140 results about "Dbscan clustering" patented technology

The invention relates to the technical field of federated learning security, and discloses a federated learning poisoning defense method based on time-frequency spectrogram and comparative learning, which comprises the following steps: receiving model update uploaded by each client, grouping and vectorizing parameters according to model layers, and generating a time-frequency spectrogram by applying short-time Fourier transform to parameter vectors of each layer; based on the time-frequency spectrogram, constructing a positive sample pair through data enhancement, carrying out difficult negative sample mining, and training an encoder by using a contrast loss function to extract an embedded vector with high discriminant power; and performing unsupervised clustering on the embedded vector by using a DBSCAN clustering algorithm, judging the maximum cluster as a benign client, performing final judgment in combination with historical malicious records, and only aggregating model parameters of the benign client to update a global model. According to the invention, high-precision detection of attack features can be realized, and a more universal, more efficient and more practical federal learning poisoning attack defense method is realized.

The invention belongs to the technical field of integrated circuits, and particularly relates to a time mismatch extraction method based on parallel windowing autocorrelation. According to the method, firstly, clustering processing is carried out on signal points; thirdly, preliminarily screening out legal signals based on the time occupancy rate of the signals, and constructing an initial legal signal library; a similarity score between the signals is further calculated through an IDK algorithm, and adjacent frequency bands are divided through a Pettitt method; and finally, continuation of legal signals is realized by means of a DBSCAN clustering algorithm, and automatic construction of a legal signal library is completed. The method is not only suitable for scenes without prior information, but also can be applied under the condition with partial prior information, and can be dynamically updated according to actual requirements. The method is based on radio frequency spectrum monitoring big data, can construct a legal signal library with high reliability and strong generalization ability, provides effective support for subsequent illegal signal monitoring, and has important application value.

The invention provides a method for identifying a microscopic association body structure in a heavy oil molecule simulation trajectory, which comprises the following steps of: analyzing a molecular dynamics trajectory, identifying an aromatic ring structure in a heavy oil molecule component, calculating a geometric center of the aromatic ring structure, combining a periodic boundary condition, and applying an improved DBSCAN clustering algorithm to realize automatic identification and clustering of an association body; and performing post-processing optimization on the clustering result, merging the shared clusters to generate a final association body set, and analyzing the number, size distribution, morphological characteristics and dynamic evolution information of the association bodies based on the structural characteristics of the final association body set. The method can accurately capture pi-pi accumulation behaviors of heavy oil molecules, further reveals morphological characteristics and dynamic evolution laws of heavy oil microassociates and influences of the heavy oil microassociates on the apparent viscosity of heavy oil, provides theoretical support for heavy oil viscosity-causing mechanism analysis and viscosity reducer design, can complement experimental research, and has a wide application prospect. And efficient development and green utilization of a complex thickened oil system are promoted.

The invention discloses a reverse BIM modeling method, system and equipment for an existing building space rod system composite structure, and belongs to the technical field of BIM modeling. In order to solve the problem that in the prior art, point cloud processing software lacks a special recognition and reconstruction tool for a'ball node-rod piece 'class space rod system combined structure, and efficient and accurate reverse generation of a rod piece axis is difficult to achieve, through curvature analysis, DBSCAN clustering, RANSAC spherical surface fitting and other technologies, the position of a ball node is accurately recognized from actually measured point cloud data; and finally generating a high-precision BIM model of a space rod system combined structure according with engineering practice by combining a two-stage geometric filtering strategy according to a sphere center connecting line of sphere nodes and the space constraint of the rod piece point cloud data. According to the method, the problem that actually measured point cloud data is discrete and difficult to process is effectively solved, and reliable technical support is provided for health monitoring, digital filing and accurate modeling of the existing building space rod system composite structure.

The invention provides a three-dimensional point cloud change detection method based on Siamese AdaptConv, and belongs to the field of intelligent scene reconstruction. The method comprises the following steps: firstly, acquiring three-dimensional point cloud data of a scene to be detected at two different time points, and constructing an adaptive neighborhood for each point; secondly, inputting the preprocessed three-dimensional point cloud data into a Siamese AdaptConv double-branch network, fusing the multi-scale features of each layer to obtain respective corresponding fused features, further calculating the change probability of the same position point, mapping the change probability into a candidate change point set, executing DBSCAN clustering, and outputting a candidate region; and finally, performing semantic verification on the candidate region, pre-constructing a geographic knowledge graph, and calculating semantic relevancy: when the semantic relevancy exceeds a set threshold value, considering that the three-dimensional point cloud change of the candidate region is consistent with the existing entity semantics in the knowledge graph, and judging that the change is reasonable. According to the invention, end-to-end multi-time sequence feature alignment and change identification are realized.

The invention discloses an underground mine pipeline type point cloud completion method based on axial slicing, which relates to the technical field of point cloud data processing, is used for performing data completion on pipeline type point clouds, and comprises the steps of axially slicing preprocessed pipeline point cloud data, performing dimension reduction on three-dimensional point clouds to two-dimensional plane processing, and calculating related geometric parameters of pipelines. The method comprises the following steps: extracting a pipeline center line based on improved DBSCAN clustering and least square circle fitting, constructing a skeleton model under geometric topology constraints, combining local geometric features and global topology constraints, realizing geometric reconstruction of a missing region through rotating surface generation and a local interpolation algorithm, and realizing robust repair of the missing region. According to the method, a convex hull and an RANSAC method are combined to extract a pipeline center line and geometric parameters, pipeline point cloud data complementation is realized through a continuous rotation correction method, and the pipeline point cloud complementation quality is remarkably improved.

The invention discloses a linear guide rail lightweight optimization design method and device, a medium and a program product, and the method comprises the steps: (1) constructing a lightweight optimization mathematical model on the basis of a linear guide rail structure and load characteristics under the condition of satisfying frictional resistance and first-order modal constraints; (2) quantizing a parameter effect based on a PCE model and constructing an effect space to generate a population; (3) designing an evolutionary strategy guided by a bidirectional information individual; (4) constructing a double-layer Stacking integration model based on DBSCAN clustering, and screening an optimal filial generation guide rail; and (5) if the current optimal filial generation guide rail meets the optimization requirement, outputting an optimal guide rail parameter value, otherwise, returning to the step (3) until the optimization requirement is met. According to the method, the weight of the guide rail can be minimized for the effect space of high-effect guide rail parameters, transition optimization for redundancy and low-effect parameters is avoided, and better lightweight performance is achieved.

The invention discloses a line loss positioning method based on phase feature clustering, belongs to the technical field of power system line loss treatment, and constructs a three-dimensional feature space through high-frequency collection of HPLC carrier signal phase offset, current ripple frequency spectrum features and intelligent electric meter load rate to comprehensively characterize the equipment operation state. An improved DBSCAN clustering algorithm is utilized, the neighborhood radius is dynamically calculated according to the feature space density, the minimum sample number MinPts is adaptively set according to the transformer area equipment scale, and clustering parameter adaptation is achieved; meanwhile, a noise filtering mechanism is introduced, secondary verification is carried out on initial clustering noise points, effective abnormal points are screened out by calculating local density and overall average density, and the misjudgment rate is reduced. The method can quickly and accurately position the position of the line loss abnormal equipment, greatly improves the line loss abnormal processing efficiency, can be widely applied to power distribution network line loss management, effectively improves the line loss analysis accuracy and treatment efficiency, and provides powerful technical support for reducing the power grid loss and improving the power supply reliability.

The invention discloses an NRLC enhanced two-stage DBSCAN clustering rock discontinuous surface identification method, and relates to the field of rock mass structural surface intelligent identification, and the method comprises the steps: S1, identifying concave, convex and boundary feature points in a rock point cloud; s2, calculating a rock point cloud normal vector based on local fitting; s3, based on the rock point cloud normal vector, identifying a group discontinuous surface through DBSCAN clustering; s4, DBSCAN clustering is carried out based on the group discontinuous surfaces to obtain all discontinuous surfaces in the point clouds, automatic recognition and accurate representation of the rock mass discontinuous surfaces are achieved through normal vector clustering analysis, a set of efficient and accurate rock mass structural surface intelligent recognition technology system is established, and reliable three-dimensional data support is provided for rock mass stability evaluation.

The invention discloses an AI marking closed-loop method based on active learning and difficult case mining, and the method comprises the steps: carrying out the parallel calculation of uncertainty, representativeness and diversity three-dimensional value indexes after training an initial model, and carrying out the dynamic weighting screening of a high-value sample through a closed-loop feedback controller; difficult cases are recognized, DBSCAN clustering is adopted, similar sample expansion is retrieved, and the samples are stored in a dynamic pool; annotations are distributed, intelligent judgment is triggered for dispute samples after consistency verification, and labels are determined by fusing node reputation, model confidence and feature similarity; a mixed loss function is adopted for incremental training, and if the performance does not reach the standard, sampling, difficult examples and training parameters are adjusted in a linkage mode and then retry is conducted; the effectiveness of the difficult cases is evaluated after each round of iteration, the invalid difficult cases are eliminated, redundant clusters are combined, and the pool capacity is adaptively maintained; and the performance of the monitoring model is iteratively optimized until the standard is reached. According to the method, the labeling cost is reduced, and the boundary sample and long tail category recognition capability is improved.

The invention discloses a tunnel blasting lumpiness and muck pile form two-dimensional correlation evaluation method and system, and constructs a set of complete blasting effect intelligent evaluation system by introducing a low illumination enhancement algorithm based on Retinex, improving a YOLOv8 instance segmentation model and a three-dimensional point cloud reconstruction technology. According to the system, an explosion-proof binocular camera is used for collecting muck pile images, point cloud data is generated through stereo matching after image enhancement, rock block recognition and mask extraction, the three-dimensional size of rock blocks is calculated through DBSCAN clustering and PCA analysis, parameters such as the particle size and the uniformity index are calculated, and the quality of the muck pile is improved. Meanwhile, the overall form of the muck pile is reconstructed to extract form parameters such as the stacking height, the looseness and the form expansion degree, and finally a correlation model of the blasting fragmentation and the muck pile form is established, so that all-around and automatic quantitative evaluation of the blasting'crushing 'and'throwing' effects is realized; the innovative instance segmentation and point cloud processing algorithm effectively solves the detection problem in complex scenes such as rock adhesion and overlapping, and the detection precision and reliability are remarkably improved.

The invention provides a comprehensive analysis method for compaction quality of a lime treated soft soil roadbed based on intelligent compaction. The method comprises the following steps: S1, acquiring vibration modulus data corresponding to a plurality of detection units respectively; s2, calculating a compaction degree passing rate and a stability passing rate based on the vibration modulus data corresponding to the plurality of detection units, obtaining a uniformity passing rate by adopting a multi-scale variation structure analysis method, and calculating an abnormal aggregation index through a DBSCAN clustering algorithm; s3, determining the weight of each index, wherein the weight comprises the weight of the compaction degree passing rate, the weight of the uniformity passing rate and the weight of the stability passing rate; s4, calculating a compaction quality value based on each index and the corresponding weight; s5, the roadbed compaction quality grade is judged based on the compaction quality value, and roadbed repairing is conducted based on the roadbed compaction quality grade and the abnormal aggregation index. Compared with the prior art, the method has the advantages of improving the spatial resolution and defect identification precision of uniformity detection of the roadbed compactness and the like.

The embodiment of the invention provides a call quality detection method and device, electronic equipment and a storage medium, and is applied to the technical field of communication. The method provided by the embodiment of the invention is applied. The method comprises the following steps: acquiring a single call MOS value, a single call packet loss rate, single call average jitter, single call average delay, single call maximum jitter, a single call packet loss rate peak value, a single call codec type, a single call network type and a single call duration of each call of a user in a preset historical duration; and calculating the first data and constructing a user feature vector to obtain the user feature vector representing the call quality of the user. And respectively analyzing the feature vectors of the users through a K-means clustering algorithm and a DBSCAN clustering algorithm, identifying abnormal call users, and clustering the users, so that the users with the same or similar call quality are clustered in the same cluster. Therefore, the long-term call quality of the user is judged.

The application discloses a kind of multi-beam outlier automatic filtering methods combined with uncertainty and density clustering algorithm, belong to multi-beam sounding data processing technical field.The application constructs grid node absorbable water depth point selection model according to CUBE filtering algorithm, and gridding is carried out to sounding data;The one-dimensional clustering of node absorbable water depth value is carried out using DBSCAN clustering algorithm, a plurality of water depth hypotheses are constructed according to the difference between water depth values, then the water depth hypothesis and uncertainty of node are updated using Kalman filter;Finally, the water depth hypothesis with higher reliability is selected from a plurality of water depth hypotheses as the real water depth value of node.The application can provide a kind of multi-beam sounding data automatic cleaning algorithm selection for domestic multi-beam sounding data processing software.

The application discloses a kind of based on data-driven wasserstein fuzzy set active distribution network distribution robust day-ahead scheduling method, realize the construction compact DRO fuzzy set, and effectively reduce the conservativeness of day-ahead scheduling result;The method constructs the wasserstein condition generated adversarial network model (CWGAN-GP) based on gradient penalty norm, for wind, light output day-ahead scene generation, and proposes the abnormal sample identification method of improved DBSCAN clustering combined with CNN-BiGRU automatic encoder, to improve the credibility of generated scene set;Adopt the compact boundary of data support set determined based on non-parametric kernel density estimation (NKDE) confidence interval, and combined with wasserstein metric to construct DRO fuzzy set.The application compared with the existing DRO day-ahead scheduling method, realizes the deep combination of data-driven method and DRO model, effectively reduces the conservativeness of fuzzy set, and improves the economy of day-ahead scheduling scheme and the adaptability of coping with new energy output uncertainty under the premise of guaranteeing decision robustness.

The application provides a kind of intelligent government affair hotline mass appeal analysis, detection method, the method includes the following contents: pull daily work order data regularly;The work order content in work order data is vectorized coding;For vectorized work order data, using DBSCAN clustering algorithm, obtain classification cluster;Traversal classification cluster, utilize the address feature of classification cluster, complaint feature, with the data not included in any cluster, again using DBSCAN clustering algorithm expands classification cluster, for obtaining more accurate complaint quantity and complaint number information;Obtain the complaint summary of each classification cluster through large model;Data is written into data.The application can realize mass appeal detection considering semantics and spatial information, replace the redundancy, repetitive work of original manual statistics in an intelligent way, can effectively help to identify and integrate similar appeals, so as to improve the processing efficiency, optimize the service quality, quickly and efficiently provide daily mass event list.

The invention belongs to the technical field of constructional engineering measurement, and discloses a constructional engineering measurement system based on segmentation and clustering, which comprises a multi-source data acquisition module, a data processing and analysis module and a control and display module. The multi-source data acquisition module is composed of a three-dimensional laser radar, unmanned aerial vehicle oblique photography equipment, a total station and GPS positioning equipment, and is used for acquiring and fusing multi-source data. And the data processing and analysis module realizes automatic and accurate identification and measurement of building components by adopting a process of data preprocessing, point cloud segmentation based on deep learning and combination of DBSCAN clustering and energy function optimization. And the control and display module is used for system control and result three-dimensional visualization. Through combination of multi-source data fusion and an intelligent algorithm, the problems of low efficiency, insufficient precision and poor automation degree of a traditional measurement method are solved, and the intelligent level and the operation efficiency of constructional engineering measurement are improved.

The application discloses a kind of road width automatic calculation and quality checking method of road network data, it is related to road network data processing technical field, to solve the problem of low efficiency of multi-source data fusion in existing method;The application is through collecting multi-source road network data and pre-standardization processing, unified coordinate system mapping and metadata annotation generate labeled original dataset;Perform noise adaptive filtering and occlusion detection, utilize U-Net semantic segmentation to identify interference area, and apply GAN local completion output purification dataset;Construct the multi-modal network calculation dynamic fusion weight of attention mechanism, and parallel integration forms fusion feature vector;Through Canny optimization edge extraction and DBSCAN clustering calculation width parameter, embedding topological structure;Use multi-scale consistency index to check result, if threshold backtracking optimization;Output result and iteratively update parameter, form closed loop mechanism.The method improves fusion efficiency and calculation accuracy, applicable to traffic navigation and urban planning, supports real-time dynamic application.

A kind of distributed power supply power prediction method, system and equipment based on LSTM, method is first obtained the meteorological data of distributed power supply target area, calculate theoretical electric power data, then based on DBSCAN algorithm clustering analysis is carried out, and the dimension of data is expanded, obtain normal value and abnormal value data set, then the loss function of LSTM prediction network is optimized, and the future normal value and abnormal value prediction result is obtained, finally set parameter adjustment condition, repeat the above steps until completion prediction;The present application is aimed at the problem that the type of historical electric power data is various, and the abnormal value is much, the distance definition of DBSCAN clustering algorithm is improved, the distance between data point and theoretical output power is combined, the stability of normal value extraction is improved, and LSTM prediction network is introduced, the data limitation of only predicting normal value is avoided, the loss function is improved by combining physical constraint and theoretical power, and adaptive adjustment parameter is optimized, so that the algorithm is more efficient.

The invention discloses a high-dimensional vector clustering algorithm based on GPU acceleration, and the algorithm comprises the steps: obtaining a high-dimensional space vector set, accelerating the construction process of a k-nearest neighbor graph through a GPU, and completing the construction of a global k-nearest neighbor graph; obtaining a high-dimensional space vector set, recursively executing k-means clustering on the high-dimensional space vector set, and completing partition operation of vector objects; independently executing DBSCAN clustering in parallel among different partitions by using a global k-neighbor graph, a partitioning scheme and a GPU thread block to complete partition clustering; vector objects conforming to core point definition are obtained, a core neighbor graph is constructed based on the density direct relation among the vector objects, the core neighbor graph and the partition clustering results are utilized, the partition clustering results are combined, and a global clustering result of the high-dimensional space vector set is obtained. According to the method, the execution time of the DBSCAN algorithm can be remarkably shortened, and the efficiency of large-scale high-dimensional vector clustering is greatly improved on the premise that the clustering precision is guaranteed.

The invention belongs to the technical field of geophysical exploration, and discloses a mine transient electromagnetic anomaly boundary identification method based on DBSCAN clustering. The method comprises the following steps: step 1, establishing a three-dimensional geophysical model; 2, calculating apparent resistivity data by using a small loop transient electromagnetic method; 3, drawing a k-distance map, and obtaining an optimal neighborhood radius according to the k-distance map; step 4, performing DBSCAN clustering on the apparent resistivity data to obtain a clustering result; 5, replacing the intra-cluster apparent resistivity value with a cluster center value to obtain replaced apparent resistivity data; step 6, imaging the replaced apparent resistivity data and identifying an abnormal body; 7, comparing the anomalous body boundary with the anomalous body boundary in the model, and if the difference is too large, adjusting the optimal neighborhood radius and returning to the step 4; and otherwise, taking the current abnormal body as a final identified abnormal body. The method can effectively identify irregular anomalous body boundaries of non-uniform complex stratums, and can adapt to real-time identification requirements of underground anomalous bodies.

The application provides a three-dimensional point cloud change detection method based on Siamese AdaptConv, and belongs to the field of intelligent scene reconstruction; firstly, three-dimensional point cloud data of a to-be-detected scene at two different time points is acquired, and an adaptive neighborhood is constructed for each point; then, the pretreated three-dimensional point cloud data is input into a Siamese AdaptConv double-branch network, multi-scale features of each layer are fused respectively, corresponding fusion features are obtained, the change probability of the same position point is further calculated, and a candidate change point set is mapped; DBSCAN clustering is performed, and a candidate region is output; finally, semantic verification is performed on the candidate region, a geographical knowledge graph is pre-constructed, and semantic correlation is calculated; when the semantic correlation exceeds a set threshold value, it is considered that the three-dimensional point cloud change of the candidate region is consistent with the existing entity semantics in the knowledge graph, and the reasonable change is determined; and the application realizes end-to-end multi-sequential feature alignment and change identification.

The invention provides a Lamb wave benchmark-free damage imaging method based on adaptive clustering and semantic weighting, and belongs to the technical field of air coupling ultrasonic detection.The Lamb wave benchmark-free damage imaging method comprises the steps that a full-path response signal is collected through orthogonal scanning, a wavelet low-frequency approximation coefficient and a normalized symmetric difference factor are extracted to construct a joint feature vector; and performing unsupervised division of'health-damage 'states on a scanning path by adopting DBSCAN clustering. And semantic tags obtained by clustering are further used as adaptive weights to be embedded into the improved RAPID probability imaging model, and dynamic construction of artifact path suppression and soft reference is realized. In a layering defect detection experiment of the carbon fiber reinforced composite material, compared with a traditional RAPID method, the average size measurement error is reduced by 50.5% under the condition that an independent health reference is completely not needed, the measurement precision of 40 * 20 * 0.05 mm defects in the X / Y direction is improved by 81.7%, the measurement precision of 40 * 20 * 0.05 mm defects in the Y / X direction is improved by 65.5%, and meanwhile 92.3% of boundary artifacts are effectively restrained.

This invention discloses a context-based copy-paste data augmentation method, system, and storage medium for multi-class remote sensing target detection, belonging to the field of image processing and target detection technology. The method is implemented through the following steps: First, prepare a multi-class remote sensing dataset and set hyperparameters; second, generate copy regions of spatial context, using the DBSCAN clustering method to preserve target spatial context information; next, generate paste regions of semantic context, using a ResNet50 network to extract background feature vectors and match candidate paste regions; finally, perform the paste operation and update the annotation information. This invention generates copy regions through clustering, preserves spatial context, and matches paste regions based on semantic context, making the augmented image closer to the real image. It effectively alleviates the long-tail problem of datasets, enhances the network's learning balance, improves detection performance in low-sample classes and complex scenes, and can be seamlessly integrated with various remote sensing target detectors.

The application discloses a power grid real-time event detection and positioning method and system based on synchronous phasor data. Firstly, linear regression and Chebyshev detector are used for abnormal detection and cleaning of PMU synchronous phasor data, and a DBSCAN clustering algorithm is used for detecting clustering change points to classify events. Then, PMU data is statistically analyzed from five dimensions, and PMU scores are calculated. Finally, subgraph construction and scanning are performed to obtain the event location. The simulation results prove the effectiveness of the application in event detection, classification and positioning. The results of industrial data prove the necessity of abnormal detection and the effectiveness of the event detection algorithm. The application can provide a very effective PMU-based power grid real-time event detection tool, and can help power system operators to improve decision-making efficiency.

The application discloses a kind of based on image analysis digital printing quality detection method, it is related to digital printing quality intelligent detection technical field, including, obtain the surface texture feature of visible light channel in HDR image group and the substrate penetration feature of infrared channel, cross-modal correlation is carried out through space-time attention mechanism, generates three-dimensional feature cube;Material physical characteristics of printed matter are obtained using reverse Monte Carlo ray tracing algorithm, and enhanced feature tensor is generated by three-dimensional convolution network splicing, and defect detection and positioning are carried out through cross-scale feature aggregation;Improved DBSCAN clustering algorithm is used to generate complete defect area by spatial aggregation of defect coordinates, and defect detection report is output by dynamic threshold classification.The application realizes the accurate inversion of ink thickness, roughness and other material characteristics by constructing three-dimensional feature cube containing surface texture and substrate penetration feature, and improves the accuracy and reliability of digital printing quality detection.

The invention provides a PM2.5 prediction method based on meteorological factor space partitioning, which performs scientific partitioning on monitoring stations based on spatial distribution characteristics of meteorological factors through an ST-DBSCAN clustering algorithm, divides stations with similar meteorological conditions into the same partition, fully considers spatial heterogeneity of the meteorological factors, and improves the accuracy of PM2.5 prediction. Noise interference caused by mixed modeling of all stations is avoided; the spatial constraint value is calculated in each partition so as to quantify the spatial influence of the surrounding stations on the target station, the perception ability of the prediction model on the spatial correlation is enhanced, and the prediction accuracy is improved; and meanwhile, the LSTM neural network model is adopted to fuse the historical PM2.5 concentration and the spatial constraint value for prediction, so that the time dynamic characteristics and the spatial correlation characteristics of the PM2.5 concentration change can be effectively captured, and the space-time modeling capability is higher.

The present application relates to a kind of road drivable area detection methods based on laser radar and millimeter wave radar fusion, belong to vehicle-road cooperation and intelligent transportation field.The method is by adaptive DBSCAN clustering algorithm to process the point cloud data of laser radar, can improve the consistency in class and difference between classes of clustering result;By adaptive threshold segmentation method based on the law of the big to construct the drivable area electronic fence of road, can avoid the problem that traditional fixed global threshold segmentation method cannot consider the situation of point cloud chart everywhere and thus segmentation effect is poor;By fan-shaped encoding, point cloud coordinate system is converted into polar coordinate system to express the position and direction information of point, and by the selection of radius, the problem of different distance under different sparsity can be solved;By pseudo-image fusion method, attention mechanism is used to adjust weight dynamically to improve environmental adaptability.

The application discloses a UAV detection method for a tree barrier in a distribution network and a related device. Based on the idea of statistical function parameter estimation, two-dimensional point cloud data collected by a single laser radar is merged into three-dimensional point cloud data. On the basis of the three-dimensional point cloud data, an improved DBSCAN clustering algorithm is used to achieve high-precision clustering effect. The number of nearest neighbor point clouds of the DBSCAN clustering algorithm does not need to be manually set, the manual trial and error cost is reduced, tree barrier misjudgment is reduced, and tree barrier detection accuracy is improved. Thus, the technical problems of the prior art, such as the need for manual selection of the number of nearest neighbor point clouds, the inability to adaptively adjust parameters to data, low tree barrier detection accuracy, and low efficiency, are solved.

The invention discloses a deep-buried tunnel disaster early warning method, system and equipment, and the method comprises the following steps: S1, initial screening of original micro-seismic data: firstly, carrying out the relative coordinate conversion of MS events, and carrying out the initial screening of the MS events participating in the subsequent clustering analysis according to the division range of an early warning unit; s2, extracting effective MS events: performing clustering analysis on the MS events in the early warning unit by using a WOA-DBSCAN clustering algorithm, and identifying the effective MS events and clusters to which the effective MS events belong; s3, calculating an energy density logarithm logED value: calculating the convex hull volume of the main MS event cluster through a Quickhull3D algorithm, and calculating the logED value in combination with the total energy and the volume; s4, triggering graded early warning: according to comparison between the logED value and a preset threshold value, identifying a disaster breeding stage and outputting an early warning signal; according to the method, effective micro-seismic events can be efficiently extracted, and the distribution characteristics of the micro-seismic events in the three-dimensional space can be quantified by calculating the energy density logED in the early warning unit.