425 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 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 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 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 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 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.

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 relates to the technical field of echelon utilization of decommissioned lithium ion batteries, in particular to a decommissioned power lithium battery sorting method and system based on DBSCAN and WOA-FCM. The method comprises the steps that static characteristics of decommissioned power lithium batteries are determined; based on the static characteristics, first-stage sorting is carried out on the retired power lithium batteries through a density-based noise application space clustering algorithm, so that abnormal batteries are eliminated, and the clustering number of normal batteries is determined; and for the remaining normal batteries after the abnormal batteries are removed, dynamic characteristics are extracted based on a battery discharge voltage curve, second-stage sorting is carried out through a whale optimization-based fuzzy C-means clustering algorithm, and a battery sorting result is obtained. Therefore, through two-stage collaborative optimization, the sorting accuracy and efficiency are improved, and the high consistency of the recombined battery packs is ensured.

The invention provides an intelligent government affair hotline group appeal analysis and detection method. The method comprises the following steps: regularly pulling daily work order data; performing vectorization coding on work order content in the work order data; aiming at the vector chemical order data, using a DBSCAN clustering algorithm to obtain a classification cluster; traversing the classification clusters, and expanding the classification clusters by using the DBSCAN clustering algorithm again by using address features and complaint features of the classification clusters and data which are not included in any cluster, so as to obtain more accurate complaint amount and complaint number information; obtaining a complaint summary of each classification cluster through a large model; and writing the data into the data. According to the method, group appeal detection considering semantic and spatial information can be realized, redundant and repeated work of original manual statistics is replaced by an intelligent mode, and similar appeals can be effectively identified and integrated, so that the processing efficiency is improved, the service quality is optimized, and a daily group event list is quickly and efficiently provided.

The invention provides an improved DBSCAN dry-type transformer vibration signal fault early warning method and system, and belongs to the technical field of dry-type transformer fault diagnosis, and the method comprises the steps: obtaining a traction rectifier transformer vibration signal of a to-be-detected urban rail power supply system; processing the acquired vibration signal by using a pre-trained fault diagnosis model to obtain a fault diagnosis result; and if the number of the abnormal points in the fault diagnosis result exceeds a set threshold value, performing early warning. According to the method, the DBSCAN parameters are automatically optimized through multi-objective optimization and a simulated annealing algorithm, so that the judgment of abnormal points reaches a most accurate state, and the workload is reduced.

The invention discloses a road advertisement putting method and system based on intelligent travel data, and relates to the technical field of intelligent traffic and precise advertisement putting, and the method comprises the steps: collecting intelligent travel data; the intelligent travel data comprises vehicle real-time trajectory data, vehicle attributes, a road network topological structure and environment data; inputting the standard trajectory data set into an improved ST-DBSCAN spatial clustering algorithm, performing spatial-temporal density clustering analysis to identify a high-frequency passage path and an associated vehicle set, and outputting a high-frequency path list; associating the high-frequency path list with an advertisement exposure log, and performing strong rule mining through an FP-Growth algorithm to generate an advertisement putting rule base; and based on the high-frequency path list and the advertisement putting rule base, combining the real-time track data of the vehicle, and generating a dynamic advertisement putting instruction. According to the invention, the accuracy, the automation level and the resource utilization efficiency of advertisement putting are integrally improved.

The invention discloses a water supply pipe network monitoring point arrangement method based on a risk assessment and optimization algorithm, and relates to the technical field of intelligent pipe network monitoring, and the method comprises the steps: collecting water supply pipe network data, carrying out the preprocessing, and storing the data in an InfluxDB time sequence database; constructing a multi-dimensional risk assessment model, and generating risk scores of the pipe sections; a greedy algorithm and genetic algorithm hybrid optimization strategy is adopted to generate a water supply network monitoring point layout scheme; the method comprises the following steps: deploying an edge computing gateway, distributing water supply network data to the edge computing gateway through an MQTT protocol, generating a pipe section risk probability by using a pipe section risk probability model, setting a four-level judgment threshold, and triggering a hierarchical alarm mechanism; a DBSCAN algorithm is used for identifying a water supply network risk high-incidence area, water supply network monitoring points are increased, and the monitoring radius of the water supply network monitoring points is dynamically adjusted. According to the method, through a greedy-genetic hybrid optimization strategy, a global optimal solution and rapid convergence of monitoring point layout are realized.