183 results about "Dynamic clustering" patented technology

The invention relates to the technical field of ship navigation risk assessment, in particular to a ship navigation risk assessment system based on multi-source heterogeneous data fusion, which comprises a multi-source data integration module, a spatial-temporal feature mapping module, a dynamic risk detection module, a linkage decision control module and a feedback optimization module. According to the method, standardized operation data is generated through multi-source data cleaning and fusion, a spatial-temporal feature distribution map is generated by using a multi-dimensional dynamic clustering algorithm, a risk index set is extracted in combination with adaptive boundary adjustment and a nonlinear optimization algorithm, and accurate path planning and real-time regulation are realized. In addition, a global sensitivity analysis framework and an early warning module are introduced into the system, and the ship navigation safety and reliability are improved. According to the method, the risk prediction accuracy can be remarkably improved, the navigation accident probability is reduced, the navigation efficiency is optimized, and safe operation of the ship is guaranteed.

The invention discloses a power grid air-ground cooperative emergency control system and method based on unmanned aerial vehicle multi-agent reinforcement learning, and belongs to the technical field of power system emergency control and intelligent cooperation. Comprising the following steps: a central coordination unit obtains a voltage state and a load importance degree of a power grid node and a position, energy and a communication state of an unmanned aerial vehicle cluster in real time, an emergency priority node is identified based on voltage recovery deviation and communication link quality, dynamic clustering is carried out, and a comprehensive communication demand priority of each node cluster is calculated; according to the method, the strong coupling optimization problem of power grid voltage recovery and emergency communication guarantee in a disaster environment is solved, the power supply reliability, the communication connectivity and the emergency response efficiency of the system are improved, the energy utilization of the unmanned aerial vehicle is optimized at the same time, and the energy utilization rate of the unmanned aerial vehicle is improved. The method is suitable for rapid recovery and cooperative scheduling of key infrastructures in extreme scenes such as earthquakes and typhoons.

The invention discloses a transformer area topological structure estimation method, system, equipment and medium based on multi-dimensional power utilization characteristics, and belongs to the technical field of power distribution transformer area topologies, and the method comprises the steps: collecting multi-source data, carrying out dynamic weight distribution and abnormal value correction, carrying out the characteristic extraction according to the collected multi-source data, and generating a high-dimensional characteristic vector; clustering user nodes, dividing cluster labels, performing topological modeling according to a cluster division result, generating a topological graph, and performing anomaly verification through multi-dimensional anomaly scoring and abnormal power utilization detection; and performing incremental model parameter correction and multi-objective optimization according to anomaly verification feedback, and generating dynamic topological graph rendering and multi-dimensional decision suggestions by integrating topological modeling, anomaly verification and optimization results. According to the method, accurate estimation of a topological structure is realized through dynamic clustering and hidden node recognition, real-time diagnosis of abnormal nodes and self-correction of a model are realized by means of a multi-dimensional abnormal scoring and self-adaptive optimization mechanism, and the accuracy and operation and maintenance efficiency of transformer area management are improved.

The invention provides an operation and maintenance alarm intelligent filtering and grading processing method based on a self-adaptive algorithm, and relates to the technical field of computer operation and maintenance management, and the method comprises the steps: obtaining an alarm event, extracting semantic and time sequence characteristics, carrying out the dynamic clustering through employing a self-adaptive similarity measurement mechanism, calculating a priority score based on an influence range and an emergency degree, and carrying out the calculation of an alarm result. And a self-adaptive filtering strategy is implemented in combination with the operation and maintenance resource state, and finally, filtered alarms are distributed to corresponding operation and maintenance units for processing, collection and feedback. According to the method, redundant alarms can be effectively reduced, resource allocation is optimized, and the operation and maintenance efficiency and the alarm processing accuracy are improved.

The invention discloses an anti-unmanned ship unmanned aerial vehicle swarm dynamic clustering algorithm and a saturation attack path planning method, and belongs to the technical field of path planning. The invention discloses an anti-unmanned ship unmanned aerial vehicle swarm dynamic clustering algorithm and saturation attack path planning method. The method comprises the steps of constructing a multi-dimensional situation awareness index system; constructing a clustering decision function; and establishing a cluster structure dynamic adjustment mechanism, and carrying out collaborative scheduling optimization on the cluster structure. According to the invention, the problem of decision error caused by incapability of timely obtaining accurate information when communication is interrupted in the prior art is solved. According to the method, dynamic changes of the bee colony targets can be more flexibly coped with, it is ensured that each bee colony target is covered with the corresponding interception cluster, even if part of communication is interrupted, all clusters can still make decisions autonomously according to local information, certain interception capacity is maintained, basic interception tasks continue to be executed under the condition that communication is limited, and the communication efficiency is improved. It is ensured that interception preparation is completed before the bee colony reaches the defense target, and defense failure caused by response delay is effectively avoided.

The invention provides a source-load collaborative load prediction method and system based on dynamic clustering and trust management, and belongs to the technical field of power distribution network optimization operation, and the method comprises the steps: carrying out the seasonal scene division based on the historical load data of each distribution transformer device in a region, carrying out the clustering of the devices based on a dynamic time warping distance algorithm, and carrying out the clustering of the devices; outputting an equipment cluster type and a corresponding standardized load form curve; according to the cluster type to which the equipment belongs, combining with the real-time output data of the new energy, dynamically correcting the equipment demand coefficient and generating an equipment-level load prediction value; overlapping the load prediction values of the same cluster equipment to generate a cluster-level load value, and aggregating all clusters to obtain a regional total load prediction value; calculating a source load matching degree quantitative index and a new energy output prediction volatility, fusing the two to generate a time-varying trust value, dynamically adjusting a new energy output prediction weight based on the time-varying trust value, and outputting a regional net load prediction value; and according to the regional net load prediction error closed-loop optimization demand coefficient correction parameter.

The invention discloses a source load multi-body dynamic clustering and regulation method based on a double-layer coupling local subgraph, and belongs to the technical field of source load regulation. The method comprises the following steps: firstly, constructing a local sub-graph model with coupled topology layer and physical layer, fusing node power, energy storage state and load characteristics, and realizing accurate description of high-order interaction relationship between source and load; secondly, providing a multi-scale feature extraction and incremental random walk clustering algorithm, and realizing dynamic updating and feature adaptive adjustment of node clustering in a local subgraph; and finally, constructing a layered autonomous regulation and control and cross-sub-graph cooperation mechanism, realizing local power balance in a sub-graph layer, realizing energy storage coordination and optimal energy distribution in a global layer, and realizing continuous updating and closed-loop optimization of a regulation and control scheme in combination with an incremental feedback actuator. According to the method, an integrated framework of coupling modeling-dynamic clustering-distributed regulation and control is formed, and the stability and toughness of multi-main-body cooperative operation of the power system are remarkably improved.

The invention discloses an anti-interference unmanned aerial vehicle cellular edge removal computing resource allocation method in an air-space-ground integrated network, which comprises the following steps of: firstly, constructing an air-space-ground integrated network architecture comprising a low-orbit satellite, an unmanned aerial vehicle cluster and a ground user, and establishing satellite interference space distribution mapping based on a satellite orbit and an interference measurement result; and then adopting a user-centered dynamic clustering strategy, combining with a large-scale fading coefficient and interference intensity, adaptively selecting a cooperation cluster scale and member composition, and forming an unmanned aerial vehicle cooperation cluster. Then establishing an uplink communication model of an explicitly introduced satellite direct-connected user co-frequency interference term, and on the basis, constructing a three-section end-to-end time delay model comprising wireless transmission time delay, parallel computing time delay and forward pass and backhaul time delay related to a cooperative cluster scale and a quantization bit number; according to the method, the cooperative cluster scale and the quantization bit number are used as time delay optimization variables together, controllable optimization of end-to-end time delay is achieved, the propulsion energy consumption of the unmanned aerial vehicle is considered, and therefore the unloading efficiency and the overall energy efficiency of the cellular edge removal computing system are improved.

The invention discloses an optical remote sensing image salient target detection method based on Mama dynamic clustering and bidirectional calibration, and belongs to the field of computer vision. The method comprises the following steps: preprocessing an original data set; inputting the preprocessed image into a lightweight encoder, capturing multi-scale features and refining local textures and edges; the multi-scale features output by the lightweight encoder are input into a dynamic clustering module based on Mamba, and interaction enhancement of global semantic modeling and dynamic local feature capture is achieved; inputting the output features of the Mama-based dynamic clustering module into a bidirectional cross-scale calibration module to realize cross-scale feature bidirectional complementation and semantic detail enhancement; inputting the output features of the bidirectional cross-scale calibration module into an edge attention combined repair module to realize attention hole repair and boundary precision enhancement; and finally, realizing feature aggregation and spatial resolution recovery through a decoder, and finally generating a saliency map. The method is used for solving the problems of target scale inconsistency and boundary blur in the remote sensing image.

The invention provides a doctor influence comprehensive evaluation method based on multi-modal data fusion, and the method comprises the steps: collecting media reports, clinical information, social platforms and other multi-source heterogeneous data, introducing timestamp marks, and achieving the time sequence semantic vector representation of a doctor entity through a medical field pre-training model and sine function embedding; in combination with BiLSTM-CRF and an attention mechanism, doctor attributes and key events are identified, and an LSTM and a dynamic clustering algorithm are adopted to extract and divide doctor attribute evolution trajectories in stages; an influence score is calculated through an exponential decay function, stage knowledge graph nodes are generated, and incremental updating and node merging and splitting are supported; a graph convolutional network and a graph attention mechanism are adopted, a doctor influence evolution chain is constructed, information dynamic association and trend prediction are achieved, and the time sequence precision and data comprehensiveness of doctor influence evaluation and the dynamic evolution ability of a knowledge graph are improved.

The invention discloses a multi-source data fusion-oriented comprehensive geological cause analysis method and system. The method comprises the following steps of 1, generating semantic mapping data; 2, constructing a cause characteristic pedigree tree, and outputting cause main chain data; step 3, generating geological space-time interlacing data; 4, limiting a time window and a space window, and generating spectrum fusion modeling data through a spectrum-guided fusion converter; 5, constructing a three-dimensional voxel grid, and carrying out dynamic clustering to generate a three-dimensional construction model; step 6, generating geological evolution field data; and 7, inputting the geological evolution field data into the improved CrossViT model, setting an interlayer cross attention unit, reconstructing a Token interaction mode, introducing a cause field constraint attention modulation mechanism, and outputting a geological cause analysis result. According to the method, high-precision analysis of complex geological causes is realized through multi-source data semantic mapping, cause pedigree reasoning and CrossViT model improvement.

A hierarchical personalized federal learning method based on cross-client prototype clustering relates to the field of artificial intelligence and distributed machine learning, and comprises the following steps: a client maps a local sample to a semantic prototype space for information compression, and generates a category prototype matrix; the server receives the prototype matrix uploaded by each client, dynamically clusters and aggregates the prototype matrix, constructs a multi-level knowledge structure including a cluster-level expert model and a global semantic prototype, and maintains the global semantic prototype through index moving average; the client receives the cluster-level expert model, the global model parameters and the global semantic prototype from the server, and carries out a new round of local training on the basis; the client integrates local data and multi-level knowledge issued by the server to optimize three types of loss updating models; through loop iteration, the local model of the client gradually realizes personalized adaptation and global alignment. According to the method, the communication efficiency, the privacy protection safety, the model convergence speed and the generalization ability are improved.

The invention relates to the technical field of hospital budget management, and discloses a comprehensive budget management optimization system and method based on budget items. A budget project basic database of the system stores department attributes, budget project classification and historical execution data. The budget compilation permission allocation engine automatically divides compilation, auditing and compilation department permissions according to a hospital organization structure, and generates a budget permission topological graph. The budget project dynamic collection module receives department budget application data and performs dynamic clustering according to classification to form a budget project collection matrix. And the budget auditing process control module constructs a multi-stage auditing path according to the authority topological graph to realize step-by-step circulation verification of the budget project. The budget execution dynamic monitoring module collects execution progress data in real time, and performs time sequence comparison with the collection matrix to generate a deviation coefficient sequence. The hospital budget management system and the hospital budget management method realize refined, process and dynamic management of the hospital budget.

This invention discloses a comprehensive management method and system for base station power consumption, belonging to the field of base station power consumption. The method includes: topological mapping of the access relationships of AC / DC equipment within the base station to form a hierarchical power consumption association list; dynamic clustering parsing of the list to generate a time-series evolution chain reflecting state changes and remote control history; constructing a device differentiation identification model based on the time-series evolution chain, determining abnormal metering factors and outputting candidate correction events; labeling the corresponding areas of the candidate events as local anomalies, and generating a correction reference group by fusing multi-source information such as meter readings and sensor data; dynamically iteratively correcting the global metering results based on the correction reference group, and generating intelligent management instructions by combining the corrected load sharing ratio and branch authorization strategy. The system includes corresponding functional modules. This invention can systematically solve the problems of unclear base station power consumption topology, inaccurate metering, and extensive management, achieving high-precision metering and automated intelligent control.

The invention discloses a response reliability evaluation method and system for a virtual power plant and a related product, and belongs to the technical field of virtual power plant evaluation optimization. Stochastic state simulation is carried out based on a Monte Carlo method, a stochastic state sequence is generated, uncertain factors such as wind and light output fluctuation and energy storage state randomness are quantified into the stochastic state sequence capable of being analyzed, and the limitation of deterministic hypothesis is broken through; through energy storage dynamic division and real-time updating of a division result, and in combination with a design of calculating a reliability index by a random state sequence and an energy storage execution model, a virtual power plant dynamic reconstruction demand caused by wind and light output fluctuation and user demand change can be responded; and through dynamic clustering and attenuation quantification, the operation adaptability of the system under the uncertain working condition is ensured, the finally calculated reliability index can comprehensively reflect the real response capability of the virtual power plant under the uncertain environment, and an accurate evaluation basis is provided for improving the adaptive capability of the virtual power plant under the uncertain environment.

The invention discloses a hierarchical and classified dynamic cluster grouping method and system for air storage and charging resources in a building park, and relates to the technical field of resource allocation. According to the method, the double representative point design of the cluster center and the maximum dispersion point is adopted, the traditional inter-cluster distance calculation amount and complexity are greatly reduced, the calculation power demand and the operation cost are reduced, and the clustering efficiency is improved to adapt to a large-scale resource scene; the inter-cluster distance is measured by a median, cluster features are comprehensively represented by combining double representative points, deviated samples, data deviation and noisy points are effectively filtered, and even in a scene with multiple operation subject data and few samples, it can still be ensured that a clustering result is stable and accurate, and resource response characteristics are truly reflected; time sequence characteristics and dynamic iteration capability are considered, resource changes are adapted in real time, a scientific basis is provided for fine scheduling and optimal configuration, and dual improvement of resource utilization efficiency and management level is realized.

The invention provides a temperature and humidity price tag application control instruction scheduling method and system, and the method comprises the steps: extracting the deployment position information of a task, the environment response characteristics of a commodity type, the temperature and humidity trend, standardizing the multi-dimensional characteristics, and carrying out the dynamic clustering, thereby achieving the optimal task grouping under environment consistency and resource constraint; according to the method, the task execution priority in the cluster is determined in combination with a distributed state machine, a control instruction is generated and issued according to a collaboration result, clustering parameters are adaptively adjusted through a feedback closed-loop mechanism, and the execution efficiency of batch electronic price tag updating, the environment response adaptive capacity and the edge resource utilization rate are improved.

The invention discloses an electrified flexible source load characteristic identification and adjustable potential measurement and calculation method, and particularly relates to the technical field of trunk line potential measurement and calculation, and the method comprises the steps: S1, multi-dimensional dynamic data collection and processing, S2, flexible source load spatial-temporal characteristic dynamic clustering, S3, adjustable potential prediction model construction of a spatial-temporal mechanism, and S4, AC trunk line power grid safety bundle potential verification. S5, dynamic potential visualization scheduling interface development; and S6, method validity verification adaptive optimization. By constructing a multi-dimensional and high-frequency dynamic data acquisition and processing system, data of different types and different scales can be effectively fused and utilized by the model, so that the defect that full utilization of the flexible load in power grid dispatching is limited due to single data source and poor quality in the prior art is overcome, and the flexible load dispatching efficiency is improved. The problems of inaccurate evaluation and poor predictability in the prior art are fundamentally solved.

This invention discloses a multi-parameter coupling-based deep peak-shaving full-condition optimization control method for coal-fired power units. Addressing the problems of existing technologies, this invention constructs a multi-parameter coupling index system, calculates the comprehensive coupling strength between each parameter and the load, and selects core parameters. Dynamic clustering is used to finely divide the full-condition operation from 20% to 100% rated load into multiple operating zones. An LSTM-GRU dual-channel deep learning prediction model is established for each operating zone. A three-layer hierarchical control architecture is designed, comprising load optimization scheduling, boiler-turbine coordination control, and combustion optimization execution. The boiler-turbine coordination layer uses multivariate model predictive control and online self-tuning of multi-objective weights through fuzzy inference. The combustion execution layer uses the NSGA-III multi-objective evolutionary algorithm to optimize air distribution. A constraint-adaptive tuning mechanism based on safety margin is established to form a closed-loop iterative optimization across all operating conditions. This invention can significantly improve the load response speed, main parameter stability, and combustion economy of the unit during wide-load operation.

The invention particularly relates to a neural decoding method based on multi-scale electroencephalogram signals and heterogeneous visual stimulation alignment, which comprises the following steps: firstly, performing frequency domain transformation, processing and inverse mapping on original EEG signals through an FADE module to effectively integrate frequency specific information; then, the signals are input into an ACC module, and the module achieves self-adaptive multi-channel relation modeling based on a functional relation instead of a fixed anatomical position through a dynamic clustering center and a cross attention mechanism; then, the encoded EEG features and CLIP visual features are subjected to alignment training under a joint loss function, and the loss function optimizes the semantic fidelity and discrimination of the features at the same time; finally, the EVA frame obtained through training can be used for heterogeneous visual stimulation decoding tasks such as zero-sample image retrieval, video classification and high-quality image reconstruction. According to the method, the capture capability of the EEG features on visual semantic information and the generalization of the model can be effectively improved, and the challenge of multi-scale and multi-modal neural decoding is solved to a certain extent.

The application discloses a kind of multi-source perception fusion's railway station air conditioning system dynamic optimization control method.The method collects static building parameters, real-time equipment operation and passenger flow perception data, constructs unified space-time index multi-source fusion basic data set;Establish partition heat and humidity balance model, use state estimation algorithm to online inversion equivalent sensible heat and latent heat load of physical partition, carry out short-time rolling prediction;Based on real-time passenger flow thermal map and load characteristics, spatial dynamic clustering is carried out, and virtual partition across physical boundary is generated, and optimization model is constructed to dynamically allocate VAV terminal air volume;Combined with train operation plan, the linkage passenger flow migration and load surge between platform and waiting room are predicted, and the joint control trajectory containing precooling, preheating and ventilation strategy is generated.The application solves the problem of fixed partition and dynamic passenger flow mismatch and control lag through virtual partition reconstruction and load feedforward control, and improves the thermal comfort and operation energy efficiency of passenger station.

The invention discloses a medicine data intelligent optimization management system and method based on machine vision, and relates to the technical field of medicine quality management and control. Through linkage of a medicine visual feature clustering analysis module and a quality fluctuation traceability module, an industrial camera is used for collecting an appearance image of each medicine unit, and multi-dimensional feature vectors are extracted; and performing real-time dynamic clustering on all drugs in batches by adopting an unsupervised clustering algorithm, dividing feature clusters with similar appearance features, calculating proportion distribution of each feature cluster, and when the system detects that the proportion of a certain feature cluster exceeds a threshold range, performing real-time dynamic clustering on all drugs in batches. The quality fluctuation traceability module reversely correlates and analyzes the process visual feature data on the production line in the same time period, and positions the process link most related to the quality fluctuation by calculating the Pearson correlation coefficient between the key appearance parameters of the abnormal feature cluster and each process parameter sequence, so that the response and handling time of the quality abnormality is remarkably compressed.

The invention provides a process parameter optimization method and device. Irrelevant noise factors are removed under data driving through a dynamic clustering algorithm, core factors are reserved, and blindness of factor selection is avoided; the improved optimization algorithm uses a dynamic penalty function mechanism, and a search path of the algorithm is forced to be always kept in a high-quality safety region, so that the occurrence of a process parameter combination causing defects is effectively prevented, and the application effect of the process parameter combination is improved; the experiment matrix is generated by combining the split area experiment structure designed by the parameter adjustment cost of each core factor, so that the experiment time consumption can be reduced, and the experiment efficiency is improved; dynamic adaptation to experimental data fluctuation is achieved through dynamic feasible region constraint of core factors, it is guaranteed that experimental points are in a safe interval, and the application effect of technological parameter combination is guaranteed.

The present application relates to the technical field of biomass raw material data management, and particularly relates to a system and method for storing and operating characteristic data of alcohol biomass raw materials based on cloud computing, comprising: generating raw material characteristic data in a unified format by collecting characteristic data of biomass raw materials according to a hierarchical system and performing standardization processing; mapping the raw material characteristic data into a multi-dimensional feature vector to form a raw material performance vector space, and generating a hierarchical index by using an incremental dynamic clustering, while updating the feature vector and the hierarchical information; correlating and mapping the feature vector with alcohol processing parameters to form a cause-and-effect mapping result; performing nonlinear pattern recognition based on the feature vector, the hierarchical information and the cause-and-effect mapping result to generate a combination mode library and record the feature vector and the process parameters of each combination; and receiving an external request through an intelligent query interface and returning a structured analysis result. The present application realizes dynamic management of cross-regional and cross-type raw material performance, combination mode analysis and efficient retrieval.

This invention discloses an intelligent voice assistant system and method for the elderly based on speech recognition, belonging to the field of intelligent speech recognition technology. It addresses the lack of dynamic adaptation mechanisms in current systems. Traditional speech recognition systems, which use a uniform model for training, often fail to consider the unique physiological characteristics of different elderly individuals, such as vocal cord aging and unclear pronunciation. This invention employs a k-means clustering method based on MFCC (Mel-frequency cepstral coefficients) to divide elderly speech data into multiple clusters with similar characteristics. A dynamic clustering analysis method, which comprehensively monitors data distribution patterns and noise ratios, is used to determine the triggering conditions for updating clusters. Each cluster is trained with a separate speech recognition model to determine the optimal model parameters for each cluster. This technology integrates data analysis methods such as feature extraction, cluster analysis, and scoring functions, enabling the speech recognition system to dynamically model physiological speech characteristics, thereby improving the accuracy of speech recognition for the elderly.

The embodiment of the present disclosure discloses a network space asset fingerprint identification method and system. The method comprises the following steps: forming original fingerprint fragments according to four types of metadata of captured network traffic record time sequence, protocol, payload length and direction; extracting equipment communication feature vectors from the original fingerprint fragments; performing dimension reduction processing on the feature vectors to obtain micro-signatures with fixed length; performing unsupervised dynamic clustering on the micro-signatures as input to establish equipment type identification; grouping the micro-signatures according to the equipment type identification, arranging the micro-signatures in time sequence, and splicing the micro-signatures into behavior chains; encrypting the equipment identification and the equipment type identification to generate anonymized equipment ID, securely encoding the micro-signatures, abstracting the behavior chains, and constructing a three-tuple data structure containing equipment ID, secure micro-signature and behavior abstract. The method can solve the technical problems of relying on a large number of pre-labeled training samples, being difficult to identify unknown equipment types, and lacking a secure output mechanism.

The invention discloses a high-dimensional data evolution feature selection method based on dynamic clustering and mixed agent combined assistance, and belongs to the field of high-dimensional data feature screening, and the method comprises the following steps: S1, constructing an initial population; s2, on the basis of the initial population, combining a paired comparison agent model and a sample agent model to form a mixed agent model; s3, approximately evaluating the individual fitness of the initial population by using the sample agent model, initializing an individual optimal solution and a global optimal solution, entering an iterative optimization process, and outputting an optimal feature subset; and S4, based on the classification performance of the output optimal feature subset, the feature scale and the operation time verification result, feeding back the update frequency of the optimization hybrid agent model and the increment adjustment parameter of the feature clustering. The high-dimensional data evolution feature selection method based on dynamic clustering and mixed agent combined assistance has high robustness and engineering adaptability, and realizes optimal balance of classification precision, feature simplification and calculation efficiency in a high-dimensional data scene.

The invention discloses a power utilization mode consistency analysis method, system and device based on load time-frequency domain feature fusion and a medium, and the method comprises the steps: obtaining power utilization load mode data of a client, extracting time domain features through dimension reduction, carrying out the Fourier transformation of the power utilization load mode data, calculating the amplitude of current harmonic waves, and obtaining the consistency of the current harmonic waves; selecting a preset number of significant harmonics to construct a frequency domain load feature vector; inputting the load characteristic template library and the transient current waveform template library into a clustering algorithm for time domain dominant clustering to obtain an initial clustering result, performing quality analysis, and triggering frequency domain clustering correction if the initial clustering result does not meet the standard; and the initial clustering result is combined with the frequency domain load feature vector to be input into an improved following leader algorithm to be corrected, so that a final consistency result is obtained. According to the method, time domain and frequency domain collaborative optimization is combined with the dynamic cluster number to adapt to a complex scene to ensure quality analysis reliability, accurate customer classification is achieved, and the method is suitable for personalized services of the electricity market.

The invention discloses a color high contrast processing algorithm for four-color electronic paper, and relates to the technical field of electronic paper display technology and image processing, and the method comprises the specific steps: firstly, carrying out feature extraction, converting an original RGB image to an HSV space, carrying out the statistics of the pixel proportion of a brightness interval, screening red and yellow pixels, and calculating the feature and the proportion of four colors; a dynamic clustering center is generated; performing weighted clustering mapping to generate an intermediate image; enhancing the saturation and boundary contrast of the adaptive image in a linkage manner; and finally, modular adaptive output: selecting scheme optimization data, converting the scheme optimization data into a 2-bit format, and outputting the two-bit format after verification. According to the method, through multi-dimensional feature extraction and dynamic clustering center generation, accurate color mapping is realized, and the color rendition degree and comfort degree are improved; and through multi-factor linkage enhancement and modular adaptive output design, the display effect and hardware adaptability are considered, the detail expressive force is enhanced, the integration process is simplified, reliable data transmission is guaranteed, and the application adaptation and display quality is improved.