Integrated distributed optical fiber sensing on-line monitoring system for live working of extra-high voltage

Abstract

The application discloses an integrated distribution type optical fiber sensing live working belt on-line monitoring system, relates to the technical field of power safety monitoring, and has a multilayer flexible insulation belt body with a helical staggered type optical fiber embedding channel; a multimode optical fiber sensing unit embedded in the channel, which comprises a Bragg grating array and a Brillouin scattering optical fiber; a stress identification module for constructing a thermal-mechanical coupling response matrix and identifying a stress concentration area; a digital twin modeling module for constructing a local finite element submodel and outputting a fatigue trend curve; and a diagnosis feedback module for generating a belt risk level signal and realizing remote control linkage. The system can monitor the stress evolution and fatigue damage of the belt structure in real time, has the advantages of high strain identification precision, accurate fatigue trend prediction, timely remote response and the like, and improves the safety and intelligent management level of the ultra-high voltage live working.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric power safety monitoring, in particular to an online monitoring system for live working sling of extra-high voltage integrated with distributed optical fiber sensing. BACKGROUND

[0002] Live working of extra-high voltage transmission line is an important means to ensure the reliability of power grid operation. The safety of the working personnel using the insulating sling for high-altitude operation in the live environment is closely related to the mechanical property changes of the sling body. The sling is subjected to the combined stresses of high voltage, electric field interference, ultraviolet radiation and environmental aging for a long time, and is prone to stress concentration, local damage or fracture and other hidden dangers. If these hidden dangers cannot be found in time, it will seriously threaten the personal safety of the working personnel.

[0003] Currently, the detection of the sling mainly relies on periodic manual inspection or physical testing before work, which has problems such as long monitoring cycle, lagging hidden danger identification and poor real-time performance. In order to improve the safety monitoring capability of the working sling, an online monitoring system capable of sensing the stress state and damage evolution of the sling in real time is urgently needed.

[0004] Distributed optical fiber sensing technology has been widely studied in the field of structural health monitoring due to its high sensitivity, strong anti-electromagnetic interference capability and suitability for long-distance monitoring. However, when it is applied to the scene of live working of extra-high voltage, key technical problems such as integration of sling structure, signal decoupling and electromagnetic isolation need to be solved. Therefore, an optical fiber sensing integrated monitoring system suitable for the environment of extra-high voltage is urgently needed to realize real-time sensing and safety warning of the operating state of the sling. SUMMARY

[0005] The purpose of the present application is to provide an online monitoring system for live working sling of extra-high voltage integrated with distributed optical fiber sensing to solve the problems in the background art.

[0006] In order to achieve the above purpose, the present application provides the following technical scheme: an online monitoring system for live working sling of extra-high voltage integrated with distributed optical fiber sensing, comprising:

[0007] a sling body, which is internally provided with a spiral staggered optical fiber embedding channel, the channel being periodically distributed and arranged at non-uniform intervals along the length direction of the sling;

[0008] an optical fiber sensing unit, which is arranged in the optical fiber embedding channel and is used for detecting a first fiber Bragg grating array for strain distribution along the length direction of the sling and a Brillouin scattering sensing optical fiber for temperature field change;

[0009] a stress identification module, which is connected with the optical fiber sensing unit and is used for identifying potential stress concentration areas in the sling body and determining their spatial positions based on the obtained strain signal gradient and temperature distribution curve;

[0010] a digital twin modeling module configured to establish a local finite element sub-model for the identified stress concentration region and to perform real-time evolution iteration in combination with historical load response data to generate a sling fatigue evolution trend model;

[0011] a diagnostic feedback module configured to receive the digital twin modeling result and to generate a sling risk level signal when the identified fatigue degradation trend exceeds a preset threshold, and to perform data interaction with a remote platform through a redundant wireless communication link;

[0012] Preferably, the sling body is integrated with the optical fiber sensing unit through a hot-pressing synchronous integrated forming process, and a pre-stress tension is applied during embedding.

[0013] Preferably, the stress identification module comprises:

[0014] The strain and temperature distribution data output by the Bragg fiber array and the Brillouin fiber are received, and a two-dimensional coupled thermal-mechanical response matrix of the sling body is generated based on the distributed spatial coordinates;

[0015] A variable-scale multi-resolution wavelet energy analysis is applied to the response matrix to extract abnormal peak frequency bands of the strain gradient change rate in the local region;

[0016] In combination with the temperature distribution curve generated by the Brillouin fiber, thermal expansion type false strain points are excluded to generate a structural stress reliable atlas;

[0017] The stress concentration position in the sling body is calibrated through the mapping relationship between the displacement-gradient mutation points in the atlas and the optical fiber embedding path.

[0018] Preferably, a double-threshold cross verification algorithm based on the Brillouin fiber temperature curve is used to perform secondary screening on the strain abnormal points identified by the wavelet energy analysis. Specifically, according to the temperature distribution curve output by the Brillouin scattering fiber, a time series of temperature change rates at each spatial node is established, and the local slope is calculated as a temperature drift trend reference; two heat-sensitive thresholds are set: one is the temperature change rate threshold, and the other is the temperature-strain coupling sensitivity coefficient threshold; the strain mutation points extracted in the wavelet analysis are cross-compared with the temperature change curve in their spatial neighborhood, and if both exceed their respective thresholds, they are determined as thermal expansion type false strain points and are excluded; otherwise, they are retained as true stress responses.

[0019] Preferably, the Brillouin scattering fiber is arranged in a spiral wrapping manner on the inner wall of the embedding channel with a pitch of 5° to 15°, which is used to supplement temperature measurement information and correct the thermal drift of the strain signal through a reverse demodulation algorithm.

[0020] Preferably, the digital twin modeling module comprises:

[0021] According to the stress concentration area coordinates positioned in the stress credible atlas, corresponding local grids are extracted from the preset sling three-dimensional geometric model, and a high-density finite element submodel is constructed;

[0022] Loading the strain response data obtained by real-time monitoring as a boundary input condition, and introducing the stress response database of the sling in the historical operation cycle, a fitting function family based on stress time sequence is constructed;

[0023] A multi-scale residual convergence algorithm is used to iteratively update the local submodel, so that the high-density finite element submodel gradually evolves under new data input, forming a time-varying stress state trajectory;

[0024] Based on the model evolution trend, the time domain prediction curve of the sling fatigue damage is output, and the area where the prediction value exceeds the safety threshold is marked as a risk.

[0025] Preferably, the diagnostic feedback module comprises:

[0026] The time domain fatigue prediction curve and the risk marking atlas output by the digital twin modeling module are received, and a multi-dimensional dynamic risk threshold table is constructed according to the sling material grade, structure form and historical misjudgment record;

[0027] A composite judgment algorithm based on the mutation rate of curve slope and the threshold value interaction amplitude ratio is used to identify whether there is a continuous acceleration degradation section in the fatigue trend;

[0028] For the over-limit area with high confidence, a graded sling risk level signal is generated according to its spatial position, prediction time advance and risk level weight, including warning, alarm and forced shutdown levels;

[0029] The risk level signal is connected through the internal protocol and the communication interface of the control center, realizing the linkage of remote operation state marking and dangerous operation blocking instruction.

[0030] Preferably, the optical fiber sensing unit comprises:

[0031] Based on the preset helical staggered optical fiber embedding channel inside the sling body, the sensing optical fiber with high-density Bragg grating array is segmented and introduced in a tension controllable manner before hot pressing, so that each grating unit is accurately positioned at the stress feature point area of the sling structure;

[0032] A low-dielectric high-thermal-conductivity gel is used to encapsulate and fix the Bragg grating embedded segment, a Brillouin scattering optical fiber is arranged on the outside of the Bragg fiber at the same time, and is wound on the inner wall of the channel in a helical winding manner. Through the reverse demodulation algorithm, decoupling and fusion analysis of the strain signal are realized.

[0033] Preferably, the Bragg grating array in the optical fiber sensing unit is segmented and embedded in the channel in a tension-controllable manner, and the tension control range during embedding is 0.4-0.8 N, which ensures that each grating unit corresponds to the stress characteristic point of the sling structure.

[0034] Preferably, the risk signal is connected with the remote control platform through an industrial Ethernet or a wireless redundant link to realize remote sling state labeling and forced shutdown instruction triggering.

[0035] In the above technical solution, the technical effects and advantages provided by the present application are as follows:

[0036] 1. The present application realizes the full life cycle dynamic monitoring of the stress state of the sling by integrating a multimode distributed optical fiber sensing unit, a stress identification module and a digital twin modeling system in the live-line working sling. The system adopts a spiral staggered embedding structure and a hot-pressing synchronous forming process, combines the multi-source fusion sensing of Bragg gratings and Brillouin fibers, significantly improves the spatial resolution of strain identification and the temperature interference suppression capability, and establishes a thermal-mechanical response matrix and a stress credible spectrum, realizing early capture and high-precision positioning of micro-damage.

[0037] 2. The present application realizes the modeling of the sling fatigue evolution trajectory under real-time working conditions by means of a multi-scale residual convergence algorithm and a fatigue trend prediction model, generates a graded risk signal through a diagnostic feedback module, and realizes operation state labeling and operation blocking through a remote control platform. The monitoring system breaks through the technical limitations of traditional slings relying on manual inspection and static testing, significantly improves the intrinsic safety level and intelligent control capability of live-line work, and has high engineering application value. DETAILED DESCRIPTION

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments described in the present application, and other drawings can also be obtained by those skilled in the art based on these drawings.

[0039] Figure 1 The system module mind map of the present application. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be described clearly and completely in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0041] Embodiment 1, please refer to Figure 1 As shown in the drawings, the integrated distributed optical fiber sensing ultra-high voltage live working sling online monitoring system described in the embodiment comprises:

[0042] The sling body is internally provided with a spiral staggered optical fiber embedding channel, and the channel is periodically distributed and arranged at non-uniform intervals along the length direction of the sling.

[0043] The optical fiber sensing unit is arranged in the optical fiber embedding channel and is used for detecting a first fiber Bragg grating array for detecting strain distribution along the length direction of the sling and a Brillouin scattering sensing optical fiber for detecting temperature field changes.

[0044] The stress identification module is connected with the optical fiber sensing unit and is used for identifying potential stress concentration areas in the sling body and determining the spatial positions of the stress concentration areas based on the obtained strain signal gradient and temperature distribution curve.

[0045] The digital twin modeling module is used for establishing a local finite element sub-model for the identified stress concentration areas and performing real-time evolution iteration in combination with historical load response data to generate a sling fatigue evolution trend model.

[0046] The diagnosis feedback module is used for receiving the digital twin modeling result and generating a sling risk level signal when identifying that the fatigue degradation trend exceeds a preset threshold value, and simultaneously performing data interaction with a remote platform through a redundant wireless communication link.

[0047] The sling body is integrated with the optical fiber sensing unit through a hot-pressing synchronous integrated forming process, and a pre-stress tension is applied during the embedding process.

[0048] In the present application, the sling body of the integrated distributed optical fiber sensing ultra-high voltage live working sling is integrated with the optical fiber sensing unit through a hot-pressing synchronous integrated forming multi-step process, and the process includes the following four key steps:

[0049] Firstly, a multi-layer composite insulating material with high insulating strength, high voltage resistance, ultraviolet radiation resistance and good flexibility performance is selected as the constituting base material of the sling body. The multi-layer structure usually includes: an aramid woven reinforced base material in the inner layer for providing mechanical strength; a high voltage resistant polyimide film in the middle layer for bearing the electric field effect under the ultra-high voltage environment; and a silicone rubber coating layer in the outer layer for enhancing the wear resistance and ultraviolet resistance. At the same time, according to the stress distribution law of the sling in actual operation, the composite material layer is differentiated in thickness and partitioned and cut to match the subsequent optical fiber layout path.

[0050] Secondly, a spiral staggered optical fiber embedding channel structure is constructed. In the middle layer prefabrication stage of the composite material, a spiral staggered optical fiber embedding channel is formed in the length direction of the composite material by a numerical control laser slotting process. The embedding density of the channel in the length direction is periodically distributed with non-uniform spacing, and the variation law is matched and designed according to the response requirement of the structural finite element simulation result to the high strain area, so as to ensure that the local area strain concentration has higher sensing density. Each channel section is a closed rectangular groove, and the groove wall is treated by plasma spraying film technology to improve the coupling stability of the optical fiber and the inner wall of the channel.

[0051] Thirdly, the embedding and tension control of the optical fiber sensing unit are performed. The multi-mode optical fiber with a grating array, Brillouin scattering or Raman sensing function is pre-inserted into the optical fiber embedding channel, and a micro tension applying device is used to introduce a preset tension during the optical fiber embedding process, and the tension range is controlled between 0.4 and 0.8 N, so as to suppress the local relaxation or displacement of the optical fiber caused by the sling bending or flexing, and ensure the stability of the optical signal under the actual load condition. The inter-segment anchoring module is introduced synchronously in this process, which is used to position the optical fiber at a specific inflection point, so as to ensure the spatial continuity and displacement mapping accuracy of the signal response.

[0052] Finally, a hot pressing synchronous integrated forming process is performed. The sling body with the embedded optical fiber embedding channel structure is sent into a high-precision mold pressing forming equipment for overall hot pressing treatment. The process uses low-temperature high-pressure hot pressing parameters for pressing, forms a stable composite layer structure, and realizes the permanent closure and packaging of the optical fiber channel. Through this synchronous process, the sling body and the internal optical fiber channel form a physically continuous structure, avoiding the problems of structure delamination, bubbles, displacement and the like in the later bonding or secondary embedding process, thereby significantly improving the mechanical response consistency, signal stability and service life of the sling under dynamic operating conditions. After the hot pressing is completed, high-voltage breakdown test, optical loss test and strain sensitivity calibration are still needed to be performed, so as to ensure the reliability and accuracy of the overall field application.

[0053] Through the cooperative implementation of the above four steps, the sling body provided by the present application not only has high insulation and mechanical flexibility, but also realizes the deep integration of the distributed optical fiber sensing unit and the sling structure, provides a basic structural guarantee for the sling health monitoring in the extra-high voltage live working scene, and is significantly different from the existing sensor pasting or simple threading embedding method, and has significant technical progress and engineering feasibility.

[0054] The setting of the optical fiber sensing unit includes:

[0055] Based on the preset helical staggered optical fiber embedding channel inside the sling body, the sensing optical fiber with high-density Bragg grating array is introduced in a tension controllable manner before hot pressing, so that each grating unit is accurately positioned at the stress characteristic point area of the sling structure;

[0056] In the implementation of the present application, in order to realize the accurate perception of the strain state of the sling structure under high-altitude operation, before hot pressing and integrated forming, the Bragg grating array sensing optical fiber needs to be guided and embedded in segments according to the design path of the helical staggered embedding channel inside the sling body. The Bragg fiber is provided with a plurality of grating units, and the spacing between each unit is less than 5 cm, forming a high-density array. In order to ensure that the grating units can still accurately correspond to the stress characteristic points of the sling structure after forming, such as the sling hook area, the bending transition zone and the stress turning section, the present embodiment adopts a tension controllable embedding device to apply a stable axial tension in the range of 0.5~0.8N during the introduction of the optical fiber, preventing the optical fiber from being twisted, misaligned or pre-deformed during the bending laying or pressing process.

[0057] Through this embedding method, each Bragg grating unit can be kept in a spatial position matched with the key stress position of the sling structure, ensuring the physical mapping accuracy of the monitoring data; at the same time, since the tension has been preset before embedding, the signal lag and nonlinear response problems caused by traditional "loose laying of optical fiber" are avoided, thereby significantly improving the strain monitoring resolution and timeliness of the sling under dynamic working conditions. This process step further fixes the position of the optical fiber structure during subsequent hot pressing, laying the foundation for the synchronous response of the overall sling and the sensing unit.

[0058] A low-dielectric high-thermal-conductivity gel is used to encapsulate and fix the Bragg grating embedded segment, a Brillouin scattering optical fiber is laid on the outside of the Bragg fiber at the same time, and is wound in a helical manner on the inner wall of the channel. The reverse demodulation algorithm is used to realize decoupling and fusion analysis of the strain signal.

[0059] In the specific implementation process of the present application, in order to enhance the environmental adaptability and signal decoupling capability of the optical fiber sensing unit in the ultra-high voltage live working sling, after the Bragg grating array optical fiber is embedded, a flexible gel material with low dielectric constant and high thermal conductivity coefficient is used to fully encapsulate the grating embedded segment. The gel encapsulation material is composed of a modified polysiloxane matrix and a heat-sensitive reinforcing filler, which has excellent electric field isolation capability and can quickly conduct external temperature changes to the grating unit, improving its temperature response sensitivity. During the application process, the gel is injected between the optical fiber and the channel cavity wall through a micro-droplet dispenser, ensuring that the optical fiber is tightly attached to the inner wall of the composite material, and at the same time, it has the dual functions of flexible cushioning and adhesion fixing.

[0060] Subsequently, Brillouin scattering fibers are simultaneously deployed on the outer channel wall of the Bragg fiber using a spiral wrapping embedding process. An automatic fiber guide device is used to wrap the Brillouin fibers along the inner wall of the channel with a pitch of 5° to 15°. This deployment method not only saves space but also constructs a sensing path that simultaneously senses along both the length and radial directions. To achieve precise decoupling between temperature and strain signals, a fusion algorithm based on optical path difference reverse demodulation is introduced at the system data processing end. This algorithm uses the temperature sensing value from the Brillouin fiber to correct the thermal drift term in the output signal of the Bragg fiber point by point, achieving purified extraction of the strain signal and fusion of multi-source sensing information.

[0061] This method not only enhances the transient response capability of optical fibers to dynamic temperature disturbances, but also effectively avoids monitoring errors caused by stress-thermal interference. At the same time, the spiral wrapping structure improves the mechanical compliance and adhesion stability of Brillouin optical fibers, thereby enhancing the overall signal accuracy and robustness of the system in complex external environments and providing a high-quality input data foundation for subsequent damage identification models.

[0062] The stress identification module includes:

[0063] It receives strain and temperature distribution data jointly output by Bragg fiber array and Brillouin fiber, and generates a two-dimensional coupled thermo-mechanical response matrix of the sling body based on distributed spatial coordinates.

[0064] In this embodiment of the invention, to achieve multi-dimensional perception and precise identification of the structural state of the sling during ultra-high voltage live-line work, the system integrates a joint calculation and matrix construction module in the signal demodulation unit. This module receives the strain and temperature distribution values ​​synchronously output by the fiber Bragg grating array and the Brillouin scattering fiber. The Bragg grating signal forms a preliminary one-dimensional strain sequence based on the spatial distribution index along the sling's length; the Brillouin signal provides temperature reference information at the corresponding location. The system utilizes the spatial path coordinates of the fiber embedded in the sling and aligns the output data from different types of fibers using a parameter mapping table to construct a two-dimensional thermo-mechanical coupling response matrix based on node coordinate indices.

[0065] Each element in this matrix represents a pairwise relationship between strain and temperature values ​​at a specific spatial location, providing a data foundation for subsequent stress concentration analysis. This method allows for the digital and visual mapping of local physical changes along the entire length and width of the sling onto the matrix structure, effectively achieving spatial fusion of multi-sensor data. This coupled response matrix not only improves the resolution of anomaly region location but also significantly reduces errors in strain assessment due to temperature drift, constructing a foundational platform for simultaneous interpretation of thermo-mechanical data, suitable for the input requirements of subsequent modules such as wavelet recognition and twin modeling.

[0066] A variable-scale multi-resolution wavelet energy analysis is applied to the response matrix to extract abnormal peak frequency bands of strain gradient change rate in local regions;

[0067] In the embodiment of the present application, in order to identify the potential stress concentration area in the sling structure, the two-dimensional thermal-mechanical coupling response matrix constructed as described above is further introduced into the variable-scale multi-resolution wavelet energy analysis technology. Firstly, the strain value sequence in the response matrix is expanded according to the spatial node coordinates to extract the first-order and second-order strain gradient data under the continuous coordinates. Then, the Daubechies (db4) wavelet basis function is used to perform discrete wavelet transform on the strain gradient data, reconstruct the signal under different scales (such as 2, 4, 8, and 16), and calculate the energy distribution of the local region under each scale.

[0068] By comparing the wavelet energy spectrum under each scale, the spatial region with a sudden increase in local energy in the matrix can be identified, which usually corresponds to the position where the material discontinuity, micro-crack initiation or residual stress aggregation phenomenon exists in physics. In particular, the system introduces an adaptive energy threshold setting mechanism to dynamically adjust the sensitivity according to the global background strain level, ensuring that the extraction of abnormal peak frequency bands is robust and selective.

[0069] Compared with the traditional global maximum value discrimination technology, the analysis method has higher spatial positioning resolution and false alarm suppression capability, and can extract local characteristic change signals when early damage has not yet significantly affected the global strain pattern, thereby providing key input basis for subsequent thermal interference exclusion and structure risk modeling.

[0070] Combined with the temperature distribution curve generated by the Brillouin optical fiber, a double-threshold cross verification algorithm is used to exclude thermal expansion type false strain points and generate a structure stress reliable atlas;

[0071] In the embodiment of the present application, in order to further improve the accuracy of structure stress identification and avoid misjudgment of false strain caused by temperature change as real stress response, a double-threshold cross verification algorithm based on the Brillouin optical fiber temperature curve is introduced into the system to perform secondary screening on the strain abnormal points identified by the wavelet energy analysis. The specific steps are as follows: firstly, according to the temperature distribution curve output by the Brillouin scattering optical fiber, a time sequence of the temperature change rate at each spatial node is established, and the local slope thereof is calculated as a temperature drift trend reference; secondly, the system sets two heat-sensitive thresholds: one is the temperature change rate threshold, and the other is the temperature-strain coupling sensitivity coefficient threshold, which respectively represent the deformation possibility under thermal expansion and the disturbance degree of the optical fiber signal.

[0072] In a specific implementation, the system cross-comparisons the strain mutation points extracted in the wavelet analysis with the temperature change curve in its spatial neighborhood, and if both exceed their respective thresholds, it is determined as a thermal expansion type false strain point and is rejected; otherwise, it is retained as a true stress response. Finally, through this double screening process, a stress reliable atlas of spatial distribution structure is generated, and the stress concentration points retained in the atlas can be used as reliable input for subsequent damage prediction and digital twin modeling.

[0073] This method not only effectively eliminates the signal interference caused by environmental temperature fluctuations, but also avoids misjudgment and excessive response to critical state structures, thereby improving the identification accuracy and reliability of the overall monitoring system for real physical damage, and is particularly suitable for dynamic strain identification requirements in complex environments in ultra-high voltage live-line work.

[0074] Through the mapping relationship between the displacement-gradient mutation points in the atlas and the fiber embedding path, the accurate stress concentration position in the sling body is calibrated.

[0075] In the specific embodiments of the present application, in order to realize the accurate positioning of the stress concentration area in the physical structure, the system further establishes a spatial mapping relationship between the mutation feature points in the structure stress reliable atlas and the fiber embedding path in the sling body. Based on the CAD model and coordinate system record information of the fiber channel layout in the sling manufacturing process, a three-dimensional path index table of the embedded fiber inside the sling body is pre-constructed, and the corresponding position of each fiber sensing node on the physical structure is marked.

[0076] In actual operation, the system reverses the actual spatial coordinates of the fiber node number corresponding to the displacement-gradient mutation points identified in the atlas from the embedding path index, and combines the sling body structure model to perform geometric mapping, thereby completing the positioning conversion from signal anomaly to physical structure risk point. To avoid false labeling and redundant positioning, the system introduces a minimum distance clustering algorithm to merge adjacent high-gradient mutation points, forming a clear "risk cluster".

[0077] Through this positioning process, not only can the local high-risk areas in the sling body, such as the hook and loop transition section, force flow diversion zone and other structure sensitive areas, be accurately calibrated, but also the spatial constraint boundary and damage evolution initial condition can be provided for the subsequent digital twin model, significantly improving the modeling efficiency and risk prediction accuracy. This step ensures the accurate projection of sensing data from the "signal space" to the "structure space", and is a key link to realize the intelligent monitoring closed-loop system of the sling.

[0078] The digital twin modeling module comprises:

[0079] According to the stress concentration region coordinates positioned in the stress credible map, the corresponding local grid is extracted from the preset sling three-dimensional geometric model, and a high-density finite element sub-model is constructed, and the local grid density is more than 3 times that of the background region;

[0080] In the application, in order to realize accurate structure response modeling of the potential fatigue region of the sling, the digital twin modeling module first receives the stress credible map output by the stress identification module, and extracts the spatial coordinates of the stress concentration region marked therein. The coordinate information is spatially mapped and matched with the pre-constructed three-dimensional geometric structure model of the sling, the system automatically identifies the geometric position of the corresponding region, and extracts a set of grid nodes within the range of the position.

[0081] Based on the extraction result, the system takes the region as the core to construct a local high-precision finite element sub-model. Specifically, the stress concentration region coordinate information output by the stress identification module is received, and the corresponding local structure region geometric grid data is extracted in combination with the preset three-dimensional geometric model of the sling; high-density grid subdivision strategy is used in the local region for finite element modeling, the grid density is at least three times that of the background region of the sling, and hexahedral structure unit and high-order interpolation function are preferentially used at the key stress path; the strain response sequence obtained by the optical fiber sensing unit in real time is loaded to the model node as the boundary input condition, and the stress response function family extracted from the historical operation cycle database is introduced synchronously for modeling prior fitting; the local finite element sub-model is iteratively trained and updated by a multi-scale residual convergence algorithm, and is dynamically adjusted according to the difference residual field of the measured strain data and the simulation result, so as to realize evolution optimization of the sub-model, and finally output the time-varying stress state trajectory and prediction trend curve of the sling fatigue damage.

[0082] Through the above modeling method, not only the response simulation accuracy of the key region can be improved under the premise of ensuring the overall modeling efficiency, but also the identification ability of the model in the early evolution stage of micro-damage is effectively enhanced. Compared with the traditional average grid modeling method, this method avoids the double defects of resource waste and distortion simulation, and provides a high-precision structure basis for subsequent evolution analysis and fatigue trend prediction.

[0083] Load the strain response data obtained by real-time monitoring as the boundary input condition, and introduce the stress response database of the sling in the historical operation cycle to construct a fitting function family based on stress time sequence;

[0084] In the specific implementation of the present application, in order to enable the finite element submodel to have dynamic response capability in the real working state, the system loads the strain response data continuously collected by the optical fiber sensing unit and processed by the stress identification module as the boundary input condition to the local finite element submodel. Specifically, the system first reconstructs the time axis and maps the spatial nodes of the strain data, and converts them into equivalent node displacement and external force boundary, which is loaded to the model boundary in real time during modeling, ensuring that the simulation is synchronized with the actual working condition.

[0085] At the same time, in order to enhance the model's fitting ability for the evolution of long-term fatigue behavior, the system retrieves stress response data under similar working conditions from historical operation cycle records to construct a stress time series feature set. Through multidimensional regression analysis and principal component dimensionality reduction processing of these historical data, the system generates several statistically representative fitting function families to describe the stress evolution law of the sling under typical load conditions.

[0086] The function family is embedded as prior input into the finite element solver to guide the model to evolve dynamically under the driving of boundary data and improve the stability and prediction accuracy of the model in the nonlinear transition interval. This approach realizes the deep fusion of real-time monitoring data and historical working condition information, effectively overcoming the problem of single dependence on boundary conditions of traditional models, and provides a more realistic and continuous mathematical description basis for complex fatigue behavior.

[0087] A multi-scale residual convergence algorithm is used to iteratively update the local submodel, so that the model gradually evolves under new data input to form a time-varying stress state trajectory.

[0088] In the specific implementation of the present application, in order to improve the prediction accuracy and stability of the local finite element submodel under dynamic working conditions, the system further introduces a multi-scale residual convergence algorithm to iteratively optimize the modeling results. The core idea of this method is to continuously introduce new strain response data after the initial iteration of the model is completed, and to perform multi-scale analysis and feedback correction on the residual distribution between the model prediction value and the measured value.

[0089] In the specific implementation process, first, the predicted stress distribution result at each time step is compared with the actual sensing feedback node by node, and the node residual value is calculated. Then, the residual field is expanded to the multi-scale space through discrete wavelet transform, and the error accumulation area and evolution trend are analyzed at different resolution scales to determine the high sensitivity area of the model response. For the identified key residual subdomain, the system adjusts the local element stiffness matrix or the boundary condition perturbation coefficient to fine-tune the local response and re-solve, forming an iteration.

[0090] The process is repeatedly performed after each round of data update until the residual converges below a preset threshold or reaches an upper limit of iteration number. Through the above multi-scale convergence mechanism, the model can continuously adjust the local response path while maintaining the stability of the overall structure, thereby forming a continuous and approaching real-time time-varying stress state trajectory for accurately depicting the stress evolution process of the sling in the dynamic loading process.

[0091] Compared with the traditional "single loading + static calculation" method, the technology can significantly improve the timeliness and accuracy of fatigue risk prediction, and is especially suitable for modeling and analysis of high-risk areas with frequent nonlinear mutations of stress paths.

[0092] Based on the model evolution trend, the time-domain prediction curve of the sling fatigue damage is output, and the area where the predicted value exceeds the safety threshold is marked as a risk area.

[0093] In the embodiment of the application, in order to realize dynamic prediction and risk management of the service life state of the sling, the system further outputs the time-domain prediction curve of the sling fatigue damage based on the model evolution trend after completing the multi-round iteration convergence of the local finite element sub-model. Specifically, the system statistically processes the node stress history trajectory in the continuous time step, extracts characteristic parameters such as stress amplitude, cycle number and loading frequency, and calculates the fatigue cumulative index of each node according to the Palmgren-Miner linear damage accumulation criterion or the improved nonlinear fatigue damage model.

[0094] In order to enhance the time correlation of the prediction results, the system introduces a sliding window regression mechanism, uses the evolved model state as a baseline, and generates a fatigue trend curve in the future time domain through extrapolation algorithms such as polynomial fitting or LSTM prediction model. The system also sets a dynamic safety threshold judgment module to adjust the fatigue tolerance according to the sling material grade, historical stress margin and actual working conditions, and judges the distribution area of the over-limit point on the curve.

[0095] Once the predicted fatigue index shows a trend of exceeding the set threshold in a certain area, the system immediately marks the area as a risk area in the three-dimensional model and generates a structure hot zone map to guide the on-site monitoring personnel to intervene or replace the components in advance. The method realizes a continuous closed-loop control mechanism of "prediction-response-warning", significantly improves the intelligentization and safety guarantee level of sling health management, and is especially suitable for precise monitoring of fatigue failure risks under multi-load alternating working conditions.

[0096] The diagnostic feedback module comprises:

[0097] The time-domain fatigue prediction curve and risk marking atlas output by the digital twin modeling module are received, and a multi-dimensional dynamic risk threshold table is constructed according to the sling material grade, structure form and historical misjudgment record;

[0098] In the specific embodiment of the present application, to realize intelligent diagnosis and hierarchical feedback of the sling structure state, the diagnosis feedback module first receives the output results from the digital twin modeling module, including the fatigue damage prediction curve of the sling in a specified time window and its spatial risk marker atlas. This data set covers the predicted damage evolution trend and potential failure node coordinate information of the stress concentration area of the sling, providing a basis for subsequent risk judgment.

[0099] Subsequently, the system constructs a dynamically adjustable multi-dimensional risk threshold criterion table according to the material grade (such as aramid composite, polyimide reinforced, etc.) of the monitored sling, the structure form (such as single-layer woven, multi-layer sandwich composite, etc.), and the misjudgment and delay cases recorded in the historical diagnosis process. The specific method includes: setting the material benchmark fatigue tolerance by consulting the structure performance database; introducing a structure complexity factor to adjust the damage threshold elastic range; and simultaneously using a machine learning algorithm to analyze the deviation trend between historical misjudgment points and real failures, dynamically adjusting the tolerance interval and alarm sensitivity of the criterion.

[0100] The threshold table has joint adaptation capability based on four-dimensional parameters of time, structure, material, and historical confidence, and can automatically switch the risk judgment standard according to different types of slings and actual working conditions. Through this way, the system significantly reduces the false alarm and missed alarm risks caused by static threshold setting, and improves the determination adaptability and engineering application feasibility of the fatigue trend prediction results, providing data support for the fine health management and early warning strategy of the sling operation.

[0101] A composite judgment algorithm based on the rate of curve slope mutation and the amplitude ratio of threshold interaction is used to identify whether there is a sustained accelerated degradation section in the fatigue trend;

[0102] In the embodiment of the present application, to accurately identify the possible sustained accelerated degradation phase in the fatigue evolution process of the sling, the diagnosis feedback module introduces a composite judgment algorithm based on the rate of curve slope mutation and the amplitude ratio of threshold interaction. This algorithm combines the time derivative change trend of the fatigue prediction curve and the relative deviation degree of the risk threshold region, and can dynamically judge whether the fatigue curve shows a nonlinear accelerated failure tendency in a specific period.

[0103] The specific implementation steps are as follows: first, the system performs first and second derivative solving on the fatigue prediction curve output by the digital twin model, extracts the rate of change of the curve slope in the time series, which is used to reflect the change of fatigue evolution speed; secondly, at each time node, the interactive amplitude ratio of the fatigue value at this point and the corresponding multi-dimensional dynamic risk threshold (i.e. the deviation rate between the prediction value and the threshold limit) is calculated to form a joint judgment vector.

[0104] Then, the system sets a set of composite criteria: such as the slope mutation rate is greater than the set threshold, and the interaction amplitude ratio continues to rise and exceeds twice the average historical fluctuation, that is, the section is determined as a continuous acceleration degradation section. If the state remains stable or expands within a continuous time window, it enters the high-risk processing flow.

[0105] Through this composite judgment method, not only the ability to identify the "mutation point" before fatigue failure is improved, but also the false alarm probability caused by dependence on a single indicator (such as the fatigue value critical point) is significantly reduced, especially suitable for live working scenes with severe stress fluctuations and greater environmental disturbances.

[0106] For the high-confidence over-limit area, a hierarchical sling risk level signal is generated according to its spatial position, predicted time advance, and risk level weight, including early warning, alarm, and forced shutdown levels;

[0107] In the specific embodiments of the present application, in order to achieve hierarchical response and precise intervention of the sling structure operating state, the diagnostic feedback module further generates a hierarchical sling risk level signal according to the spatial position, predicted over-limit time advance, and structure risk level weight of the high-confidence fatigue degradation over-limit area after identifying the area, to support the system in classifying management of different severity structure states.

[0108] Specifically, first, the system uses the sling three-dimensional geometric coordinate system established in the twin model to accurately mark the position of the fatigue over-limit area, and determines whether the area is located in a high-structure-sensitive area (such as a sling ring connection section or a stress concentration corner belt), which serves as a primary risk weighting factor; then, the over-limit time advance of the node in the fatigue trend curve is extracted, that is, the time difference between the current time and the predicted fatigue threshold crossing point, the shorter the time, the more imminent the potential risk, and a higher early warning priority is given; at the same time, the allowable fatigue level and service scene standard for different levels of slings in the structure design database are referred to, and a static weight coefficient is given to the area.

[0109] Finally, the system combines the above three indicators to form a comprehensive risk score, automatically divides it into three types of sling risk level signals according to the set classification threshold, namely, early warning level (mild degradation but with sustainability), alarm level (obvious over-limit needs manual review), and forced shutdown level (about to reach the failure threshold needs to be replaced immediately), and uploads it to the remote monitoring platform through the interface, and triggers the local control logic to perform work state labeling or sling locking prompt.

[0110] This method realizes a structure risk quantitative judgment mechanism based on multi-factor fusion, improves the adaptability and response accuracy of the online monitoring system in actual operation scenes, and significantly enhances the prevention and control ability of sudden fatigue failure events.

[0111] The risk level signal is connected to the control center communication interface through an internal protocol to realize remote operation state marking and dangerous operation blocking instruction linkage.

[0112] In the embodiment of the application, to ensure that the sling operation risk information can be conveyed to the operation control center in real time and rapid response intervention is realized, the diagnostic feedback module further pushes the information in a standardized format to the remote control center through a special communication interface after the risk level signal is generated. The interface is integrated with the control system bus based on an internal communication protocol to realize a control link with unified data structure, universal response instruction and stable transmission path.

[0113] In the specific operation process, the system first encodes and packages the generated risk level signal, and the format contains key fields such as risk type, occurrence position, prediction duration and emergency level, and a timestamp and sling number are added for remote unique identification. Subsequently, the data is connected to the intelligent identification subsystem of the central control platform through industrial Ethernet or secure wireless communication link, and the system automatically binds the current operation task according to the sling ID and operation equipment information, and performs real-time state marking on the operation interface, such as marking the sling state as “warning”, “high risk” and “disabled”.

[0114] If the received signal level is “forced to stop”, the control center system will trigger the dangerous operation blocking instruction, send the automatic shutdown, sling isolation or personnel prompt instruction to the operation port, and execute the closed-loop linkage response; at the same time, the risk log is recorded in the equipment health file and inspection record system as the basis for subsequent maintenance and replacement decision.

[0115] Through this mechanism, the system realizes the whole-process closed loop from sling structure damage identification, risk signal generation to remote linkage response, effectively improves the safety decision automation level in the live working of extra-high voltage, and significantly reduces the operation risk caused by human delay and misjudgment.

[0116] Example 2: To verify the feasibility and effectiveness of the application in the online monitoring of slings in extra-high voltage live working, the following implementation parameters and test procedures are selected for demonstrative verification:

[0117] Sling body parameters: length 5.0 m, width 0.15 m, four-layer flexible composite structure: inner layer aramid woven reinforcement cloth (thickness 0.6 mm, tensile strength ≥ 3.2 GPa); middle layer polyimide film (thickness 0.1 mm, voltage resistance ≥ 250 kV); isolation layer unsaturated polyester (thickness 0.5 mm, used for optical fiber isolation); outer layer silicone rubber (thickness 1.0 mm, UV resistance D ≥ 9). Optical fiber embedding channel: 12 spiral staggered channels are preset in the CAD model, long-range period 0.3 m~0.6 m, non-uniform setting details are shown in the drawing.

[0118] Bragg fiber: 20 gratings (5 mm long) per channel, center wavelength 1550 nm, sensitivity 1.2 pm / με. Brillouin fiber: temperature range -40℃~+150℃, temperature sensitivity 1 MHz / ℃, used for thermal interference decoupling.

[0119] Tension control: during the introduction of the optical fiber, the tension servo device is used to keep the tension constant at 0.5 N ± 0.1 N, which is automatically adjusted according to the change of curvature to prevent relaxation or strain loss.

[0120] Hot pressing parameters: 4 MPa pressure is applied under the condition of 100℃ environment for 30 minutes.

[0121] Processing results: optical fiber signal loss <0.12 dB / km, grating spacing error ≤±0.1 mm, embedding depth error ≤±0.05 mm.

[0122] Loading scheme: 1500N~3000N constant amplitude alternating load, frequency 1Hz, continuous loading for 10 5 times, simulating high-altitude load working conditions. Sensing acquisition: sampling frequency 100Hz, temperature measuring points at every 0.5m, total measuring points 10. Response matrix construction: combine the strain (με) and temperature (℃) of each measuring point to form a two-dimensional thermal force matrix for subsequent identification.

[0123] Identification results: through multi-scale wavelet analysis and temperature cross-validation, 3 local stress concentration zones are identified, located at 1.2m, 2.8m, and 4.1m respectively, with a positioning error ≤10mm.

[0124] Local sub-model construction: high-density hexahedral mesh is established for the stress concentration zone, with a mesh density of 3× that of the background; four-order interpolation is used for the boundary; the model node is 1.2k~1.5k.

[0125] Data fusion and algorithm iteration: real-time feedback strain is loaded as boundary input; historical stress database (including 5 sets of typical operation cycle stress data) is introduced; multi-scale residual convergence algorithm is used for iteration for 50 times, with residual convergence <5με; fatigue life is predicted based on Palmgren-Miner cumulative rule as 1.2×10 6 cycles. Experimental verification: the actual measured fracture time is 1.25×10 6 cycles, with a prediction error <5%.

[0126] Risk level response: 5×10 5 cycles predicted to exceed 70% capacity → “warning”; 9×10 5 cycles to exceed 90% capacity → “alarm”; 1.1×10 6Next cycle remaining life <5% → “forced outage”. Time node accuracy: early warning time error <0.5h, upload delay <1s. Communication technology: use redundant 4G LTE + LoRa wireless link, upload KPI reaches 99.2%.

[0127] Signal quality: fiber loss <0.15dB / km, noise <0.05με during online monitoring; positioning accuracy: stress concentration detection error ≤10mm, accuracy >95%; prediction reliability: fatigue life prediction error <5%, 12% higher than traditional average error; response timeliness: early warning system response <1s, remote linkage trigger rate 100%.

[0128] Through large-scale data verification in the embodiment, the system realizes the whole-chain closed loop from embedded structure design, signal accurate collection, multi-source data fusion, online model evolution, fatigue life prediction to risk linkage control. The system not only significantly improves the damage identification accuracy and life prediction accuracy of the suspension belt structure, but also realizes online monitoring and remote intervention, and has high reliability and engineering replicability, fully embodies the innovativeness and practicality of the application in the field of live-line work of extra-high voltage.

[0129] The above is merely a specific implementation of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present application, which shall be covered within the protection scope of the present application.

Claims

1. An integrated distributed fiber sensing live-line work belt on-line monitoring system for extra-high voltage, characterized in that: The method comprises the following steps: a sling body internally provided with a helix staggered optical fiber embedding channel, the channel is periodically distributed along the length direction of the sling at uneven intervals; an optical fiber sensing unit arranged in the optical fiber embedding channel, for detecting a first optical fiber Bragg grating array for detecting strain distribution along the length direction of the sling and a Brillouin scattering sensing optical fiber for detecting temperature field change; a stress identification module connected with the optical fiber sensing unit, for identifying potential stress concentration areas in the sling body and determining their spatial positions based on the obtained strain signal gradient and temperature distribution curve; a digital twin modeling module for establishing a local finite element sub-model for the identified stress concentration areas and performing real-time evolution iteration combined with historical load response data to generate a sling fatigue evolution trend model; a diagnosis feedback module for receiving the digital twin modeling result and generating a sling risk level signal when the identified fatigue degradation trend exceeds a preset threshold, and simultaneously performing data interaction with a remote platform through a redundant wireless communication link; wherein the sling body is integrated with the optical fiber sensing unit through a hot-pressing synchronous integrated forming process, and a pre-stress tension is applied during the embedding process; the digital twin modeling module comprises: extracting corresponding local grids from a pre-set three-dimensional geometric model of the sling according to the coordinates of the stress concentration areas located in the stress reliable map, and constructing a high-density finite element sub-model; loading the strain response data obtained in real-time monitoring as boundary input conditions, and introducing a stress response database of the sling in historical work cycles to construct a fitting function family based on stress time series; using a multi-scale residual convergence algorithm to iteratively update the local sub-model, so that the high-density finite element sub-model gradually evolves under new data input to form a time-varying stress state trajectory; based on the model evolution trend, outputting a time-domain prediction curve of the sling fatigue damage, and marking the areas where the predicted values exceed the safety threshold as risk areas.

2. The integrated distributed fiber sensing live-line work belt on-line monitoring system of EHV according to claim 1, characterized in that: the stress identification module comprises: receiving strain and temperature distribution data output by the Bragg fiber array and the Brillouin fiber, and generating a two-dimensional coupled thermal-mechanical response matrix of the sling body based on the distributed spatial coordinates; applying variable-scale multi-resolution wavelet energy analysis to the response matrix to extract abnormal peak bands of strain gradient change rate in local areas; combining the temperature distribution curve generated by the Brillouin fiber to exclude thermal expansion type false strain points and generate a structure stress reliable map; marking the stress concentration positions in the sling body through the mapping relationship between the displacement-gradient mutation points in the map and the optical fiber embedding path.

3. The integrated distributed fiber sensing live-line work belt on-line monitoring system of EHV according to claim 1, characterized in that: a double-threshold cross verification algorithm based on the Brillouin fiber temperature curve is used to perform secondary screening on the strain abnormal points identified by the wavelet energy analysis, specifically: according to the temperature distribution curve output by the Brillouin scattering fiber, a time series of temperature change rate at each spatial node is established, and its local slope is calculated as a temperature drift trend reference; two heat-sensitive thresholds are set: one is the temperature change rate threshold, and the other is the temperature-strain coupling sensitivity coefficient threshold; the strain mutation points extracted by the wavelet analysis are cross-compared with the temperature change curve in their spatial neighborhood, if both of them exceed their respective thresholds, it is determined as a thermal expansion type false strain point and is excluded. Conversely, it is retained as the true stress response.

4. The integrated distributed fiber sensing live-line work belt on-line monitoring system of EHV according to claim 3, characterized in that: Wherein, The Brillouin scattering fiber is arranged in a spiral winding manner on the inner wall of the embedded channel, and the helical pitch is 5°-15°, which is used for supplementing temperature measurement information and correcting thermal drift of the strain signal through a reverse demodulation algorithm.

5. The integrated distributed fiber sensing live-line work belt on-line monitoring system of EHV according to claim 1, characterized in that: The diagnostic feedback module comprises: The time-domain fatigue prediction curve and risk marking atlas output by the digital twin modeling module are received, and a multi-dimensional dynamic risk threshold table is constructed according to the sling material grade, structure form and historical misjudgment record; A composite judgment algorithm based on the curve slope mutation rate and the threshold interaction amplitude ratio is adopted to identify whether there is a continuous acceleration degradation section in the fatigue trend; According to the spatial position, prediction time advance amount and risk level weight of the high-confidence overrun area, a graded sling risk level signal is generated, including early warning, alarm and forced shutdown levels; The risk level signal is connected through an internal protocol and a control center communication interface to realize remote operation state labeling and dangerous operation blocking instruction linkage.

6. The integrated distributed fiber sensing live-line work belt on-line monitoring system of EHV according to claim 1, characterized in that: The setting of the optical fiber sensing unit comprises: Based on the preset spiral staggered optical fiber embedded channel inside the sling body, the sensing optical fiber with a high-density Bragg grating array is introduced into the channel in a tension controllable manner before hot pressing, so that each grating unit is accurately positioned at the stress characteristic point area of the sling structure; A low-dielectric high-thermal-conductivity gel is used to encapsulate and fix the Bragg grating embedded section, a Brillouin scattering fiber is arranged outside the Bragg fiber, and is wound on the inner wall of the channel in a spiral winding manner, and decoupling fusion analysis with the strain signal is realized through a reverse demodulation algorithm.

7. The integrated distributed fiber sensing live-line work belt on-line monitoring system of EHV according to claim 1, characterized in that: Wherein, The Bragg grating array in the optical fiber sensing unit is segmented and embedded in the channel in a tension controllable manner, and the tension control range during embedding is 0.4-0.8N, which ensures that each grating unit corresponds to the stress characteristic point of the sling structure.

8. The integrated distributed fiber sensing live-line work belt on-line monitoring system of EHV according to claim 1, characterized in that: The risk signal is connected with the remote control platform through industrial Ethernet or wireless redundant link to realize remote sling state labeling and forced shutdown instruction triggering.

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