296 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.

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 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 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 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 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 discloses a point cloud clustering method based on normal vector information, and belongs to the technical field of point cloud segmentation. Comprising the following steps: acquiring point cloud data; calculating a normal vector of each point in the point cloud data; performing preliminary clustering on the point cloud data based on a DBSCAN algorithm according to the normal vector; points which are not classified into any cluster of the preliminary cluster are added into a noise point set; carrying out plane and curved surface distinguishing on a cluster set subjected to preliminary clustering based on a normal vector included angle; further plane distinguishing is conducted on the obtained plane cluster set according to the projection distance in the main normal vector direction, and a final plane cluster set is obtained; and completing point cloud clustering based on the final plane cluster set, the curved surface cluster set and the noise point set. By combining the normal vector information of the point cloud and the DBSCAN clustering algorithm, the clustering and classification problems of the point cloud data are effectively solved, and a more efficient and stable point cloud processing method is provided especially for a complex three-dimensional object surface.

The invention discloses a multi-level water supply and drainage balance monitoring method and system for the water affair industry, and relates to the technical field of water affair monitoring. The method comprises the following steps: deploying a multi-level monitoring network; the method comprises the following steps: collecting real-time data of a multi-level monitoring network, executing space-time alignment operation, and preprocessing the data; constructing a quarterly three-dimensional water balance equation, and dynamically estimating through Kalman filtering; dBSCAN clustering is implemented based on pipe network topology, and leakage positioning is carried out to generate a leakage probability thermodynamic diagram; and establishing a hierarchical response mechanism to carry out multi-level early warning linkage, and rendering a multi-layer perspective view in a GIS (Geographic Information System) engine. According to the method, a quarterly three-dimensional water balance equation is constructed through a city-region-enterprise three-level dynamic weight model and a hierarchical penetration algorithm, and dynamic estimation is performed through Kalman filtering; dBSCAN clustering is implemented based on the pipe network topology, leakage positioning is performed, a leakage probability thermodynamic diagram is generated, the water affair monitoring efficiency is improved, and the manual troubleshooting workload is reduced.

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

The invention discloses a coal seam roof pre-grouting optimization method based on data driving. The coal seam roof pre-grouting optimization method comprises the following steps: S1, fine exploration and original information extraction of a geologic body in a research area; s2, construction information extraction; s3, performing grouting project classification by adopting a DBSCAN clustering algorithm; s4, establishing a quantitative relation model based on a classification result; the method is suitable for the technical field of coal water prevention and control, multi-source geological exploration data and dynamic grouting engineering parameters are fused, a geological-engineering feature coupling model is established, a nonlinear association rule of a geological structure and grouting response is quantitatively analyzed based on a machine learning algorithm, and the method has the advantages of being high in practicability and high in reliability. And a multi-objective collaborative optimization system is developed, and intelligent matching of grouting parameters, accurate prediction of the construction effect and dynamic optimization of an engineering scheme are achieved. Finally, the technical purposes of improving the historical data utilization rate, enhancing the grouting and water plugging success rate and reducing the engineering cost are achieved, and scientific decision support is provided for coal mine water disaster prevention and control.

The invention provides an electrical load release method based on clustering analysis, and relates to the technical field of electrical load adjustment. The electrical load release method based on clustering analysis comprises the steps of data collection, collection of historical data of electrical loads in a target area through a power grid load power monitoring system, data preprocessing, elimination of abnormal values in the collected electrical data in the target area, and extraction of static characteristics and dynamic characteristics of power data. And the data features are standardized. According to the method, the K-means clustering algorithm and the DBSCAN clustering algorithm are adopted, the convergence speed of the algorithms and the stability of the clustering result are effectively improved by combining the characteristics of the two algorithms, and the load release strategy which is high in pertinence and feasible is formulated based on different electrical load categories obtained through clustering analysis.

The invention provides an ROP prediction method based on an LSTM and an ensemble learning algorithm, and the method comprises the steps: carrying out the clustering and denoising: carrying out the denoising of data through employing a DBSCAN clustering algorithm, and removing outliers; data interpolation: applying a Hermite interpolation method to missing data, filling missing values, and ensuring the smoothness of the data; time sequence generation: converting the data into a time sequence format suitable for LSTM, and segmenting the data by using a sliding window; lSTM training: using an LSTM model to train data, learning a time dependency relationship and performing ROP prediction; on the basis of data preprocessing and an LSTM model, regression prediction is carried out by using LightGBM and XGBoost, so that the accuracy of ROP prediction is improved; evaluating a prediction result: evaluating prediction precision on training and measurement data through mean square errors and R2 evaluation indexes; and model optimization: carrying out model optimization according to an evaluation result. According to the method, more accurate prediction can be provided for the drilling operation, and then the efficiency and safety of the drilling operation are optimized.

The invention discloses a target tracking method and system based on DBSCAN and Kmeans, and relates to the technical field of target tracking, and the method comprises the steps: marking all point cloud data sets as unaccessed; all the point cloud data sets are sorted in a descending order according to the signal-to-noise ratio; sequentially selecting a point as a research object; setting a correlation radius according to the speed of the research object, and setting a density threshold according to the signal-to-noise ratio of the research object; carrying out clustering analysis on the research object by adopting a DBSCAN method, and outputting a cluster; performing clustering judgment on the clustering clusters; if not, outputting a clustering cluster; if yes, the number of possible targets is calculated; and adopting a Kmeans algorithm to perform secondary clustering on the DBSCAN cluster, and outputting a target number of new clusters. According to the method, clustering judgment is added after DBSCAN clustering, the same target is prevented from being divided into multiple clusters, the accuracy of a clustering result is improved, and the accuracy of traffic flow statistics is improved.

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

The invention particularly relates to a point cloud self-supervision quality evaluation and completion method, which relates to the technical field of point cloud processing and computer vision, and comprises the following steps of: constructing a normal data set and simulating refined defects; performing double-branch network training and normal feature deep learning; carrying out cloud processing and completion reasoning on the point to be detected; and carrying out residual calculation and multi-dimensional anomaly positioning analysis. According to the invention, the two-dimensional analysis of the point-by-point residual error and the voxelization residual error comprehensively depicts the single-point abnormal intensity and the density and position difference of the local area, and the candidate abnormal points are screened in combination with the optimization threshold, so that the false drop rate is effectively reduced; a continuous abnormal area is further obtained through DBSCAN clustering, the residual coefficient and the two-dimensional volume coefficient are fused to calculate the abnormal index, accurate classification of the anomalies is achieved, and the problem that classification of different types of anomalies is not accurate through a traditional method is solved.

The invention relates to the technical field of gas turbine unit data optimization processing, and provides a gas turbine unit data anomaly identification method based on group decision, which comprises the following steps: step 1, preprocessing original operation data; 2, performing data detection by adopting multiple detection methods to obtain an abnormal score of each detection method; 3, adopting a voting mechanism to judge abnormal points; and step 4, outputting all abnormal points which are finally judged. The detection method comprises but not limited to a standard deviation method, an isolated forest method, a DBSCAN clustering method, an OCSVM method, an LOF method, a CBLOF method, a KNN method and an HBOS method. According to the method, a plurality of different abnormal data identification methods can be integrated, the idea of majority voting is used, an abnormal value is selected by a proposed group decision-making method, and the abnormal value can be better detected.

The invention discloses an intelligent segmental arc correlation method based on a minimum optical admissible domain, belongs to the field of intelligent calculation optimization of space target monitoring, solves the problem of large calculation amount of segmental arc correlation, and comprises the following steps: constructing the minimum optical admissible domain by a first observation segmental arc; uniformly sampling in the minimum optical tolerance domain, performing orbit propagation on each sampling point under a J2 perturbation model to obtain theoretical optical observation data of a second observation arc section, and calculating an angle root-mean-square error of the second observation arc section to obtain an angle loss function; using a DBSCAN clustering algorithm to identify wave troughs; selecting an initial value of a simplex method in each wave trough to obtain an angle optimal solution; a probability loss function is obtained through Bayesian theorem calculation; substituting the angle optimal solution into a probability loss function, and obtaining a probability optimal solution by using an LM algorithm; a threshold value is set according to the probability loss function obeying chi-square distribution, and when the probability optimal solution is smaller than the threshold value, the two arc segments are associated successfully; according to the invention, the calculation cost is saved, and the space target observation accuracy is improved.

The invention discloses a distributed power supply power prediction method, system and equipment based on LSTM, and the method comprises the steps: firstly obtaining meteorological data of a distributed power supply target region, calculating theoretical power data, then carrying out the clustering analysis based on a DBSCAN algorithm, expanding the data dimension, obtaining a normal value and abnormal value data set, carrying out the optimization of a loss function of an LSTM prediction network, and carrying out the prediction of the power supply power of the distributed power supply. And predicting to obtain future normal value and abnormal value prediction results, finally setting parameter adjustment conditions, and repeating the steps until the prediction is completed. According to the method, for the problems that historical electric power data are various in type and have many abnormal values, the distance definition of a DBSCAN clustering algorithm is improved, the distance between a data point and theoretical output power is combined, the stability of normal value extraction is improved, an LSTM prediction network is introduced, the data limitation that only the normal value is predicted is avoided, and the prediction efficiency is improved. And meanwhile, a loss function is improved by combining physical constraints and theoretical power, and adaptive adjustment parameters are optimized, so that the algorithm is more efficient.