528 results about "DBSCAN" patented technology

The invention provides an unmanned aerial vehicle optimal route planning method and system fusing image recognition and M-RRT and APF algorithms. The point cloud and visual data of a fan are obtained in real time through a multi-mode sensor, key parts of blades are recognized based on deep learning, a geometric mapping model is constructed, the blade tip points are corrected in a clustering mode in combination with DBSCAN, and the shutdown posture is predicted. A global path adopts an M-RRT algorithm improved by a direction heuristic factor to guide and search a key area of a blade; a local track is optimized through a dynamic weight APF algorithm, and a repulsive force field is adjusted in real time to cope with attitude changes. An online re-planning mechanism is introduced, NSGA-III multi-target optimization is triggered when the environment suddenly changes or the tracking error exceeds a threshold value, and the optimal track is generated by integrating energy consumption, time and safety. The system integrates high-precision sensing, self-adaptive planning and dynamic optimization, and the inspection coverage rate and the track safety under the complex shutdown attitude are remarkably improved.

The invention provides a full-layout defect rapid detection system based on pattern matching and classification, which relates to the technical field of semiconductor manufacturing and comprises a data acquisition and multi-source fusion module, a self-adaptive preprocessing module, a dynamic pattern matching module, a multi-source feature fusion module and a closed-loop optimization and output module, the dynamic mode matching module dynamically generates a defect template from real-time data through a clustering algorithm (such as DBSCAN) and a generative adversarial network (GAN), the speed and accuracy of defect detection are remarkably improved in combination with reinforcement learning filtering and region focusing technologies of the self-adaptive preprocessing module, a detection strategy can be adaptively adjusted according to the real-time data, and the defect detection accuracy is improved. According to the method, a high-risk area is preferentially matched, and GPU parallel computing acceleration processing is performed, so that the detection time is shortened to 50% or below of that of a traditional method, deformation or fuzzy defects can be effectively identified, the false alarm rate is reduced by about 30%, and high-quality input is provided for subsequent matching and classification.

The invention discloses a power prediction method considering photovoltaic power clustering, which comprises the following steps of: firstly, acquiring historical data of a photovoltaic power station, decomposing a photovoltaic power time sequence by using an STL time sequence decomposition method, performing DBSCAN clustering on the decomposed photovoltaic power data, identifying a low-power cluster and a high-power cluster, and predicting the power of the photovoltaic power station according to the low-power cluster and the high-power cluster; establishing a photovoltaic power prediction model based on a time convolutional network and Times Net for low power and high power respectively; secondly, a badger algorithm is improved, Chebyshev chaotic mapping is adopted to improve initialization of a badger population, and a Gaussian mutation strategy and a lens imaging reverse learning strategy are introduced to obtain MHBA; and optimizing hyper-parameters of the TCN photovoltaic low-power prediction model and the TimesNet photovoltaic high-power prediction model by using an improved badger algorithm, and predicting photovoltaic power by using the optimized models to obtain a final prediction value. Compared with the prior art, the precision and effectiveness of the photovoltaic power prediction model can be improved.

The invention discloses a mobile tunnel detection method and system, and relates to the field of tunnel detection, and the method comprises the steps: dividing a tunnel into a shield tunnel and a mine tunnel; for a shield tunnel, obtaining a three-dimensional point cloud of the shield tunnel, and extracting bolt holes by adopting a CSF algorithm and a DBSCAN algorithm; adopting a mean-shift algorithm to obtain a central point of the bolt hole, and performing ring-dividing and block-dividing on the three-dimensional point cloud of the shield tunnel according to the central point of the bolt hole to obtain a shield tunnel point cloud after ring-dividing and block-dividing; the method comprises the following steps of: for a mine tunnel, arranging one target every preset distance along the axial direction of the tunnel by using mobile tunnel laser equipment for target arrangement to obtain a mine tunnel point cloud with target position information; generating a mine tunnel lining diagram according to the mine tunnel point cloud with the target position information; establishing an index relationship between the mine tunnel point cloud and the mine tunnel lining graph; and positioning the mine tunnel point cloud according to the index relationship to obtain the positioned mine tunnel point cloud and the like, so that the tunnel defect detection efficiency is improved.

The invention relates to the technical field of chemical safety and intelligent control, in particular to an explosion-proof intelligent temperature control method and system for a hydrogen peroxide storage tank based on multi-modal monitoring, and the method comprises the steps: collecting multi-dimensional data such as temperature, pressure and H2O2 concentration through a sensor array, carrying out the Kalman filtering and wavelet transform denoising, recognizing local abnormal points through an improved WMDS-DBSCAN algorithm, and obtaining the explosion-proof intelligent temperature control of the hydrogen peroxide storage tank based on the WMDS-DBSCAN algorithm. Predicting a hot spot diffusion trend in combination with an unsteady heat conduction equation and an RF-ADI algorithm; weighted Delaunay triangulation is utilized to construct a self-adaptive monitoring grid, risk area probability distribution is output through an SAM-LSTM-CNN hybrid neural network, and a dynamic amplitude limiting fuzzy PID controller adjusts the opening degree of a nozzle to achieve temperature control. According to the method, multi-modal data and an intelligent algorithm are fused, abnormal accurate detection, risk prediction and self-adaptive temperature control are realized, a closed-loop optimization mechanism is formed, and the operation safety and reliability of the hydrogen peroxide storage tank are effectively improved.

The invention discloses a micro-seismic multichannel data processing method and system based on a DBSCAN algorithm, and belongs to the technical field of micro-seismic monitoring. According to the invention, through feature vector construction and optimization distance calculation, the clustering reliability is enhanced; a multi-dimensional feature vector Xi containing P-wave refinement arrival time ti, an amplitude Ai and space coordinates (xi, yi, zi) is constructed, distance calculation is performed by combining a normalized cosine distance with wave velocity constraint, a micro-seismic wave propagation rule is fully utilized, the discrimination capability of DBSCAN clustering on physical consistency is enhanced, and the robustness of a screening result is improved, so that noise interference can be effectively processed in a complex environment, and the accuracy and the robustness of the screening result are improved. And the stability and the reliability of a micro-seismic positioning result under different geological conditions are ensured.

The invention discloses a high-precision DEM construction method based on a point cloud model. The method comprises the following steps: step 1, performing grid division on urban point cloud data, dynamically adjusting the grid size and the neighborhood range based on a weighted gradient value, and calculating the weighted gradient value through the height difference and the distance between the lowest points of a central grid and a neighborhood grid; 2, clustering the urban point clouds by using DBSCAN (Density Based Spatial Clustering of Applications with Noise) and combining the features of color, intensity and texture of the point clouds; 3, taking the extracted ground feature line as a constraint during point cloud filtering, and determining the boundary of a ground point and a non-ground point; step 4, based on the ground feature line constraint in the step 3, executing multi-scale adaptive filtering, and removing non-ground points; 5, taking the extracted ground feature line as a constraint during point cloud filtering, and determining the boundary of a ground point and a non-ground point; and step 6, aiming at different partitions, respectively using a nearest neighbor interpolation up-sampling algorithm to construct a TIN (Triangulated Irregular Network) method to construct an urban high-precision DEM (Digital Elevation Model).

The invention discloses a data processing method and system for micro-motion detection array station distribution, and relates to the technical field of micro-motion detection, and the method comprises the steps: according to the actually measured starting point coordinates and the azimuth angle of a measuring line, setting the measuring point distance, and the radius and number of nested triangle circumcircles, generating field station coordinates in batches; collecting and preprocessing station waveform data; calculating a spatial autocorrelation coefficient of any center station and a non-center station; dBSCAN clustering is carried out based on the station distance; performing spatial averaging on the spatial autocorrelation coefficient according to a clustering result; fitting the averaged spatial autocorrelation coefficient and a zero-order Bessel function to obtain a frequency dispersion spectrum, picking up a surface wave phase velocity in the frequency dispersion spectrum, and calculating an apparent shear wave velocity; inverting the shear wave velocity of the waveform data by adopting a self-adaptive gradient inversion strategy; and generating a velocity comprehensive profile of a depth domain and a frequency domain according to the surface wave phase velocity, the apparent shear wave velocity and the inverted shear wave velocity. The invention provides a new method for station distribution to data processing for micro-motion detection.

The invention discloses a target detection method and system based on a millimeter wave radar. The target detection method and system are used for realizing accurate detection, positioning and dynamic and static recognition of multiple targets under a complex background. According to the method, a distance-Doppler spectrogram is generated through the technical means of sliding window construction, spectral analysis, clutter suppression and the like, and candidate target points are detected by adopting an SO-CFAR algorithm. Then, determining a target position through high-resolution direction estimation and coordinate transformation, performing spatial clustering in combination with a density-based DBSCAN algorithm, and extracting a target geometric center and a bounding box; in the aspect of target tracking, Kalman filtering is used for predicting and updating the position and speed of the target, and a beam forming technology is used for enhancing a target signal, so that the target recognition stability is improved. And finally, the system performs robust dynamic and static state recognition on the target through a dynamic and static judgment module, so that high precision and robustness of the target detection process are ensured. The method can effectively cope with static background interference and dynamic target changes, and is suitable for target detection and tracking in a complex environment.

The invention relates to the technical field of data processing, in particular to a wind turbine generator running state monitoring method based on machine learning, and the method comprises the steps: carrying out the logarithmic transformation of each piece of data in a data sequence corresponding to a plurality of data indexes in a preset time period during the running of a wind turbine generator, and obtaining a corresponding logarithmic value; constructing a scatter diagram of all logarithm values; obtaining a neighborhood radius and a minimum sample number of a DBSCAN algorithm, and obtaining a clustering result of all data points in the scatter diagram according to the neighborhood radius and the minimum sample number; a plurality of boundary points are obtained according to the clustering result, the isolation degree of each boundary point is obtained according to the distribution characteristics of the data points within the neighborhood radius range of each boundary point, and abnormal points in the scatter diagram are obtained according to the isolation degree of each boundary point; and the operation state of the wind turbine generator is monitored according to the abnormal points in the scatter diagram, so that the accuracy of monitoring the operation state of the wind turbine generator by the DBSCAN algorithm is improved.

The invention discloses an adaptive distributed network threat detection and response system and method, and the system comprises a data collection module which is used for capturing network traffic in real time and converting the network traffic into structured data; the data analysis module is used for performing parallel analysis on the structured data by adopting a plurality of anomaly detection algorithms; the algorithm fusion module is used for counting the abnormal judgment times of each algorithm on the same sample through a voting mechanism, and outputting a final classification result according to a preset threshold value; the method comprises the following working steps: concurrently capturing network traffic through distributed nodes, analyzing the network traffic into structured data, operating an isolated forest algorithm, a DBSCAN algorithm and a K-means algorithm in parallel, and outputting an anomaly judgment result; and fusing multi-algorithm results and generating final classification through a voting mechanism. The invention provides an efficient, intelligent and distributed network security protection system, which can effectively detect and respond to various network threats, and is particularly suitable for large-scale distributed environments and dynamic network security defense requirements.

The invention relates to a bridge modal parameter automatic identification method and system considering multi-channel information, and the method comprises the following steps: S1, directly analyzing a multi-channel monitoring signal through COV-SSI, generating a stability diagram, and automatically extracting a stability axis in combination with a DBSCAN clustering algorithm, thereby achieving the automatic identification of a modal frequency; s2, performing signal decomposition on the multi-channel monitoring data by adopting MvFIF to generate an intrinsic mode function (IMF) group with a mode alignment characteristic; s3, the instantaneous frequency and bandwidth of the IMF in the step S2 are calculated through HHT, IMF components containing target modal frequency are screened out, and linear reconstruction is carried out; s4, taking the multi-channel monitoring data reconstructed in the step S3 as system input of a COV-SSI algorithm, calculating a system matrix and an output matrix, and calculating a modal damping ratio by utilizing eigenvalue decomposition; according to the method, the spatial-temporal correlation among multi-channel data can be considered, and synchronous decomposition of multi-channel monitoring data is realized; and automatic and accurate identification of the structural modal parameters under different noise levels can be realized.

The invention further provides an unmanned ship collision avoidance method and device based on the DBSCAN algorithm, and the method comprises the following steps: 1, collecting ship data, and training a six-degree-of-freedom ship motion model through a neural network; step 2, acquiring a wind speed by using a wind speed sensor, and acquiring ocean current information by using an ocean current sensor; step 3, acquiring speed and position information of other ships by using AIS / radar; step 4, collecting ship captain driving information, establishing a ship driving model, and predicting the future motion state of the ship; 5, performing risk analysis on other ships by using a DBSCAN algorithm, and calculating ship collision risks; step 6, according to the step 5, performing corresponding collision avoidance behaviors aiming at the risk; and 7, judging an action execution condition, and returning a reward value and a result. According to the technical scheme, the collision avoidance capability of the unmanned ship can be improved, the generalization capability is good, and the efficiency of collision avoidance and safe navigation of the unmanned ship on the water surface is improved on the whole.

The invention belongs to the technical field of smart cities, and discloses a point cloud-based urban building roof wireframe reconstruction method, which comprises the following steps of: fusing a fast point feature histogram and a multi-scale roof geometric descriptor, generating robust point-by-point features, screening candidate angular point clusters in combination with a classification head, and adaptively segmenting the candidate point clusters based on density parameters by utilizing DBSCAN (Density-Based Spatial Clustering of Applications with Noise), so as to reconstruct a point cloud-based urban building roof wireframe. Initial inflection points are extracted through unsupervised clustering, noise is effectively suppressed, and irregular distribution is adapted; then multi-scale geometric information of an initial inflection point neighborhood is aggregated through an inflection point correction network, offset is learned to correct position deviation, and inflection point positioning precision is improved; and finally, the edge classification network automatically deduces the topological connection of the roof wireframe based on the geometrical relationship and feature relevance of prediction inflection points, so that error accumulation caused by dependence on manual rules in a traditional method is avoided, the generalization ability of a complex roof structure is enhanced, the correction network learns offset through a multi-scale context, and the inflection point positioning precision is remarkably improved.

The invention discloses an unknown radar radiation source sorting method based on time sequence feature clustering. The unknown radar radiation source sorting method comprises the following steps: acquiring radar signal pulse description words of pulse signals; according to the radar signal pulse description word, performing spatial clustering on the pulse signal to obtain a spatial clustering result; for each spatial clustering result, according to the radar signal pulse description word of the pulse signal in each spatial clustering result, performing time feature clustering on the pulse signal in each spatial clustering result to obtain a time sequence clustering result; and analyzing a time parallel relationship and a time continuous relationship of the time sequence clustering results, and performing pulse group sequence blending on the time sequence clustering results to obtain a sorting result. On the basis of the existing clustering algorithm, the information of the pulse signal in the time dimension is introduced, the time sequence feature clustering of the pulse signal is realized by using the ST-DBSCAN algorithm thought and introducing the TOA-PA constraint interval, and the method has strong robustness for the complex electromagnetic environment.

The invention discloses an improved DBSCAN abnormal node identification method based on space-time fusion features in the technical field of power systems, and the method comprises the steps: extracting five types of spatial topology features, including node proximity centrality, betweenness centrality and hierarchy depth, through constructing a power distribution area adjacency matrix; according to the method, 16 time sequence features such as a voltage fluctuation index, a current peak-valley difference and power integration are combined to form space-time dual feature representation, principal component analysis (PCA) is adopted to carry out dimension reduction processing on fusion features, and more than 90% of variance information is reserved to reduce calculation complexity. According to the method, the propagation rule of electricity stealing disturbance in a power grid is effectively described through spatio-temporal feature fusion, the problem of dimension disasters is solved through PCA dimension reduction, adaptation of DBSCAN to a complex topology scene is improved by means of dynamic parameter optimization, compared with a traditional detection technology, the false detection rate is reduced by 50% or above, the calculation efficiency is improved by 80%, and the method is suitable for large-scale popularization and application. The method can be widely applied to the fields of high-loss transformer area electricity larceny screening, abnormal electricity consumption behavior monitoring and the like, and provides technical support for safe operation of an electric power system.

The invention discloses an engineering rock mass fracture surface dynamic extension prediction method and system, and the method comprises the following steps: obtaining original sound wave data, carrying out the positioning calculation and seismic source mechanism inversion of the original sound wave data, obtaining the core parameters of a coal rock fracture source at each moment, and calculating a fracture penetration possibility index BCI; fracture similarity is calculated through multi-dimensional features, density clustering is performed by using a DBSCAN clustering algorithm, and a reconstructed fracture surface time sequence image is obtained in combination with BCI optimization and a B spline surface fitting technology. A fracture surface image spatio-temporal data set is obtained through preprocessing and is divided, an improved SimVP spatio-temporal neural network is utilized to construct a fracture surface image sequence prediction model for training, the multi-head attention mechanism and local residual error connection are introduced into an encoder and a decoder, a Poolform network is used as a translator, and a fracture surface image sequence prediction model is constructed through the improved SimVP spatio-temporal neural network. And finally, utilizing the trained prediction model to accurately predict the dynamic expansion of the fracture surface at the future moment.

The present invention discloses a method for clustering millimeter-wave radar point cloud targets based on KDE-DBSCAN, comprising the following steps: S1. For each point in the millimeter-wave radar point cloud data set D, calculate the corresponding associated window width W and the associated adjacent points RN of this point according to the k value corresponding to the region where this point is located; S2. According to the associated window width W and the set of associated adjacent points RN of each point, preliminarily divide the radar point cloud data set D into different pseudo-clusters FC; S3. Count the number of points in each pseudo-cluster FC generated in step S2 i , and delete the pseudo-clusters with the number of points less than λ; S4. Determine the optimal associated radius ε and the minimum number of associated points minP of each pseudo-cluster; S5. From the optimal associated radius ε and the minimum number of associated points minP of each pseudo-cluster obtained in step S4, then use the DBSCAN algorithm to cluster each pseudo-cluster respectively. The method of the present invention can adapt to the point cloud distribution characteristics of the millimeter-wave radar, thereby improving the clustering quality of the millimeter-wave radar targets.

The invention belongs to the technical field of public safety, and particularly relates to a map-based focused person management and control large model visualization system and method, and the system comprises a multi-source data collection layer which is used for constructing a multi-modal data set comprising identities, tracks and biological characteristics; the large model analysis layer comprises a space-time trajectory prediction model, a risk integral calculation engine, a group event early warning model and data processing; the visual interaction layer dynamically renders personnel tracks, risk thermodynamic diagrams and police resource distribution based on a three-dimensional map engine, and supports hierarchical early warning display and emergency plan linkage; the monitoring and tracking layer is matched with the tracking and early warning layer, frequently-going / non-frequently-going areas are divided through an improved space-time ST-DBSCAN algorithm, dangerous behaviors of management and control personnel are monitored and tracked, and a differential early warning mechanism is triggered. The multi-modal data fusion technology is combined, real-time loading of ten thousand people-level track points is achieved, data collaborative training of public security organizations in multiple places is supported, and dangerous behaviors of management and control personnel can be monitored and tracked.

The invention belongs to the technical field of electric digital data processing, and more specifically relates to an agricultural machinery behavior identification method fusing interpolation enhancement and depth spatial-temporal feature modeling. The method comprises the following steps: acquiring GNSS trajectory data of an agricultural machine and preprocessing the trajectory data; performing data enhancement on the road points in the data set by adopting a local interpolation method based on DBSCAN (Density Based Spatial Clustering of Applications with Noise) clustering; designing a set of spatial distribution feature extraction method; performing feature extraction on the trajectory data after data enhancement by combining motion feature extraction and spatial distribution feature extraction; introducing a variational auto-encoder VAE to carry out potential feature extraction on the initial features; then, a ResBiLSTM network is adopted to carry out space-time modeling; and finally, an end-to-end classification decision is realized through a linear classifier, so that intelligent identification of the operation behavior of the agricultural machine is realized. According to the method, two key problems in the existing track identification technology are solved, namely, data distribution is seriously unbalanced, and the spatial distribution characteristics of track points are often neglected.

The invention relates to the technical field of artificial intelligence prediction, in particular to an artificial intelligence-based teenager psychological health development trajectory prediction system, which comprises a data acquisition module, an emotion recognition module, a trajectory prediction module, a risk assessment module and an intelligent feedback module, and provides a feature extraction method based on a point cloud structure. Time sequence feature modeling is carried out by combining K nearest neighbor with Transform, and global feature extraction is carried out by using position sensing optimization and Max Pooling, so that key features of the mental health state of the teenagers are extracted more comprehensively; according to the method, a clustering-based bidirectional LSTM is combined with a GRU prediction model, firstly, DBSCAN is used for clustering, different mental health state modes are defined, Bayesian optimization and Bayesian filtering are adopted for improving the prediction precision, and finally more accurate mental health development trajectory prediction is provided.

The invention discloses an operation state and characterization state parameter method of a GIS isolation switch, and relates to the field of GIS isolation switch operation. Multi-source heterogeneous sensors including vibration, temperature, strain gauges, fiber bragg gratings, acoustic cameras and the like are deployed at key parts, and data are collected in a self-adaptive mode according to working conditions; processing data by using a deep learning noise reduction auto-encoder, adaptive weighted fusion and DBSCAN; deeply mining features by using empirical wavelet transform, a heat conduction model and the like; constructing a composite state parameter by means of a deep belief network and principal component analysis in combination with particle swarm optimization; the state is evaluated through a convolutional neural network of transfer learning, LSTM prediction and early warning are carried out, and a fault tree and a Bayesian network are combined to locate a fault. According to the invention, data is comprehensively collected, intelligent processing and deep mining are carried out, scientific state parameters are constructed, and accurate state evaluation and early warning are realized in combination with transfer learning; the adaptability is improved through self-learning and multi-modal fusion, the operation and maintenance efficiency is improved through a VR / AR visual platform, the risk of the power system is reduced, and benefits are remarkable.

The present invention discloses a 4D millimeter wave radar clustering method and storage medium based on DBSCAN, comprising the following steps: S1, obtaining vehicle signals, lane line signals and 4D millimeter wave radar point cloud signals, and parsing and preprocessing the signals; S2, calculating the local search radius of each point in the point cloud signal, and sorting the calculated local search radius of each point; S3, clustering the point cloud signal based on the DBSCAN algorithm. The present invention is based on the traditional DBSCAN algorithm, in which data preprocessing based on other sensors and the physical properties of each point is added, a local search radius ε is determined for each point cloud according to the size of its physical quantity, all original detections are sorted and clustered, and the number of clustering result points is dynamically judged whether it is a single-frame observation target. Comparing the clustering results of the present invention with the traditional DBSCAN clustering method, it can be found that the clustering effect of the present algorithm is better.

The invention requests to protect an aerial photography small target detection method based on regional clustering and a feature attention mechanism. The method comprises the following steps: firstly, taking a central coordinate of a candidate target extracted from an input image as an input feature of a clustering algorithm; then clustering the targets by using a density-based DBSCAN clustering algorithm, cutting out the regions from the image according to a clustering result, performing center filling processing on the cut-out regions to keep the scale and the ratio in a reasonable range, and integrating the cut-out regions with original data to obtain a new expanded data set; and putting the expanded data set into the improved single-stage target detection network RetinaNet for training, and performing final target classification and positioning by using a detection head based on an attention mechanism. The method can achieve the effect of data enhancement. In addition, the detection head based on the attention mechanism can remove interference of useless information in classification and regression tasks, so that the network can adaptively pay attention to more appropriate information for classification and regression.

The invention provides a demand response bus intelligent scheduling method and system driven by real-time data. A demand response bus intelligent scheduling method driven by real-time data comprises the following steps: S1, collecting and fusing multi-source data in real time, and constructing a dynamic data set; s2, carrying out short-time demand prediction and clustering analysis based on LSTM and DBSCAN; s3, dynamic service area division and greedy algorithm line generation; s4, elastic vehicle scheduling based on combination of mixed integer programming and a simulated annealing algorithm; s5, dynamically adjusting the real-time ticket price driven by the supply and demand game model; s6, passenger personalized service matching is achieved through a collaborative filtering algorithm; s7, performing multi-target simulation verification on the digital twin platform; and S8, reinforcing a learning-driven dynamic feedback optimization mechanism. According to the real-time data-driven demand response bus intelligent scheduling method and system provided by the invention, the operation efficiency, the passenger satisfaction degree and the enterprise benefit of the demand response type customized bus can be remarkably improved, and the method and the system are suitable for construction of an urban intelligent bus system and have remarkable advantages.

The invention discloses a seismic source inversion and clustering reconstruction integrated unstable structural plane identification method and system, and the method comprises the following steps: obtaining original sound wave data, carrying out the positioning calculation and seismic source mechanism inversion of the original sound wave data, and obtaining coal rock fracture source core parameters directly related to fracture; calculating a fracture penetration possibility index BCI in combination with the standardized fracture volume, the seismic source radius and the spatial distance; the similarity between the fractures is calculated according to the multi-dimensional features of the fractures, and density clustering is conducted on the fractures through a DBSCAN clustering algorithm; by calculating a fracture and penetration possibility index value on each fracture cluster, the fractures with relatively high fracture and penetration possibility indexes are screened out, so that a potential instability surface is identified; generating a macroscopic instability surface by adopting a B-spline surface fitting algorithm; a control point is selected through a principal component analysis method to determine a fracture main direction, a B-spline curve is constructed based on fracture distribution to generate a boundary contour, and a B-spline algorithm is used for accurately fitting an unstable structural surface.

The invention relates to the technical field of fan blade fault diagnosis, and discloses a fan blade fault diagnosis system and method based on HHT and DBSCAN. Synchronously acquiring vibration signals acquired by a three-axis acceleration sensor and wind speed and rotating speed working condition data acquired by an SCADA (Supervisory Control and Data Acquisition) system; eMD empirical mode decomposition is carried out on the collected vibration signals, the vibration signals are decomposed into a limited number of IMF signals, and effective IMF signals are screened; generating a Hilbert spectrum through HHT, extracting multi-scale frequency band energy features, and fusing SCADA data to carry out working condition adaptive normalization on the features; and then, processing the normalized features by adopting a self-adaptive DBSCAN clustering algorithm based on K-distance map parameters, and realizing fault diagnosis by identifying an abnormal cluster with a sample proportion of less than 5% and combining with a frequency band energy deviation threshold. According to the method, the problems that a traditional method is not thorough in decomposition of non-stationary signals, depends on labeled samples and is insufficient in clustering robustness are solved, and unsupervised and high-precision blade state monitoring is achieved.

The invention discloses a method for detecting and tracking a dynamic obstacle in a weak light environment. The method comprises the following steps: 1) acquiring an image; (2) a U-depth detector and a DBSCAN detector are used for conducting rapid detection on a near obstacle, and after weak light images of a far obstacle are enhanced through a lightweight image enhancement network LPF, a lightweight FastDet model is input to conduct deep learning auxiliary detection; 3) calculating the intersection-to-union ratio of the U-depth detector, the DBSCAN detector and the LPF-FastDet detector, fusing the intersection-to-union ratio results of the U-depth detector, the DBSCAN detector and the LPF-FastDet detector, and generating a set bounding box; 4) performing data association on the detected obstacles through a feature association method, and performing continuous tracking by using Kalman filtering; and 5) identifying a dynamic obstacle from all detected obstacles, and outputting the size of a three-dimensional bounding box of the dynamic obstacle and the center position coordinates of the dynamic obstacle in the unmanned aerial vehicle coordinate system.The method provides a high-precision and low-delay solution for obstacle perception of the unmanned aerial vehicle in a weak light environment, and is suitable for different scene requirements.

The invention relates to the field of gas leakage identification, and discloses a gas leakage identification method and system based on sound localization, a medium and equipment, and the method comprises the steps: collecting a leakage sound wave signal through a microphone array, and extracting acoustic features in a complex background; simulating a diffusion process of gas in a turbulence environment after gas leakage through an established leakage gas convection-diffusion model, and combining gas concentration distribution in the simulated diffusion process with acoustic characteristics to predict sound source localization at a leakage position; modeling a sound source localization search process as POMDP, and iteratively updating a confidence state of a sound source position through a particle filter; spatial distribution features are extracted from the confidence state through DBSCAN clustering to serve as input of the LSTM-DQN network, spatial and temporal features are fused through the constructed LSTM-DQN network, cross-scene generalization is achieved through transfer learning, and sound source coordinates are dynamically optimized and output through the Q network. According to the invention, the positioning accuracy and real-time performance in a complex environment are improved.

The invention provides an arc fault detection method based on an improved empirical mode decomposition algorithm, and the method comprises the steps: collecting an arc fault signal through building a benchmark test platform, carrying out the preprocessing, and carrying out the decomposition of the signal through employing a completely adaptive noise set empirical mode decomposition algorithm, according to the method, useful intrinsic mode functions containing main characteristics and information of arc fault signals are screened out, then the signals are reconstructed, spectrum characteristics are extracted, and finally, a DBSCAN clustering algorithm is used for carrying out clustering analysis on characteristic vectors so as to judge whether the arc faults occur or not. The modal aliasing phenomenon and the endpoint effect are effectively avoided, and the accuracy of arc fault detection is improved. Meanwhile, through adaptive spectrum segmentation and feature extraction, arc fault signals can be accurately identified under strong noise interference.