87 results about "Medoid" patented technology

The invention discloses an obstacle avoidance method and device for unmanned aerial vehicle power inspection autonomous flight based on a laser radar and a storage medium, and relates to the technical field of unmanned aerial vehicle obstacle avoidance, and the method comprises the steps: configuring the laser radar and a visual sensor on an unmanned aerial vehicle, and collecting the three-dimensional point cloud data and image data of a flight environment in real time; optimizing the point cloud data to generate a three-dimensional environment model; and based on the three-dimensional environment model, classifying the obstacles into static obstacles and dynamic obstacles, if the obstacles are static obstacles, identifying the types of the obstacles through a CNN model and performing positioning, and if the obstacles are dynamic obstacles, clustering motion tracks through a K-Medoids clustering method. According to the invention, static obstacles can be identified and positioned, and motion trail prediction can be carried out on dynamic obstacles such as flying birds through a dynamic obstacle prediction technology, so that a preliminary flight path is generated, surrounding obstacles are sensed and avoided in real time in the flight process, and it is ensured that the unmanned aerial vehicle can be smoothly executed in an electric power inspection task.

The invention discloses an intelligent news broadcasting method and system based on artificial intelligence, and the method comprises the following steps: S1, obtaining data through a news crawling tool, and generating structured event data; s2, generating an event knowledge graph by using an improved BERT model, and performing clustering analysis by using an improved K-Medoids clustering algorithm; s3, generating a dynamic broadcast script through the multi-modal generative adversarial network; s4, analyzing the emotional state of the user, and generating personalized voice broadcast content; s5, generating an enhanced news abstract in combination with an event-driven mechanism and a time sensitivity optimization rule; s6, distributing dynamic weights by using a hierarchical attention mechanism, and dynamically adjusting a news broadcasting sequence in combination with a time sensitivity optimization rule; s7, performing logic consistency detection on the news broadcast content; and S8, adjusting the broadcast content and generating updated news broadcast content in real time. According to the method, personalized news broadcasting is realized by combining an improved algorithm, sentiment analysis, a dynamic broadcasting technology and the like.

The invention discloses an electric vehicle charging load prediction method and system, and the method comprises the steps: collecting and obtaining the historical charging load time series data of an electric vehicle, and employing an ICEEMDAN model to carry out the decomposition of the charging load time series data, and obtaining a plurality of components; performing sample entropy calculation on the components, and performing signal classification by adopting K-medoids clustering according to a sample entropy result to obtain a high-frequency component F1, an intermediate-frequency component F2 and a low-frequency component F3; carrying out secondary decomposition on the high-frequency component F1 by adopting a characteristic mode decomposition (FMD) model to obtain a plurality of components; the MTS-Mixers model, the Crossform model and the DeepESN model are optimized by using an improved artemisinin AO optimization algorithm, and a high-frequency component F1, an intermediate-frequency component F2 and a low-frequency component F3 are predicted; the prediction results of the AO-MTSMixers model, the AO-Crossform model and the AO-DeepESN model are reconstructed, and the prediction result of the charging load is obtained; compared with a traditional method, the charging load prediction accuracy can be improved, and powerful support is provided for charging use and management of the electric vehicle.

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 provides a distribution network power failure risk assessment method considering emergency resource dynamic scheduling in strong wind and rain weather. The method comprises the following steps: in extreme weather of strong wind and rain, firstly, predicting tower and lead element fault probabilities of a distribution line according to forecast data of wind speed and rainfall intensity, and combining to obtain the fault probability of the whole line; generating an original fault scene set of the power distribution network by adopting Latin hypercube sampling, clustering original scenes by adopting a k-medoids method, and constructing a representative fault scene set by using a final clustering center to realize scene screening; further, aiming at each scene, constructing a power distribution network recovery optimization model considering mobile energy storage and maintenance resource dynamic collaborative scheduling, solving to obtain an optimal recovery decision, and calculating a power failure risk index of each node; and after all scenes are traversed, performing statistics to obtain a final power failure risk index of the power distribution network. According to the method, the technical problem that the real risk level of the power distribution network is difficult to accurately and objectively reflect in the prior art is solved.

The invention discloses a zero sample object instance segmentation system and method in a robot environment, and belongs to the technical field of robot visual perception, and the method comprises the steps: firstly carrying out the Vibriis color mapping preprocessing of a depth image, and then generating an initial object mask candidate through an SAM (Segment Anything Model) model; meanwhile, a pre-trained Vision Transform is used as a feature description model of a scene to process the image, an attention map of the last layer is extracted, and a feature weighting mechanism is constructed based on the information entropy of the attention map; a similarity matrix of the image blocks and the background blocks is calculated to remove non-object masks, a K-Medoids clustering algorithm is adopted for each remaining object mask candidate to obtain representative sampling points, and finally the points serve as prompt information to be input into an SAM model to obtain an accurate object instance segmentation result. According to the method, the zero sample generalization ability of the visual basic model is fully utilized, accurate segmentation of the unseen object can be realized without additional training, and the method has good universality and practical value.

The invention belongs to the technical field of intrusion detection, and discloses an industrial Internet of Things intrusion detection method based on time sequence clustering. An intrusion detection model based on TCN-GRU-Temporal Attention is provided, and the model combines the advantages of long and short term memory processing, dynamic feature capture and key information focusing, and is especially suitable for processing complex time sequence tasks. A multi-dimensional time sequence clustering algorithm based on an evaluation index is provided, k-medoids clustering edge nodes based on DTW are used, and the intrusion detection accuracy and the system response speed in a federated learning environment are improved. The industrial network intrusion detection method provided by the invention also shows good performance under non-independent identically distributed data, and effectively improves the recognition capability for complex attack modes.

The invention discloses a method for predicting the charging load of an electric vehicle, and the method comprises the steps: collecting the historical charging load data of the electric vehicle, and carrying out the one-time decomposition of the time series data of the charging load through employing an ICEEMDAN model, and obtaining 10 components; performing sample entropy calculation on components obtained by primary decomposition, performing signal classification by adopting K-medoids clustering according to a sample entropy result to obtain a high-frequency component, an intermediate-frequency component and a low-frequency component respectively, optimizing parameters in an MTS-Mixers model, a Crossform model and a DeepESN model by using an improved AO optimization algorithm, and performing signal classification by adopting K-medoids clustering to obtain a high-frequency component, an intermediate-frequency component and a low-frequency component; and predicting the high-frequency component, the intermediate-frequency component and the low-frequency component by using an MTS-Mixers model, a Crossform model and a DeepESN model, and finally reconstructing prediction results of the three models to obtain a prediction result of the charging load.

Embodiments include techniques to determine a set of credit risk assessment data samples, generate local credit risk assessment attributions for the set of credit risk assessment samples, and normalize each local credit risk assessment attribution of the local credit risk assessment attributions. Further, embodiments techniques to compare each pair of normalized local credit risk assessment attributions and assign a rank distance thereto proportional to a degree of ranking differences between the pair of normalized local credit risk assessment attributions. The techniques also include applying a K-medoids clustering algorithm to generate clusters of the local risk assessment attributions, generating global attributions, and determining insights for the neural network based on the global attributions.

The invention relates to the technical field of photovoltaic power generation system monitoring, in particular to a photovoltaic power generation power medium and short term prediction method and system. Preprocessing the photovoltaic power generation power data, and performing feature selection; carrying out similarity evaluation by adopting a DTW algorithm, and carrying out modeling on a relationship between weather conditions and generated power; similar day clustering is carried out through K-Medoids in combination with a DTW algorithm; and training the prediction model and evaluating the model effect. According to the method, firstly, the correlation degree of meteorological factors and photovoltaic power generation power is analyzed through K-Medoids-DTW, and similar day clustering analysis is carried out according to weather conditions; a TimeXer model is used for performing eco-eco variable and eco-eco variable joint time sequence prediction modeling, and accurate prediction of photovoltaic power generation power under three weather conditions of sunny days, cloudy days and cloudy and rainy days is realized. Medium-short term photovoltaic power generation power prediction can be carried out under three common weather conditions, and the accuracy and efficiency of online monitoring and energy management of a photovoltaic system are improved.

The invention discloses a transformer fault intelligent diagnosis system and method based on a K-Medoid and SMOTE optimization support vector machine, and the system comprises a data collection module which is used for collecting dissolved gas and operation parameters in transformer oil in real time; the data preprocessing module is connected with the data acquisition module and is used for completing data standardization and abnormal value detection; the sample balancing module is connected with the data preprocessing module and used for reducing majority class redundancy through K-Medoid clustering and expanding minority classes through an SMOTE algorithm; the intelligent classification module is connected with the sample balance module and adopts a weighted SVM classifier to train and predict; and the result output and visualization module is connected with the intelligent classification module and is used for displaying the classification result, the confidence coefficient and the key performance indexes. According to the method, the problem of classification performance degradation caused by sample imbalance in the prior art is solved, and the recognition capability of the SVM model on minority class faults and the overall diagnosis precision are improved.

The invention discloses a rural energy system multi-energy load scene construction method, and relates to the field of comprehensive energy, and the method comprises the steps: obtaining a historical multi-energy load time sequence of a rural energy system; according to the historical multi-energy load time sequence, a scene generation model is adopted to generate a multi-energy load scene set in a future time period; the scene generation model is constructed based on a Transform model and a regularization relative loss generative adversarial network, and a cosine annealing algorithm and a hot restart mechanism are adopted to dynamically adjust the learning rate when the scene generation model is trained; and reducing scenes in the multi-energy load scene set by adopting a dynamic time warping distance method and a K-Medoids clustering method to obtain a typical multi-energy load representative scene set. According to the method, on the basis that the generated scene keeps a certain diversity, the historical data evolution law and the coupling characteristic between the source and the load are restored to the maximum extent, and extraction and reduction of the representative scene are achieved.

The invention discloses a flood classification method and system based on dynamic time warping and multi-cluster coupling. The method comprises the following steps: firstly, collecting and processing flood flow time sequence data to obtain a data set composed of standardized floods; secondly, calculating the similarity between every two of the K floods by adopting a dynamic time warping method to form a similar matrix; and flood classification is carried out based on DTW coupling hierarchical clustering and K-medoids multi-clustering. The system comprises a data acquisition module, a data preprocessing module, a comparison module and a classification output module. According to the method, hierarchical clustering is optimized again, and a multi-clustering method is combined, so that the reasonability of flood classification is improved, and the accuracy of flood classification is improved. The method can be applied to the fields of flood early warning, flood control dispatching and the like, and has important practical value and social significance.

The invention discloses a cold-wave weather photovoltaic power generation prediction method under semi-supervised learning, and the method comprises the following steps: (1), obtaining the historical power generation data and the same-period meteorological data of a photovoltaic power station, and defining a cold-wave discrimination standard based on the sudden temperature drop characteristics; (2) detecting and removing abnormal values by adopting Grubbs inspection, complementing missing data through a segmented cubic Hermite interpolation method, and constructing a cold wave small sample data set; (3) performing data expansion on the cold wave small samples by using an SMOTE algorithm, screening high-confidence samples in combination with K-medoids clustering, and training and initializing a CNN-LSTM model by using labeled data; (4) optimizing CNN-LSTM model parameters by using a whale optimization algorithm WOA, and finally outputting a photovoltaic output prediction result and performing error verification; the method is of great significance to technical research in the fields of new energy power prediction, power grid dispatching and the like.

The invention discloses a vehicle route optimization method based on K-Medoids clustering and an improved genetic algorithm, and the method comprises the steps: obtaining input data which comprises a distribution center position, a customer demand, a road distance, a traffic flow, vehicle information and a time window constraint; dividing the customer points into K clusters by adopting a K-Medoids clustering algorithm according to the distribution center position, the customer demand and the road section distance; numbering each customer point in the K clusters, and generating K initial chromosome populations in combination with a time sequence considering road congestion; in each chromosome population, a new fitness function is adopted to calculate the fitness value of each chromosome, and genetic iteration operation is executed; and judging whether a termination condition is met, if the termination condition is met, outputting an optimal solution, otherwise, continuing iteration. According to the method, K-Medoids clustering and the improved genetic algorithm are combined, and the technical problems that when a traditional genetic algorithm is used for solving the VRP problem, the convergence speed of the solving process is low, and the problem is prone to falling into a local optimal solution are solved.

The invention discloses a new energy distribution and storage power station day-ahead bidding method and system considering wind and light uncertainty, and belongs to the field of power market design, and the method comprises the steps: generating a scene considering wind and light output uncertainty based on the historical data information of a new energy distribution and storage power station; after wind power output scenes are reduced by using a K-medoids clustering algorithm, a day-ahead bidding strategy is formulated with the purpose of maximizing the total power generation income of each scene, and a day-ahead bidding plan is formulated and dynamically adjusted according to the real-time condition of the new energy distribution and storage power station. According to the method, a more accurate day-ahead market bidding strategy is formulated, the problem of uncertainty of wind and light output is solved, maximum utilization of natural resources such as wind and light is realized, and sustainable development of new energy is promoted.

The invention relates to the technical field of electric digital data processing, in particular to an ophthalmology department clinical nursing data preprocessing method and system. The method comprises the following steps: realizing stage clustering of pathological images through a K-Medoids algorithm based on predefined typical images; carrying out deformable image registration on the images in the same stage, and extracting key focus features through group difference analysis and structural pattern recognition; generating a weighted mask by using the lesion features, and realizing local enhancement processing of the original image; further extracting non-image modal data corresponding to the image and carrying out structured coding; and finally, constructing a time axis taking the image acquisition time as a reference, and aligning and fusing the image modality and the non-image modality to generate multi-modality comprehensive information. The system supports co-processing and feature enhancement of multi-source data, provides more accurate input for subsequent intelligent analysis and nursing intervention, and improves clinical aid decision-making efficiency and reliability.

A wind storage joint optimization regulation and control method considering conditional value-at-risk comprises the following steps: acquiring prediction data of wind power plant output and day-ahead electric energy market clearing price in each transaction period, and generating a large number of uncertain scenes through a Monte Carlo simulation method; then scene reduction is carried out through a K-medoids clustering algorithm to generate a reasonable number of typical day-ahead spot electricity price and new energy output scenes, the two typical scenes are permutated and combined to obtain final scene data, and the final scene data is input into a pre-constructed wind storage joint participation day-ahead electricity market output regulation and control optimization model; taking the day-ahead expected net income maximization as the target, introducing CVaR to measure the income risk, setting a corresponding target function and constraint conditions, and solving to obtain an output regulation and control plan and a bidding decision of the wind power plant and the novel energy storage in each simulation transaction scene. According to the method, the complementary advantages of novel energy storage and new energy are utilized, the new energy output fluctuation and deviation punishment risks caused by prediction deviation are reduced, the market competitiveness of clean energy can be improved, and new energy consumption is promoted.

The invention discloses an extreme weather power grid fault scene generation method and system. The method comprises the following steps: collecting fault ledger data, meteorological big event records and meteorological observation data of power grid operation and maintenance management; data preprocessing and data analysis are carried out; based on the constructed extreme weather fault scene data sample, constructing a WGAN-GP network model, and training the network model to generate fault scene sample data; evaluating the generated scene data, determining training parameters according to an evaluation index and distribution analysis of the generated data and the original data, and obtaining optimal generated scene data; and cutting down the generated scenes, cutting down a large number of generated scenes through a K-medoids method, and screening out high-frequency scenes. The method has the advantages that the quality of the generated data is improved, the method is closer to the distribution of real data, the generated samples can be better generalized, the human deviation is reduced, and the transparency of the data generation capability of the model is enhanced.

The present invention discloses a method and device for generating a fake location based on improved K-Medoids clustering, the method comprising seven steps: preprocessing historical query data to generate time series data; dividing time using an improved K-Medoids clustering algorithm; calculating query probability; selecting fake locations with similar query probabilities in corresponding time periods to construct an anonymous set, and initiating location service queries; and the service provider returning query results. The present invention mines out the regularity of location service requests in different time periods from historical information, realizes the reasonable division of time periods, realizes k-anonymity by dynamically selecting fake locations with similar query probabilities in corresponding time periods, effectively resists malicious attackers who have the ability to mine historical data, and improves the security of user location privacy.

The invention relates to the technical field of big data management and database optimization, in particular to an index tuning method and system for medical multi-modal data mixed query, and the method comprises the following steps: accessing structured and unstructured medical data into a data lake; performing expansion processing on a nested structure in the structured data according to field types, extracting semantic feature vectors from unstructured data, packaging the unstructured data into a standardized JSON format, performing data fusion, and loading the fused data to a PostgreSQL database; analyzing a mixed query SQL statement submitted by a user, and constructing high-dimensional mixed feature representation; carrying out clustering compression on the query request set by adopting a K-Medoids clustering algorithm to generate a representative query set; performing virtual index simulation evaluation on the structure field by utilizing HypoPG, creating a real index for the vector field in combination with a pgvector plug-in, and collecting a query performance index; and based on IBST multiplexing historical evaluation data, in combination with a structure change prediction model based on a Transform architecture, outputting an optimal index combination. According to the method, the query performance and the index recommendation efficiency are remarkably improved.

The invention discloses a periodic power consumption mode optimization method based on an encryption technology and a K-Medoids algorithm, and relates to the technical field of intelligent power grids, and the method comprises the steps: obtaining the historical load data of periodic power consumption of a consumer and a cost saving rate expected value; selecting and updating each evaluation factor based on the historical load data of different periods, and iteratively distributing the historical load data of each period of non-evaluation factors to the evaluation factor class with the highest similarity level, so as to determine each optimal evaluation factor and obtain the corresponding power consumption mode of each optimal period; and obtaining each power consumption cost saving rate of each optimal period power consumption mode compared with the recent period power consumption, determining the power consumption cost saving rate closest to the cost saving rate expected value, and determining the corresponding optimal period power consumption mode as the expected periodic power consumption mode. According to the invention, it is ensured that the power consumption cost saving rate reaches the expectation, the periodic power consumption mode with the minimum total power consumption cost is found, and the encryption technology is used to guarantee the communication security.

The invention relates to an ocean data extraction method of k-medoids based on neighborhood variance optimization. The method comprises the following steps: calculating a plurality of first neighborhood data points of a first data point in a target ocean environment data set; determining a variance between the first data point and a plurality of first neighborhood data points; selecting K first data points with variances smaller than a second threshold as K initial clustering center points; clustering the target marine environment data set according to the K initial clustering center points; constructing a retrieval database according to a clustering result; and extracting target data points according to the retrieval database. According to the method, multiple first neighborhood data points corresponding to each data point are found for each data point, the variance of each data point in the neighborhood of the data point is calculated based on the multiple first neighborhood data points, the compactness of data distribution in the neighborhood is quantized through the variance, and part of data points with relatively small variance are selected as initial clustering center points; the method can select an initial clustering center point with a scientific basis, and improves the extraction efficiency and accuracy of the marine environment data.

The invention relates to a hydrogen energy dynamic scheduling method and system considering source load uncertainty in an industrial park, and belongs to the technical field of energy scheduling, and the method comprises the steps: obtaining wind-solar power generation and hydrogen load historical data, and constructing a data set; obtaining an intrinsic mode function component through variational mode decomposition; constructing a modal feature matrix based on the components, and performing K-medoids clustering by adopting a dynamic time warping distance to generate a typical scene and probability; establishing a day-ahead scheduling optimization model, and making a hydrogen production and energy storage plan by taking total cost minimization as a target; and in actual operation, a real-time scheduling instruction is generated by minimizing the plan deviation and the equipment variation through the intra-day rolling optimization model. Through multi-scale optimization and scene generation, the scheduling precision, economical efficiency and real-time adaptability are improved, and the method is suitable for an electric-hydrogen coupling system in an industrial park.

The application discloses a differential privacy data desensitization method based on time sequence random mapping and related devices, comprising: obtaining various target time sequence data that needs to be desensitized in a power transaction center, and clustering the target time sequence data by using a k-medoids clustering algorithm based on dynamic time warping distance measurement to obtain time sequence data sets of different clusters; based on the maximum compression ratio of the target time sequence data, the window value size corresponding to different target time sequence data in the same cluster is calculated, and the average compression ratio of all target time sequence data is determined, and the average compression ratio is taken as the target window size set during desensitization; based on the pre-determined security protection requirement level of the target time sequence data, the privacy budget required by differential privacy is determined, and the target time sequence data is processed according to the target window value size and the differential privacy desensitization mode of time sequence random mapping to obtain power market subject data after time sequence desensitization.

The invention discloses a cross-border ecological economy zoning method. The method comprises the following steps: S1, constructing a cross-border ecological economy zoning index system; s2, a PartitioningAround Medoid clustering algorithm, namely a PAM clustering algorithm, is divided around the central point, and the PartitioningAround Medoid clustering algorithm is divided around the central point; and S3, selecting the optimal clustering number. According to the method, multidisciplinary theories and methods of geography, resource science, ecology, environmental science, economics, sociology and the like are crossly fused, a national boundary system is broken through, an ecological economy division scheme of a cross-administrative region boundary based on a grid scale is completed, and a certain method and theoretical reference is provided for cross-border region ecological economy division and collaborative development mode research.

The invention discloses a method and system for intelligent news broadcasting based on artificial intelligence, comprising the following steps: S1, using a news crawling tool to obtain data and generate structured event data; S2, using an improved BERT model to generate an event knowledge graph, and using an improved K-Medoids clustering algorithm for cluster analysis; S3, generating a dynamic broadcast script through a multimodal generative adversarial network; S4, analyzing the user's emotional state, and generating personalized voice broadcast content; S5, combining an event-driven mechanism and time-sensitivity optimization rules to generate an enhanced news summary; S6, using a hierarchical attention mechanism to allocate dynamic weights, and combining time-sensitivity optimization rules to dynamically adjust the order of news broadcasting; S7, performing logical consistency detection on news broadcast content; S8, adjusting the broadcast content and generating updated news broadcast content in real time. The present invention combines improved algorithms, sentiment analysis, and dynamic broadcasting technology to achieve personalized news broadcasting.

The application discloses a transformer winding looseness fault diagnosis method based on an improved MPE and K-medoids algorithm, which is used for forming a transformer winding looseness MPE value criterion and realizing winding looseness fault diagnosis. The method steps are as follows: 1, measuring points are arranged on a transformer box body, vibration signals of each measuring point are acquired, and a vibration amplitude maximum measuring point D is selected as an optimal measuring point; 2, vibration signals of the transformer winding in different states are measured; 3, a particle swarm optimization algorithm is used to optimize parameters in a traditional MPE algorithm, so that the overall change of the MPE value is stable; 4, the MPE value of the measuring point D is calculated by using the optimized MPE algorithm; 5, the MPE values under two adjacent scale factors are selected as horizontal and vertical coordinates of a clustering coordinate system; 6, the K-medoids algorithm is used in the coordinate system to realize accurate classification of the transformer winding fault type; and 7, the MPE value criterion is summarized and formed, and a database is established. The method reduces the cross aliasing phenomenon of the traditional MPE algorithm, and realizes accurate judgment of the transformer fault type.

The application discloses a kind of based on trust value improvement K-medoids's blockchain trusted sensor anomaly detection method, comprising the following steps: S1, trust value initialization, and the trust value of the BTS node participating in this consensus is updated by node trust value update formula;S2, n BTS node is clustered into k groups, and the optimal BTS node is determined as center point;S3, partition for the rest BTS node;S4, when there is client to provide new transaction request, BTS layer according to partition voting result, the same partition vote different from other BTS node is regarded as abnormal target set A, the node set of credit value below 60 points is recorded as B, the node in the intersection of A intersection B is judged as abnormal node, the beneficial effects of the application are: improved K-medoids clustering algorithm, more in line with the demand of BTS practical application, can more accurately and efficiently exclude BTS abnormal node;While clustering result has strong stability, abnormal BTS node under different environments can maintain higher detection rate.

The invention belongs to the field of Internet of Things technology and reinforcement learning, and discloses a method for optimizing information age of data acquisition under a wireless rechargeable sensor network, which comprises the following steps of: firstly, determining initial cluster division of hovering points and nodes of an unmanned aerial vehicle according to geographic positions, electric quantity states and data priorities of sensor nodes by adopting a weighted K-Medoids clustering algorithm; and then, a dual depth Q network (DDQN) algorithm in deep reinforcement learning is used for performing unmanned aerial vehicle path planning and dynamic adjustment of a hovering strategy, minimization of intra-cluster average information age (AoI) and overall network average AoI is taken as an optimization target, and node weights in a clustering algorithm are dynamically adjusted through a feedback mechanism of the DDQN algorithm, so that continuous optimization of the AoI is realized. The data acquisition delay is effectively reduced, the information acquisition real-time performance and the network overall performance are improved, and the method has the advantages of being efficient, flexible, high in adaptability and high in expandability.