184 results about "Spectral clustering algorithm" patented technology

The invention discloses a computer task scheduling method based on artificial intelligence, and the method comprises the following steps: 1, data collection: employing a double-flow feature fusion mechanism, and generating global feature representation containing long-term dependence and an instantaneous state; step 2, generating a global optimization scheduling strategy: constructing a hierarchical federal reinforcement learning system, dividing a cluster into a plurality of super nodes through an enhanced spectral clustering algorithm, independently training a Dueling DQN network by each super node, performing global strategy cooperation by adopting Shapley value weighted aggregation and differential privacy protection, and generating a scheduling strategy of global optimization; distilling a global strategy into a lightweight decision tree through a strategy distillation technology, and deploying the lightweight decision tree to a physical node; 3, task priority control and elastic resource allocation are carried out, wherein elastic control over resource allocation is carried out through a dynamic time slice bank mechanism; and 4, self-adaptive evolution: establishing a closed-loop optimization system, and carrying out strategy self-evolution by adopting a double-layer optimization architecture.

The invention discloses a layered energy management method of a fuel cell hybrid tramcar with self-adaptive working condition perception. In the recognition layer, a sliding window mechanism is adopted to extract time domain and frequency domain features of load conditions, feature data are clustered based on a spectral clustering algorithm driven by a deep auto-encoder, a data set with category labels is obtained, and a deep dynamic learning vector quantization neural network classifier is trained; in the strategy layer, a double-delay depth deterministic strategy gradient reinforcement learning algorithm is adopted, a reward function is constructed, and lithium battery SOC fluctuation penalty term limit parameters in the reward function are adaptively adjusted according to the real-time load working condition category output by the recognition layer; training the reinforcement learning agent to obtain an optimal power distribution scheme between the multi-stack fuel cell power generation system and the lithium battery; and according to the performance degradation degrees of different fuel cell stacks, a distributed cooperative control strategy considering performance difference is adopted to distribute the output power of each stack, so that the coordinated control of the running state of the multi-stack fuel cell power generation system is realized.

The invention discloses an automatic flow chart layout method fusing semantic analysis, which relates to the technical field of automatic flow chart layout, and comprises the following steps of: inputting a node set with a business ring dependence condition into a time sequence conflict analysis engine, and combining a semantic vector and a time sequence vector to obtain a time sequence conflict analysis result; calculating a time sequence conflict index of the annular dependency set by adopting a weighted path consistency check algorithm so as to determine a time sequence conflict degree under the condition that the nodes have service annular dependency, and generating corresponding conflict description data; and inputting the conflict description data and the semantic vector into a conflict perception clustering optimizer, introducing a time sequence conflict penalty term into a clustering objective function, and performing cluster boundary adjustment on the annular dependency set through a spectral clustering algorithm so as to adjust a semantic clustering structure according to a determination result. According to the method, the problem that a semantic clustering structure cannot be optimized in combination with a time sequence conflict under business annular dependence is solved, and the effects of conflict accurate identification, clustering boundary dynamic adjustment and layout saliency enhancement are achieved.

The invention relates to the technical field of artificial intelligence and data processing, in particular to a method for predicting the influence of tunneling blasting on surface building vibration, which specifically comprises the following steps of: collecting a vibration signal generated by blasting operation in a tunneling process and denoising the vibration signal as a sample; according to the energy distribution characteristics of the signals, a spectral clustering algorithm is adopted to generate meta-tasks; performing feature extraction on samples in each meta-task through a neural network model; a hierarchical attention mechanism is adopted to capture time response characteristics of the vibration signals in different time durations, key characteristics of all stages are obtained and fused, a classification feature vector is obtained, and then a classification prediction result of the influence of blasting on surface building vibration is determined according to the classification feature vector; and optimizing the classification prediction process. According to the method, the tunneling blasting vibration data is processed based on the neural network model of the improved meta-learning strategy, and the accuracy of the vibration influence on the surface building and the construction safety can be improved.

The invention provides an underwater propeller control method and system based on tensor recognition and fuzzy control. The underwater propeller control method and system are suitable for improving the propelling efficiency and adjustment intelligence in a complex flow field. According to the method, a wake flow simulation model is constructed based on geometric parameters and boundary conditions of a propeller, a rotation tensor and a strain tensor are derived after flow field data are obtained, and a tensor field index is extracted through function space mapping so as to determine a target grid region. A high-rotation candidate area is screened through a spectral clustering algorithm, a vortex structure is identified, and multi-dimensional features such as the scale, the strength, the axial direction and the vortex core position of the vortex structure are extracted. And constructing a state vector by combining the current propulsive efficiency and flow field disturbance parameters, inputting an adaptive fuzzy neural network model, reasoning a relationship between a vortex and an operation state, outputting a propeller rotating speed and an attack angle adjusting quantity, and realizing intelligent response and energy efficiency optimization of wake flow disturbance.

The invention discloses an intelligent early warning analysis platform based on management and control of a product life cycle, and the platform comprises a data collection and preprocessing module which is used for collecting multi-source heterogeneous data and carrying out the preprocessing of the data, and obtaining a standardized input data set; the spectral clustering grouping module is used for carrying out unsupervised grouping by adopting a spectral clustering algorithm and generating a grouping label and a mapping result; the abnormal sample counting and generating module is used for counting the number of abnormal samples and data distribution of each data sub-group and generating abnormal sample data matched with sub-group characteristics through a stable diffusion model; the training and risk detection module is used for constructing a training data set and training a risk identification and anomaly detection method; and the intelligent early warning and response module is used for automatically triggering early warning for the detected abnormal data and linking platform management and control. According to the method, intelligent grouping, abnormal sample enhancement and efficient risk early warning analysis of product full-life-cycle multi-source heterogeneous data are realized.

The invention discloses an internet of things-based amusement equipment light dynamic control method and system, and the method comprises the steps: collecting the acceleration, angular velocity and coordinate data of equipment, carrying out the wavelet denoising and Z-score standardization processing, and constructing a dynamic topology model based on a graph attention network and a graph convolution network. A spectral clustering algorithm is utilized to carry out space partitioning on equipment nodes to generate a topological sub-graph, and an LSTM network is combined to predict an equipment motion track and generate a continuous track sequence. Track correlation features are extracted through a graph attention network, a genetic algorithm is fused to optimize and generate a light conversion sequence, dynamic time warping alignment timestamps are synchronously adopted, and phase synchronization of the light sequence and the motion track is ensured. And finally, brightness, color and flicker frequency are adjusted in real time based on a PID control algorithm, and a closed-loop feedback mechanism is formed. According to the method, high-precision dynamic matching of light and equipment movement is realized, and immersive experience and visual interactivity are improved.

The invention discloses an electric field analysis and optimization method for a transformer withstand voltage and partial discharge test platform, and the method comprises the steps: building a digital model of the test platform, obtaining a preparation technology standard and historical manufacturing measurement data of a target test platform, defining an influence variable which influences the distribution of an electric field, simulating the distribution of the electric field under different preparation differences, and carrying out the optimization of the electric field. Establishing an electric field simulation database; identifying test platform electric field sensitive areas under different preparation differences by adopting a spectral clustering algorithm based on the electric field simulation database, constructing a knowledge graph, constructing a sensitive area identification model based on a graph neural network, and performing model training through the knowledge graph; structural features and preparation features of the to-be-analyzed test platform are obtained, a local sensitive area of the to-be-analyzed test platform is recognized in the sensitive area recognition model, and if the local sensitive area exists, an optimization scheme is formulated for test platform optimization assistance, so that the design reliability and operation safety of the transformer test platform are improved.

The invention discloses an airport apron operation risk assessment method based on a complex network, and belongs to the technical field of risk control. The method comprises the following steps: analyzing an airport apron safety report by adopting an event chain analysis method to construct a complex network; determining a risk static index of each node; simulating a dynamic propagation process of risks in the complex network based on an SIRS model, and determining a risk dynamic index of each node; by taking the dynamic and static indexes of each node as characteristics, classifying the nodes by adopting a spectral clustering algorithm, respectively calculating an average risk comprehensive value of each cluster node, and determining a risk level of each cluster node based on a quantile; and acquiring risk events of the airport apron at a certain moment, determining a risk comprehensive value and a risk level of each risk event, and performing hierarchical and classified management on the risk events. According to the method, a multi-dimensional risk assessment index system is constructed to assess the risk propagation capability and blocking capability of the nodes, classification and grading of the risk nodes are realized, and scientific decision support can be provided for airport managers.

The invention discloses a fault modeling method, device and equipment based on seismic data and a storage medium, relates to the field of geological modeling, and aims to process initial seismic data by adopting a dip angle-oriented median filtering method and effectively remove noise while retaining geological structure information such as faults and the like. And then the trained fault identification model is introduced to process the target seismic data, fault information can be rapidly extracted from massive seismic data, and the identification efficiency is greatly improved. And then similarity is calculated from two dimensions of spatial distance and direction attribute based on a fault identification result, clustering is carried out by using a spectral clustering algorithm, correlation of fault lines in spatial distribution and fault extension direction characteristics are considered, and more reasonable and accurate fault line groups are obtained. And finally, constructing an initial B-spline curved surface by utilizing a grouping result, and enabling the finally constructed three-dimensional fault model not only to fit actual seismic data, but also to accord with the smoothness and direction rule of a geological structure through variation energy minimization optimization, so as to accurately reflect the real form of an underground fault.

The invention discloses an intelligent set top box video processing system based on multimode decoding, which relates to the field of terminal video processing, and comprises the following steps: an original data stream processing module outputs a scene complexity grading label through a spectral clustering algorithm; the video metadata analysis module is used for analyzing metadata of an original video stream in real time and identifying a content type identifier; the edge-cloud cooperative decoding module allocates the high-complexity video frames to edge nodes for decoding, allocates the low-complexity video frames to a local terminal for decoding, and outputs a decoded video frame sequence; the video restoration module detects and restores picture distortion caused by transmission frame loss or signal interference; the dynamic code rate control module dynamically distributes the code rate weight of each block-level area according to the scene complexity grading labels; and the energy efficiency self-optimization module dynamically controls the states of the heating sheet and the idle decoding channel to adjust the core voltage and the decoding frame rate of the CPU. The method has the advantages of low power consumption, strong adaptability to weak network smoothness through multi-scene high-definition cloud edge collaboration.

A honeycomb-shaped active power distribution network topological structure selection and optimization method based on a graph theory comprises the following steps: taking line impedance and rated apparent power as constraints, constructing a multi-target weight factor, adopting an improved spectral clustering algorithm to cluster and distribute power grid nodes, and transiting a micro-grid group after cluster division into a honeycomb-shaped active power distribution network; abstracting each micro-grid as a honeycomb distribution network single node, constructing a connection relation mathematical representation of the HSPH and the micro-grid, and realizing sparse matrix method modeling of the honeycomb active power distribution network; optimizing a site selection strategy of the HSPH by taking a maximized HSPH return on investment index and a system stability index as a target function; and optimizing the energy storage capacity of the HSPH based on the net load time sequence data of the micro-grid group, and finally generating an optimal topological structure of the honeycomb-shaped active power distribution network. And through honeycomb topology and HSPH addressing and sizing optimization, the new energy consumption capability of the power distribution network is improved, and the economy, reliability and flexibility of the power distribution network are improved.

The invention discloses an analog IC synchronization test optimization method and system based on crosstalk simulation. The method comprises the following steps: firstly, acquiring position layout data and test signal data of each analog IC in a synchronous test process of the analog ICs, determining electromagnetic crosstalk information by combining test signals, and constructing a crosstalk schematic diagram according to the electromagnetic crosstalk information and the position layout data; clustering the schematic diagrams by using a spectral clustering algorithm, and identifying a crosstalk path in the synchronization test; designing an electromagnetic crosstalk signal isolation barrier based on the schematic diagram and the crosstalk path, deploying the electromagnetic crosstalk signal isolation barrier in a test environment, performing simulation evaluation on an independently tested analog IC by constructing and injecting a crosstalk simulation signal, and judging an isolation effect; and optimizing the isolation barrier according to an evaluation result to obtain an optimization scheme. According to the method, the electromagnetic crosstalk path in the synchronous test process can be effectively identified and isolated, and the accuracy and reliability of the simulation IC test are improved.

The invention relates to the technical field of sea area intelligent shipping, in particular to a ship dynamic risk group identification method and system based on spectral clustering, which are used for identifying high-risk ship interaction groups and evaluating spatial distribution characteristics of the high-risk ship interaction groups. According to the method, the collision risk of each pair of ships is calculated in real time by obtaining AIS dynamic data, and a risk weight matrix is constructed by indexes; introducing modularity as a ship clustering cluster number estimation standard, and realizing ship group division based on a spectral clustering algorithm; further combining modularity optimization and risk potential field calculation, and determining and identifying the positions of a dynamic high-risk group and a water area hot spot area; according to the method, potential conflict groups can be dynamically extracted in a complex navigation environment, accurate modeling of risk structures among ships is achieved, intelligent and prospective support is provided for maritime affair supervision and risk intervention, and therefore ship navigation safety is guaranteed.

The invention discloses a topological communication coverage path planning method and system of an unmanned ship cluster for wide-area target search and detection, and belongs to the technical field of multi-agent collaborative path planning. According to the method, turning frequency optimization is converted into a minimum rectangular coverage problem, an integer programming model is established, a layered relaxation integer programming acceleration strategy based on maximum extension candidates is proposed, and the bottleneck of model expansion and calculation efficiency caused by candidate solution space explosion in a large-scale scene is broken through; then, constructing a rectangular topological connected graph, designing an improved spectral clustering algorithm fusing load balancing constraint and subgraph connectivity guarantee, realizing efficient and balanced distribution of regions in a complex environment, and ensuring internal connectivity of subregions; and finally, providing a dynamic pruning optimization method for the minimum corner spanning tree based on strong and weak connection characteristics, iteratively evaluating the corner cost and removing redundant edges, and finally generating a coverage path spanning tree with the minimum corner cost, thereby providing an accurate and efficient full-coverage operation path for scenes such as regional exploration and environment monitoring.

The invention discloses a method and system for automatically segmenting and extracting hyperspectral information of oil tea fruits in situ, and the method comprises the steps: obtaining an RGB image and a hyperspectral image of the oil tea fruits in a natural environment, and constructing a source domain and target domain data set; performing pre-training on the CA-TransUNet + + model by using the source domain, and then introducing a target domain based on a pre-training weight to perform fine tuning so as to obtain a final camellia oleifera fruit semantic segmentation model; and then, performing refined segmentation on a fruit adhesion region in the segmented image through a spectral clustering algorithm, and combining a result with an original hyperspectral image to realize automatic extraction of hyperspectral information of the camellia oleifera fruits. According to the method, through the innovative CA-TransUNet + + image segmentation model, the camellia oleifera fruits can be rapidly and efficiently segmented, the hyperspectral information of the camellia oleifera fruits can be extracted, the linear decision coefficient (R2) of the method and a manual extraction result can reach 99.03%, and the processing time of a single fruit image is about 1-2 seconds. According to the method, the hyperspectral data of the camellia oleifera fruits can be efficiently and accurately acquired in a natural scene.

A power distribution network voltage cooperative control method considering a cluster division-optical storage all-in-one machine comprises the following steps: step 1, combining an electrical modularity index, a balance index and a membership index to construct a cluster comprehensive division index; 2, taking the comprehensive division index as a convergence condition for dividing a cluster region, and solving an optimal cluster division result of the power distribution network by utilizing an improved spectral clustering algorithm; 3, with the minimum power distribution network node voltage deviation punishment cost, the minimum network loss cost and the minimum power generation loss cost of the optical storage all-in-one machine as the target, a power distribution network voltage optimization model containing the optical storage all-in-one machine is constructed; and 4, based on the optimal cluster division result, solving the power distribution network voltage optimization model of the optical storage all-in-one machine through a cluster voltage optimization control strategy, optimizing active power, reactive power and voltage of the power distribution network, and improving power flow distribution of the whole network. The voltage regulation task sharing and power balance are realized, the voltage regulation effect is more reasonable, and the voltage out-of-limit problem under the random fluctuation of the source load power can be effectively solved.

The invention provides a sewing thread defect classification method based on multi-scale fusion and adaptive learning, which solves the technical problems of extremely unbalanced data and low expert labeling efficiency in industrial quality inspection, and can be extensively applied to surface defect detection tasks in multiple manufacturing fields such as spinning, packaging and electronics. According to the method, four core modules of multi-scale depth feature extraction, dynamic loss prediction, adaptive spectral clustering and intelligent pre-classification are creatively fused, and multi-level semantic features are extracted through a multi-scale feature pyramid network; designing a dynamic weight loss prediction network, and accurately evaluating the sample information amount; constructing an adaptive spectral clustering algorithm, and adaptively adjusting clustering parameters; a multi-feature fusion pre-classification algorithm is combined to assist experts in efficient labeling. According to the method, the defect classification precision can be remarkably improved in a few-sample scene.

The invention discloses a small and micro enterprise loan availability evaluation method, which belongs to the technical field of data processing, and integrates unstructured operation and semi-structured behavior data, establishes a dynamic data confidence evaluation matrix, and starts artificial recheck to guarantee data quality. And constructing a three-layer fuzzy membership function system, and generating a standardized score by using an improved interval type-2 fuzzy set and an alpha-cut algorithm. Defining an interest rate sensitive dimension, generating an initial interest rate hierarchy through an improved spectral clustering algorithm, and predicting operation fluctuation and adjusting parameters by using a double-layer LSTM (Long Short Term Memory). And through IPC cross validation, carrying out residual analysis on the fuzzy data and on-site collected data, generating a joint confidence interval through D-S evidence fusion, and evaluating loan availability and outputting a suggestion report on the basis of the joint confidence interval. According to the method, the existing evaluation data processing problem is effectively solved, evaluation scientificity, accuracy and comprehensiveness are improved, a reliable decision basis is provided for financial institutions, and accurate customer layering and product adaptation are assisted to be achieved.

The invention relates to the technical field of semiconductor manufacturing and detection, and discloses a wafer defect detection data processing method and system.The wafer defect detection data processing method comprises the steps that a structured random measurement matrix is constructed based on wafer surface characteristics, and compression measurement data is collected; identifying local manifold structures corresponding to different defect types by using a spectral clustering algorithm; constructing a multi-scale image Laplacian operator, and extracting defect geometric characteristics; performing differential reconstruction processing according to the defect type, and solving an optimization target containing a nuclear norm and a graph Laplacian regularization item; dynamically adjusting a sampling strategy based on reconstruction quality evaluation; according to the method, high-precision defect reconstruction under a low sampling rate is realized, the data processing efficiency is improved, the complex defect reconstruction quality is improved, the method adapts to diversified defect processing, the storage and transmission burden is reduced, and the computing resource allocation is optimized.

A clustering integration-based electroencephalogram identification analysis method under complex visual stimulation comprises the following steps of designing an experimental normal form of electroencephalogram signal acquisition under a complex visual stimulation condition, selecting N subjects, and respectively operating according to the experimental normal form, synchronously collecting and recording multi-channel electroencephalogram signal data of N subjects under complex visual stimulation in the experimental normal form; starting an experiment, preprocessing electroencephalogram signals of the N subjects, obtaining preprocessed electroencephalogram signal data corresponding to the N subjects, and forming a rapid sequence to present an electroencephalogram signal data set; carrying out clustering analysis on the electroencephalogram signal data of the N subjects by utilizing a spectral clustering algorithm to obtain N sub-clusters; fusing the N tested sub-clusters by using a spectral clustering integrated optimization strategy based on hypergraph division; and testing and evaluating the fusion model to obtain a performance attribute value of the fusion model. According to the method, decision-making layer integration of a plurality of tested brain visual cognition image classification models is realized, so that a cross-individual electroencephalogram signal analysis method under a visual stimulation target with relatively high generalization, accuracy and robustness is constructed.

The present invention belongs to the technical field of data security of industrial Internet, and provides a method and system for detecting abnormal nodes in industrial Internet, a medium and a device. In the industrial Internet, different data holders firstly transform their own local node data into graphic data. Before local model training, the data holders firstly use a spectral clustering algorithm to perform certain clustering operations on local data, cluster the node data of the same category into the same cluster, and then perform local model training on a clustered result to obtain partial aggregation features. The trained partial features are uploaded to a trusted third-party server for global feature aggregation. Through an attention mechanism, different weights are assigned for partial features uploaded by different data holders, and the aggregated global features are delivered to each data holder for a new round of training.

The invention relates to the technical field of Web security and artificial intelligence, and discloses a Web reverse analysis method based on a large model and dynamic feedback, which comprises the following steps: constructing an AST-LLM joint analysis engine, defining an AST node importance evaluation formula, carrying out weight quantification on AST nodes, and screening out key functions or code snippets in reverse analysis; semantic analysis is carried out through LLM in combination with context, and candidate names of confusion variables are generated; semantic similarity calculation is carried out on the key nodes screened out through weight quantification and candidate variable names generated by LLM, and semantic grouping is carried out on confusion variables associated with AST nodes through a spectral clustering algorithm; a code structure is optimized through AST node remapping, and codes are corrected through real-time interaction; a closed-loop optimization system of LLM and log instrumentation is constructed, and an optimization engine is dynamically fed back; the method has the advantages that multi-state confusion is resisted, the semantic consistency of reverse output and original logic is ensured, the problem of reverse analysis distortion in a dynamic confusion scene is solved, and the readability, the performability and the maintainability of codes are kept.

The invention provides a cache management method and system of a work meeting welfare platform in a high-concurrency scene, and belongs to the field of electric digital data processing.The cache management method comprises the steps that work meeting member request metadata is collected, burst mode detection is carried out based on a spectral clustering algorithm, potential high-demand welfare is marked, and dynamic fragmentation is carried out on inventory Key of the potential high-demand welfare; constructing a three-level collaborative cache based on the inventory Key after dynamic fragmentation, and realizing distributed inventory deduction through cooperation of a lease lock and atomic operation; establishing a multi-level asynchronous buffer queue including an edge queue, a region queue and a global queue, performing aggregation batch processing on the inventory change request, and combining incremental synchronization and block chain operation Hash uplink to guarantee final consistency; routing and fusing threshold values are dynamically adjusted based on potential high-demand welfare, different routes are allocated to high-concurrency requests, millisecond-level flow switching is achieved when regional nodes fail, and the stability of worker welfare issuing activities and the member right priority are guaranteed.

The invention discloses a data analysis management method and system based on ocean network security, and relates to the technical field of ocean network security, and the technical key points comprise the following steps: collecting the flow and log data of multi-source ocean network equipment in real time, constructing a dynamic heterogeneous data stream, and extracting a space-time correlation feature tensor; behavior topology modeling is carried out on the space-time correlation feature tensor, a network behavior dynamic topological graph is generated, and topology stability measure is calculated; abnormal behavior detection is carried out based on topological stability measurement, an abnormal behavior cluster is identified through a multi-scale spectral clustering algorithm, and a high-risk threat area coordinate set is generated; the technical effects are that the dynamic topology modeling adapts to the characteristics of ocean network equipment movement, communication intermittence and the like, and the environment adaptability is improved; and manifold mapping and adaptive clustering are combined, so that the anomaly recognition accuracy is improved, the missing report rate is reduced, and accurate threat positioning is realized.

The invention discloses an anomaly monitoring method for substation equipment and related equipment, relates to the technical field of anomaly monitoring of a power system, and solves the problem of large misjudgment rate difference during anomaly monitoring of different types of substation equipment in the prior art. According to the method, feature weight distribution is independently performed on various types of substation equipment, so that the spectral clustering algorithm can extract key features of each type of equipment and perform clustering based on the key features, and misjudgment caused by feature distribution difference is avoided; and in combination with unified computational logic, the cross-device type consistency of the abnormal monitoring result can be ensured. By adopting the embodiment provided by the invention, the abnormal conditions of different types of substation equipment can be accurately detected.

The invention belongs to the field of electric power material quality control, and discloses an electric power material equipment defect analysis method and a related device, and the method comprises the steps: obtaining knowledge graph quality detection sub-graphs of electric power material equipment of the same type, carrying out the clustering according to a spectral clustering algorithm, and determining defective electric power material equipment; obtaining a knowledge graph quality detection sub-graph set of the defective electric power material equipment, and performing frequent sub-graph analysis to obtain a maximum frequent sub-graph set of the defective electric power material equipment; and obtaining familial defects of the electric power material equipment according to the maximum frequent sub-graph set of the defective electric power material equipment. The defect identification of the electric power material equipment is realized, and the familial defects of the electric power material equipment are deeply analyzed, so that the state of the electric power material equipment can be more comprehensively known, potential risks can be timely found and processed, the operation and maintenance effect of the electric power material equipment is improved, and the service life of the electric power material equipment is prolonged.

The invention relates to the technical field of indoor layout design, and discloses an indoor layout design system based on user behavior data analysis. The system comprises a user behavior data acquisition module which captures and transmits a user movement track coordinate sequence, residence time distribution data and equipment interaction event records in real time through a multi-sensor network; the layout analysis module constructs a dynamic layout model by using a decision tree integration algorithm, and outputs theoretical layout parameters including a space occupancy rate and functional region division; the layout difference detection module performs multi-dimensional comparison of space overlapping degree, path conflict index and regional utilization efficiency difference on theoretical and actual layout parameters to generate a difference index vector; the problem region positioning module identifies a layout abnormal region through a spectral clustering algorithm based on the difference index vector and predefined spatial topology information; and the adaptive design strategy generation module automatically generates a layout adjustment strategy instruction according to the abnormal region position and the attribute data.

The invention discloses a semi-supervised image clustering method based on adaptive graph learning, and the method comprises the steps: iteratively updating a sparse representation matrix and a paired constraint matrix until the value of a target function is unchanged or reaches a maximum iteration number; taking the updated pairwise constraint matrix as an input similar matrix, calling a spectral clustering algorithm to divide image sample data into a plurality of sample groups to finish clustering output; wherein the objective function is a weighted sum of a propagation consistency error based on a sparse representation matrix and a paired constraint matrix, an image sample data reconstruction error based on the sparse representation matrix, an L1 norm of the sparse representation matrix and a matrix correlation error based on the sparse representation matrix and the paired constraint matrix. The invention further discloses a system, a computer storage medium and a program for implementing the method. According to the method, the problem of separation of similar graph learning and constraint propagation can be solved, and the image clustering performance is improved.

The invention provides a seismic fault identification method and system based on multi-attribute collaboration and spectral domain graph enhancement, and relates to the technical field of seismic exploration, and the method comprises the steps: carrying out the all-directional dip angle and azimuth angle scanning of a seismic data volume, extracting multi-scale guide field information through combining structure tensor decomposition, and executing anisotropic diffusion filtering; calculating characteristic value distribution through a characteristic value coherence algorithm based on the filtering data volume, and determining the structural consistency difference of adjacent seismic traces to obtain a fracture coherence attribute data volume; carrying out azimuth gather sorting and pre-stack time migration processing on the seismic data volume, extracting seismic wave dynamic response characteristics, carrying out Fourier series expansion on the azimuth change rate to obtain a crack indication information data volume, splicing the data volume and executing multi-scale three-dimensional convolution solution, and constructing a spatial dependency graph through a spectral clustering algorithm; spectral domain enhancement features are obtained through spectral domain graph transformation and frequency selective filtering reconstruction, and morphological connectivity analysis is executed to obtain a crack prediction result.