322 results about "Dynamic clustering" patented technology

Provided is a method for perceiving and eliminating an abnormal state of active distribution network based on data enhancement, including: acquiring, by synchrophasor measurement device, data of each node of active distribution network in target domain in real-time and transmitting to processor; inputting the acquired data into a classification model, and outputting abnormal detection and classification results in real time; and analyzing the abnormal detection and classification results, and transmitting an abnormal state eliminating instruction to a distribution terminal to eliminate the abnormal state. Wherein, hidden distribution features in node data of active distribution network are mined through dynamic clustering, a large amount of unlabeled data are clustered, a data label is generated through self-coding and label correction rule, training samples with balanced category distribution is generated through data enhancement and is used to train the classification model based on dynamic graph attention network by domain adaption method.

The invention discloses an aero-engine group health evaluation method based on multi-working-condition dynamic clustering, and belongs to the field of aero-engine health state evaluation. The method comprises the following steps: firstly, carrying out clustering analysis on set parameters in engine operation data, and carrying out merging processing on small-scale abnormal clusters to obtain a working condition category division result; secondly, constructing a health baseline data set, carrying out standardized preprocessing on sample data in a working condition category division result, and carrying out nonlinear dimensionality reduction to obtain a low-dimensional feature data set; thirdly, performing clustering analysis on the low-dimensional feature data set by adopting a Gaussian mixture model, and calculating an average mahalanobis distance between a sample of each clustering category and a health reference center to obtain multi-level health levels corresponding to different clustering categories; and finally, through fusing the membership soft probability and the sample individual mahalanobis distance, constructing a continuous health score and obtaining a health grade determination interval. According to the method, health state characteristics under different working conditions can be effectively identified, individual difference modeling and group transverse comparison evaluation are supported, and the accuracy is improved.

The invention relates to the technical field of power plant data processing, in particular to a virtual power plant integrated management system, which comprises a data acquisition module for acquiring voltage fluctuation characteristics and power climbing rate parameters of energy equipment in real time; the equipment fingerprint database construction module generates an equipment fingerprint code comprising a dynamic response time-lag coefficient and an adjustment margin quantized value; the dynamic clustering module is used for dividing simulation sub-models of regional topology constraints based on the geographic position of the equipment and a power regulation threshold value; the multi-time scale prediction module is used for generating source load prediction data by fusing meteorological parameters through a space-time diagram convolutional network; the digital twin simulation module outputs an energy storage charging and discharging threshold value and a demand response priority strategy; and the cross-domain collaboration module adopts a double-chain block chain architecture to realize security verification and data integrity binding of federated learning feature parameters. According to the invention, through equipment characteristic digital modeling, regional co-simulation and multi-energy flow coupling regulation and control, the resource aggregation precision and response real-time performance in a new energy strong fluctuation scene are improved.

The invention relates to the technical field of Internet, and discloses an Internet hotspot data mining system and method based on artificial intelligence, and the system comprises a data collection module, a semantic understanding module, a dynamic clustering module, a popularity evaluation module, a visual output module and a feedback optimization module. By setting a multi-source calibration unit, when cross-platform hotspot data acquisition is carried out, standardized processing of multi-source heterogeneous data acquisition is ensured by establishing a dynamic semantic feature library and configuring adaptive weight parameters for different data platforms; meanwhile, by monitoring the semantic offset of the collected data in real time, characterization deviation generated during cross-platform data collection can be detected and eliminated, the accuracy of hotspot clustering similarity calculation is guaranteed, hotspot recognition errors are reduced, and by deploying a semantic tracking engine, when hotspot event propagation analysis is carried out, the analysis accuracy is improved. The deviation degree of event core elements is calculated by constructing spatio-temporal feature vectors, and whether topic semantics are migrated or not is judged in real time.

The invention provides a mountain power station current collection system state online monitoring method and device, and relates to the field of data processing. According to the method, distributed optical fiber data, environmental meteorological data and electrical measurement data are acquired to construct a multi-mode time sequence matrix, an operation state diagram is formed through graph structure representation and graph signal processing, a working condition mode cluster is obtained by combining dynamic clustering, a constraint diagram optimization framework is input, thermal, electrical and mechanical constraints are introduced, and an operation state track is generated. And constructing and fusing a non-stationary degradation path library and environment exposure history to form a dynamic degradation evolution model, and finally outputting in-service reliability and predicting residual life by a risk inference engine to realize online monitoring of the state of the current collection system of the mountain power station. By implementing the technical scheme provided by the invention, the accuracy of online monitoring of the state of the current collection system of the mountain power station is improved.

The invention belongs to the technical field of power system operation control, and discloses a virtual power plant aggregation operation optimization method considering flexible and adjustable resource hierarchical and partitioned regulation, which is characterized in that dynamic clustering and clustering are performed on a distributed power supply based on an improved K-medoids algorithm, and response characteristic analysis is performed on an electric vehicle, an energy storage load and a temperature control load; by establishing a self-adaptive layering and partitioning mechanism, dynamic layer region boundary adjustment is realized. An optimization model with the lowest cost as the target is constructed, the optimal resource scheduling scheme is solved by comprehensively considering various cost factors and operation constraint conditions, and efficient configuration of resources and effective control of the cost are achieved; and the adjustable load and the energy storage resource are guided to participate in system regulation and control, so that the peak-valley difference of the system is reduced with relatively low regulation and control cost. According to the method, the resource scheduling efficiency is effectively improved, high-quality peak regulation auxiliary service is provided for the power grid, and efficient utilization of distributed energy is assisted.

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 relates to the technical field of communication production management, and discloses a communication equipment production intelligent management system based on machine learning. The system comprises a production data acquisition module, a feature engineering construction module, a dynamic clustering analysis module, an anomaly detection engine module and a production decision optimization module. The production data acquisition module acquires multi-source sensor data in real time and converts the multi-source sensor data into a standardized sequence with a unified timestamp; the feature engineering module extracts a time domain statistical feature, a frequency domain energy feature and an equipment state association feature to generate a high-dimensional feature vector set; the dynamic clustering module adopts an incremental algorithm to divide clusters online; the anomaly detection module establishes a multi-level Gaussian mixture model based on a clustering label, and quantifies an anomaly probability through a mahalanobis distance; and the production decision module integrates the results to generate an equipment maintenance priority sequence and a production takt adjustment instruction. According to the system, intelligent monitoring and dynamic optimization of the whole production process of the communication equipment are realized, and the real-time change requirement of a complex production environment is met.

The invention discloses an integrated federated learning optimization method based on clustering weight sampling, and the method specifically comprises the following steps: a federated learning system comprises a plurality of clients and a server, and the server calculates the similarity between the clients through model updating information uploaded by the clients, clustering the clients by adopting a dynamic clustering method according to the similarity; the server carries out secondary clustering according to a set sampling rule and judges whether a first-stage iteration threshold value is reached, all the clients obtain a latest global model and freeze a model feature recognition layer for fine tuning, the server collects parameters of all the clients after fine tuning, and then the parameters are clustered according to similarity and are subjected to secondary clustering according to the sampling rule; and combining into an enhanced global model through an ensemble learning strategy. The method can be widely applied to data privacy protection scenes in the fields of medical image analysis, financial risk control, intelligent transportation and the like, and a new technical solution is provided for efficient application of federal learning in a heterogeneous environment.

The invention discloses a power distribution station equipment monitoring and health evaluation method based on multi-modal information, and the method comprises the steps: collecting multi-dimensional parameters of power distribution station equipment, carrying out the dynamic clustering through an improved fuzzy C-means clustering algorithm, and carrying out the aggregation of related parameter subsets; and constructing a hypergraph neural network model, taking the parameter subset as a node, generating a hypergraph adjacency matrix containing a multi-parameter coupling relationship, extracting deep features through multilayer hyperedge convolution, generating a quantitative evaluation result through reasoning, and dividing health levels. According to the method, parameter adaptability is enhanced through a model formula, key steps are refined, parameter association is mined, the defects that traditional clustering precision is insufficient and a conventional neural network cannot model high-order association are overcome, and comprehensiveness and accuracy of health management of power distribution station equipment are improved.

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 a rolling quality parameter uncertainty quantification method based on mixed entropy-fuzzy clustering. The method comprises the following steps: S1, multi-source data fusion: integrating rolling compaction parameters, meteorological data and real-time monitoring data in an engineering construction process, and constructing a multi-dimensional feature matrix; s2, calculating mixed entropy, namely quantifying the randomness and fuzziness of parameter distribution in combination with information entropy and fuzzy entropy; s3, performing dynamic fuzzy clustering, namely, optimizing a clustering center based on an improved firefly algorithm, and dividing parameter uncertainty levels; step S4, uncertainty contribution degree analysis: quantifying the influence weight of each parameter on the rolling quality through an entropy weight-grey correlation method; according to the method, the information entropy and the fuzzy entropy are fused, and dynamic clustering and an intelligent optimization algorithm are combined, so that precise quantification and hierarchical management and control of the rolling parameter uncertainty are realized.

The invention discloses a method for identifying traditional Chinese medicinal materials by combining infrared spectroscopy with clustering analysis, which comprises the following steps of: acquiring original infrared spectral data, averaging the original infrared spectral data to obtain single-sample original spectral data, constructing a sample graph and a wavelength graph based on standardized spectral characteristics, and fusing the sample graph and the wavelength graph to obtain the single-sample original spectral data. Obtaining fusion image data, inputting the fusion image data into a pre-trained image neural network model, extracting a low-dimensional feature vector of a target sample through forward propagation, and inputting the low-dimensional feature vector into a pre-trained dynamic cluster diffusion module to obtain a clustering label and distance data; and determining and outputting the quality grade of the rhizome traditional Chinese medicinal material sample based on the clustering label and the distance data. Therefore, interference can be effectively reduced, key features can be extracted, associated features of fused graph data are extracted in combination with a graph neural network, and the quality grade of traditional Chinese medicinal materials can be accurately determined in combination with clustering analysis of a dynamic cluster diffusion module.

The invention provides a power plant combustion optimization method, system and device and a program product. The method comprises the steps that historical operation data of a power plant are collected; constructing a self-adaptive physical information coupling model and obtaining a target state parameter calculation value; training a coupling model based on historical operation data; wherein the data loss item of the loss function is constructed based on the target state parameter measured value and the target state parameter predicted value, the physical loss item of the loss function is constructed based on the target state parameter measured value and the target state parameter calculated value, and the coupling model is trained based on the loss function; acquiring real-time operation data of the power plant, and inputting the real-time operation data into the coupling model to obtain a target state parameter predicted value output by the coupling model at the next moment; and obtaining an optimal control quantity based on the target state parameter predicted value at the next moment, and controlling the working state of the actuator dynamic cluster based on the optimal control quantity. According to the invention, fine tuning of the power plant is realized.

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 a digital aeronautical communication system design based on a cellular-free network architecture, which belongs to the technical field of aeronautical communication and comprises an aeronautical communication system and a communication service execution module. The aeronautical communication system comprises a network infrastructure and a communication link layer and is responsible for providing underlying physical connection and transmission environment support. The communication service execution module covers a network protocol and information interaction and task execution process; the network infrastructure adopts distributed AP topology planning, airport and route area APs are deployed in a differentiated manner, and parameters are dynamically adjusted in combination with a three-dimensional coverage optimization model; and software and hardware modules comprise ground APs and airborne terminals, so that the problems caused by high-speed movement are solved. The communication link layer carries out multi-dimensional channel modeling, and LDPC code coding and dynamic cluster generation are adopted. The working process comprises the steps of initialization, cluster construction, state monitoring and the like, stable communication in the whole flight process is achieved, and the aviation communication efficiency and safety are improved.

The invention relates to the technical field of data processing, can be applied to business scenes of battery management, financial science and technology, medical health and the like, and discloses a battery unit anomaly detection method, device, equipment and medium, and the method comprises the steps: obtaining static attribute data and operation state parameters of a plurality of battery units, and generating a homogenized battery unit group through dynamic clustering; performing dynamic reference analysis by using the internal resistance data, identifying abnormal battery units and generating a preliminary abnormal battery unit set; classifying the abnormal battery units by using a time sequence analysis model, and generating and confirming an abnormal battery unit list; and generating a battery unit maintenance schedule based on the confirmation list and a preset maintenance knowledge base. According to the invention, through combination of the dynamic reference analysis model and the time sequence analysis model, the abnormal state of the battery unit is accurately identified, a potential problem battery can be found in time, the efficiency and accuracy of a battery monitoring system are improved, and the reliability of the battery at a critical moment is guaranteed.

The invention discloses a cloud edge federal learning method and system and a storage medium, and belongs to the field of model training optimization. Firstly, the cloud constructs a dynamic clustering mechanism and reduces intra-group statistical heterogeneity based on model features and data distribution information uploaded by an edge terminal, and the edge terminal performs local training and intra-group model aggregation according to a cloud clustering result to improve the consistency and adaptability of a local cluster model; secondly, a decoupling knowledge transfer mechanism is adopted, the global model and the local cluster model are decoupled into a feature layer and a classification layer respectively, hierarchical knowledge alignment is carried out in the distillation process, the learning ability of the local model for intermediate feature expression and classification decision boundaries is enhanced, and the distillation efficiency is improved; therefore, the convergence speed and generalization performance of the model in the heterogeneous data environment are improved. Therefore, the technical problems of model performance reduction and weak generalization ability caused by data heterogeneity in the cloud-edge federation in the prior art are solved.

The invention discloses a distributed photovoltaic power collaborative prediction system based on gridding meteorological fusion and dynamic cluster modeling, and the system mainly comprises a meteorological grid processing module, a dynamic cluster modeling module, a standby power demand processing module, a collaborative distribution module, a meteorological prediction processing module, and a power prediction module. The meteorological grid processing module is used for acquiring meteorological grid data of a target area and generating a photovoltaic output coefficient corresponding to each grid through a grid output calculation model; the dynamic cluster modeling module is used for generating a dynamic cluster topology matrix according to the output fluctuation similarity; the standby power demand processing module is used for generating a standby power demand priority weight through a standby power demand prediction model; the collaborative distribution module is used for calculating the output priority proportion of each photovoltaic cluster; the power prediction module is used for generating a collaborative output baseline value of each photovoltaic cluster. According to the invention, prediction precision can be improved, scheduling robustness can be enhanced, and reasonable distribution of photovoltaic resources can be realized.

A system for managing cache coherency comprises memory areas, processing cores, cache nodes each associated with at least one of the processing cores, and a hardware processor configured to: for each of the memory areas: cluster the processing cores into clusters according to memory access metrics in relation to the memory area; and for each of the clusters, associate the memory area with a caching scheme; and configure the processing cores to: receive from a first core a memory access command comprising a memory address associated with a memory area, where the first core is a member of a first cluster for the memory area; compute a determination of a target cache node according to the memory access command, where the target cache node is associated with a second core; and access the memory area according to the caching scheme associated with the memory area for the first cluster.

The invention relates to the technical field of power battery echelon utilization, and discloses a decommissioned power battery system reliability optimization design method, which comprises the following steps: acquiring and preprocessing battery operation attenuation data; constructing an attenuation prediction model; performing dynamic clustering and grouping after multi-dimensional consistency evaluation; and generating a strategy and feeding back iterative optimization. The system comprises a data acquisition module, a preprocessing module, an attenuation prediction module, a consistency evaluation module, a dynamic grouping module and a strategy execution and feedback module. By adopting the scheme, the problems are solved, the grouping accuracy and stability are improved, the secondary utilization efficiency is improved, the cycle life is prolonged, the operation reliability is improved, and the maintenance cost is reduced.

The invention discloses a pet physiological and psychological collaborative treatment system and method, and relates to the technical field of pet medical treatment, and the method comprises the following steps: continuously obtaining first behavior data, first respiration data and first physiological data of a pet at a time window; performing spatio-temporal change analysis on the first behavior data to obtain second behavior data representing behavior complexity; and performing nonlinear calculation on the first breathing data and the second behavior data to obtain second breathing data. According to the scheme, abnormal fluctuation caused by psychological factors is effectively separated through phase difference analysis and nonlinear calculation, in addition, judgment standards can be adaptively adjusted according to individual differences through dynamic clustering and mapping modeling, the suitability of a treatment scheme is remarkably improved, and accurate recognition and quantitative evaluation of the psychological stress state of the pet are achieved.

The invention relates to the technical field of air purification, in particular to an air purification method and system based on an ion air supply technology. The method comprises the following steps of obtaining target area data and reconstructing a spatial model, performing block division, determining virtual layout nodes and performing layout rehearsal, obtaining a unit layout topological structure through spatial node dynamic clustering and redundancy elimination, performing ion air supply action range superposition simulation, analyzing action range collaborative enabling parameters, and performing layout rehearsal on the basis of the action range collaborative enabling parameters. Ion transmission is monitored in real time, diffusion path tracking and blind area correction are carried out, heterogeneous response unit dynamic coupling and purification intensity boundary migration are achieved, adjustment simulation is carried out based on adjustment of purification intensity, feedback data are collected, coordination control is carried out through an air purification threshold value, and intelligent air purification is achieved. According to the invention, the air purification process is accurately controlled and optimized, the purification efficiency is improved, the energy consumption is reduced, and a technical guarantee is provided for efficient and intelligent air purification.

The invention relates to the technical field of highway tunnel lining disease evaluation, and discloses a highway tunnel lining disease dynamic clustering evaluation system, which comprises a data acquisition module used for acquiring multi-dimensional disease data of a tunnel lining and preprocessing the data so as to construct a disease dynamic matrix. And the data analysis module is connected with the data acquisition module, establishes a fuzzy similar matrix among the disease space units based on the disease dynamic matrix, generates a closed similar matrix in combination with a transitive closure algorithm, and further analyzes the closed similar matrix through a dynamic clustering algorithm to obtain a disease clustering result. And the data output module is connected with the data analysis module, constructs a comprehensive evaluation model according to the clustering result and the historical maintenance data, and finally determines and outputs the risk score and the maintenance priority of the tunnel lining disease. According to the invention, tunnel disease identification precision and maintenance resource configuration efficiency are improved, and safe operation of the tunnel is guaranteed.

The invention discloses an industrial key point data cleaning and abnormal point removing method and a related device, and the method comprises the steps: obtaining to-be-processed industrial key point coordinate data, and carrying out the preprocessing of the coordinate data; dynamically determining the optimal clustering number of the preprocessed coordinate data; based on the optimal clustering number, clustering the coordinate data by adopting a first clustering algorithm, and identifying a first type of abnormal points according to distance distribution statistical characteristics in each cluster; performing secondary cleaning on the data from which the first type of abnormal points are removed by adopting a density-based second clustering algorithm so as to identify a second type of abnormal points which are judged as noise points; and integrating the first type of abnormal points and the second type of abnormal points to generate a cleaned data set. According to the method, dynamic clustering, anomaly detection based on intra-cluster statistics and density secondary cleaning are combined, so that the adaptive capacity, accuracy and robustness of the data cleaning process are enhanced, and a purer and more reliable data set can be produced.

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 provides a cluster wireless ad hoc network method for port intelligent operation equipment, and belongs to the technical field of wireless communication. The method comprises the steps that firstly, port communication system layered architecture initialization is carried out, port intelligent operation equipment neighbor discovery is achieved, and meanwhile neighbor equipment access information packets and channel quality index information are recorded; secondly, performing cluster head node election through a maximum score priority rule to form a local cluster structure consisting of cluster head nodes and terminal equipment, and introducing a cluster wireless ad hoc network to complete a judgment mechanism after the cluster head node election of the intelligent operation equipment is completed; and finally, performing multi-path construction, standby path caching and a main and standby path seamless switching mechanism. The method supports dynamic election of cluster head nodes, supports multi-path cache, can realize seamless communication switching, and solves the problems of slow remote control response speed and high failure rate of port intelligent operation equipment.

The application discloses a kind of millimeter wave radar human body fall detection methods based on multi-feature fusion, comprising: obtaining original point cloud frame from each radar, time stamp calibration and space coordinate system conversion are carried out;Noise filtering, ground segmentation and human body point cloud extraction are carried out to each frame point cloud;After pre-processing, point cloud is divided into static cluster and dynamic cluster;Filter false dynamic cluster;Extract the remaining dynamic cluster set, adopt tracking algorithm to identify and track human body dynamic cluster in continuous frame;From the tracked human cluster, extract the corrected time sequence feature;The time sequence feature is input into the pre-trained deep time sequence network, learns the fall time sequence dependency relationship, and outputs the abnormal index;Multi-source fusion is carried out with the abnormal index, and the comprehensive index is obtained to determine whether to trigger alarm. It can adapt to complex home environment, improve detection accuracy and real-time performance, and reduce false alarm and missed alarm.