175 results about "Location technology" patented technology

The invention discloses a fault root cause positioning method and system driven by a dynamic knowledge graph, and relates to the technical field of fault root cause localization, and the method comprises the steps: collecting and obtaining a multi-source fault associated data set, carrying out the entity association extraction of the multi-source fault associated data set, and obtaining a fault entity set and an entity relationship set; performing graph node cascading and incremental learning updating, and constructing a fault updating knowledge graph; monitoring and acquiring target fault data, performing mode matching reasoning, and generating a fault mode candidate root cause set; and performing similarity matching on the fault mode candidate root cause set in combination with a historical fault case library, and determining a target fault root cause positioning result. The technical problem of low fault diagnosis efficiency caused by inaccurate fault root cause positioning and knowledge graph updating lagging in the prior art is solved, and the technical effects of realizing accurate positioning of the fault root cause and dynamic improvement of the knowledge graph and improving the fault diagnosis efficiency and accuracy are achieved.

The invention discloses a fault tracing and positioning method in an FTU section, and belongs to the technical field of distribution automation fault positioning. The method comprises the following steps: synchronously acquiring current abrupt change signals of multiple FTU sections, reconstructing a transient waveform through EMD decomposition and cubic spline interpolation, extracting wavelet packet energy characteristics, and generating multi-dimensional transient characteristics in combination with wave head polarity and timestamps; fusing the power distribution network topology and traveling wave time delay to construct a space-time correlation graph, introducing virtual nodes to compensate communication interruption, dynamically assigning node attributes and marking a reflection path; based on graph neural network cooperative training, iteratively aggregating neighborhood information and dynamically optimizing edge weights, and generating candidate section fault probability distribution; and judging a conflict level by using information entropy, carrying out multi-level digestion in combination with polarity matching and time delay consistency, and outputting a high-confidence positioning result. According to the method, the problems of difficulty in multi-FTU cooperative positioning, poor communication interruption adaptability, inaccurate feature fusion and the like are solved, and the accuracy and robustness of power distribution network fault tracing are remarkably improved.

The invention belongs to the technical field of power fault positioning, and provides a power distribution network fault positioning method and system based on topological characteristics, and the method comprises the steps: carrying out the statistics of the reason type of each fault sub-region, constructing a multi-abnormal-fault sequence and a main abnormal-influence sequence, determining coexisting abnormal points, and carrying out the analysis of the coexisting abnormal points in a plurality of historical analysis periods, the method comprises the following steps: obtaining an abnormal tracing base point, analyzing the abnormal tracing base point, obtaining a fault tracing value, and positioning a fault source type, so that an area where a fault source is located can be more accurately determined, a propagation mechanism of a fault in a power distribution network can be deeply understood, tracing deviation caused by dispersion and interference of fault signals is avoided, and the fault tracing accuracy is improved. And the fault source tracing precision and efficiency are greatly improved.

The invention relates to the technical field of intelligent substation fault positioning, and discloses a fault positioning method based on Sdd-Scd fusion, which comprises the following steps of: 1, analyzing an SDD file and an SCD file of an intelligent substation, extracting physical link information and logic link information, and establishing a mapping relationship between a physical port and logic signal subscription; 2, based on the mapping relation between the physical link and the logic link, a fault propagation matrix M is constructed, and elements of the matrix M represent the fault propagation probability between equipment nodes; the fault propagation model is constructed based on SDD-SCD fusion, the fault propagation probability is calculated through the fault propagation matrix M, a multi-fault scene under a complex topological structure can be effectively handled, the limitation that the prior art is only based on an expert rule or a simple graph theory algorithm is overcome, and the accuracy and reliability of fault positioning are improved.

The invention relates to the technical field of multi-sensor fusion positioning, and discloses an outdoor robot multi-sensor fusion method. Comprising the following steps that robot operation data are obtained through multiple sensors in the running process of a robot; taking the preprocessed operation data as measurement input of an unscented Kalman filter state estimation process, and obtaining first position data after fusion processing; learning the noise characteristics of each sensor by using a long-short-term memory network of a sliding window, and compensating the position deviation of the robot to obtain second position data; performing time synchronization processing on the first position data and the second position data, and inputting the first position data and the second position data into an improved double-layer multi-model adaptive estimation framework; the improved double-layer multi-model adaptive estimation framework adopts a double-layer structure; and obtaining final real-time position data. The method can inhibit noise and drift of a single sensor, reduces error accumulation caused by factors such as environment shielding, improves positioning precision, robustness and continuity, and is suitable for scenes such as outdoor driving robots.

The invention belongs to the technical field of indoor positioning, and discloses a Bluetooth path loss model parameter adaptive calibration method and system. According to the method, the historical learning mechanism and the hybrid optimization strategy are fused, so that the global search capability and the local optimization precision are considered while the dynamic updating of the path loss model parameters is realized. Compared with an existing calibration method of a fixed parameter or a single optimization algorithm, the method can automatically correct the model parameters according to the environment change, and solves the problem that the precision of a traditional model is reduced under the conditions of multipath effect, shielding interference and environment sudden change. A hybrid optimization framework of a genetic algorithm and a particle swarm algorithm is introduced, so that a parameter optimization process obtains a high-quality initial value in a global search stage, rapid convergence is realized in a local fine adjustment stage, and the calibration efficiency and precision are remarkably improved. And meanwhile, a historical learning mechanism is added, so that the model has a time memory characteristic, smooth mutation of historical parameter information can be fused, and the stability and robustness of the method in a complex dynamic environment are improved.

The invention relates to the technical field of fault positioning, and discloses an indoor distribution multi-source data fusion fault positioning system based on reinforcement learning, which comprises a space-time tensor construction module, a federated collaborative detection module, a map reasoning positioning module and a prediction scheduling module, and is characterized in that a multi-source space-time data federated learning framework under privacy protection is constructed; and cross-physical domain dynamic reasoning and resource elastic scheduling of the fault propagation chain are realized. According to the method, through fusion of multi-source spatio-temporal data and federal collaborative learning, fundamental transformation from passive response to active prediction is realized, fault root causes in a complex environment can be accurately identified, and the false alarm rate is significantly reduced; equipment deadlock is effectively avoided by a dynamic resource scheduling mechanism, and the operation and maintenance efficiency is improved; the privacy protection design ensures the safety of cross-domain cooperation, meanwhile, the environmental perception capability enhances the robustness to hidden faults such as electromagnetic interference, and finally, a fault prediction-positioning-scheduling full-process closed loop is constructed.

The invention relates to the technical field of intelligent positioning of building construction, in particular to an intelligent positioning method and system for fabricated building laminated slabs based on AR and UWB. According to the method, a physical monitoring network comprising a UWB positioning base station and a visual positioning target is constructed, a digital operation instruction generated by analysis of a cloud BIM model is converted into a virtual contour superposed on a real operation plane by using an AR augmented reality technology, and further, the position deviation is calculated in real time and a sound-light alarm mechanism is combined, so that the real-time positioning of the cloud BIM model is realized. A construction quality closed-loop control system of real-time monitoring, alarming, adjusting and compliance is established, it is ensured that the laminated slab meets the design precision requirement in the installation process, and later reworking and rectification caused by installation errors are effectively avoided; meanwhile, based on data interaction of the cloud BIM management platform, full-process digital management from design model issuing to final installation position data returning is achieved, and reliable data support is provided for construction quality tracing and acceptance of the fabricated building.

The invention relates to the technical field of computers, in particular to a municipal environmental sanitation cleaning task scheduling method and system based on big data analysis, and the method comprises the steps: obtaining the historical garbage amount data of an urban traffic station, and carrying out the garbage amount partitioning through a K-means + + clustering algorithm according to the historical garbage amount data; acquiring urban meteorological data, and predicting an urban weather condition through a wavelet transform and LSTM neural network hybrid model according to the urban meteorological data; the state of the sanitation vehicle is monitored in real time through a vehicle-mounted sensor, and the position and the driving track of the sanitation vehicle are tracked in real time through the GPS positioning technology; according to the garbage quantity partition, the predicted urban weather condition, the environmental sanitation vehicle state and the position and the driving track of the environmental sanitation vehicle, a cleaning task is distributed to the environmental sanitation vehicle; and real-time traffic conditions are obtained, and the sanitation vehicles are scheduled to go to the garbage quantity subareas to execute cleaning tasks in combination with the real-time traffic conditions. The environmental sanitation management efficiency can be improved.

The embodiment of the invention provides a power supply state fault positioning method and system, electronic equipment and a storage medium, and belongs to the technical field of fault positioning. The method comprises the following steps: carrying out residual calculation according to an original time domain electromagnetic signal and a reference frequency spectrum template to obtain a frequency spectrum residual matrix; and performing cross-frame disturbance integral scoring on the spectrum residual vector according to the integral residual scoring function to obtain disturbance scoring data. And obtaining a disturbance event set according to the disturbance score data and the disturbance threshold. And fusing the disturbance event set and the operation log to obtain a state event sequence. And constructing a state semantic tree according to the state event sequence and calculating an edge weight. And scoring each path according to the path scoring function and the edge weight to obtain path scoring data. And selecting a path corresponding to the maximum path score data as a main cause path, wherein a head node of the path is a fault root cause node. And entering a preset execution output process according to the main cause path and the fault root cause node. And the accuracy of power supply fault positioning is improved.

The invention discloses a server fault diagnosis system based on service topology correlation analysis, and relates to the technical field of computer data processing and automatic fault localization, and the system comprises a service topology construction module which is used for collecting multi-source heterogeneous data to construct infrastructure, runtime environment and business performance feature vectors, a service dependence topological graph is maintained; the fault storm diagnosis module is used for identifying a fault storm by calculating node state weights and utilizing spatial compactness and a time synchronization coefficient, and positioning a root cause node based on reverse breadth-first search and a fault propagation index; and the fusion alarm generation module is used for calculating the response time contribution degree of the root cause node to verify the causal relationship so as to distinguish dominant storm and hidden faults. The system executes cascade alarm convergence for dominant storm, triggers snapshot capture during deep operation for hidden faults, locates hidden root causes under complex topology, and realizes automatic fault closed-loop processing.

The invention discloses a power distribution network fault positioning method and device based on a quantum ant colony and a PSO algorithm, and belongs to the technical field of power system fault positioning. The method comprises the steps of obtaining topological structure data and real-time operation data of a power distribution network, and performing preprocessing; parameters of the quantum ant colony algorithm are initialized, path selection is carried out based on the quantum bit state transition probability, global search is carried out on the whole power distribution network, and a suspected fault area is obtained; and taking the suspected fault region obtained by the quantum ant colony algorithm as a search space of a PSO algorithm, initializing PSO algorithm parameters, performing local fine search through particle position and speed updating by using the PSO algorithm, and determining a fault position. Through organic fusion and cooperative work of the two algorithms, the global search capability of the quantum ant colony algorithm and the local search advantage of the PSO algorithm are fully played, the accuracy and efficiency of power distribution network fault positioning are effectively improved, and the adaptability of the algorithms to a complex topological structure is enhanced.

The invention discloses a railway power distribution network fault information automatic diagnosis and positioning method and system, and belongs to the technical field of power distribution fault diagnosis and positioning, and the method comprises the steps: employing an auxiliary classification generation adversarial network, obtaining a class-balanced standardized training data set, and carrying out the preprocessing; mapping the preprocessed data in the standardized training data set into a snowflake-shaped SDP image under a two-dimensional polar coordinate system, inputting the snowflake-shaped SDP image into a fault diagnosis model based on a deep convolutional neural network, and outputting a fault section and a preliminary fault distance; constructing a physical circuit model of railway power distribution network distribution parameters, and calculating theoretical simulation voltage and simulation current by using a transmission line equation; and starting iterative correction based on the Pearson's correlation coefficient of each data, and outputting a diagnosis result. According to the method, high-precision, high-reliability and self-adaptive fault information automatic diagnosis and positioning are realized by fusing data enhancement, feature transformation, intelligent diagnosis, physical inversion check and topology self-adaptive adjustment.

Disclosed are a key facility gas safety assurance method and a system, comprising the steps: 1. acquiring basic data; 2. using BIM technology to perform three-dimensional modeling of a key facility; 3. reviewing a model; 4. performing lightweight model handover of the reviewed model; 5. performing model splitting, control dashboard design, function development, and project deployment on a plan by means of a corresponding platform side of a key facility digital delivery platform adopting a B / S architecture; and optimizing the model by means of a corresponding mobile client utilizing AR and positioning technologies, then uploading the model to an AR collaborative platform, and performing location verification by means of creating a two-dimensional code on the AR collaborative platform and deploying the two-dimensional code. The present invention enables real-time monitoring, rapid linkage, intelligent command, and safety control of a gas system in a key facility.

This invention discloses a fire-fighting location and communication method and system that integrates multi-source sensing data, relating to the fields of fire-fighting location technology and communication technology. The invention significantly suppresses time drift at each node through a time calibration mechanism within the topological neighborhood. The corrected timestamp set generated by iterative correction ensures high consistency in cross-node timing, thereby improving the synchronization and reliability of multi-source data fusion. A dynamic generation strategy based on local gradients and communication status heartbeat signals enables the broadcast period to adapt to link fluctuations, improving network robustness in scenarios with thermal interference or smoke obstruction. Furthermore, the location and communication status table uniformly quantifies the location information interval, movement trend, and communication availability level, enabling synchronous optimization of location inference and communication routing selection, significantly improving the location accuracy and communication continuity for firefighters in complex environments.

This invention relates to the field of fault location technology and discloses a fault location method for a long-distance conveying inspection robot. The method includes: when the inspection robot performing long-distance conveying is detected to have reached an entrance equipped with an RFID tag, reading the RFID tag to determine the section of the long-distance conveying route to which the inspection robot belongs, and calculating the distance between the inspection robot and the entrance; acquiring an image of the idler roller taken by a camera mounted on the inspection robot, and using an image recognition algorithm to identify the idler roller code; when a fault signal is received, concatenating the section of the long-distance conveying route to which the inspection robot belongs, the distance between the inspection robot and the entrance, and the idler roller code identification result to obtain the position of the inspection robot in the long-distance conveying, and generating an alarm message. This invention combines RFID and visual recognition to accurately locate the inspection robot's position, improving positioning accuracy and thus improving fault handling efficiency.

This application relates to the field of three-phase distribution network fault detection and location technology, and discloses a method, device, system, and storage medium for fault location in distribution systems. It addresses the problem of low accuracy in fault identification and location in existing solutions due to poor traceability of PMU measurement data and high computational costs. The method includes: acquiring power grid data collected by acquisition units located on the distribution network of the distribution system; fusing the three-phase π model and state estimation equations characterizing the distribution network branches to obtain equations corresponding to the distribution branches, and solving them to obtain theoretical values; using a threshold filtering method to remove data with numerical fluctuations exceeding preset conditions from the power grid data, and compensating the removed power grid data based on the data acquisition accuracy to obtain target power grid data; using the LNR algorithm to perform fault detection based on the target power grid data and theoretical values; and employing a multi-stage candidate branch location strategy to locate the fault when it is detected.

The application discloses an AI-driven cloud rendering service fault rapid positioning method, relates to the technical field of service fault positioning, and is used for solving the problems of low efficiency and large resource waste of rendering retry behavior, acquiring a cloud rendering operation target, performing regional division, collecting the resolution and artifact area of each region, comprehensively calculating the cloud rendering fault indexes of the regions, evaluating fault region positioning features based on the fault indexes and storing the fault region positioning features in a temporary database, setting multiple rendering retries within a retry time, counting retry fault frequencies, detecting the rendering execution time of each time and calculating rendering levels, evaluating fault type positioning features in combination with the retry fault frequencies and the rendering levels, fusing the fault region positioning features in the temporary database, selecting an adaptive fault region or type positioning method for processing, intelligently analyzing multidimensional fault features, realizing rapid and accurate positioning of cloud rendering faults, and improving service stability and operation and maintenance efficiency.

The invention discloses an intelligent automatic test system and a test method, and belongs to the technical field of software testing, the system comprises a hardware layer and six software modules, namely a demand analysis module, a large model processing module, a test execution module, a self-healing optimization module, a result analysis module and a knowledge base management module, which are deployed on the hardware layer. The method comprises the following steps: extracting structured data through a demand analysis module, and intelligently generating test assets by a large model core processing module; the test execution module adopts a fusion positioning technology to carry out multi-terminal automatic test; the self-healing optimization module monitors the interface change in real time and automatically corrects the script; the result analysis module identifies defects and generates a report; and the knowledge base module continuously iterates to provide support. According to the invention, full-process automatic closed loop from demand to report is realized, the problems of low efficiency, poor compatibility and high maintenance cost of the traditional test technology are solved, and the test intelligence level and the adaptive capability are obviously improved.

The invention discloses a power distribution network partial discharge fault positioning device and method, and relates to the technical field of power distribution network fault positioning, and the device comprises a data collection module, a multi-dimensional analysis module, an intelligent traceability module, a directional verification module, a report generation module and a local database. All the modules collaboratively integrate discharge pulses and load data of corresponding lines, through feature extraction, correlation analysis and historical data verification, a plurality of discharge sources are accurately distinguished, a fault branch is locked, then through space-time signal propagation path analysis and multi-algorithm collaborative optimization, a fault is accurately positioned to a specific equipment node in the power supply branch, and the fault is accurately positioned. And a structured report is output, so that the positioning accuracy and efficiency are greatly improved, and reliable support is provided for operation and maintenance of the power distribution network.

The invention relates to the technical field of three-dimensional environment perception and positioning, in particular to a three-dimensional point cloud dynamic object deletion method, three-dimensional reconstruction equipment and a computer storage medium. According to the three-dimensional point cloud dynamic object deletion method, feature vectors of a local connection graph, a central voxel and a neighborhood voxel adjacent to the central voxel are generated through a voxel-based domain relationship, a dynamic probability value of the central voxel is predicted by introducing a graph neural network, and when the dynamic probability value is determined, the dynamic probability value of the central voxel is determined. The states of adjacent voxels in the neighborhood relation are fully considered, and the accuracy of dynamic voxel determination is greatly improved.

The invention belongs to the technical field of carry-scraper positioning, and discloses an underground unmanned carry-scraper global rapid positioning method based on FASTLIO improvement. The method comprises the following steps: acquiring a high-precision underground map, and performing parameter initialization on a FASTLIO algorithm; a sampling strategy is adaptively set, point cloud data and inertial data are collected in real time, and a FASTLIO algorithm is dynamically optimized; feature classification is carried out on the point cloud data, and differential configuration of feature weights is carried out on the point cloud data according to a classification result and the high-precision underground map; adopting a dynamically optimized FASTLIO algorithm to perform tight coupling optimization on the point cloud data and the inertial data after feature weight configuration to obtain initial positioning data, and performing dynamic correction to obtain final positioning data; the problems of drifting and error accumulation of a traditional positioning technology in a narrow closed tunnel can be effectively solved, and the positioning precision and stability of the unmanned carry-scraper in a complex underground environment are improved.

The embodiment of the invention discloses an indoor positioning method and system, a storage medium and a server, and is applied to the technical field of computer vision, three-dimensional reconstruction and indoor positioning. The method comprises the following steps: a server extracts a semantic landmark in a current view of a mobile terminal and semantic information for describing a relationship between the semantic landmark and the mobile terminal through a semantic understanding technology based on a received video stream sent by the mobile terminal; and the real-time feature vector obtained based on the semantic landmark and the relational semantic information is matched with the preset semantic landmark feature library, so that the current position of the mobile terminal can be quickly determined. In this way, the snapshot type positioning experience of scanning and knowing is achieved, the method does not depend on the initial position and the continuous movement track of the mobile terminal, and the complexity of indoor positioning is greatly reduced.

The present application relates to VR auxiliary debugging and bearing fault positioning technical field, specifically, the present application relates to VR auxiliary debugging and fault positioning method for equipment maintenance, aiming at solving the problem that traditional bearing fault detection adopts visual and static detection, and cannot collect full angle torque dynamic characteristics and large fault positioning error, in the bearing press fitting link, the dynamic torque sensor integrated with the angle encoder is used to collect the instantaneous torque of the full angle, the torque curve of the rotation angle is generated and is associated with the coding storage, when the fault occurs, the measured curve and the standard curve of the same type are called, and are bound to the double virtual bearing model of the VR scene, the virtual driving model is synchronously rotated, the signal module generates the resistance signal according to the torque to control the force feedback data glove, the real rotation feeling is restored by superimposing the viscous damping, the comparison module compares the resistance difference point by point and in the sliding window, if the threshold value is exceeded, the abnormal rotation angle interval is locked, the effective fault area is verified and confirmed through the original point, and the positioning result is output, and the fault positioning precision is improved.

The application discloses a power distribution network line fault positioning method, device and equipment, relates to the technical field of power distribution network line fault positioning, and comprises the following steps: acquiring operation data and lightning ground flash data; performing feature extraction on the operation data, performing space-time feature mapping on the lightning ground flash data, and generating multi-source feature parameters; performing probability reasoning on the input multi-source feature parameters through a distributedly arranged section intelligent agent and a Bayesian network submodule thereof, and outputting real-time fault probability values of each independent section of the power distribution network; and taking the real-time fault probability values of each independent section as input, dynamically coordinating output results of each intelligent agent through a Nash equilibrium strategy, and generating a globally consistent fault positioning decision. The application is used to solve the problems that a traditional line fault positioning method is difficult to effectively fuse heterogeneous data and difficult to coordinate local judgment conflicts.

The application discloses a remote personnel positioning and high-definition image monitoring system based on a 5G network, relates to the technical field of 5G intelligent monitoring and positioning, and comprises a 5G fusion positioning module, an intelligent image acquisition and processing module, and a 5G edge computing and transmission module.The 5G fusion positioning module uses composite positioning and multipath separation technology.The intelligent image acquisition and processing module is equipped with a dynamic aperture camera and super-resolution reconstruction, supports 8K quality and full-scene perception, and comprises a federal learning framework and dynamic code rate adaptation.The system also has a dynamic networking module, a multi-modal fusion module, an intelligent early warning module and the like, and improves the anti-interference and cooperative capabilities.The application realizes 5G fusion positioning and 8K high-definition monitoring, has strong anti-interference capability, intelligent early warning, privacy protection and long endurance, is suitable for intelligent security, industrial inspection and the like, greatly improves the emergency response efficiency, and promotes the landing of 5G intelligent application.

The shapes and sizes of teeth are greatly different in different crowds, and in addition, the problems of crowding, missing, overlapping and the like also exist, so that the complexity of tooth positioning is increased. At present, tooth segmentation and positioning are mainly based on tooth X-ray films, and shooting of the X-ray films in remote areas with backward medical resources is limited. In order to solve the above problems, a deep convolutional neural network is planned to be used in the research, and teeth are positioned and classified based on intraoral images. The method comprises the following steps: firstly, preprocessing a collected data set to improve the capturing capability of important features; secondly, a mixed attention mechanism is introduced into the project, and important information in feature maps of different scales is accurately captured; and finally, the convergence and generalization ability of the model are improved through a residual module. The specific innovation points are as follows: 1, a mixed attention mechanism is introduced into a basic framework based on U-Net + + to dynamically adjust the weight of each feature map in the network; 2, the low-level features and the high-level features are connected by adopting skip connection, so that the problem of local information loss possibly caused by the low-level features and the high-level features can be solved;

The invention discloses a calculation graph compilation error attribution method, system and equipment based on a benchmark reference model and a medium, belongs to the technical field of computers, and aims to solve the technical problem that in the model compilation optimization process of an AI compiler, when end-to-end test results are inconsistent, error sources are difficult to effectively attribut and position, and the calculation graph compilation error attribution accuracy is poor. According to the technical scheme, a reference path, an isolation to-be-measured path and an accumulation to-be-measured path are cooperatively executed for each node in a calculation graph, corresponding reference output, isolation to-be-measured output and accumulation to-be-measured output are respectively obtained, and differential analysis is carried out on the reference output, the isolation to-be-measured output and the accumulation to-be-measured output, so that the calculation graph is obtained. Compiling errors are accurately attributed to function errors of operators, internal calculation errors or transmission characteristics accumulated to the errors, and accurate positioning and quantitative analysis of the compiling errors are achieved; the method specifically comprises the following steps: constructing an initial calculation graph; initializing node attributes; and constructing a benchmark reference model.

This invention discloses an auxiliary detection system and method for fault location in hollow optical cables. By deploying moisture and dust detection devices in each splice box of the hollow optical cable and combining them with a splice box detection management platform, this invention achieves proactive monitoring and accurate fault diagnosis of environmentally sensitive factors in hollow optical cables. Specifically, addressing the pain point of hollow optical cables being highly sensitive to moisture and dust, and the difficulty of accurate fault location by traditional OTDRs, this invention utilizes real-time collected moisture and dust data, combined with signal reception power and historical detection data for comprehensive analysis. This effectively distinguishes abnormal environmental conditions within different splice boxes, thereby accurately identifying the specific location of the fault and solving the problem of traditional detection methods failing. Therefore, this invention provides an auxiliary judgment and location technology for faults in hollow optical cables caused by moisture and dust, improving maintenance efficiency and reducing hollow optical cable faults.