281 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 computer network equipment fault positioning method and system and a storage medium, and relates to the technical field of fault positioning. The method comprises the following steps: performing multi-source operation acquisition on computer network equipment to generate an equipment state data set; according to the equipment state data set, abnormity judgment is carried out according to the dynamic baseline, monitoring extraction is carried out according to a judgment result, and an abnormal event sequence is constructed; performing association mapping based on a computer network equipment topological graph, constructing a network equipment dependency graph, traversing the network equipment dependency graph according to an abnormal event sequence to perform traceability positioning, and determining a fault source equipment identifier set; and performing fault root cause analysis according to the fault source equipment identification set, determining fault generation root cause data, and feeding back the fault generation root cause data to the network operation and maintenance unit to execute automatic fault handling. The technical problems of low network equipment fault positioning efficiency and poor positioning accuracy in the prior art are solved, and the technical effect of accurately and efficiently positioning the fault source is 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 discloses an unmanned aerial vehicle track maintenance and autonomous positioning method in a communication decline scene, and relates to the technical field of navigation maintenance positioning, and the method comprises the steps: carrying out the signal intensity monitoring and smoothing of signal quality and decline information collected in real time, carrying out the decline trend prediction through a long-short-term memory network, and outputting a signal decline report; in combination with multi-unmanned aerial vehicle sensor data, local sensor data is processed in real time through edge calculation, and shared information is fused through particle filtering; a cooperative positioning algorithm and a distributed positioning algorithm are used to correct the shared information of the multiple unmanned aerial vehicles and output the precision for autonomous positioning; and adjusting the unmanned aerial vehicle to an energy-saving flight mode by using an energy consumption model in combination with the signal recession report and the optimal path. According to the method, the flight path of the unmanned aerial vehicle is optimized by adopting deep reinforcement learning and the game theory, and adaptive flight path adjustment is realized in combination with a model prediction control algorithm, so that the unmanned aerial vehicle can dynamically avoid a low-signal area, and flight stability is ensured.

The invention discloses a server fault diagnosis method and device, a storage medium and a program product, and relates to the technical field of server fault localization, and the method comprises the steps: carrying out the preprocessing of original log data, determining a state space according to the obtained preprocessed log data, a fault classification model containing a value network and an action network is obtained through reinforcement learning in advance, the value network in the fault classification model is used for carrying out value evaluation on the state space, and the action network in the fault classification model is used for carrying out action probability distribution calculation on the state space according to a target value obtained through value evaluation; and determining the target fault type according to the calculated probability distribution result, thereby solving the technical problem that new features or anomalies cannot be effectively judged, effectively improving the quality and integrity of log data, improving the server fault diagnosis efficiency and accuracy, realizing intelligent identification of the fault type, and improving the fault diagnosis efficiency and accuracy. The method has the technical effects of good training stability and generalization ability.

The invention provides a multifunctional inspection device detection system based on a distribution network mobile operation terminal, and relates to the technical field of electric power detection, and the multifunctional inspection device detection system comprises a portable main terminal of a dynamic collaborative architecture, a wearable terminal for augmented reality interaction and a distributed heterogeneous sensing cluster, which form a closed-loop data chain through an electric power dedicated low-delay wireless communication network; the portable main terminal is integrated with a heterogeneous computing unit with adaptive computing power distribution, can perform real-time fusion analysis on multi-dimensional sensing data, and outputs a visual result containing fault location and confidence; the wearable terminal superposes fault information to a real scene in a three-dimensional marking form through a virtual-real fusion positioning technology, and supports eye movement and voice collaborative interaction; multi-module collaborative acquisition of the distributed heterogeneous sensing cluster is combined with a nanosecond timestamp synchronization mechanism, so that the limitation of traditional single parameter detection is broken through, rich and synchronous basic data is provided for subsequent analysis, and the comprehensiveness of routing inspection is greatly improved.

Various approaches for the generation and verification of user equipment (UE) location, using communications and processing capabilities of non-geostationary (NGSO) satellite networks and equipment are discussed. Among other examples, communications between a UE and a low-earth orbit (LEO) satellite may be used to substantiate or corroborate, to a location management function (LMF) of a 5G network, that a UE is located in or at a particular geographical location or area. Additionally, based on this location information and related SV ephemeris data, a satellite coverage area may be determined for a UE at a location at any one moment in time, for coordinating and managing connectivity of the UE with terrestrial and non-terrestrial networks.

The invention belongs to the technical field of intelligent robot navigation and positioning, and discloses an intelligent robot autonomous mapping method in a GNSS rejection environment, which comprises the following steps of: monitoring a GNSS signal state in real time, and switching to pure laser SLAM mapping when detecting that the number of available satellites, PDOP or pseudo-range residual exceeds a threshold; the method comprises the following steps: acquiring three-dimensional point cloud data, filtering and denoising, obtaining a filtered and denoised current frame point cloud, performing laser radar mapping and positioning registration, selecting a key frame point cloud, constructing lightweight neural implicit scene representation, and training an MLP network; loopback detection is carried out, a sparse factor graph is constructed, key frame poses are corrected, then the key frame poses are applied to original three-dimensional point cloud data, and a two-dimensional grid map is generated. According to the invention, under the condition that the GNSS signal is blocked or fails, the environment map with high precision and low drift is continuously generated, and the real-time performance and robustness of autonomous mapping are remarkably improved.

The invention provides a speed reducer fault positioning method and system based on artificial intelligence, and relates to the technical field of intelligent fault detection and positioning of industrial equipment. The method comprises the following steps: synchronously acquiring vibration, temperature and acoustic emission signals by using a multi-mode sensing module, carrying out filtering, correction and improved wavelet packet decomposition processing through an edge calculation unit, extracting time domain, frequency domain and nonlinear characteristics, calculating a vibration degradation degree and a state deviation index to construct a characteristic space, and calculating a state deviation index; fault large-class identification and fault source accurate positioning are realized through deep metric learning and a graph attention network, and credibility evaluation and online incremental learning optimization are carried out in combination with a D-S evidence theory. The system comprises a multi-modal sensing module, an edge computing unit, a cloud analysis platform, a self-adaptive optimization module and a man-machine interaction terminal. The method overcomes the defects that in a traditional method, a signal source is single, feature extraction depends on experience, real-time performance is insufficient, positioning is inaccurate and the like, and fault detection accuracy and equipment operation safety are remarkably improved.

The invention relates to the technical field of shopping, and discloses a map shopping method based on AI voice interaction. Through deep fusion of AI voice interaction and a map visualization technology, the user experience and the operation efficiency of a geographic shopping scene are remarkably improved. According to the method, a real-time association system of the map POI and the commodity inventory is constructed, multi-modal interaction of voice instructions, dragging operation and cross-shop settlement is supported, and intelligent complementation of fuzzy instructions and dynamic distribution route optimization are realized by adopting BERT semantic understanding and a Q-learning path planning algorithm, so that the order response speed is improved, and the distribution distance is optimal; voiceprint DTW verification and Redis distributed inventory management are integrated, and real-time inventory scheduling in a 3-kilometer business district is realized while payment security is guaranteed; full-link state visual management is established through SSE push and WebSocket positioning technologies, and closed-loop experience of'search-purchase-payment-tracking 'is formed in combination with a thermodynamic layer recommendation system, so that operation is convenient.

The invention discloses a positioning method and system based on an RSSI fingerprint database and a generalized regression neural network, and relates to the technical field of indoor positioning. The method comprises the following steps: acquiring RSSI values and corresponding position information of a plurality of sampling points in a target space, and constructing a fingerprint database based on the RSSI values and the position information; establishing a generalized regression neural network prediction model based on the fingerprint database, and globally optimizing model parameters of the generalized regression neural network prediction model by adopting a quantum particle swarm algorithm to obtain preliminary model parameters; performing local optimization on the preliminary model parameters by adopting a simulated annealing algorithm to obtain target model parameters, and training based on the target model parameters to obtain a target generalized regression neural network prediction model; and collecting a real-time RSSI value of a to-be-positioned target, inputting the real-time RSSI value into the target generalized regression neural network prediction model, and outputting a target position coordinate of the to-be-positioned target. By implementing the technical scheme provided by the invention, parameter optimization can be prevented from falling into a locally optimal solution, and the positioning precision is 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 discloses a power distribution network fault positioning method and positioning system, and relates to the technical field of power distribution network fault positioning, and the method comprises the steps: collecting multiple groups of historical data of different faults of a power distribution network, obtaining the missing intervals of voltage and current sequences in each group of historical data, and positioning the missing intervals to the front, middle and back of the whole sequence; different interpolation methods of different missing intervals are obtained by combining the length of the missing interval, the distance from the starting point position of the missing interval to the previous nearest missing interval and the distance from the ending point position of the missing interval to the next nearest missing interval, an abnormal value is marked for a sequence after interpolation is completed, and the sequence is equally divided into S < front > and S < back >; the abnormal values are repaired by utilizing ELM, a standard data set belonging to the fault is obtained, clustering processing is carried out on data in the standard data set, irrelevant clusters are removed, and a specific position is positioned according to the similarity between the fault and each group of faults in the target cluster. The power distribution network fault positioning method effectively improves the power distribution network fault positioning precision.

The invention discloses a maintenance management system and method for intelligent office equipment, and relates to the technical field of maintenance management of the intelligent office equipment, and the system comprises an equipment fault diagnosis grading module which is used for installing a multi-dimensional sensor on the intelligent office equipment, collecting the information of the multi-dimensional sensor in real time, carrying out the analysis and judgment of a fault type through combining historical fault data, and carrying out the maintenance management of the intelligent office equipment. A fault severity index is dynamically calculated, a maintenance priority is automatically divided, and a maintenance result is output and transmitted to a maintenance order generation module; the maintenance priority is automatically divided by dynamically calculating the fault severity index, the fault diagnosis accuracy is improved, the GIS positioning technology and the dynamic routing optimization algorithm are adopted, appropriate maintenance personnel are determined, the optimal maintenance work order is generated, intelligent order dispatching is achieved, and the maintenance efficiency is improved. Maintenance personnel can respond quickly and perform maintenance operation through dynamic authority management, so that the maintenance time is shortened, the maintenance efficiency is improved, the downtime of equipment is shortened, and the business loss caused by equipment faults is reduced.

The invention discloses a power distribution network line fault positioning method, device and equipment, and relates to the technical field of power distribution network line fault positioning, and the method comprises the steps: obtaining operation data and lightning ground lightning data; feature extraction is carried out on the operation data, spatial-temporal feature mapping is carried out on the lightning and ground lightning data, and multi-source feature parameters are generated; probabilistic reasoning is carried out on the input multi-source characteristic parameters through section agents deployed in a distributed mode and Bayesian network submodules of the section agents, and real-time fault probability values of all independent sections of the power distribution network are output; and taking the real-time fault probability value of each independent section as an input, dynamically coordinating an output result of each agent through a Nash equilibrium strategy, and generating a global consistency fault positioning decision. The method is used for solving the problem that a traditional line fault positioning method is difficult to effectively fuse heterogeneous data and is difficult to coordinate local judgment conflicts.

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 discloses a primary and secondary fusion ring main unit fault positioning communication method and system, and relates to the technical field of power system fault positioning, and the communication method comprises the specific steps: S100, multi-source parameter cooperative collection: collecting the parameters of current, voltage and power factor of each loop through an electronic multifunctional sensor arranged in a ring main unit, meanwhile, an intelligent electric meter with a communication function and a wireless data acquisition terminal based on the Internet of Things technology are utilized to obtain operation parameters of flexible loads of the charging pile and the air conditioner cluster, and data are transmitted to a server to be subjected to format conversion and abnormal value elimination; according to the invention, through a multi-source parameter collaborative acquisition technology, real-time and accurate acquisition of key parameters of current, voltage and power factors of each loop of the ring main unit is realized, and at the same time, the operation parameters of the flexible load are comprehensively monitored by combining an intelligent electric meter with a communication function and an Internet of Things technology wireless data acquisition terminal. And the comprehensiveness and accuracy of data acquisition are improved.

The invention relates to the technical field of power grid information system fault positioning, and particularly discloses a power grid information system fault positioning system based on adaptive learning, and the system comprises a data collection module which is used for collecting electrical quantity data and state quantity data in the operation process of a power grid; the data preprocessing module is connected with the data acquisition module and is used for carrying out preprocessing operations such as filtering, denoising and normalization on the acquired data and removing interference and invalid information in the data; and the fault feature extraction module is connected with the data preprocessing module and is used for extracting feature parameters related to the power grid fault based on the preprocessed data. According to the power grid information system fault positioning system based on adaptive learning, a dynamic multi-scale adaptive learning algorithm is fused with reinforcement learning and transfer learning mechanisms, model parameters and structures are dynamically adjusted according to the real-time operation state of a power grid, similar scene experience is migrated, and the fault positioning accuracy and adaptability are remarkably improved.

The invention relates to the technical field of fault positioning, and discloses a fault position positioning method and device based on multiple acquisition devices, equipment and a medium, and the method comprises the steps: obtaining a plurality of device positions of the acquisition devices of a plurality of traveling wave signals, the time difference of arrival of the traveling wave signals acquired by every two acquisition devices, and the propagation speed of the traveling wave signals; a plurality of hypersphere equations are established according to the positions of every two devices, the time difference of arrival and the propagation speed, and the hypersphere equations are used for reflecting the difference value relation of the distances between the positions of every two devices and the positions of the fault points corresponding to the traveling wave signals; and using a preset particle swarm optimization algorithm and a hyperspherical equation to carry out fault point position positioning processing to obtain an optimal fault point position. Through the above mode, a plurality of acquisition devices can be adopted to capture traveling wave signals, then the hyperspherical equation is established, and finally, the particle swarm optimization algorithm and the hyperspherical equation are used to carry out fault point position positioning processing, so that the optimal fault point position is obtained, and the fault positioning precision is 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 discloses a UWB-IMU-ODOM sequential fusion positioning algorithm based on AUKF, a storage medium and equipment, and relates to the technical field of indoor positioning, and the algorithm comprises the steps: completing the preliminary calculation of UWB data and a track plotting result in respective time steps; performing state prediction by a central UKF (Unscented Kalman Filter) based on a CTRV motion model, and eliminating abnormal measurement values by comparing a residual range with a chi-square distribution value so as to reduce the influence of noise peaks and burst errors on the positioning precision; through an adaptive noise adjustment mechanism, a sliding window is adopted to calculate the moving average of the residual error, and the noise covariance is dynamically adjusted according to the residual error change amplitude to adapt to the change of noise characteristics in a complex dynamic environment. A centralized sequential fusion framework is adopted, independence of UWB and IMU / ODOM data is reserved, sensor data at different moments are fused through a central UKF filter, and the method has low calculation complexity and good robustness and is suitable for an embedded system with limited resources.

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 relates to the technical field of intelligent environment detection, and particularly discloses an underground pipe gallery detection method based on a robot and a pipe gallery detection robot, and the method comprises the steps: obtaining the topological data of a pipe gallery through a multi-source sensing device, constructing a three-dimensional space model of the pipe gallery through the combination of a high-precision Beidou / UWB fusion positioning technology, and obtaining a three-dimensional space model of the pipe gallery; geometric parameters and initial defect distribution of the to-be-detected pipe gallery are marked; based on the three-dimensional space model, a pipe gallery detection robot is deployed, and multi-dimensional data of the pipe gallery environment and the equipment state are synchronously collected in the advancing process; according to the invention, robot inspection is introduced, so that personnel are prevented from being directly exposed in a dangerous environment, the safety risk of the inspection personnel is remarkably reduced, and the problem of high risk in manual inspection is effectively solved; meanwhile, through an automatic and intelligent robot inspection mode, the robot flexibly shuttles around the pipe gallery, all-directional and dead-corner-free detection of the pipe gallery is achieved, the inspection time is greatly shortened, and the detection comprehensiveness and accuracy are remarkably improved.

The invention provides an intelligent fault positioning system and method based on a DC system, and relates to the technical field of power fault positioning, and the system comprises a digital twin module, a sensor network module, a real-time data synchronization module, a graph neural network model module, an online reasoning module, an intelligent decision module and a decision self-healing module. Through deep fusion of the digital twinning technology and the graph neural network, complex electrical, thermal, mechanical and environmental parameters in the direct current system can be comprehensively analyzed. The digital twin module provides a high-precision virtual model of the system through multi-physics field integration, so that a graph neural network model can accurately capture tiny anomalies and complex fault modes in the system under the support of rich and high-quality data. Through combination of the graph convolutional network and the graph attention network, the graph neural network model can effectively identify relevance between key nodes and edges, weights of neighbor nodes are dynamically adjusted, and the fault detection and positioning precision is further improved.