Cable external damage intelligent early warning method and system based on optical fiber vibration sensing

Abstract

The application provides a cable external damage intelligent early warning method and system based on optical fiber vibration sensing, relates to the technical field of power cable safety protection, and solves the technical problem in the prior art that previous precise early warning cannot be based on the movement trend of a vibration source. The method specifically comprises the following steps: acquiring a cable vibration signal along a line collected by a distributed optical fiber; extracting a movement vector of an external vibration source relative to the cable based on the phase characteristics of the vibration signal; the movement vector represents the radial approaching speed and the azimuth angle of the external vibration source; predicting the movement trajectory of the external vibration source based on the movement vector, and calculating the minimum intersection distance and the predicted intersection time between the movement trajectory and the spatial position of the cable; and generating hierarchical early warning information based on the minimum intersection distance and the predicted intersection time. The application is used in cable external damage early warning.

Description

Technical Field

[0001] This application relates to the field of power cable safety protection technology, and in particular to an intelligent early warning method and system for preventing external damage to cables based on fiber optic vibration sensing. Background Technology

[0002] In the field of power cable safety protection, the use of distributed fiber optic vibration sensing technology to monitor cable damage from external forces is currently the mainstream approach. Existing technologies typically collect vibration signals along the cable route, extract statistical characteristics such as vibration amplitude, energy, or spectrum, and compare them with preset fixed thresholds. An alarm is triggered when the characteristic value exceeds the threshold. However, this monitoring method based on static threshold comparison is essentially a post-event or in-event alarm mechanism; it can only determine "whether abnormal vibration has occurred," but cannot further analyze the motion state of the vibration source. In actual engineering scenarios, various mechanical operations frequently occur around cable channels. The vibrations generated by vehicles or machinery passing parallel to the cable channel and the vibrations generated by excavation work approaching the cable are often highly similar in amplitude and energy characteristics, making it difficult to effectively distinguish them based solely on statistical characteristics, leading to a high false alarm rate. More importantly, even if the vibration source is continuously approaching the cable, existing technologies cannot infer the direction and speed of the vibration source based on the changing trend of the vibration signal, thus failing to provide effective pre-warning before the external damaging source contacts the cable. Therefore, existing technologies suffer from the technical problem of being unable to provide accurate pre-warning based on the motion trend of the vibration source. Summary of the Invention

[0003] This application provides a method and system for intelligent early warning of cable damage prevention based on fiber optic vibration sensing, which solves the technical problem that existing technologies cannot provide accurate early warning based on the movement trend of vibration sources.

[0004] To achieve the above objectives, this application adopts the following technical solution: Firstly, a method for intelligent early warning of cable damage prevention based on fiber optic vibration sensing is provided, comprising: acquiring vibration signals along the cable obtained by distributed fiber optic acquisition; extracting the motion vector of the external vibration source relative to the cable based on the phase characteristics of the vibration signal; the motion vector characterizing the radial approach velocity and azimuth angle of the external vibration source; predicting the motion trajectory of the external vibration source based on the motion vector, and calculating the minimum intersection distance and expected intersection time between the motion trajectory and the spatial position of the cable; and generating graded early warning information based on the minimum intersection distance and expected intersection time.

[0005] The above solution transforms one-dimensional vibration signals into motion vectors containing velocity and orientation, and predicts the spatial intersection relationship between the vibration source and the cable accordingly. This enables the shift from static threshold alarms to dynamic trend prediction, allowing for the identification of threat levels before damage occurs.

[0006] In conjunction with the first aspect mentioned above, in one possible implementation, the motion vector of the external vibration source relative to the cable is extracted based on the phase characteristics of the vibration signal, including: performing phase demodulation on the vibration signal to obtain phase change data of adjacent sensing units; calculating the radial approach velocity of the external vibration source based on the phase change data; and calculating the azimuth angle of the external vibration source based on the Doppler frequency shift or phase gradient of the phase change data.

[0007] In conjunction with the first aspect above, in one possible implementation, the radial approach velocity of the external vibration source is calculated based on the phase change data, including: filtering out environmental background noise through adaptive phase compensation to obtain compensated phase change data; and determining the radial approach velocity based on the phase difference of the compensated phase change data within a continuous time window.

[0008] In conjunction with the first aspect mentioned above, in one possible implementation, the motion trajectory of the external vibration source is predicted based on the motion vector, and the minimum intersection distance and expected intersection time between the motion trajectory and the spatial position of the cable are calculated. This includes: constructing a three-dimensional predicted trajectory of the external vibration source based on the motion vector and historical motion states; calculating the shortest spatial distance between the three-dimensional predicted trajectory and the spatial position of the cable as the minimum intersection distance; the spatial position of the cable is a three-dimensional spatial curve; and calculating the expected intersection time based on the radial approximation velocity and the shortest spatial distance.

[0009] In conjunction with the first aspect above, in one possible implementation, calculating the shortest spatial distance between the three-dimensional predicted trajectory and the spatial position of the cable includes: discretizing the three-dimensional predicted trajectory into multiple predicted spatial points; calculating the perpendicular distance from each predicted spatial point to the three-dimensional spatial curve; and determining the minimum perpendicular distance as the shortest spatial distance.

[0010] In conjunction with the first aspect mentioned above, in one possible implementation, a graded early warning information is generated based on the minimum intersection distance and the expected intersection time, including: dividing the initial early warning level according to the minimum intersection distance and the expected intersection time; when the expected intersection time of multiple consecutive sampling periods shows a decreasing trend, the initial early warning level is increased to generate the final graded early warning information.

[0011] In conjunction with the first aspect mentioned above, in one possible implementation, before extracting the motion vector of the external vibration source relative to the cable based on the phase characteristics of the vibration signal, the method further includes: screening out effective vibration sources based on the energy distribution and phase continuity of the vibration signal; and extracting motion vectors only from the effective vibration sources.

[0012] In conjunction with the first aspect mentioned above, in one possible implementation, phase continuity means that the phase change direction of the effective vibration source remains consistent within a continuous time window, and the phase change rate is greater than a preset parallel movement interference threshold.

[0013] In conjunction with the first aspect mentioned above, in one possible implementation, the graded early warning information includes the early warning level, the current coordinates of the external vibration source, the expected convergence time, and the corresponding linkage control command; the linkage control command is used to trigger the corresponding level of on-site audible and visual alarm or power failure protection.

[0014] Secondly, a cable external damage prevention intelligent early warning system based on fiber optic vibration sensing is provided, comprising: a signal acquisition module for acquiring vibration signals along the cable collected by distributed optical fibers; a vector extraction module for extracting the motion vector of the external vibration source relative to the cable based on the phase characteristics of the vibration signal, wherein the motion vector represents the radial approach velocity and azimuth angle of the external vibration source; a trajectory prediction module for predicting the motion trajectory of the external vibration source based on the motion vector, and calculating the minimum intersection distance and the expected intersection time between the motion trajectory and the spatial position of the cable; and an early warning generation module for generating graded early warning information based on the minimum intersection distance and the expected intersection time.

[0015] The technical solution provided in this application extracts the radial approach velocity and azimuth angle of an external vibration source by demodulating the phase characteristics of the distributed optical fiber vibration signal, constructing a motion vector characterizing the motion state of the threat source. This overcomes the limitation of traditional technologies that rely solely on scalar characteristics such as vibration amplitude or energy, which cannot distinguish the intention of the vibration source. Based on this, the three-dimensional trajectory of the vibration source is predicted using the motion vector, and the minimum intersection distance and expected intersection time with the three-dimensional spatial curve of the cable are calculated. This upgrades the assessment of external damage risk from static threshold discrimination to dynamic spatiotemporal trend prediction. Furthermore, the warning level is dynamically adjusted based on the temporal decreasing trend of the expected intersection time, ensuring timely response to real approaching threats while effectively suppressing false alarms caused by parallel movement or instantaneous interference. From a physical mechanism perspective, this achieves accurate pre-emptive identification and hierarchical control of external cable damage threats, significantly improving the intelligence level of power cable safety protection and the efficiency of operation and maintenance response.

[0016] It should be understood that the descriptions of technical features, technical solutions, beneficial effects, or similar language in this application do not imply that all features and advantages can be achieved in any single embodiment. Rather, it is understood that the description of a feature or beneficial effect means that a specific technical feature, technical solution, or beneficial effect is included in at least one embodiment. Therefore, the descriptions of technical features, technical solutions, or beneficial effects in this specification do not necessarily refer to the same embodiment. Furthermore, the technical features, technical solutions, and beneficial effects described in this embodiment can be combined in any suitable manner. Those skilled in the art will understand that embodiments can be implemented without one or more specific technical features, technical solutions, or beneficial effects of a particular embodiment. In other embodiments, additional technical features and beneficial effects may be identified in specific embodiments that do not embody all embodiments. Attached Figure Description

[0017] Figure 1 A flowchart illustrating an intelligent early warning method for preventing external damage to cables based on fiber optic vibration sensing, provided in this application embodiment. Figure 1 ; Figure 2 A flowchart illustrating an intelligent early warning method for preventing external damage to cables based on fiber optic vibration sensing, provided in this application embodiment. Figure 2 ; Figure 3 A flowchart illustrating an intelligent early warning method for preventing external damage to cables based on fiber optic vibration sensing, provided in this application embodiment. Figure 3 ; Figure 4 This application provides a schematic diagram of a shortest spatial distance calculation process. Figure 5 A flowchart illustrating an intelligent early warning method for preventing external damage to cables based on fiber optic vibration sensing, provided in this application embodiment. Figure 4 ; Figure 6 This is a system architecture diagram of an intelligent early warning system for preventing external damage to cables based on fiber optic vibration sensing, provided in an embodiment of this application. Detailed Implementation

[0018] In the description of this application, unless otherwise stated, " / " means "or," for example, A / B can mean A or B. The "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone. Furthermore, "at least one" means one or more, and "multiple" means two or more. The terms "first," "second," etc., do not limit the quantity or order of execution, and "first," "second," etc., do not necessarily imply differences.

[0019] It should be noted that, in this application, the terms "exemplary" or "for example" are used to indicate that something is being described as an example, illustration, or illustration. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or design solutions. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0020] Example 1: like Figure 1 As shown in the figure, this application provides an intelligent early warning method for cable damage prevention based on fiber optic vibration sensing. The method includes the following steps: S101. Acquire vibration signals along the cable obtained from distributed optical fiber acquisition.

[0021] Among them, the cable vibration signal acquired by distributed optical fiber refers to the continuous signal along the entire line that includes the phase information of external disturbances, obtained by using communication optical fibers laid in the cable body or cable channel as sensors and phase-sensitive optical time-domain reflectometry (Φ-OTDR).

[0022] In this embodiment, by injecting pulsed laser light into an optical fiber and receiving backscattered Rayleigh light, the phase change sequence of each sensing unit along the length of the optical fiber is demodulated. This signal not only contains the intensity information of the vibration, but also retains the original phase characteristics reflecting the mode and position of external mechanical action.

[0023] It should be noted that the acquired signal is the raw phase data stream that has not undergone amplitude threshold truncation or energy averaging. This is fundamentally different from the traditional approach that relies solely on vibration energy for alarms, ensuring that weak but directional approaching signals are not filtered out.

[0024] Based on the above steps, the vibration field along the cable was perceived from a physical perspective, preserving the key phase dimension used to analyze the motion properties of external threat sources.

[0025] S102. Based on the phase characteristics of the vibration signal, extract the motion vector of the external vibration source relative to the cable.

[0026] Among them, the motion vector represents the radial approach velocity and azimuth angle of the external vibration source. It is a two-dimensional or three-dimensional physical quantity, which consists of two core components: radial approach velocity and azimuth angle. The radial approach velocity represents the rate at which the external vibration source approaches or moves away from the cable along a direction perpendicular to the cable axis, and the azimuth angle represents the spatial orientation of the external vibration source relative to the cable.

[0027] In this embodiment of the application, by performing spatiotemporal correlation analysis on the phase difference between adjacent sensing units, the projected motion component of the external vibration source on the cross section perpendicular to the cable is calculated, and its spatial direction of arrival is determined by combining Doppler frequency shift or phase gradient information, thereby reconstructing the one-dimensional fiber axial strain signal into a vector description characterizing the true motion intention of the threat source.

[0028] Based on the above steps, a precise quantitative characterization of the motion state of the external vibration source is achieved, providing input parameters with clear physical meaning for subsequent trajectory prediction, and solving the technical problem that traditional methods cannot identify threatening intent.

[0029] S103. Predict the motion trajectory of the external vibration source based on the motion vector, and calculate the minimum intersection distance and expected intersection time between the motion trajectory and the spatial position of the cable.

[0030] Among them, the minimum intersection distance refers to the shortest Euclidean distance in three-dimensional geometric space between the predicted motion trajectory of the external vibration source and the actual spatial position of the cable within a preset future time window, and the expected intersection time is the theoretical contact time calculated based on the current radial approximation velocity and the minimum intersection distance.

[0031] In this embodiment, the future motion path of the external vibration source is constructed by combining the motion vector at the current moment with the historical state estimation, and the actual laying shape of the cable is modeled as a three-dimensional spatial curve. By solving the geometric extrema between the predicted trajectory point set and the cable curve model, spatial distance parameters and time parameters reflecting the degree of collision risk are obtained.

[0032] It should be noted that the minimum intersection distance is not a simple horizontal projection distance, but a three-dimensional spatial measurement that fully considers cable burial depth, terrain undulation, and mechanical operation depth. This geometric extreme value calculation method avoids underreporting or false reporting caused by simplified models.

[0033] Based on the above steps, a spatiotemporal quantitative assessment of external damage risks was achieved, transforming abstract vibration signals into collision countdowns and safety margins that maintenance personnel can intuitively understand, thus supporting the realization of accurate pre-emptive prediction capabilities.

[0034] S104. Generate graded early warning information based on the minimum intersection distance and the expected intersection time.

[0035] Among them, the graded early warning information is a multi-level alarm instruction divided according to the urgency and severity of the risk. Its classification depends on two dimensions: the minimum intersection distance in space and the expected intersection time in time.

[0036] In this embodiment, a dual-parameter joint decision-making mechanism is established. When the minimum intersection distance is less than the safety threshold and the expected intersection time is shorter than the response window, a high-level warning is triggered. When the distance is far or there is enough time, a low-level monitoring state is maintained, thereby achieving dynamic matching between the warning strategy and the actual danger on site.

[0037] Based on the above steps, a shift from passive response to proactive hierarchical management has been achieved. By integrating risk assessments in both temporal and spatial dimensions, the accuracy and practicality of early warning information have been significantly improved, buying time for handling cable damage.

[0038] Based on the above technical solution, by acquiring vibration signals containing phase characteristics and extracting motion vectors representing radial approach velocity and azimuth angle, the trajectory is predicted and the minimum intersection distance and expected intersection time are calculated. Finally, graded early warning information is generated, completing the technical paradigm shift from scalar vibration monitoring to vector trajectory prediction, effectively supporting the accurate pre-emptive identification and graded control of external cable damage threats.

[0039] Example 2: In one possible implementation of the embodiments of this application, combined with Figure 1 ,like Figure 2 As shown, the above S102 can be specifically implemented through the following S201, S202, S203 and S204, which are explained in detail below: S201. Perform phase demodulation on the vibration signal to obtain phase change data of adjacent sensing units.

[0040] Phase demodulation refers to the process of converting the acquired backscattered Rayleigh light signal into a phase sequence containing external disturbance information by using the coherent detection principle in phase-sensitive optical time-domain reflectometry. The phase change data of adjacent sensing units reflects the spatial phase gradient when the vibration wave propagates along the optical fiber axis.

[0041] In this embodiment, the IQ quadrature demodulation algorithm is used to process the original beat frequency signal. The absolute phase value of each sensing unit at each sampling time is extracted by arctangent operation. Then, the absolute phase of two spatially adjacent sensing units is differentially calculated to obtain phase change data after eliminating common-mode noise. The phase change data directly characterizes the local strain state of the optical fiber caused by external vibration.

[0042] As an example, when the system spatial resolution is 5 meters, two sensing points A and B with a spacing of 5 meters are selected. If the phase of point A is 1.2 rad and the phase of point B is 1.5 rad at a certain moment, then the phase change data is 0.3 rad. This positive value indicates that the optical fiber is subjected to tensile strain in this section.

[0043] Based on the above steps, a lossless conversion from optical intensity signal to electric phase information was achieved, preserving the original physical fingerprint of the direction and intensity of the external vibration source, thus providing a clean data foundation for subsequent high-precision vector calculations.

[0044] S202. Environmental background noise is filtered out through adaptive phase compensation to obtain compensated phase change data.

[0045] Among them, adaptive phase compensation is a dynamic suppression mechanism for low-frequency environmental drift and steady-state background noise in distributed optical fiber sensing systems. Its core lies in automatically adjusting the filtering parameters according to the statistical characteristics of the real-time signal, thereby separating the effective phase components caused by external transient vibrations.

[0046] In this embodiment, an adaptive filter based on a sliding time window is constructed. The mean value of the phase change data within the window is calculated in real time as the background noise estimate. The estimated value is subtracted from the original phase change data to obtain the compensated phase change data. When a sudden change in the environmental noise level is detected, the window width is automatically shortened to improve the tracking ability, and vice versa to enhance the smoothing effect.

[0047] Based on the above steps, non-stationary background interference in complex environments is effectively eliminated, ensuring that the phase data input to the speed calculation module only contains dynamic components related to external mechanical actions, which significantly improves the robustness of the system under harsh working conditions.

[0048] S203. Determine the radial approximation velocity based on the phase difference of the compensated phase change data within a continuous time window.

[0049] Among them, radial approach velocity is the instantaneous rate at which an external vibration source approaches or moves away from the cable along a direction perpendicular to the cable axis. Its magnitude directly reflects the urgency of the threat source approaching the cable and is one of the most critical state variables in the trajectory prediction model.

[0050] In this embodiment, the radial approximation velocity is calculated using the cumulative phase difference of the compensated phase change data within a preset time window, combined with the fiber elastic-optic coefficient and geometric parameters, through the following formula:

[0051] in, Indicates the radial approximation velocity. Indicates the center wavelength of the probe pulse laser. This represents the normalized cumulative phase difference after compensation within a continuous time window. Indicates the effective refractive index of the optical fiber core. The optical elastic coefficient represents the efficiency of conversion between strain and phase change in optical fiber. Indicates the duration of a continuous time window.

[0052] Based on the above steps, a precise quantitative mapping from the optical phase domain to the mechanical velocity domain is achieved, enabling the system to perceive the dynamic approach trend of threat sources in real time in a physically interpretable way, rather than merely remaining at the level of qualitative alarm.

[0053] S204. Calculate the azimuth angle of the external vibration source based on the Doppler frequency shift or phase gradient of the phase change data.

[0054] Among them, the azimuth angle is the spatial direction of the external vibration source relative to the cable axis. It is defined as the angle between the incident direction of the vibration source and the radial reference line of the cable. This parameter, together with the radial approach velocity, constitutes a complete two-dimensional motion vector, which is used to determine the specific spatial location of the threat source.

[0055] In this embodiment, the Doppler frequency shift component is extracted by performing a short-time Fourier transform on the phase change data of adjacent sensing units, or the spatial gradient of the phase along the fiber axis is calculated. The azimuth angle of the external vibration source is obtained by utilizing the directional sensitivity of the vibration wave propagating in the fiber. When the Doppler frequency shift method is used, the azimuth angle has a sinusoidal relationship with the frequency shift amount. When the phase gradient method is used, the azimuth angle is determined by the ratio of the axial and radial strain components.

[0056] As an example, when the excavator is operating 3 meters to the right of the cable, the phase of the left sensing unit lags behind that of the right sensing unit due to the path difference of the vibration wave reaching different sides of the optical fiber. Based on this, the system calculates that the phase gradient points to the right, and then calculates the azimuth angle as +90 degrees (with the right side defined as positive), accurately locking the location of the threat source.

[0057] Based on the above steps, the angular dimension information of the motion vector is completed, enabling the system not only to know "something is approaching" but also to accurately know "from which direction it is approaching", providing an indispensable spatial orientation basis for subsequent three-dimensional trajectory prediction and accurate early warning.

[0058] Based on the above technical solution, this embodiment constructs a complete extraction link from the original optical signal to the high-dimensional motion vector through four closely linked steps: phase demodulation, adaptive compensation, velocity calculation, and azimuth angle calculation.

[0059] Example 3: In one possible implementation of the embodiments of this application, combined with Figure 1 ,like Figure 3 As shown, S103 and S104 can be specifically implemented through the following S301, S302, S303 and S304, which are explained in detail below: S301. Based on motion vectors and historical motion states, construct a three-dimensional predicted trajectory of an external vibration source.

[0060] Among them, the three-dimensional predicted trajectory is the future spatial path derived by combining the current instantaneous motion vector of the external vibration source with the historical motion state over a period of time. It not only includes the current position and velocity direction, but also incorporates inertial features such as the acceleration change trend.

[0061] In this embodiment, Kalman filtering or particle filtering algorithms are used to perform temporal smoothing and state estimation on the motion vector sequence. The radial approximation velocity and azimuth angle extracted at the current moment are used as observation inputs. The position, velocity and acceleration state vectors of the previous moment are combined for iterative updates, thereby outputting a three-dimensional spatial curve trajectory with probability confidence within a preset time window in the future. This trajectory can effectively correct vector jumps caused by noise from a single measurement.

[0062] As an example, when the excavator's radial approach speed at time t0 is 0.5 m / s, and its speeds at times t-1 to t-3 are 0.48 m / s, 0.49 m / s, and 0.51 m / s respectively, the system determines that it is in a stable approach state, and the predicted trajectory will extend along the current direction. If the speed suddenly changes to 2 m / s at time t0 but the historical average is only 0.5 m / s, the filter will suppress this outlier to avoid unreasonable and drastic deviations in the predicted trajectory.

[0063] Based on the above steps, a robust estimation of the future motion path of the external vibration source is achieved, eliminating the influence of random disturbances in the original phase signal on trajectory prediction, and providing a reliable state input for subsequent accurate calculation of spatial intersection relationships.

[0064] S302. Calculate the shortest spatial distance between the three-dimensional predicted trajectory and the spatial position of the cable, as the minimum intersection distance.

[0065] Among them, the spatial location of the cable is a three-dimensional spatial curve, and the shortest spatial distance refers to the minimum value of the geometric perpendicular distance from any point on the three-dimensional predicted trajectory to the three-dimensional spatial curve. This parameter truly reflects the physical proximity of the threat source and the cable entity in the complex underground environment.

[0066] In this embodiment of the application, the overall calculation process is divided into three stages, combined with Figure 4 The illustration is as follows: The first stage is to generate and discretize the predicted trajectory: First, based on the cable laying completion data or GIS geographic information data, the generated three-dimensional predicted trajectory is discretized and sampled at a fixed spatial step size to obtain a series of discretized predicted spatial points such as P1, P2, P3, and P4. The distance solution of the continuous trajectory is transformed into a point-by-point distance solution problem of the discrete point set. Based on this, the actual route of the cable is modeled as a cable spatial curve model composed of multiple ordered adjacent cable segments such as L1, L2, and L3 connected in sequence.

[0067] The second stage involves calculating the perpendicular distance from a point to a line segment, using the vector cross product method to solve point by point: For each prediction spatial point to be processed, all adjacent cable segments on the cable spatial curve model are traversed, and the perpendicular distance from that point to each segment is calculated. Taking prediction spatial point P2 and adjacent cable segment L2 as an example, the following is defined... Let L2 be the line segment vector. To predict the point-to-endpoint vector from spatial point P2 to the endpoint of line segment L2, calculate the cross product of the two vectors and take their modulus. The geometric meaning of this modulus is the area of ​​the parallelogram formed by the line segment vector and the point-to-endpoint vector as adjacent sides. Dividing this area by the length of line segment L2 yields the perpendicular distance d2 from the predicted spatial point P2 to line segment L2, with the corresponding foot of the perpendicular denoted as H2. The perpendicular distances d3, d4, ... from this predicted spatial point to all other adjacent cable segments L1, L3, L4, etc., are calculated sequentially in the same manner, forming the set of perpendicular distances corresponding to this point.

[0068] The third stage involves determining the shortest spatial distance: for a single predicted spatial point, the set of its corresponding perpendicular distances... Perform minimum value operation The shortest distance from the discrete point to the cable space curve is obtained; all discretized prediction space points P are traversed. i After finding the closest distance, the minimum value is taken as the shortest spatial distance between the entire 3D predicted trajectory and the cable space curve. ,satisfy The foot of the perpendicular on the cable curve is denoted as . That is, the spatial location closest to the cable body on the predicted trajectory.

[0069] It should be noted that cables in actual engineering are not ideal straight lines, but three-dimensional curves that bend and undulate with the terrain of the trench. If the cable is simply simplified into a straight line or a horizontal projection line for distance calculation, significant geometric errors will occur at places with large terrain elevation differences or cable bends, leading to missed or false alarms. This step strictly adapts to the actual spatial shape of the cable by discretizing the point cloud to calculate the vertical distance.

[0070] Based on the above steps, a leap from an ideal geometric model to a real engineering model was achieved, ensuring that the minimum intersection distance calculation results can accurately characterize the actual physical threats faced by cables under complex terrain conditions, and avoiding safety assessment deviations caused by model simplification.

[0071] S303. Calculate the expected rendezvous time based on the radial approximation velocity and the shortest spatial distance.

[0072] The estimated convergence time is the theoretical time required for the external vibration source to move from its current position to the point where it reaches the shortest spatial distance from the cable, assuming that the current radial approach velocity remains constant. It is the core time indicator for measuring the urgency of the risk.

[0073] In this embodiment of the application, the expected meeting time satisfies the following formula:

[0074] in, This indicates the estimated time of convergence, in seconds. This represents the shortest spatial distance, in meters (m). This represents the radial approximation velocity at the current moment, in m / s. When the speed is less than the preset dead zone threshold, Set to infinity to represent the lack of approximation tendency.

[0075] Based on the above steps, the abstract spatial distance is transformed into an intuitive countdown parameter, enabling maintenance personnel to quantify the urgency of the perceived risk and solving the technical deficiency of traditional methods that can only inform "there is danger" but cannot inform "when the danger will occur".

[0076] S304. Generate tiered early warning information based on the minimum intersection distance and the expected intersection time.

[0077] The generation of graded early warning information not only relies on static values ​​calculated in a single instance, but also introduces a dynamic adjustment mechanism based on time series. That is, when the expected convergence time of multiple consecutive sampling periods shows a decreasing trend, the initial early warning level is increased to generate the final graded early warning information.

[0078] In this embodiment, the initial warning level is first determined by looking up the current values ​​of the minimum intersection distance and the expected intersection time in a table. Then, time-series trend monitoring is started. If the expected intersection time of each period is less than that of the previous period and the difference exceeds the preset jitter tolerance within N consecutive sampling periods, it is determined to be a continuous approaching event, and the initial warning level is automatically raised by one level. Conversely, if the expected intersection time fluctuates around the threshold or shows an increasing trend, the warning level is maintained or lowered to avoid triggering high-level false alarms due to instantaneous measurement anomalies.

[0079] It should be noted that "a decreasing trend in multiple consecutive sampling periods" is the key criterion for distinguishing between real mechanical approximation and instantaneous environmental interference. Real excavation operations have a clear direction and continuity, and their convergence time will inevitably decrease monotonically. In contrast, vibrations caused by vehicles passing by or trees being shaken by the wind are often random and transient, and their convergence time series exhibits disordered fluctuations. This dynamic adjustment mechanism constructs a second line of defense against counterfeiting from the temporal dimension.

[0080] As an example, with N=3 and a sampling period of 100ms, if the estimated convergence times calculated three times consecutively are 5.0s, 4.8s, and 4.6s respectively, and the decrease is greater than the jitter tolerance of 0.05s, then it is confirmed as an effective approximation and the warning level is upgraded from Level II to Level I; if the three results are 5.0s, 5.1s, and 4.9s, although the average values ​​are similar, they do not meet the monotonically decreasing condition, so the system judges it as parallel movement interference, maintains the Level III monitoring status, and does not upgrade the alarm.

[0081] Based on the above steps, the early warning strategy has been upgraded from static threshold discrimination to dynamic trend perception, which not only ensures timely response and level matching to real approaching threats, but also effectively suppresses false alarms caused by non-destructive interference, significantly improving the practical effectiveness and reliability of the cable protection system against external damage.

[0082] Based on the above technical solutions, a precise risk assessment system adapted to real complex terrain and dynamic operation scenarios is constructed by integrating historical states to build a three-dimensional predicted trajectory, accurately solving the shortest spatial distance based on a three-dimensional spatial curve model, and introducing a dynamic hierarchical early warning mechanism with a decreasing trend over time. This solves the problem of high false alarms and false alarms caused by traditional methods due to model simplification and lack of time-series verification.

[0083] Example 4: In one possible implementation of the embodiments of this application, combined with Figure 1 ,like Figure 5 As shown, prior to S102 above, the method also includes an effective vibration source screening mechanism S501, which is described in detail below: S501. Based on the energy distribution and phase continuity of the vibration signal, select effective vibration sources and extract motion vectors only from the effective vibration sources.

[0084] Phase continuity refers to the stable directionality and significant amplitude characteristics of the phase change caused by the external vibration source within a continuous time window. Specifically, it is characterized by the fact that the effective vibration source maintains a consistent direction of phase change within a continuous time window, and the phase change rate is greater than a preset parallel movement interference threshold.

[0085] In this embodiment, before performing high-performance motion vector extraction, the original vibration signal is first lightly pre-screened. The variance of the angle between the phase change vectors and the mean of the magnitude are calculated within the sliding time window. When the angle between the phase change vectors is less than a preset angle threshold (e.g., 15 degrees) and the magnitude of the phase change rate exceeds the parallel movement interference threshold, the signal source is determined to have directional approximation characteristics and is marked as a valid vibration source. Subsequently, motion vector extraction is only performed on the valid vibration source, while signals with random jumps in phase change direction or weak change rate are directly discarded.

[0086] Based on the above steps, on-demand allocation of computing resources and preprocessing improvement of signal-to-noise ratio are achieved, avoiding the waste of computing power caused by full data processing. At the same time, the physical stability of the phase direction is used to effectively eliminate most of the parallel motion interference, significantly reducing the false alarm rate of the system.

[0087] Example 5: In one possible implementation of this application embodiment, the graded early warning information includes the early warning level, the current coordinates of the external vibration source, the expected convergence time, and the corresponding linkage control command. The linkage control command is used to trigger the corresponding level of on-site audible and visual alarm or power failure protection.

[0088] Among them, the linkage control command is the execution code that transforms the digital risk assessment results into physical world safety management actions. There is a definite mapping relationship between it and the warning level, ensuring that risks of different urgency levels can trigger matching on-site response measures.

[0089] In this embodiment, a structured data frame containing the warning level, the current coordinates of the external vibration source, the expected convergence time, and the linkage control command is constructed. Specific commands are generated according to a preset strategy table. For example, when the warning level is Level 1 (emergency), the linkage control command includes a relay closing signal to cut off the power supply to the construction area and activate a high-decibel audible and visual alarm; when the warning level is Level 2 (warning), the linkage control command only triggers a low-frequency buzzer and a yellow warning light; when the warning level is Level 3 (prompt), the linkage control command is empty or only contains a log recording identifier and does not trigger any on-site audible and visual equipment.

[0090] Based on the above steps, by linking the warning level with specific sound, light, and power-off actions, the intelligent warning system is ensured to have warning effectiveness in practical engineering applications.

[0091] Example 6: This application embodiment also provides an intelligent early warning system for preventing external damage to cables based on fiber optic vibration sensing, including a signal acquisition module 601, a vector extraction module 602, a trajectory prediction module 603, and an early warning generation module 604. The modules interact with each other in real time through a high-speed data bus or a shared memory mechanism, forming a tightly coupled processing link.

[0092] The signal acquisition module 601 is used to acquire the vibration signal along the cable obtained by distributed optical fiber acquisition.

[0093] Among them, the signal acquisition module 601 is the interaction interface between the system and the physical sensing layer. It is responsible for reading the original backscattered Rayleigh light signal or the phase data stream after preliminary demodulation from the distributed optical fiber vibration sensor host, and converting it into a standard digital signal format that can be recognized by the subsequent processing module.

[0094] In this embodiment, the signal acquisition module 601 typically includes a photoelectric conversion unit and a data acquisition card, or communicates directly with a commercial Φ-OTDR device via an Ethernet / PCIe interface. It continuously buffers vibration signal data according to a preset sampling rate (e.g., 10kHz) and spatial resolution (e.g., 5 meters), and performs integrity verification and timestamp synchronization on the data to ensure the timing accuracy of the input data.

[0095] It should be noted that the signal acquired by this module retains complete phase information rather than just amplitude energy information. This is a key prerequisite that distinguishes it from traditional vibration monitoring systems. Only by retaining the phase dimension can subsequent motion vector extraction and trajectory prediction functions be supported.

[0096] The vector extraction module 602 is used to extract the motion vector of the external vibration source relative to the cable based on the phase characteristics of the vibration signal. The motion vector represents the radial approach velocity and azimuth angle of the external vibration source.

[0097] Among them, the vector extraction module 602 is one of the core computing units of the system. It integrates phase demodulation, adaptive noise suppression and vector synthesis algorithms, and is specifically responsible for converting one-dimensional axial strain signals into two-dimensional or three-dimensional motion state descriptions with clear physical orientation.

[0098] In this embodiment, the vector extraction module 602 receives raw phase data from the signal acquisition module 601. First, it performs IQ quadrature demodulation and differential operation to obtain the phase change sequence of adjacent sensing units. Then, it calls an adaptive filter to remove environmental background drift. Subsequently, it calculates the radial approximation velocity and azimuth angle based on the photoelastic effect model and the Doppler frequency shift principle, respectively. Finally, it encapsulates these two components into a unified motion vector object and outputs it to the next-level module.

[0099] The trajectory prediction module 603 is used to predict the motion trajectory of an external vibration source based on motion vectors, and to calculate the minimum intersection distance and the expected intersection time between the motion trajectory and the spatial position of the cable.

[0100] Among them, the trajectory prediction module 603 undertakes the function of spatiotemporal situation simulation, uses the current motion state combined with historical inertial information to construct a future path model, and uses geometric calculations to quantitatively assess the degree of collision risk.

[0101] In this embodiment, the trajectory prediction module 603 maintains a dynamically updated three-dimensional spatial curve database of the cable and a state estimator of the external vibration source. When a new motion vector is received, the state estimator is first updated to smooth the measurement noise and predict the trajectory point set in the next few seconds. Then, the trajectory point set and the cable curve model are traversed to solve for the shortest vertical distance as the minimum intersection distance. The expected intersection time is then calculated by combining the current radial velocity. These two parameters together constitute the core indicators of risk assessment.

[0102] The early warning generation module 604 is used to generate graded early warning information based on the minimum intersection distance and the expected intersection time.

[0103] Among them, the early warning generation module 604 is the system's decision output terminal, responsible for transforming abstract risk parameters into executable control instructions and understandable intelligence information.

[0104] In this embodiment, the early warning generation module 604 has a built-in dynamic hierarchical strategy engine and linkage control mapping table. First, it determines the initial early warning level based on the current values ​​of the minimum intersection distance and the expected intersection time. Then, it monitors the changing trend of the expected intersection time in multiple consecutive sampling periods. If it shows a monotonically decreasing trend, it automatically raises the early warning level. Finally, it generates a structured message package containing the early warning level, target coordinates, countdown and linkage instructions, and executes the corresponding on-site handling actions through an audible and visual alarm, SMS gateway or relay control loop.

[0105] Based on the above technical solution, the engineering applicability and practicality of the cable damage prevention early warning system are significantly improved through the coordinated operation of four functional modules: signal acquisition, vector extraction, trajectory prediction, and early warning generation.

[0106] Although this application has been described herein in conjunction with various embodiments, those skilled in the art, by reviewing the accompanying drawings, disclosure, and appended claims, will understand and implement other variations of the disclosed embodiments in carrying out the claimed application. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude multiple instances. A single processor or other unit can implement several functions listed in the claims. While different dependent claims may recite certain measures, this does not mean that these measures cannot be combined to produce good results.

[0107] Although this application has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made thereto without departing from the spirit and scope of this application. Accordingly, this specification and drawings are merely exemplary illustrations of this application as defined by the appended claims, and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from the spirit and scope of this application. Thus, if such modifications and modifications of this application fall within the scope of the claims of this application and their equivalents, this application is also intended to include such modifications and modifications.

Claims

1. A smart early warning method for preventing external damage to cables based on fiber optic vibration sensing, characterized in that, include: Acquire vibration signals along the cable obtained from distributed optical fiber acquisition; Based on the phase characteristics of the vibration signal, the motion vector of the external vibration source relative to the cable is extracted; the motion vector represents the radial approach velocity and azimuth angle of the external vibration source. Based on the motion vector, predict the motion trajectory of the external vibration source, and calculate the minimum intersection distance and expected intersection time between the motion trajectory and the spatial position of the cable; Based on the minimum intersection distance and the expected intersection time, a graded early warning information is generated.

2. The method according to claim 1, characterized in that, The step of extracting the motion vector of the external vibration source relative to the cable based on the phase characteristics of the vibration signal includes: The vibration signal is phase demodulated to obtain phase change data of adjacent sensing units; Based on the phase change data, the radial approximation velocity of the external vibration source is calculated; The azimuth angle of the external vibration source is calculated based on the Doppler frequency shift or phase gradient of the phase change data.

3. The method according to claim 2, characterized in that, The calculation of the radial approximation velocity of the external vibration source based on the phase change data includes: Environmental background noise is filtered out through adaptive phase compensation, and the compensated phase change data is obtained. The radial approximation velocity is determined based on the phase difference of the compensated phase change data within a continuous time window.

4. The method according to claim 1, characterized in that, The method of predicting the motion trajectory of the external vibration source based on the motion vector, and calculating the minimum intersection distance and expected intersection time between the motion trajectory and the spatial position of the cable, includes: Based on the motion vector and historical motion state, a three-dimensional predicted trajectory of the external vibration source is constructed; Calculate the shortest spatial distance between the three-dimensional predicted trajectory and the spatial position of the cable, which is taken as the minimum intersection distance; the spatial position of the cable is a three-dimensional spatial curve. The estimated convergence time is calculated based on the radial approach velocity and the shortest spatial distance.

5. The method according to claim 4, characterized in that, The calculation of the shortest spatial distance between the three-dimensional predicted trajectory and the spatial position of the cable includes: The three-dimensional predicted trajectory is discretized into multiple prediction space points; Calculate the perpendicular distance from each predicted spatial point to the three-dimensional spatial curve; The minimum perpendicular distance is determined as the shortest spatial distance.

6. The method according to claim 5, characterized in that, The generation of tiered early warning information based on the minimum intersection distance and the expected intersection time includes: The initial warning level is determined based on the minimum intersection distance and the expected intersection time; When the expected convergence time of multiple consecutive sampling periods shows a decreasing trend, the initial warning level is increased to generate the final graded warning information.

7. The method according to claim 1, characterized in that, Before extracting the motion vector of the external vibration source relative to the cable based on the phase characteristics of the vibration signal, the method further includes: Based on the energy distribution and phase continuity of the vibration signal, effective vibration sources are selected. The motion vector is extracted only from the effective vibration source.

8. The method according to claim 7, characterized in that, The phase continuity means that the effective vibration source maintains a consistent phase change direction within a continuous time window, and the phase change rate is greater than a preset parallel movement interference threshold.

9. The method according to claim 8, characterized in that, The graded early warning information includes the early warning level, the current coordinates of the external vibration source, the expected convergence time, and the corresponding linkage control command; the linkage control command is used to trigger the corresponding level of on-site audible and visual alarm or power failure protection.

10. A smart early warning system for cable damage prevention based on fiber optic vibration sensing, characterized in that, The system includes: The signal acquisition module is used to acquire vibration signals along the cable obtained by distributed optical fiber acquisition. The vector extraction module is used to extract the motion vector of the external vibration source relative to the cable based on the phase characteristics of the vibration signal. The motion vector represents the radial approach velocity and azimuth angle of the external vibration source. The trajectory prediction module is used to predict the motion trajectory of the external vibration source based on the motion vector, and to calculate the minimum intersection distance and the expected intersection time between the motion trajectory and the spatial position of the cable; The early warning generation module is used to generate graded early warning information based on the minimum intersection distance and the expected intersection time.

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