455 results about "Dynamic clustering" patented technology

The invention discloses an unmanned aerial vehicle cluster intelligent cooperative control method, and the method comprises the steps: optimizing a network topology through heterogeneous unmanned aerial vehicle cluster dynamic networking and a dynamic clustering algorithm, and guaranteeing the reliability of a communication link; a layered hybrid decision architecture is designed to improve the task allocation rationality and the dynamic adaptability of the unmanned aerial vehicle cluster; a distributed control strategy network is trained by using a multi-agent near-end strategy optimization MA-PPO algorithm, and unmanned aerial vehicle cluster behavior collaboration is ensured in combination with space-time consistency constraint; an asynchronous incremental consensus protocol AICP is provided, the data transmission amount is reduced, and topology reconstruction is accelerated; real-time three-dimensional environment reconstruction and dynamic threat prediction are realized based on a neural radiation field NeRF technology; a lightweight anti-interference communication middleware is developed, and the instruction transmission stability is enhanced by adopting a space-time coding diversity technology. The method solves the problems of high delay of centralized control of the unmanned aerial vehicle cluster, poor convergence of a distributed algorithm and the like, and is suitable for high-dynamic task scenes such as urban street battle and complex terrain search.

The invention relates to the technical field of user behavior analysis, and discloses a user behavior intelligent analysis and management system based on a big data technology. The system comprises a user behavior data acquisition module for acquiring behavior data in a multi-dimensional scene; the behavior feature intelligent recognition module is used for extracting features by using a deep neural network and a time sequence analysis algorithm and generating a map; the behavior pattern dynamic analysis module is used for analyzing pattern changes through dynamic clustering and a hidden Markov model; the abnormal behavior autonomous detection module is used for detecting anomalies based on the multi-dimensional anomaly score and an adaptive threshold value; and the behavior management intelligent optimization module is used for optimizing a management strategy by utilizing a reinforcement learning algorithm. In addition, a behavior data archiving module is further arranged to guarantee safe storage of data. The system can comprehensively collect and analyze user behavior data, accurately detect abnormity, intelligently optimize a management strategy, improve user experience, system safety and operation efficiency, and have wide application value in multiple fields.

The invention discloses a ship typical channel auxiliary navigation method and system based on unmanned aerial vehicle accompanying, and belongs to the technical field of intelligent shipping auxiliary control. A channel area multi-dimensional risk perception model is constructed, and historical accident, hydrology and obstacle information is extracted to generate a risk density matrix; forming a high-risk accumulation area based on risk spectrum dynamic clustering, and endowing an adjustable intervention weight; scheduling an unmanned aerial vehicle with self-positioning and differential recognition capabilities to carry out accompanying flight, and obtaining track deviation, a velocity vector and attitude information through multi-modal sensing and a low-delay link; calculating an intervention coefficient by using a risk decision engine and generating course, speed and steering point control parameters; and finally, local path dynamic optimization and feedback closed-loop control are realized. According to the invention, the course precision and risk avoiding capability of the ship in a complex typical channel are improved, and the method is suitable for intelligent shipping application in a high-density navigation environment.

The invention discloses a millimeter wave radar human body tumble detection method based on multi-feature fusion, and the method comprises the steps: obtaining an original point cloud frame from each radar, and carrying out the timestamp calibration and space coordinate system conversion; performing noise filtering, ground segmentation and human body point cloud extraction on each frame of point cloud; dividing the preprocessed point cloud into a static cluster and a dynamic cluster; filtering false dynamic clusters; extracting a residual dynamic cluster set, and identifying and tracking the human body dynamic clusters in continuous frames by adopting a tracking algorithm; extracting a corrected time sequence feature from the tracked human body cluster; inputting the time sequence characteristics into a pre-trained deep time sequence network, learning a falling time sequence dependency relationship, and outputting an abnormal index; and performing multi-source fusion with the abnormal index to obtain a comprehensive index to judge whether to trigger an alarm. The method can adapt to a complex home environment, improves the detection accuracy and real-time performance, and reduces the false alarm and missing alarm.

The invention relates to the technical field of intelligent power grids, in particular to an intelligent design method and system for electric power engineering cloud resources based on distributed computing. Comprising the following steps: constructing an electric power engineering multi-dimensional resource modeling system, dividing calculation nodes into three types of heterogeneous resource units including a real-time processing core, a data analysis core and a disaster recovery backup core, and establishing a dynamic attribute matrix containing time delay sensitivity, an energy consumption coefficient and a risk assessment value; the real-time processing core is configured with a hardware acceleration instruction set and supports microsecond response; a self-adaptive dynamic clustering algorithm is adopted, elastic resource clusters facing task requirements are generated according to the spatial topological relation of resource units and the load change trend, a quantum genetic optimization mechanism is introduced in the clustering process to dynamically adjust the inter-cluster coupling degree, and a cross-cluster communication link based on credibility evaluation is established; according to the design, the problems of low resource utilization efficiency and service quality degradation caused by a static allocation mode can be fundamentally solved.

The invention discloses a three-dimensional point cloud data filtering method and system based on adaptive clustering segmentation and gradient compensation, and the method comprises the steps: carrying out the data preprocessing of three-dimensional point cloud data through employing a statistical filtering method, obtaining the preprocessed point cloud data, extracting the point cloud features in the preprocessed point cloud data, and obtaining the point cloud feature data; according to the invention, a function of establishing multi-dimensional features including point cloud density, spatial autocorrelation and local curvature features by using an adaptive clustering segmentation algorithm based on density spatial distribution and carrying out dynamic clustering segmentation on a ground feature-ground cluster is realized; and residual mixed point clouds can be further separated through a coarse-fine granularity grid grading processing strategy, so that ground point clouds are separated, and the technical limitations of high parameter dependence, insufficient terrain adaptability and poor real-time performance in the prior art are broken through; and the filtering precision and robustness in a complex scene are remarkably improved through multi-dimensional collaborative optimization, and the method is suitable for being widely popularized and used.

The invention relates to a city-level path guidance method and system based on dynamic clustering in a vehicle-road cloud cooperation scene, and the method comprises the steps: constructing a vehicle-road cloud cooperation architecture, and collecting traffic data; the traffic management center updates the road weight and initializes a penalty matrix, and performs congestion detection at the same time; when a congested road section is detected, vehicles possibly affected by congestion are screened out, and a to-be-planned vehicle set is formed; performing spatial clustering on the to-be-planned vehicle set to obtain a plurality of vehicle clusters, and constructing an independent path planning sub-graph for each vehicle cluster; distributing priorities for the vehicles in each vehicle cluster; planning an alternative path for the vehicle; performing batch dynamic adjustment on the road weight and the penalty value on the path planning subgraph; the traffic management center issues the planned alternative path to the corresponding vehicle through the road side unit, and the vehicle runs according to the new running path to realize path induction; finally, reasonable distribution of traffic flow, improvement of road network traffic efficiency and balanced allocation of time-space resources are realized.

The invention relates to the technical field of data analysis, in particular to a diabetes clinical test data intelligent clustering analysis system and method based on federal learning. Comprising a data acquisition and preprocessing unit; a federal privacy protection unit; the dynamic clustering modeling unit is used for constructing a clustering model of self-adaptive diabetes data features, and realizing joint clustering analysis of multi-source heterogeneous data by adopting a hierarchical federal architecture and a dynamic parameter aggregation algorithm and combining a diabetes course time decay factor and a clinical feature weight adjustment strategy; a double-track verification optimization unit; and an intelligent decision support unit. According to the method, the incidence matrix of the diabetes disease course time decay factor and the clinical characteristics is introduced, the dynamic weight vector is constructed and applied to clustering distance calculation, so that the model can adapt to dynamic changes of the clinical characteristics in different disease course stages, the adaptability to multi-center heterogeneous data is improved, and the stability of a clustering result is enhanced.

The invention relates to the technical field of electrical load management, and discloses an electrical load spatial-temporal distribution modeling and adaptive optimization regulation and control system. The system comprises a load monitoring module, an optimization decision-making module, an exception handling module, a communication coordination module and a data tracing module. The load monitoring module collects load data by using a space-time correlation analysis technology; the optimization decision-making module adopts a dynamic clustering algorithm to generate a regulation and control strategy; the exception handling module corrects load exception in a graded manner based on a multi-scale coordination mechanism; the communication coordination module realizes instruction interaction through a hierarchical routing protocol; and the data tracing module generates a topology storage index by using a multi-dimensional feature coding algorithm. The system can accurately collect load data, reasonably distribute power, effectively process load abnormity, guarantee communication stability, facilitate data tracing, improve the operation efficiency and reliability of an electric power system, and meet the requirement of a modern electric power system for electrical load management.

The invention discloses a power consumption behavior clustering analysis method and system based on big data, and relates to the field of data analysis, and the method comprises the steps: firstly fusing collected active power time sequence data, operation and maintenance logs and meteorological monitoring data, and achieving the construction of sub-sequence-level features through the alignment of a multi-scale sliding window and a time window; then, multi-source fusion feature embedding is extracted by using a pre-trained depth encoder, so that the characterization capability for complex behaviors and mutation events is remarkably improved; further, adaptive determination of the cluster number and the center is realized through a Dirichlet process driven Gaussian hybrid dynamic clustering model, soft distribution is performed on embedded vectors by adopting a probability attribution degree inference method, and different power consumption behavior modes are accurately identified; and finally, according to the clustering labels, typical power consumption behavior labels are automatically distributed to all the parks. Therefore, the refined and intelligent level of energy efficiency management of the industrial park is comprehensively improved.

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 provides a grouping federal recommendation method based on bilateral additive article embedding. A central server constructs a double-layer article embedding characterization structure under a federated learning framework: a client locally maintains user personalized embedding and an article local personalized embedding matrix, and a server generates global group shared article embedding through a dynamic clustering grouping mechanism; superposing global sharing embedding and local personalized embedding by adopting an additive fusion strategy to generate user side personalized article characterization; a progressive course learning scheme is designed, and smooth transition from complete personalization to additive representation is realized by dynamically adjusting a regularization weight coefficient; and a grouping and clustering process is optimized in combination with a knowledge migration strategy, and collaborative knowledge sharing across user groups is promoted. In a client local training stage, a personalized recommendation loss function based on binary cross entropy is constructed, global shared embedding and local embedding parameters are synchronously updated, and a server side updates a global model through grouping federation aggregation. On the premise of protecting user privacy, the problems that in traditional federated recommendation, article characterization is simplified, and personalized perception is insufficient are effectively solved, the accuracy of a recommendation system is remarkably improved, communication overhead is reduced, and the method is suitable for personalized recommendation services of privacy sensitive scenes such as e-commerce and content platforms.

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 voltage control method based on dynamic cluster division of a high-permeability photovoltaic power distribution network, and the method comprises the steps: carrying out the Newton-Raphson method load flow calculation of a power distribution network power system based on the topological structure data of the power distribution network, line parameters, the access position of a photovoltaic inverter and energy storage equipment, and equipment parameter information, and constructing a comprehensive modular function; based on the comprehensive modularization function, according to the load capacity of each moment, taking the average load capacity as a standard, utilizing an improved artificial hummingbird algorithm to dynamically divide time segments of the clusters and selecting dominant nodes of each cluster; based on the dominant node, taking the minimum node voltage deviation and the minimum system loss as objective functions, and constructing a second-order cone programming model when a preset constraint condition is satisfied; and solving the second-order cone programming model by using a solver CPLEX to obtain the reactive power regulation quantity of the distributed photovoltaic inverter and the active power regulation quantity of the energy storage equipment, thereby improving the power supply reliability and the electric energy quality of the photovoltaic power distribution network.

The invention relates to the field of data visualization, in particular to a visual scheduling method and system for a scientific and technological achievement evaluation system. The method comprises the following steps: identifying system access behavior data of a user; performing attention hotspot field prediction on system access behavior data of the user, performing deep personalized demand evolution, and constructing a personalized scientific and technological achievement demand graph; real-time data flow matching retrieval is carried out according to the personalized scientific and technological achievement demand graph, semantic feature analysis is carried out, and deep semantic features of each text item and visual semantic features of each image and table item are extracted; and performing dynamic clustering processing according to the deep semantic features of each text item and the visual semantic features of each image and table item, and performing multi-dimensional scientific and technological achievement item aggregation to construct a scientific and technological achievement display library. According to the invention, comprehensive, dynamic, accurate and personalized scientific and technological achievement evaluation visualization is realized.

The invention discloses a geological disaster real-time monitoring method and system based on a ground sensing network, and belongs to the technical field of geophysical exploration, and the method comprises the steps: obtaining an acceleration parameter and a moisture content parameter of each sensor node; according to the change rate of the acceleration parameter and the moisture content parameter, identifying an environment sudden change event, generating an environment trigger signal to activate an emergency sampling mode of a target node and an adjacent node, and constructing a dynamic monitoring cluster; requesting a plurality of nodes in the dynamic monitoring cluster to synchronously measure similar parameters, and comparing measurement results to generate a compressed alarm packet; adjusting weight factors of different monitoring parameters by using the compressed alarm packet and pre-acquired real-time environment parameters, and generating an environment calibration risk index; and when the environment calibration risk index exceeds a preset risk threshold value, outputting a geological disaster early warning instruction. According to the method, multi-parameter cooperative triggering, dynamic cluster response, weight adaptive evaluation and compression transmission technologies are adopted, and the monitoring precision, timeliness and environmental adaptability can be comprehensively improved.

The invention provides a wireless signal detection and positioning method and system, and relates to the technical field of wireless communication, and the method comprises the steps: collecting signal characteristic parameters through a plurality of monitoring devices, constructing a three-dimensional time-frequency space matrix, carrying out envelope modulation to construct a primary function, carrying out iterative projection to obtain a reconstructed signal, and extracting a characteristic fingerprint through a high-order statistical moment; constructing a topological graph according to geographic distribution to calculate information entropy, and performing dynamic clustering fusion; a scene type is identified and a location is estimated in combination with historical data. The wireless signal detection accuracy and the positioning precision can be improved, and the method adapts to a complex electromagnetic environment.

The invention discloses a Bayesian optimization-based power distribution network operation risk control method. The method comprises the following steps of S1, establishing a node-side double-space coupling dynamic graph by using multi-source operation data of a power distribution network; s2, on the basis of the dynamic graph, generating a space-time embedded vector through a self-supervision task of multilevel space-time interaction between nodes and node neighborhoods; s3, constructing a risk sensitive network with multi-scale dynamic clustering, and predicting a risk propagation trajectory; s4, forming a cross-period continuous dynamic risk evolution link by using the risk propagation trajectory, and dividing and distributing a power grid risk control sub-domain; s5, evaluating sub-domain risk dynamic evolution, and constructing a joint optimization state space; s6, constructing a distributed bidirectional information cooperation link between neighborhoods; and S7, the decision convergence condition is monitored in real time, and path switching is carried out. The real-time performance and accuracy of risk control of the power distribution network are improved, and safe and stable operation of the power distribution network is ensured.

The invention relates to the technical field of industrial Internet of Things and wireless sensor networks, in particular to a large-scale industrial fault diagnosis system and method based on Bluetooth MESH, and efficient monitoring and fault early warning of industrial equipment are realized through low-power-consumption sensor nodes, edge computing, a Bluetooth Mesh network and a clustered Mesh tree architecture. The system comprises a client, a server, a gateway, a cluster head and a cluster node, the cluster node comprises a sensor node and a relay node, a lightweight neural network model is built in the sensor node, and edge reasoning and fault detection can be realized locally. Stable communication and real-time cooperation of large-scale nodes are ensured through a decentralized architecture of Bluetooth Mesh, establishment of a dynamic cluster head chain and optimization of an RSSI threshold value. And a low-power-consumption mechanism combining event driving and fixed polling is adopted, so that the energy consumption of the system is remarkably reduced. In-cluster communication loads are reduced through a clustering architecture, and the communication efficiency and the anti-interference capability of the system are improved in combination with channel separation of Mesh and BLE protocols.

The invention discloses an unmanned aerial vehicle three-dimensional flight path planning method for wireless sensor network data collection and energy supplementation, and belongs to the technical field of Internet of Things. A network is divided into a plurality of clusters through a density peak clustering algorithm, and a dynamic cluster head selection mechanism based on a routing protocol is designed to optimize the positions of cluster heads. The shortest flight path is determined by adopting a self-organizing mapping network introducing a penetration mechanism. Through a depth deterministic strategy gradient algorithm, the flight height of the unmanned aerial vehicle is dynamically adjusted, and meanwhile, the flight route of the unmanned aerial vehicle is planned, so that the unmanned aerial vehicle can efficiently collect data and supplement energy to sensor nodes in a charging range. According to the method, the distance of the unmanned aerial vehicle for charging the sensor and collecting data is obviously optimized, the energy utilization efficiency is improved, the energy supplement and data collection efficiency of the network is improved, and the network survival time is prolonged.

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 discloses a line intelligent planning and fault early warning method suitable for electric power engineering design, and relates to the technical field of electric power engineering. The method comprises the following steps: quantifying geographic information, geological conditions and construction cost into a continuous decision curved surface through spatial semantic segmentation and a cost curved surface model, and identifying a large-area low-cost region as a primary feasible region in combination with a dynamic clustering algorithm; a multi-dimensional decision index is constructed based on spatial features, geological stability and facility association data, fault regions such as terrain abrupt change or facility conflict are identified through real-time association coefficients, and an exception handling mechanism is triggered; and selecting a seed region with an optimal construction condition as an anchor point, constructing a joint probability model of spatial features and cost, and generating an optimal path considering feasibility probability and cost effectiveness in combination with an expectation maximization algorithm and a graph optimization technology. According to the invention, scientificity and economical efficiency of cable planning under complex geological conditions are improved, and key technical support is provided for intelligentization of electric power engineering.

The invention relates to a VDE signal satellite access control method. The method comprises the following steps that a satellite end carries out dynamic clustering and communication time slot pre-distribution on ships in a satellite signal coverage area; the ship end in the dynamic cluster performs local negotiation distribution of communication time slots and periodically reports to the satellite end; and the satellite end dynamically optimizes the pre-allocated communication time slot in the next period according to the report of each region in each period. The method has the advantage of reducing the transmission conflict rate in a scene with heavy ship flow.

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 a personalized customization data mining and analysis system based on artificial intelligence, and the system comprises the following modules: an edge data collection module which is used for generating a structured multi-source heterogeneous data set; the high-order data tensor construction module is used for constructing a high-order data tensor based on the structured multi-source heterogeneous data set; the heterogeneous tensor decomposition module is used for extracting a potential feature matrix and a core tensor; the feature fusion and unified expression module is used for organizing all the fused feature vectors according to a time sequence to form a unified feature expression sequence; the dynamic clustering analysis module is used for generating a clustering evolution diagram; the clustering stability evaluation and feedback module is used for obtaining a fusion feature vector in a cluster structure mutation state; and the abnormal data labeling module outputs an abnormal data labeling result set. According to the method, the overall data availability and maintainability of the system are greatly improved.