[0066] Meanwhile, to deal with the boundary condition that the time beacon in the dynamic time delay calibration mechanism might be instantaneously contaminated or lost due to channel noise, the fault-tolerant procedure is set as follows: when the signal receiving and separating module fails to detect the valid time beacon corresponding to a specific data packet within a predetermined time window, the dynamic time delay calibration mechanism outputs an invalid time delay mark to the correlation diagnosis module; upon receiving the mark, the correlation diagnosis module immediately suspends its risk judgment based on the correlation index and enters a short diagnosis holding state; if a new valid time beacon is received within a subsequent 500 ms preset time limit, the system resumes the normal diagnosis process; if the valid beacon is continuously failed to be received, the correlation diagnosis module gives up the judgment of the external link risk and instead reports an internal state alarm to the remote control end indicating that the diagnosis system time delay calibration function is abnormal, which is used to avoid making a false external risk judgment in the case of uncertain key input data.

[0067] It is apparent for those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application.

[0068] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application but not limit the present application, and although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the present application.

Claims

1. A fiber optic communication link system for unmanned equipment, characterized in that, The system includes: An optical fiber link for transmitting signals between the unmanned equipment and a remote control terminal; and an optical fiber storage device disposed on the unmanned equipment for storing and releasing the optical fiber link during the maneuvering of the unmanned equipment. An endogenous reference signal generation module is located at one end of the optical fiber link and is configured to generate an endogenous reference signal and inject the endogenous reference signal into the optical fiber link. A signal receiving and separating module, located at the other end of the optical fiber link, is configured to separate the endogenous reference signal from the optical fiber link; and an association diagnostic module executed by one or more processors, the association diagnostic module being configured to: Based on the reference correlation pattern between the physical layer characteristics of the endogenous reference signal established during normal unmanned equipment maneuvering and the unmanned equipment's own maneuvering status data, the physical layer characteristics acquired in real time are analyzed to identify potential health risks that deviate from the reference correlation pattern; and when the potential health risk is identified, the unmanned equipment is actively driven to perform a diagnostic perturbation maneuver, and the health status of the fiber optic link is confirmed by analyzing the response characteristics of one or more physical layer characteristics of the endogenous reference signal to the diagnostic perturbation maneuver.

2. The fiber optic communication link system for unmanned equipment according to claim 1, characterized in that, The optical fiber storage device includes: An outer casing that remains stationary during fiber optic deployment; A rotatable inner core enclosed within the stationary housing for winding the optical fiber link; An optical fiber guide outlet is fixed in the central axis region of the stationary housing; wherein the optical fiber released from the rotatable inner core is led out through the central guide outlet, thereby forming a constant exit point and protecting the rotatable inner core by the stationary housing. The housing is at least partially transparent to enable real-time visualization and monitoring of the fiber optic link margin on the inner core; and the inner core and the housing are connected by a detachable structure.

3. The fiber optic communication link system for unmanned equipment according to claim 2, characterized in that, The system also includes: An endogenous reference signal generation module is located at one end of the optical fiber link and is configured to generate an endogenous reference signal and inject the endogenous reference signal into the optical fiber link. A signal receiving and separating module, located at the other end of the optical fiber link, is configured to receive signals from the optical fiber link and separate the endogenous reference signal; and an association diagnostic module executed by one or more processors, the association diagnostic module being configured to: Step a: Obtain and characterize one or more physical layer features of the separated endogenous reference signal to generate link state information; Step b: Acquire one or more self-maneuvering status data of the unmanned equipment; Step c: Based on the baseline correlation pattern between the link status information established during normal maneuvering of unmanned equipment and its own maneuver status data, continuously analyze the real-time acquired link status information, and identify any link status information that deviates from the baseline correlation pattern as a potential and uncertain health risk. Step d: When a potential and uncertain health risk is identified, an instruction is actively sent to the control system of the unmanned equipment to drive the unmanned equipment to perform a diagnostic perturbation maneuver with a predetermined frequency and amplitude. Step e involves generating an acknowledgment signal characterizing the final health status of the fiber optic link by analyzing the response characteristics of one or more physical layer features of the endogenous reference signal to diagnostic perturbation maneuvers.

4. The fiber optic communication link system for unmanned equipment according to claim 1, characterized in that, When performing step e, the associated diagnostic module is further configured to: analyze one or more spectral features of the response characteristics; and identify and classify the physical properties of the health risk based on one or more spectral features; when identifying and classifying the physical properties of the health risk, the associated diagnostic module is further configured to: calculate a harmonic distortion index of the response characteristics. ,in ,in, The reference frequency for diagnostic perturbation maneuvers. To respond to the power characteristics at the reference frequency, For the response characteristics at the reference frequency Power at the subharmonic frequency; and the calculated harmonic distortion index. When a rigid contact threshold stored in memory is exceeded, the physical nature of the health risk is identified as rigid point contact.

5. The fiber optic communication link system for unmanned equipment according to claim 1, characterized in that, The system further includes a dynamic delay calibration mechanism, which is configured to: at the unmanned equipment end, while sending a specific data packet containing its own maneuvering status data, simultaneously embed a corresponding time beacon in the endogenous reference signal; at the end where the signal receiving and separating module is located, measure the time difference between the arrival time of the time beacon and the arrival time of the specific data packet to determine a dynamic transmission delay; Before executing step c, the associated diagnostic module uses the determined dynamic transmission delay to perform time alignment calibration between the link status information and its own maneuver status data.

6. The fiber optic communication link system for unmanned equipment according to claim 1, characterized in that, The correlation diagnostic module is further configured to perform long-term statistical analysis on one or more physical layer features of an endogenous reference signal that spans multiple task cycles to obtain one or more statistical features characterizing its long-term drift trend. And to diagnose the performance degradation of one or more optoelectronic transceiver components in an optical fiber communication link system based on whether one or more statistical characteristics exceed their initial baseline range.

7. The fiber optic communication link system for unmanned equipment according to claim 1, characterized in that, The physical layer features include one or more of the optical power, polarization state, and phase of the endogenous reference signal; the endogenous reference signal generation module is configured to combine the endogenous reference signal with the main data optical signal and then inject it into the fiber optic link via wavelength division multiplexing.

8. The fiber optic communication link system for unmanned equipment according to claim 1, characterized in that, When the correlation diagnosis module performs step c, the technical solution used to establish the baseline correlation mode includes: recording the link status information and its own maneuver status data of the unmanned equipment in uniform linear motion.

9. A fiber optic communication link system for unmanned equipment according to claim 3, characterized in that, When identifying and classifying the physical properties of health risks, the correlation diagnosis module is further configured to: identify the physical property of the health risk as flexible surface dragging when the fundamental frequency signal energy shows a clear attenuation and the harmonic components are weak in the spectrum of the response characteristics; and identify the physical property of the health risk as elastic body winding when the fundamental frequency signal phase shows a clear lag or a specific resonance peak exists in the spectrum of the response characteristics. The correlation diagnosis module is further configured to: retrieve and output the corresponding action instruction for the identified physical property from a strategy library that stores the correspondence between risk properties and action measures.

10. An application method for an optical fiber communication link system for unmanned equipment, characterized in that, include: Generate an endogenous reference signal and inject it into the fiber optic link; Receive signals from the fiber optic link and separate the endogenous reference signal; The link state information is generated by acquiring and characterizing one or more physical layer features of the separated endogenous reference signal, acquiring the self-maneuvering state data of the unmanned equipment, and analyzing the link state information based on a reference association pattern established when the unmanned equipment is maneuvering normally. When a potential uncertain health risk is identified, a command is sent to the control system of the unmanned equipment to drive the unmanned equipment to perform a diagnostic perturbation maneuver with a predetermined frequency and amplitude. By analyzing the response characteristics of one or more physical layer features of the endogenous reference signal to diagnostic perturbation maneuvers, an acknowledgment signal characterizing the final health status of the fiber optic link is generated.

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