6877 results about "Distance measurement" patented technology

The invention discloses a hierarchical contrast anti-fact learning method and system for a visual question and answer model. The method comprises the steps of obtaining original image data and question text data, constructing multi-modal joint tensor data, calculating probability distribution based on a projection matrix, screening samples and dynamically adjusting weights. Constructing a multi-modal graph structure, performing modal alignment based on the feature enhancement matrix, and executing dynamic feature fusion; establishing a multi-level probability knowledge structure, generating an anti-fact sample, and executing knowledge fusion operation; constructing a multi-level distance measurement system, executing adaptive weight optimization, and performing spectral clustering optimization and boundary adjustment; and constructing a multi-dimensional evaluation index system, executing stability analysis and dynamic performance tracking, and obtaining final performance evaluation index data. According to the method, the performance of the visual question and answer model is improved through a hierarchical comparison anti-fact learning framework, and optimization of feature representation, dynamic adjustment of a knowledge structure and accurate evaluation of the model performance are realized.

An apparatus for generating obfuscated data within a computing environment, comprising a processor and a memory containing instructions configuring the processor to access a database containing a plurality of private data elements belonging to at least a private record, generate a set of obfuscated data elements, representative of the at least a private record, as a function of the plurality of private data elements using an generative model, determine a first distance measure between at least an obfuscated data element within the set of obfuscated data elements and at least a private data element of the plurality of private data elements within the database, and verify the first distance measure is within a distance range, wherein a minimum threshold of the distance range is determined as a function of a deidentification parameter and a maximum threshold of the distance range is determined as a function of an obfuscation parameter.

The invention relates to an automatic data labeling method of an ISP image signal processing visual sensor. The method comprises the following steps: carrying out noise reduction and signal enhancement on an original image data stream acquired by an ISP image signal processing visual sensor to obtain a standardized image sequence; performing time sequence feature extraction to obtain a time sequence feature tensor; performing spatial feature extraction to obtain spatial-temporal feature representation; inputting a double-fold teacher-student network for semi-supervised training to obtain an initial image annotation model; performing quality evaluation based on the prediction probability distribution and the time sequence consistency to obtain a labeling result with a quality score; and storing a high-quality sample in the labeling result with the quality score into a sample buffer pool, carrying out sample screening based on entropy and distance measurement, and carrying out parameter updating on the initial image labeling model through an incremental learning strategy to obtain a target image labeling model. According to the invention, the reliability evaluation of the labeling result is realized, and the accuracy of automatic labeling is ensured.

In some aspects, a server determines an input provided to a generative artificial intelligence, parses the input to determine: a type of content to generate, a content description, and creator identifiers. The server embeds the input into a shared language-image space to create an input embedding. The server determines a creator description comprising a creator-based embedding associated with individual creators. The server performs a comparison of the input embedding to the creator-based embedding associated with individual creators to determine a distance measurement of an embedding of individual creators in the input embedding. The server determines creator attributions based on the distance measurement and creates a creator attribution vector to provide compensation to the creators.

The invention discloses a distributed power supply access-oriented power distribution network fault distance measurement method and system, and relates to the technical field of power systems, and the method comprises the steps: collecting the electrical parameters and operation states of distributed power supply access nodes in a power distribution network in real time, building a dynamic manifold model based on an ecological niche theory, and carrying out the calculation of the dynamic manifold model; adaptively adjusting manifold learning neighborhood parameters according to power fluctuation data included in the electrical parameters, and updating node ecological niches to reconstruct a dynamic manifold model; based on the reconstructed dynamic manifold model, fault features are extracted from three scales of a current harmonic component, a feed line inter-harmonic propagation path and whole network voltage influence, and a three-dimensional feature vector is generated through fusion of a graph correlation algorithm; based on the expanded fault sample library and the power fluctuation data, constructing a fault transfer relation model to predict a ground fault and a short circuit risk area; a fault source is modeled by adopting a topological neural network, and a fault point distance measurement value is output through state prediction and strategy deduction.

The invention provides a novel power system multi-scale network construction energy storage planning optimization method based on source network load clustering. The method comprises the following steps: firstly, processing source network load data by adopting a multi-dimensional adaptive feature extraction and fusion method, and realizing accurate processing of the data through interval adaptive probability density estimation, adaptive kernel tensor decomposition and marginal information entropy maximization; then, a multi-level data clustering method is adopted, and accurate clustering is achieved through hierarchical distance measurement and boundary self-adaptive adjustment; then, a multi-scale energy storage modeling method is adopted, and accurate modeling of the energy storage system is achieved through the dynamic characteristic matrix and self-adaptive topology embedding; and finally, a multi-objective optimization solution method is adopted, and efficient solution of an optimization problem is realized through adaptive hierarchical decomposition and hybrid evolutionary optimization. According to the invention, the accuracy and reliability of energy storage planning can be obviously improved, and the system operation efficiency is improved.

The invention relates to a laser inter-satellite link test method, system and device and a computer readable medium, which are used for performing butt joint test on a laser inter-satellite link by using a laser inter-satellite link test system. The laser inter-satellite link test system comprises any one of a laser high-speed processor, a first laser terminal, a second laser terminal, a first collimator, a second collimator, a monitoring camera, a micro-vibration simulation galvanometer and a rotary table. The method comprises the following steps: carrying out a signal light capturing and tracking test; carrying out beacon light capturing and tracking and communication distance measurement test; carrying out a beacon light leapfrogging capturing and tracking test; carrying out a broken chain recapture test; performing a long-time link establishment assessment test; therefore, any one of the capture time, the link establishment time, the coherent fine tracking precision, the error rate, the ranging random error, the link reestablishment time, the link establishment interruption times and the recapture success times of the laser inter-satellite link is obtained. According to the invention, the matching performance and reliability of working processes among single machines can be effectively verified, and system risks can be effectively prevented.

The invention provides a distribution line fault location optimization method based on single-ended traveling wave location, and relates to the technical field of traveling wave detection. The method comprises the following steps: carrying out segmented modeling on a line, calculating the traveling wave propagation speed and wave impedance of each segment, and correcting the traveling wave propagation speed and wave impedance; collecting a voltage / current traveling wave signal of a detection point, and extracting a line mode component; performing wavelet transformation on the line mode component, detecting the wave head position of the initial traveling wave through a modulus maximum search algorithm, and recording the arrival time of the initial traveling wave, a corresponding first energy value and polarity; calculating a reflected wave time window based on segmented modeling parameters, screening reversed polarity waves with qualified energy attenuation, and calculating a fault distance; if no effective reflected wave is detected in the reflected wave time window, triggering a pseudo double-end mode; a result is verified through quadruple constraints; the problems of poor parameter adaptability, low signal processing precision, strong reflected wave dependence and incomplete verification in traveling wave distance measurement of a complex distribution line are solved, and the accuracy and reliability of fault distance measurement are improved.

The invention relates to the field of image processing, in particular to a paint defect detection method and system based on machine vision, paint surface images are collected through an industrial camera, and brightness correction is carried out in combination with an illumination correction factor; fusing color fluctuation and texture features to construct a fluctuation factor, and optimizing a distance measurement formula of super-pixel segmentation to realize accurate segmentation of a complex surface area; and finally, carrying out classification detection on the super-pixel region features by utilizing a convolutional neural network, and identifying the type of the paint surface defect. According to the invention, through illumination correction, fluctuation factor construction, improved superpixel segmentation algorithm and convolutional neural network classification, automatic and accurate detection and classification of paint surface defects are realized.

The invention relates to an autonomous navigation method for lunar satellite formation and a second-order filtering navigation architecture, and the autonomous navigation method comprises the steps: obtaining orbit prediction information based on a kinetic model of the lunar satellite formation, and obtaining collaborative theory observation information based on a collaborative observation model, carrying out fusion processing on orbit prediction information, collaborative theory observation information and multi-source observation data of in-orbit observation by using an extended Kalman filtering algorithm, and estimating an absolute orbit state of the lunar satellite formation; establishing a relative motion model of the slave satellite under the local coordinate system of the master satellite, and establishing a relative measurement model; and taking the absolute orbit state of the master satellite as a reference datum, obtaining a preliminary prediction relative orbit state based on the relative motion model of the slave satellite, and obtaining relative measurement data including distance measurement and angle measurement based on the relative measurement model, and fusing the relative measurement data and the preliminarily predicted relative orbit state through an extended Kalman filtering algorithm to estimate the relative orbit state of the slave satellite. Navigation may be independent of ground support.

The invention relates to the technical field of nuclear fusion devices, and discloses a nuclear fusion device and a monitoring system and an installation method thereof, the monitoring system comprises a cold shield main body, the cold shield main body is of an annular rotation structure, and the cold shield main body is arranged in a cavity and wraps the circumferential outer side of a vacuum chamber; the cold shield main body comprises a plurality of groups of fan-shaped cold shield sections which are sequentially arranged in the circumferential direction, and the plurality of groups of fan-shaped cold shield sections are encircled to form the cold shield main body; the distance measuring assembly is arranged on the cold shield main body, the distance measuring assembly comprises a plurality of groups of distance measuring sensors which are arranged in the circumferential direction of the cold shield main body at intervals, and each group of distance measuring sensors comprises a plurality of distance measuring sensors; the distance measuring sensor is used for detecting the size of a gap between the cold shield body and the longitudinal field coil and the size of a gap between the cold shield body and the vacuum chamber. Therefore, the cold shield main body can be assisted to be assembled at a position in the nuclear fusion device, and the matching position of the cold shield main body, the vacuum chamber and the longitudinal field coil can be monitored in real time.

The invention discloses a monocular vision inertial positioning method based on fine pre-integration and adaptive vision inertial weight, which comprises the following steps of: firstly, constructing key frames by using parallax by adopting a sliding window method, and processing the output of an inertial measurement unit between adjacent key frames by using fine pre-integration; initializing a visual inertial odometer to obtain external parameters and internal parameters of an initial sensor and scale and speed information of the odometer; normalizing the acceleration and the angular velocity of each pre-integration quantity by adopting an L2 norm, and weighting the components of the covariance matrix constrained by each fine pre-integration by taking the normalized acceleration and angular velocity as adaptive inertia optimization weights; based on the quality of feature matching after optical flow tracking, a visual weight is obtained according to the number of times of feature continuous tracking and changes of gray values before and after the feature continuous tracking, and the size of an information matrix of a visual re-projection residual error is adjusted in a self-adaptive mode; and finally, performing optimization iteration on the camera pose by adopting a sliding window and a factor graph to minimize a residual term based on mahalanobis distance measurement and obtain a final positioning result.

The invention discloses a mechanical arm welding detection intelligent diagnosis method and system based on laser ultrasonic guided waves. According to the system, a high-precision multi-degree-of-freedom steering engine, an infrared distance measurement array, a temperature sensor, a laser vibration exciter and a laser vibration meter are integrated at the tail end of a mechanical arm, synchronous operation of welding and online detection is achieved by presetting a welding track, and the system collects an excitation signal, a reflection ultrasonic signal and temperature data through a high-speed data collection card; and the signal is preprocessed through a temperature matching model, phase compensation and baseline correction. Then, wavelet decomposition is adopted to extract time-frequency features, intelligent diagnosis is achieved through a pre-trained one-dimensional convolutional neural network, a dynamic threshold value is set based on the 3 sigma criterion, and recheck feedback is automatically triggered when detection data exceed the limit, the welding detection efficiency and precision are remarkably improved through the scheme, data closed-loop management and intelligent quality tracing are achieved, and the reliability of welding detection is improved. And the automatic production requirement of modern steel structure manufacturing is met.

A time-of-flight based distance measuring method and a time-of-flight based distance measuring system are provided. The distance measuring method includes: sending N consecutive pulses from a transmitter side intermittently, N being a positive integer greater than one, wherein the N consecutive pulses are reflected by a target object, and a reflection signal is generated accordingly, during arrival of the reflected signal at a receiver side, sampling the reflected signal multiple times according to a predetermined sampling interval within a predetermined sampling duration and accordingly generating a sampling result; detecting time of flight of a single pulse of the N consecutive pulses traveling from the transmitter side to the receiver side according to the sampling result; and measuring a distance between the target object and a reference position according to the time of flight. The distance measuring method maintains good measurement quality, measures the distance rapidly, and consumes low power.

The present disclosure relates to the technical field of smart power grids. Disclosed are a method and apparatus for constructing a high-order tensor network of a large-scale power grid, and a device and a medium. The method comprises: acquiring multiple category attribute sets of heterogeneous nodes in a large-scale power grid; performing feature extraction on the multiple category attribute sets, so as to obtain attribute features of the heterogeneous nodes; on the basis of the attribute features of the heterogeneous nodes, using a deep hash mapping model to establish multiple feature sub-spaces; using a breadth learning strategy to align the multiple feature sub-spaces to a unified feature dimension; on the basis of an attribute feature vector of the unified feature dimension, using a distance measurement method to calculate a distance measurement value between the heterogeneous nodes; on the basis of the attribute feature vector of the unified feature dimension and the weight of a heterogeneous node interaction relationship, determining a basis tensor for representing the heterogeneous node interaction relationship; and on the basis of the basis tensor and the weight of the heterogeneous node interaction relationship, using a tensor multiplication operation rule to construct a high-order tensor network of a large-scale power grid.

The invention discloses a real-time track obstacle detection method and system and a medium. The method comprises the following steps: acquiring a visual image of a track area; segmenting the visual image by using a lightweight target segmentation model to obtain a single-frame track line, an obstacle and the category of the obstacle; according to the single-frame track line, a final track area is obtained based on dynamic multi-frame track area fitting; judging whether the obstacle is in the final track area or not, and if yes, obtaining a detection frame of the obstacle in the visual image; and according to the obtained detection frame of the obstacle in the visual image, projecting the obstacle detection frame in the visual image into the radar point cloud data to obtain an obstacle point cloud range frame, and according to the obstacle point cloud range frame, calculating the distance from the obstacle to the train head. According to the invention, real-time detection and distance measurement can be carried out on the track obstacle, and the track area can be accurately determined in the image, so that whether the obstacle is in the track area or not can be judged, and a more accurate obstacle distance measurement result can be obtained.

The invention discloses a mining area unmanned vehicle multi-mode competitive collaborative fusion sensing method, and relates to the technical field of fusion sensing, and the method comprises the following implementation steps: employing infrared thermal imaging and visible light image fusion to forward sense an obstacle, employing an ultrasonic radar and a laser infrared distance measurement sensor to sense a near-area obstacle, and employing a laser infrared distance measurement sensor to sense the near-area obstacle; whether an obstacle approaches or not is judged according to distance measurement information of sensors at four corners and four sides; a laser radar, a depth visible light imaging head and an inertial measurement unit are fused to perceive obstacles, and six modes are fused to construct a complete environment perception domain. According to the multi-mode competitive collaborative fusion perception method for the unmanned carrying vehicle in the mining area, posture changes are detected and tracked in real time, the angle of a vehicle steering engine is adjusted in advance, it is guaranteed that an image can accurately reflect an actual scene, the precision of state prediction of the carrying vehicle is further improved, and the safety and reliability of operation of the unmanned carrying vehicle in the mining area are improved.

The invention discloses a laser radar mapping method based on loopback detection and a visual-inertial odometer. The visual-inertial odometer (VIO) and laser radar data are combined with loopback detection. Through comprehensive utilization of rich perception of VIO to the environment, high-precision distance measurement provided by the laser radar and a correction function of loopback detection, the method can significantly improve the accuracy and robustness of the autonomous navigation system. In a dynamic and uncertain environment, the method can effectively solve the technical problems of sensor error accumulation, environment change, positioning errors caused by dynamic obstacles and the like, and provides a more reliable and effective solution for navigation of an automatic driving vehicle and an advanced robot in a complex environment.

The invention provides a road marking quality intelligent evaluation system based on image analysis, and relates to the technical field of road facility monitoring, and the system comprises a detection unit, a marking quality evaluation unit, a marking wear prediction unit, a GPS positioning unit, a vehicle driving information unit, a control unit and a remote central control unit. The marking quality evaluation unit is constructed based on a differential geometry theory and comprises a curvature flow edge detection module, a marking geometric characteristic manifold representation module and a multi-scale differential invariant evaluation module, the system regards a marking as a two-dimensional manifold, and the marking quality is evaluated by calculating differential geometric quantities such as a Gaussian curvature, an average curvature and a shape index. And constructing geodesic distance measurement on the feature manifold, and evaluating the completeness, visibility and reflective performance of the marked line. And the marking wear prediction unit predicts the service life of the marking based on the traffic flow information and the environment characteristic mapping relation model, and generates a maintenance suggestion.

A distance measuring device includes: a pixel array unit that performs a light receiving operation for time-of-flight distance measurement using a modulation frequency; a distance measurement processing unit that performs dual mode distance measurement that causes the pixel array unit to alternately perform a light receiving operation using a first frequency as the modulation frequency and a row receiving operation using a second frequency having a higher frequency than the first frequency as the modulation frequency, and single mode distance measurement that causes the pixel array unit to perform only a light receiving operation using a predetermined frequency as the modulation frequency; and a switching processing unit that switches the distance measurement in the dual mode and the distance measurement in the single mode according to a predetermined condition.

Provided is an image processing device that is capable of storing three-dimensional information relating to a road surface (3D road surface information), transforming / converting the stored 3D road surface information into a current time by using motion of a vehicle, using the three-dimensional information from the road surface together with monocular-based distance estimation processing, increasing the distance measurement accuracy without requiring a special sensor while maintaining the obstacle detection accuracy, and, in particular, supporting a case where an ego vehicle is turning at an intersection that may include a sidewalk area (that is, which includes a difference in ground height relative to a roadway area) that requires accurate monocular-based distance measurement for a detected obstacle. The obstacle ranging unit 161 performs monocular-based distance estimation processing, and uses the 3D road surface information stored via the 3D road surface information acquisition unit 131 and converted to the current time by the 3D road surface information management unit 141.

The invention relates to the field of government affair work order intelligent processing, in particular to a government affair work order intelligent processing method and system based on space-time semantic clustering and a large language model. According to the scheme, unified data feature modeling is conducted on a work order to be processed, an improved DBSCAN clustering algorithm is executed on the work order through a weighted space-time semantic three-dimensional distance measurement formula, and combined clustering of space, time and semantic features is achieved; calculating priority scores of the work orders, and dynamically allocating scheduling resources according to the clustering scale and the priority of the work orders; based on a retrieval enhancement generation technology of an RAG framework and an FAISS vector retrieval library, historical similar work orders are matched, a few-sample learning case is generated, and two sets of differential treatment schemes are generated by controlling temperature parameters of a large language model; visual display and interactive analysis of work order clustering are realized through an interactive GIS platform; and establishing a quality feedback closed loop of work order reconstruction. The method is suitable for intelligent government affair work order processing.

The invention relates to a machine vision-based electric power operation risk behavior violation intelligent identification method, which comprises the following steps of: acquiring an image of a violation scene, and labeling the collected image; preprocessing and storing the image; based on the preprocessed image, carrying out classification training on the extracted feature information, and constructing a classifier model used for identifying violation behaviors; performing autonomous feature learning based on calibrated positive and negative samples, and training a classifier model; and collecting behavior pictures of the on-site overhead working personnel in real time, and performing violation information intelligent identification through the trained classifier model. According to the method, the supervision efficiency of electric power safety supervision personnel is improved, electric power operation site violation behaviors can be timely found, effectively handled and reasonably solved, a template matching method, a deep learning algorithm and a feature extraction and video image processing analysis identification technology are utilized, an intelligent violation identification function is realized, and the method is suitable for popularization and application. Intelligent identification, automatic research and judgment, instantaneous alarm and log recording are carried out on common high-altitude operation personnel behaviors (such as ladder climbing), electrified interval or open fire area calibration, scaffold erection, foundation pit soil piling height, fence limb distance measurement and other violation behaviors on an electric power operation site, and a breakthrough from remote inspection to intelligent supervision is achieved.

An intelligent drone system for surveying and monitoring construction sites and industrial areas, consisting of: an AI processor for processing and analyzing data from multiple sensors, including LiDAR, thermal imaging, and high-resolution cameras, a laser-based distance measurement sensor for generating high-resolution 3D maps of the surveyed environment for topographic accuracy and real-time obstacle detection to improve navigation safety. a heat detection sensor for capturing infrared radiation to detect thermal anomalies that may indicate potential device or structural problems. an optical imaging system to provide detailed visual documentation of the surveyed construction site, where: The optical imaging system captures high-quality images to support inspections and monitoring, complements LiDAR and thermal data, provides a visual context for the captured data, and enables reporting for project tracking and compliance.

The invention discloses a high-precision laser ranging system and method based on multi-frequency modulation, and relates to the field of laser ranging, and the system comprises a multi-frequency laser transmitting module which comprises generators of different frequency bands and can be independently regulated, rapidly switched and matched with a high-precision frequency modulation unit; the laser receiving and signal conditioning module is used for processing signals by using an avalanche photodiode, a low-noise preamplifier and the like; the signal processing and analyzing module is used for calculating the distance by means of a high-speed digital signal processor in combination with a multi-frequency distance measurement algorithm; a display and control module and a temperature compensation module are further included, the method comprises the steps of multi-frequency laser emission, receiving conditioning, signal processing, display control and temperature compensation, and high-precision distance measurement is achieved through fine operation according to environment and target characteristics. The method is high in distance measurement precision, and error compensation in a complex environment exceeds 90%; the adaptability is high, and automatic adjustment is achieved in different distances and environments; the real-time performance is good, and a result can be obtained within 10 milliseconds; operation is convenient, wireless and data management is supported, and laser ranging innovation is promoted.

The invention discloses a high-precision flow online monitoring device and method for a siltation section, and belongs to the technical field of hydrological monitoring. The device comprises a sensing terminal, an edge terminal and a mounting mechanism, the sensing terminal is integrated with an upper and lower section layered speed measurement module, an underwater distance measurement module, a first absolute pressure type water level measurement module and an attitude and temperature sensor, and vertical section flow velocity data is obtained by receiving water body reflection signals in a time-sharing manner; and the dynamic deposition section area is corrected in real time in combination with ultrasonic ranging. And the edge terminal compensates water level measurement data by using atmospheric pressure, and calculates the flow through fusion of a telemetering module. The installation mechanism supports fixed or telescopic installation, and the height of the installation mechanism is automatically adjusted according to the distance between a deposition layer and equipment. The method comprises the steps of dynamically correcting the section, calculating the flow through layered integration and compensating errors of multiple sensors, the problems of large area deviation, high operation and maintenance cost and the like caused by sedimentation in a traditional flow measurement technology are solved, and high-precision online monitoring under the high-sand-content complex flow state is achieved.

The invention relates to the technical field of space ranging, in particular to a smart agriculture big data cloud service supervision platform fusing aerial remote sensing and space ranging information. The method comprises the steps that a data acquisition unit acquires agricultural data based on a multi-source acquisition module; the data processing unit performs cleaning, correction and matching processing based on the agricultural data acquired by the data acquisition unit; the data analysis unit performs multi-aspect evaluation analysis through a multi-party analysis technology based on the agricultural data processed by the data processing unit; and the storage management unit manages and protects agricultural data through a cloud computing distributed storage technology based on an analysis result of the data analysis unit. According to the design of the invention, multi-dimensional evaluation and prediction are carried out on agricultural data by adopting a multi-party analysis technology, and a scientific basis and intelligent decision support are provided for agricultural production; through a cloud computing distributed storage technology, rapid access, backup and sharing of large-scale agricultural data are realized, and meanwhile, the security and privacy of the data are ensured.

The invention discloses a sports video commentary generation method and system based on a multi-modal large language model, and the method comprises the steps: obtaining a multi-modal data set which comprises a sports video, and an audio and a commentary text corresponding to the sports video; constructing a multi-modal large language model, encoding the sports video, the audio and the explanation text so as to project corresponding video frames, audio waveforms and metadata to a shared embedding space, and determining a multi-modal embedding vector; a multi-modal clustering memory unit is set, multi-modal embedded vectors are grouped, and feature alignment between modals is optimized through comparative learning and information entropy regularization; based on a retrieval enhanced context learning mechanism, a historical instance is retrieved through sparse regularization distance measurement to serve as reference input of a current input multi-mode embedded vector; and jointly inputting the current multi-modal embedded vector and the reference input into the multi-modal large language model to obtain a sports video commentary. According to the method and the device, the problems of insufficient multi-modal information integration and insufficient context utilization are solved.

The invention relates to the technical field of fault positioning, and particularly discloses a digital distribution network fault traveling wave range finder and method, and the range finder employs fault traveling wave collection devices disposed at all key nodes of a distribution network to collect fault traveling wave signals in real time, and obtains stable line mode traveling wave signals through signal filtering and line mode component extraction. Then introducing a signal processing algorithm based on deep learning to carry out time domain feature analysis on the line mode traveling wave signal of each key node so as to detect the wave head position of the signal, further determining a power distribution network section where a fault point is located by comparing the signal arrival time of each key node, and determining the fault location of the power distribution network based on a double-end traveling wave distance measurement algorithm. And calculating the distance between the fault point and the nodes at the two ends according to the signal arrival time difference of the nodes at the two ends of the section and the signal propagation speed. Thus, through deep time domain analysis of the fault traveling wave signal, the wave head position of the signal can be accurately identified, and the reliability of distribution network fault traveling wave distance measurement is improved.

The invention discloses a power transmission line channel hidden danger distance measurement method based on point cloud image fusion. The method comprises the steps of data joint calibration, target recognition and modeling of a power transmission line channel scene, target segmentation and positioning of the power transmission line channel scene and judgment of the distance between a hidden danger target and a wire. The method has the advantages that the point cloud data and the image data are fused, the space coordinate conversion relation between the point cloud data and the image data is calibrated and solved, the point cloud data are projected to the image view angle to generate a new point cloud projection mapping graph, training recognition is conducted through the image data and the point cloud data, the recognition accuracy of the hidden danger target is improved, the SAM model serves as a basic model, and the recognition efficiency is improved. The point cloud projection mapping graph and the hidden danger target in the image data are used as samples, training is carried out, a hidden danger target segmentation model is generated, accurate contour information of the hidden danger target is extracted, and effective early warning information is provided for the safety of a power transmission line channel.