357 results about "Dbscan clustering" patented technology

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

A multipath identification method and apparatus, a vehicle, and a medium. The method comprises: preprocessing an echo signal of a millimeter wave radar, so as to obtain static point cloud data and dynamic point cloud data in the echo signal; clustering the dynamic point cloud data by means of improved DBSCAN clustering algorithm, so as to obtain multi-cluster dynamic data; on the basis of an object reflection boundary parameter and cluster center information in the multi-cluster dynamic data, determining at least one matching set, the matching set containing at least two cluster centers, and the object reflection boundary parameter being determined on the basis of the static point cloud data; and for each matching set, performing identification on the cluster centers in the matching set to obtain a target cluster center, and determining the target cluster center and the point cloud data in a cluster where the target cluster center is located as a multipath object in the echo signal. The method can effectively identify a multipath object, thereby improving the accuracy and robustness of vehicle-mounted millimeter wave radar detection.

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 multi-source text-based dynamic hotspot clue accurate extraction method, which realizes real-time adaptation and efficient training of a sensitive intelligence recognition model through dynamic label configuration and Prompt design in combination with a lightweight fine tuning technology (RsLoRA), and can accurately recognize in a changeable intelligence scene without re-training. Through human feedback reinforcement learning (PPO) and sorting training, clue texts output by the generative large model are optimized, event subjects and contents are accurately extracted, and a generated result is structured and highly summarized. A semantic vector model and a dynamic multi-layer DBSCAN clustering algorithm are combined, the problem that an existing clustering algorithm is insufficient in adaptability to semantic density difference scenes is solved, and high-precision clue aggregation and popularity statistics are achieved. The overall scheme reduces resource consumption and training time through the RsLoRA technology, comprehensively improves intelligence processing efficiency and analysis ability, and has significant technical advantages and wide application prospects in the fields of intelligent public security, government management and the like.

The invention relates to an offshore wind power prediction method, and aims to improve the accuracy and reliability of prediction. The method comprises the following steps: (1) a data preprocessing stage: detecting an abnormal value in data by using a DBSCAN clustering algorithm, reconstructing the abnormal value by using a KNN interpolation method, detecting time sequence abnormity through an LSTM automatic encoder, and performing regression reconstruction by using LSTM to ensure the retention of time sequence features; (2) a feature engineering stage: screening out key features through correlation analysis, generating a label column through K-means clustering and wind direction classification, and extracting features such as wind direction change rate, time periodicity and wind speed interaction; and (3) a model construction stage: constructing a composite model in combination with multi-scale convolution (Inception), a bidirectional long short-term memory neural network (BiLSTM) and a multi-head self-attention mechanism, extracting local features through a convolution layer, capturing a time dependency relationship through a bidirectional LSTM layer, enhancing the attention of key features by using the multi-head self-attention mechanism, and finally realizing high-precision wind power prediction.

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 discloses an intelligent splitting method based on a data segmentation algorithm, and particularly relates to the technical field of intelligent splitting, and the method comprises the steps: carrying out the finite element multi-scale stress analysis of a fusion model, mapping the stress data to a point cloud, marking a high-stress region, a regular geometric region and a transition region based on an adaptive DBSCAN clustering algorithm, and obtaining a high-stress region, a regular geometric region and a transition region; the marking and partitioning efficiency and the transition region identification precision are effectively improved; according to the method, graph segmentation is carried out on the feature partitions to obtain a steel structure preliminary module division scheme, and geometric segmentation is carried out on the steel structure preliminary module division scheme to obtain a steel structure refined module division scheme and machining process parameters; based on an NSGA-II algorithm and a PPO algorithm, dynamic multi-objective optimization is performed on a steel structure refined module splitting scheme, and a final Pareto leading-edge solution set and an optimization scheme comparison matrix are output, so that the segmentation accuracy is improved, the module division dispersion is reduced, the adaptation rate of process parameters and the process is improved, and multi-objective collaborative optimization is realized.

The invention discloses a point cloud image data fusion method, system and device based on an improved DBSCAN clustering algorithm and a storage medium, and belongs to the field of multi-sensor data fusion, the method uses an acquisition system composed of a laser radar and a camera to complete point cloud and image shooting and acquisition of a detected target scene, and the point cloud and image shooting and acquisition have different characteristics; the sensor fusion mode is used for target detection, so that advantage complementation can be realized; an improved DBSCAN algorithm is used for completing detection target point cloud clustering and generating a three-dimensional bounding box, finally, a point cloud cluster bounding box model is projected to a corresponding image pixel position, fusion of image data and point cloud data is completed, the DBSCAN point cloud clustering algorithm is improved, automatic calculation of a minimum neighborhood and an initial radius value is achieved, and finally, point cloud detection is completed. And the efficiency of detecting the target point cloud clustering is improved, so that the robustness and the accuracy of the detected target are improved, and the omission ratio and the false detection rate of target detection under the condition that the illumination condition is insufficient are reduced.

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 invention discloses a BIM-based construction engineering visual management method and system, and relates to construction engineering management, and the method comprises the steps: collecting a data set containing geometric information, attribute information and associated information; dividing the data set into a plurality of hierarchical subsets according to the hierarchical relationship of the building engineering BIM model; an improved DBSCAN clustering algorithm is adopted to perform clustering analysis on each hierarchy subset; classifying and archiving the data set according to the data cluster, and generating a semantic association data collaboration array A according to a classifying and archiving result; carrying out dimension reduction processing on the semantically associated data collaboration array A; constructing a spatial index of the dimension reduction data array by adopting a density-based spatial index method; and dividing the building engineering BIM model into a plurality of partitions by using a distributed computing framework, and performing distributed matching on the partitions and the spatial index to obtain a data matching point set. For the problem that the BIM-based building engineering high-dimensional data clustering precision is low in the prior art, the analysis precision is improved.

The invention relates to a multi-target line spectrum characteristic distinguishing method based on DBSCAN clustering, and the method comprises the following steps: 1, receiving space low-resolution sonar platform time domain data, and carrying out the short-time frequency modulation Fourier transform of the space low-resolution sonar platform time domain data, so as to obtain a frequency modulation Fourier transform spectrum; 2, corresponding fi of peak values is extracted from the frequency modulation Fourier transform result of each time period at each frequency, so that an instantaneous frequency matrix F of different frequencies and different times is obtained, F = [f1 (tau), f2 (tau),..., fi (tau)], and i represents a corresponding spectral line frequency; and step 3, respectively removing outliers caused by noise or interference in the fi (tau). According to the method, dense data points can be found from the sparse region, and the data points in the sparse region are marked as noise, so that the interference of noise points can be better eliminated.

The invention relates to the technical field of environmental sanitation service quality management, and discloses an environmental sanitation service evaluation method based on a task decoupling and closed-loop feedback mechanism, and the method comprises the steps: carrying out the processing of environmental sanitation operation flow data and service item feature data of a target region through a task decoupling model, and generating a task decomposition thermodynamic diagram and a weight gradient field; secondly, constructing a multi-dimensional evaluation index system, and formulating a dynamic data acquisition strategy in combination with a DBSCAN clustering algorithm; the feedback data acquisition equipment acquires service feedback data according to the strategy, and identifies a service quality level and a defect item through a random forest model; and finally, based on a closed-loop feedback mechanism, the evaluation data are uploaded to a management control center to carry out adaptive correction of the index threshold and the weight. According to the method, fine modeling of environmental sanitation operation is realized through task decoupling, the evaluation standard is dynamically optimized by using closed-loop feedback, the problems of single evaluation dimension and index updating lagging in a traditional method are solved, and the accuracy and timeliness of environmental sanitation service evaluation are improved.

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 relates to the technical field of electricity consumption prediction, in particular to an electricity consumption load prediction method based on an intelligent electric meter communication module. The method comprises the following specific steps: collecting historical power utilization load data and power utilization influence factors, and extracting power utilization feature vectors; clustering the electricity utilization characteristic vectors by using a DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering method to obtain a group of clusters; performing local feature difference calculation and dynamic feature weight adjustment on the samples in each cluster to obtain a weighted feature matrix; and performing local consistency measurement and local consistency enhancement on the weighted feature matrix to obtain an enhanced matrix. And performing intra-cluster hierarchical division, hierarchical feature focusing and hierarchical fusion on the enhanced matrix to obtain a final clustering result, and evaluating the clustering result and the electrical load model. According to the method, the difference in each cluster in the clustering result can be reduced, and the effect of the clustering result is improved.

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 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 point cloud data processing, and relates to a 4D millimeter wave radar clustering method and device and electronic equipment. The method comprises the following steps: inputting 4D millimeter wave radar point cloud data; setting the first neighborhood radius and the minimum point number of the point clouds in the first neighborhood radius; performing point cloud density clustering by using a DBSCAN algorithm to obtain a point cloud density clustering result of each target area; calculating the center point position of each point cloud density cluster, and counting the width of each point cloud density cluster; setting a second neighborhood radius according to the width of each point cloud density cluster to obtain a set of the second neighborhood radiuses; and performing DBSCAN clustering on each point cloud density cluster according to the set second neighborhood radius to complete the point cloud clustering operation. The method can adapt to target point cloud data of different sizes, and the neighborhood radius of large-size clustering is adaptively adjusted according to the point cloud width, so that the point cloud clustering accuracy of the 4D millimeter wave radar is improved.

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 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 a casting joint line identification method based on point cloud, and belongs to the crossing field of three-dimensional industrial detection and intelligent manufacturing, and the method comprises the steps: carrying out the preprocessing of the obtained original three-dimensional point cloud data of a casting; forming a primary candidate set by point clouds of which the curvature change rate is greater than a curvature threshold value and which meet a normal mutation condition in the voxel three-dimensional point cloud data; screening connected domains from the primary candidate set by adopting a three-dimensional DBSCAN clustering algorithm, and setting a length-width ratio threshold value and a length threshold value of the connected domains to remove unqualified connected domains to obtain candidate connected domains; performing expansion and corrosion operation on each candidate connected domain, and performing topological constraint verification on the obtained result to obtain a consistent connected domain; and performing three-level geometric verification on the consistent connected domain in sequence. According to the invention, the joint line identification precision can be effectively improved.

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 relates to the field of temperature measurement, in particular to a temperature change monitoring system and method in a metal raw material melting process, and the temperature change trend is captured by collecting temperature data in a melting furnace in real time and combining a temperature change rate calculation formula. A DBSCAN clustering algorithm is adopted to carry out segmentation processing on temperature data to obtain different temperature change stages, curve fitting is carried out through a least square method, and error factors at all moments are calculated. And finally correcting temperature data based on the error factor to ensure that the measured value is closer to the actual temperature so as to judge whether the smelting furnace reaches the target temperature and avoid the influence of temperature error on the melting effect. The high-precision sensor, the clustering algorithm and error correction are adopted, the temperature change trend is dynamically captured, precise monitoring of the temperature in the metal melting process is achieved, and the melting uniformity and the product quality are ensured.

The human cost data storage method for the power grid engineering system comprises the steps that human cost data related to cost data in the power grid engineering system are acquired, and the human cost data comprise work type data, man-hour data and salary data of constructors; determining a DBSCAN clustering radius range based on the average man-hour salary of each type of work; based on a DBSCAN clustering radius range, performing clustering analysis in a man-hour-salary two-dimensional plane formed by the human cost data to obtain a preliminary classification result; when it is determined that the isolated data points exist based on the preliminary classification result, secondary classification is carried out on the isolated data points based on the distance between the isolated data points and the known clustering cluster, and a final classification result is obtained; and realizing partition storage of the cost data based on a final classification result. By implementing the method, optimized storage of the cost data of the power grid engineering system can be realized.

The invention relates to an unmanned surface vehicle obstacle detection method based on a laser radar. The method comprises the following steps: establishing a grid map; laser radar point cloud data are processed on the grid map, three-dimensional point cloud data are converted into two-dimensional data, and rapid processing of the laser radar point cloud data is carried out; performing multi-frame point cloud data fusion on the grid map by using the processed data; and an improved DBSCAN clustering algorithm is used to carry out filtering processing and clustering on the point cloud data after fusion of the multiple frames of point cloud data, and target detection of the laser radar point cloud data is realized. According to the invention, in a complex sea surface environment, the laser radar can be used to carry out efficient and real-time detection on water surface obstacles, and environment sensing algorithm support is provided for autonomous navigation of a water surface unmanned ship path.

The invention discloses a method for constructing an overground and underground integrated geological dynamic grid structure, and belongs to the field of geological data grid structure construction.The method comprises the steps that geological space data are collected; presetting a grid size, and dividing the geological space data based on the grid size to obtain an initial grid model; clustering the geological spatial data by using a DBSCAN clustering algorithm, and removing noise data points to obtain a plurality of clustering regions; dynamically adjusting the grid size in each clustering region in the initial grid model based on the data point density of each clustering region to obtain a primary refined grid model; on the basis of gradient refinement of a clustering region junction in the primary refinement grid model, a secondary refinement grid model is obtained; and correcting data values of data points in a ground and underground transition zone in the secondary refined grid model by using convolution operation to obtain a final ground and underground integrated grid model. The problem that a traditional grid structure cannot adapt to spatial data distribution of different areas is solved.

The invention discloses a land reclamation planning surveying and mapping collaborative operation method based on geographic information, and particularly relates to the technical field of planning surveying and mapping. A time sequence analysis method is introduced, dynamic changes of land reclamation areas are analyzed, in combination with a density-based DBSCAN clustering algorithm, time sequence dynamic clustering is performed on the areas, areas with similar characteristics are identified, a risk assessment model is constructed through a clustering result, and risk levels of different areas are assessed. The cloud platform is utilized to support collaborative operation of planning surveying and mapping personnel, and the risk assessment result of the region is updated in real time, so that the land reclamation scheme is automatically adjusted, matching of planning and actual conditions is ensured, the problem that time dynamic characteristics are neglected in the prior art is effectively solved, the timeliness and accuracy of the land reclamation scheme are improved, and the land reclamation method is suitable for large-scale popularization and application. And the management efficiency and effect of overall land reclamation are improved.

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 load regulation and control strategy considering multiple market superposition, and relates to the technical field of power systems. The method comprises the following steps: S1, inputting load side resource feature data of multiple markets; s2, calculating indexes of the fluctuation rate, the peak-valley difference ratio, the discharge load support rate, the load transfer capability and the load interruption capability of each resource main body as feature vectors of each resource main body, constructing a feature vector set of each resource main body, and hierarchically aggregating the feature vectors in the feature vector set of all the resource main bodies by adopting a DBSCAN clustering algorithm; s3, on the basis of clustering results of all the feature vectors, in combination with constraint conditions, establishing a load side resource aggregation model with the lowest operation cost as an objective function; and S4, calling a commercial solver to solve the load side resource aggregation model to obtain an optimal load side resource hierarchical aggregation scheme. According to the invention, the problems of heterogeneity of each load resource, difference in response capability and low enthusiasm of users participating in demand response in the existing load management can be effectively solved.

The embodiment of the invention provides a conductor generation method and device based on point cloud data, equipment and a storage medium, and relates to the field of three-dimensional modeling, and the method comprises the steps: obtaining the point cloud data, carrying out the classification processing of the point cloud data based on a weak supervised learning framework, obtaining the conductor point cloud data and the tower point cloud data, and obtaining the conductor point cloud data and the tower point cloud data; performing clipping processing on the conductor point cloud data according to the tower point cloud data to obtain clipped conductor point cloud data, performing classification processing on the clipped conductor point cloud data based on a DBSCAN clustering algorithm to obtain a training sample, and training a random forest model according to the training sample to obtain a conductor fitting model, the lead coordinate points are generated according to the tower point cloud data, the lead coordinate points are input into the lead fitting model, the lead list is output, the lead path in the point cloud data is automatically identified and extracted through the lead fitting model, the lead path extraction speed and accuracy are improved, and the lead path generation efficiency is improved.