116 results about "Algorithmics" patented technology

The invention provides a power system operation state intelligent monitoring method and system, and relates to the technical field of power data monitoring, and the method comprises the steps: obtaining multi-source heterogeneous operation data, obtaining a standard operation data set, adaptively extracting high-order multi-modal feature representation through a confidence weighted fusion method, and obtaining an operation state multi-modal feature set. Constructing an intelligent monitoring cognitive map by learning topological association characteristics and semantic association rules of the running state; mapping the real-time operation data into an intelligent monitoring cognitive map, dynamically sensing an operation state spatio-temporal evolution rule through a spatio-temporal dependence modeling unit and a graph feature propagation unit in an operation state graph learning model, and generating a prediction and diagnosis result; and learning and generating an optimal control strategy of the power system through a depth deterministic strategy gradient algorithm, decoupling the optimal control strategy of the power system into a plurality of local sub-strategies, issuing the local sub-strategies to corresponding power control equipment, and obtaining a distributed cooperative control scheme through a federated learning algorithm.

The invention provides an unmanned aerial vehicle return detection method and system based on AI recognition, and relates to the technical field of image recognition, and the method comprises the steps: extracting multi-scale features through an improved moving window hierarchical converter model, obtaining target attitude information through the combination of cross-modal comparative learning and a dynamic routing capsule network, and obtaining target attitude information; an alignment feature representation is obtained by using a feature enhancement module and an adversarial feature alignment method, the alignment feature representation is input into a graph attention diffusion model to obtain an accurate state representation, and a state evolution trajectory is obtained in combination with an adaptive causal convolutional network and an ordinary differential equation network; posterior distribution is determined through a variational inference network and an autoregression flow model, a state sequence is predicted by using a neural process network and a dynamic point process model, an expert strategy is learned by using a hierarchical generative adversarial imitation learning network and a distributed trust domain evolutionary strategy algorithm, and optimal parameter configuration is obtained; and accurate prediction of the return state of the unmanned aerial vehicle is realized.

The invention discloses a construction method and device of a digital power system based on a multifunctional agent. Respectively extracting feature production element data and control production element data from the power data of the power plant side and the power grid side; constructing a target data vector based on the power function demand, and determining key control production element data associated with the power function demand; learning by using an artificial intelligence algorithm to obtain a mapping function between the target data vector and the key control production element data, constructing a functional operator taking the mapping function as a kernel, and constructing an intelligent agent based on the functional operator; and adding the intelligent agents corresponding to the different electric power function requirements into the digital electric power system to serve as electric power function implementation units. According to the system, diversified and digital electric power functions based on data driving can be realized, the relation between data can be captured more accurately, and high-value data assets in the electric power system are formed.

The invention relates to a DDRL-based aircraft mission offloading method in a low earth orbit satellite network. The method comprises the following steps: (1) constructing an LEO low earth orbit satellite network system into an LEO satellite mobile edge computing network system; (2) modeling an LEO low earth orbit satellite network system utility maximization problem into a joint decision problem for optimizing task unloading and resource allocation; and (3) converting a joint decision problem into a Markov decision process MDP, learning an optimal task unloading decision by using a dual depth Q network algorithm DDQN under a dual depth reinforcement learning DDRL framework, and obtaining an optimal resource allocation strategy by using a time difference triple strategy gradient algorithm TD3PG on this basis. Simulation experiments show that compared with a reference algorithm, the scheme can effectively access and cooperatively compute concurrent tasks, and has better convergence and superiority under different environment variables.

The present invention relates to the field of pipeline leakage monitoring. Disclosed is an oil and gas pipeline leakage wave identification and monitoring system. In the present invention, an mCNN is combined with LFLBs for performing feature extraction on an acoustic wave signal collected by a DFB, and the collected data improves information completeness; a three-way parallel one-dimensional CNN used in the present invention exhibits good temporal resolution and sensitivity to high-frequency feature transformations in signals; and the present invention integrates advantages of different scales, enabling the algorithm to learn more features, and incorporating the LFLBs to further extract high-level local features. An mCNN-LFLBs network model of the present invention exhibits significant innovation and advancement on the technical level, and also demonstrates extremely high value in actual application. The network model not only provides a novel and efficient technical means for critical fields such as natural gas pipeline inspection, but also introduces new ideas and methods to research fields related to deep learning and signal processing.

The invention relates to a multi-beam interference avoidance method and system based on multi-agent reinforcement learning. The method comprises the following steps: S1, establishing an initial system model diagram according to a relationship between multiple beams generated by a satellite and a ground cell; s2, analyzing interference among multiple beams; s3, generating a service arrival model according to the ground state information; s4, constructing an M / G / 1 queuing model, and inputting an output result of the model in the S3 into the queuing model; s5, multi-beam context learning is carried out; s6, converting a beam interference avoidance problem into a multi-agent depth deterministic strategy gradient algorithm learning problem, and performing conversion definition of a Markov decision process problem; and S7, performing reinforcement learning of the MADDPG algorithm according to the conversion definition of the problem and the MADDPG algorithm, and planning an optimal strategy of satellite beam scheduling according to the maximum probability action value output by the plurality of beams of the satellite. According to the method, the problem that interference exists among multiple beams in an existing low-orbit satellite multi-beam system is solved, the method is more reasonable and flexible, interference can be effectively avoided, and the overall performance of the system is improved.

The invention discloses a power transmission line engineering quality defect acceptance method based on generative adversarial and reinforcement learning, and relates to the technical field of power engineering quality detection and intelligent image recognition, and the method comprises the steps: carrying out the sample amplification of original image data of a power transmission line tower through a generative adversarial network module, and obtaining an amplified training data set; constructing a PaFPN feature extraction network according to the amplification training data set, extracting multi-scale defect feature vectors and generating a coding feature matrix; establishing a reinforcement learning agent module, taking the coding feature matrix as state input, learning an optimal defect detection strategy through a Q-learning algorithm, and outputting a defect detection parameter combination; and performing feature fusion on the coding feature matrix to generate a fusion feature vector, inputting the fusion feature vector into a classifier network, and outputting a defect category label and a confidence score of the power transmission line project quality. According to the invention, the automation level and the detection precision of power transmission line project quality defect acceptance are improved.

The invention provides an industrial graph construction method and system based on scene type marketing, and relates to the technical field of industrial graph construction, and the method comprises the steps: processing a media text through a BERT pre-training model to generate a text data set, processing scene data through a space-time calibration algorithm to generate a behavior data set, and summarizing to generate an unstructured marketing data set. Generating a multi-modal marketing scene data set in combination with the structured marketing data set; processing the multi-modal data set by adopting a stream-batch integrated framework and an entity disambiguation technology, and constructing a four-dimensional data lake; constructing a three-dimensional feature library and training a marketing scene weight model; a data lake is mapped to a graph database, an industrial chain topological structure is formed, node relation strength is adjusted based on a weight model, node implicit features are learned through a GraphSAGE algorithm, and a scene-sensitive industrial graph topological structure is generated, so that industrial entity relations in different marketing scenes can be reflected in real time, and the marketing efficiency is improved. And the accuracy of the industrial map in marketing decision making is improved.

The invention discloses a multi-modal code stream transmission bandwidth dynamic allocation optimization method, which comprises the steps of firstly performing feature extraction on to-be-transmitted multi-modal data including images and voiceprints, calculating the number of features, further considering the influence of feature similarity among the multi-modal data on transmission marginal benefits, and optimizing the transmission bandwidth dynamic allocation of the multi-modal code stream. The suitability of an analysis result and a real environment is improved; a time sequence neural network is utilized to calculate a code stream demand predicted value, a bandwidth allocation optimization model with maximization of bandwidth transmission efficiency as an optimization target is constructed according to image feature quantities and voiceprint feature quantities, the optimization model is further modeled as a decision process, a deep actor-commentator algorithm is utilized to solve the decision process, and the decision process is optimized. A playback experience pool in a decision process is constructed by utilizing prediction deviation of a time sequence neural network, so that the algorithm learns an optimal strategy of each parameter under different prediction precisions, the adaptability of the bandwidth allocation method to different monitoring scenes is enhanced, and the problem of sparse training samples is solved.

The invention belongs to the technical field of data processing, and discloses an active intelligent operation and maintenance monitoring method for a data center, which comprises the following steps: step 1, component modeling and topology configuration; 2, carrying out distributed health detection and data acquisition; 3, performing real-time health assessment and anomaly detection; 4, performing intelligent alarm and root cause analysis; 5, unified operation and maintenance and closed-loop control are carried out; and step 6, dynamically optimizing the intelligent operation and maintenance strategy. According to the method, a monitoring object and a dependency relationship are clarified through component modeling and topological configuration, and specific scenes, such as multi-mode acquisition, active detection, index pulling, log analysis, coverage message queue theme accumulation and database connection pool exhaustion, of distributed health detection are combined. The quantitative health score is calculated based on the preset scoring model, and by combining with the dynamic baseline learned by the ARIMA or LSTM algorithm, the module abnormity can be actively detected in different periods and weekly updating, and the problems of fault discovery lagging and incomplete monitoring coverage are solved.

The invention relates to the technical field of path planning, in particular to an automatic path planning method for a mechanical arm. According to the technical scheme, the method comprises the steps of environment perception and modeling, global rough planning, learning parameter initialization based on a deep reinforcement algorithm, local fine planning and learning optimization, and path execution and real-time feedback adjustment. The rapid exploration random tree algorithm is adopted for global rough planning, a basic framework is provided for subsequent local fine planning, the global optimal path can be effectively searched through the deep reinforcement learning algorithm, the path is further optimized through local fine planning, the problem that a traditional intelligent path planning algorithm is prone to falling into a local optimal solution is solved, and the path planning efficiency is improved. The multi-sensor fusion technology is used for sensing environment information in real time, the environment model and the state space are updated in time, the robustness and reliability of the system are improved, the mechanical arm can adapt to diversified working environments and task requirements, and more intelligent decision making and path planning are achieved.

The embodiment of the invention relates to a sea battlefield intelligent confrontation method based on an online learning rate optimization mechanism, and the method comprises the steps: calculating the gradient of a network model, updating a learning rate for each layer of neural network through a learning rate optimization model, and updating the parameters of each layer of network according to the updated learning rate; and updating the basic element learning rate, updating parameters of the network model and the learning rate optimization model according to the updated basic element learning rate, and deploying the trained model to a simulation environment to generate a marine battlefield multi-ship intelligent game confrontation strategy. Compared with a traditional combat behavior modeling method, the problem of large-scale agent confrontation task state and action space explosion is solved; on the other hand, the online learning rate optimization mechanism solves the problem of hyper-parameter setting in the training process of the MAML algorithm, the stability of network training is enhanced, the algorithm learning efficiency and final performance are improved, and higher robustness and practicability are achieved.

The invention discloses a novel power distribution network real-time topology tracing method and system, and the method comprises the steps: carrying out the comprehensive collection of the voltage, current and power of a key node based on a power distribution network SCADA system, and constructing a historical measurement data set containing a topological structure label; designing and training a Transform deep learning model suitable for the characteristics of the power distribution network; the feature space of the Transform model is optimized on the basis of the maximum margin principle; constructing a topological graph model of the power distribution network, carrying out weight assignment, and rapidly tracing and positioning a problem region when a fault or an abnormal condition occurs by using an improved depth-first search algorithm; and constructing a multi-scene topology traceability collaborative decision-making system, and generating a visual traceability report including a fault area topology structure, an influence range and a key equipment state. According to the method, the problems of incomplete data collection, limited learning ability of an identification algorithm, lack of an efficient local tracing mechanism and the like in novel power distribution network topology tracing are solved, and the fault processing efficiency and the operation reliability of the power distribution network are improved.

The invention provides a three-dimensional point cloud just noticeable distortion threshold prediction method and product. The specific implementation scheme is as follows: extracting three-dimensional texture features based on a reference point cloud and a corresponding distortion point cloud; performing multi-view projection processing on the reference point cloud and the distorted point cloud to obtain a plurality of two-dimensional projection images of different views; extracting two-dimensional texture features based on the two-dimensional projection images of the reference point cloud and the distorted point cloud; splicing the three-dimensional texture features and the two-dimensional texture features to obtain feature vectors, and learning based on a machine learning algorithm to obtain a mapping relationship between the feature vectors and just perceptible distortion thresholds; through the implementation of the scheme, the mapping relation between the feature vector and the just noticeable distortion threshold is established through machine learning after the point cloud three-dimensional feature and the point cloud two-dimensional feature are spliced, a high-precision three-dimensional point cloud just noticeable distortion threshold prediction model is constructed, effective indication can be provided for three-dimensional point cloud coding, and the accuracy of the three-dimensional point cloud coding is improved. And the visual quality of the coded point cloud is ensured.

The invention discloses a memory enhancement action recognition method and system on a Riemannian manifold and a storage medium, and the method comprises the steps: 1, collecting human body action data, and representing the human body action data as a third-order tensor; 2, expanding along three modes to obtain three corresponding matrixes; 3, calculating by adopting a human short-term memory mechanism to obtain a memory enhanced weight matrix; 4, decomposing the weight matrix through a principal component analysis method to obtain a base vector with a weight; 5, recombining, normalizing and mapping to a unit hyper-sphere, and reserving an angle relation; 6, learning modal weight parameters through a Monte Carlo Markov algorithm, and calculating geometric differences between points on the hypersphere; and 7, carrying out human body action classification by adopting a K-nearest neighbor classifier. The method effectively solves the problem of time information loss in a complex scene, and is suitable for various application scenes such as medical health, virtual reality, physical training and the like.

The invention relates to the field of in-vehicle infotainment testing, in particular to a stability testing method, system and device for an in-vehicle infotainment system and a medium, and the method comprises the steps: learning a first weight corresponding to each display area, support operation and a second weight corresponding to each support operation by using a Q-learning algorithm according to a priori knowledge rule set; executing a test step for a predetermined number of times, the test step comprising: determining a target area based on the first weight according to a predetermined rule; determining a target operation corresponding to a target area according to the second weight; and executing the target operation on the target area, judging the execution validity, and generating a stability test report according to an execution result. According to the characteristics of the vehicle machine system interface, the test event is intelligently generated, and the exploration probability is dynamically adjusted, so that the test coverage rate is improved, the test efficiency is also improved, and invalid and repeated tests are reduced.

The invention discloses a wafer defect detection method and a computer program product, and relates to the technical field of semiconductor measurement. The wafer defect detection method comprises the following steps: acquiring a defect image sample with a real defect label or a false positive defect label; the method comprises the following steps: firstly, respectively performing morphological structure analysis on areas to be detected in a defect image sample to determine defect morphological characteristics, performing frequency domain transformation and energy analysis to determine defect texture characteristics and performing boundary line gray gradient analysis to determine defect boundary characteristics, and then integrating the characteristics of the three types of areas to be detected into multi-dimensional characteristics; and learning a mapping relationship between the multi-dimensional features and the defect tags through a decision tree generation algorithm in combination with the defect tags so as to construct a target decision tree model. By means of the mode that the multi-dimensional features cooperatively describe the physical and structural essence of the defects and the decision tree model is combined for classified learning, the real defects and the false positive defects can be effectively distinguished, and the accuracy of wafer defect detection is improved.

Systems and methods are provided for learning normal behavior for user roles of an application running within a cluster of container orchestration platform and based thereon proactively taking action responsive to suspicious events. According to one embodiment, an event data stream is created by an API server of the cluster. The data for each event includes information regarding a request made to an API exposed by the API server with which the event is associated and a user of the application by which the event was initiated. The data is augmented with a role associated with the user and an anomaly threshold for the role. Normal behavior is learned by an ML algorithm of respective user roles by processing the augmented data. When an anomaly score associated with a particular event is output by the ML algorithm that exceeds the anomaly threshold, a predefined or configurable action may be triggered.

The invention discloses an e-commerce platform private domain content intelligent generation and distribution method based on data analysis, and the method comprises the following steps: collecting behavior data, including browsing, clicking, collecting, purchasing and interactive recording, of a user in a private domain scene, and combining the behavior frequency and time information to generate a user behavior representation vector; a user multi-interest representation structure is constructed by using an improved Rocchio algorithm and a clustering mechanism, a heterogeneous graph model is further constructed by combining users, contents and behaviors, random walk is performed based on a graph structure, and node representation is learned by using a DeepWalk algorithm. And carrying out weighted fusion on the user interest vector and the graph embedding result to generate joint user representation, carrying out matching scoring and sorting on candidate contents, and finally realizing accurate pushing and dynamic updating of personalized contents.

The present application discloses a method and device for constructing a digital power system based on multifunctional intelligent agents. The method comprises: respectively extracting feature production element data and control production element data from power data of a power plant side and power data of a power grid side; constructing a target data vector on the basis of a power function requirement, and determining key control production element data associated with the power function requirement; using an artificial intelligence algorithm to learn a mapping function between the target data vector and the key control production element data, constructing a functional operator using the mapping function as a core, and constructing an intelligent agent on the basis of the functional operator; and adding intelligent agents corresponding to a plurality of different power function requirements into a digital power system as power function implementation units. The system can realize data-driven diversified and digital power functions, and implement more accurate capturing of the relationship between data, thereby forming high-value data assets in the power system.

The application discloses a soft measurement method for converter steelmaking endpoint carbon temperature prediction based on adaptive instant learning, adopts a WGK measurement criterion with introduced label information to construct a WGKSOM clustering algorithm for guiding a clustering direction, improves clustering quality of the algorithm and reduces influence of data fluctuation of a class cluster on modeling; secondly, a membership of a to-be-measured sample is calculated by using a Gaussian posterior probability and a DRCA measurement strategy is constructed by introducing a dynamic factor, so that adaptive selection of the sample to form an algorithm learning set is realized and is used for local model training, and finally online prediction of the converter steelmaking endpoint carbon temperature is completed. Compared with other instant learning soft measurement methods, the application is more accurate in prediction of the converter steelmaking endpoint carbon temperature.

The present invention proposes an AI-driven intelligent modeling method for self-distributing 3D printing parameters of complex geological structures, including the following steps: acquiring multi-source characteristic data of complex geological bodies and performing preprocessing; extracting geological geometric parameters and mapping them into a 3D printing voxel model, and converting them into geometric parameters that can be used for 3D printing; using intelligent algorithms to learn the non-linear mapping relationship between the physical parameters and material parameters of historical complex geological bodies, constructing and training an AI model to predict material distribution parameters; converting the geometric parameters and material distribution parameters into a three-dimensional model of complex geological structures and verifying; exporting parameterized full-model slice data; verifying the performance of the 3D printed complex geological structure model. Compared with the prior art, the present invention realizes the intelligent conversion from data to model, can accurately describe the complex spatial heterogeneity in geological disaster models, improves the adaptability and accuracy of the models, and provides a basis for subsequent prediction and prevention of complex geological body disasters.

The present invention discloses an adaptive dialogue recommendation method and system for new products and new users, the method comprising: constructing a user interaction graph based on users, products, product attributes, and dialogues; using an inductive graph algorithm to learn and obtain representations of all nodes in the current graph based on the user interaction graph; obtaining representations of states and actions based on the representations of the nodes; introducing state transfer and rewards to form a Markov decision process based on the state and action representations, and learning the action value function through a deep Q network algorithm; and querying attributes or recommending products in each round of dialogue recommendation based on the modeled action value function. The present invention dynamically obtains representations of newly added products and users through neighbor information, and simultaneously models dialogue strategies and recommendation strategies by introducing a graph model, so that they can be integrated with each other for better interaction.

1. The name of the design product: artificial intelligence comprehensive experiment box. 2. The use of the design product: for teaching simulation test algorithm learning and practice, visual detection, etc. Teaching product. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view.

The application discloses an intelligent pushing method and system based on intention recognition, relates to the technical field of data processing, and implements the following contents: S10, language information, text information and historical information of a user are collected, and the collected information is mapped to a high-dimensional space; S20, a LightGBM algorithm is used to learn high-dimensional features of the information, and then according to feature importance, unimportant features are removed, and the data dimension is reduced; S30, based on a BiGRU-Attention model, output data of step S20 is processed, and key information reflecting a user intention is output; S40, a demand scheme similar to the key information is found from a database, and the demand scheme with the highest similarity is intelligently pushed to the user. The application can effectively improve pushing efficiency and pushing accuracy.

The present application relates to the technical field of data processing, and especially relates to a box-type transformer substation intelligent integrated design method based on cloud data, which comprises: integrated storage of historical system graph data, component data, cabinet type data and heat dissipation scheme data; through a machine learning algorithm, the correlation between each data after classification and arrangement is learned, and the machine learning algorithm is applied to a prediction model; the prediction model is trained, and according to new input system graph data, corresponding component data, cabinet type data and heat dissipation scheme are output; in the data transmission process, encryption processing is carried out, and the encryption processing adopts a transmission layer security protocol. The present application simplifies the design process, improves the accuracy and speed of the design, optimizes the matching of each component and the heat dissipation scheme, solves the parameter complex correlation problem through systematic data analysis, and can ensure the safety of data in the transmission and storage process, so as to comprehensively improve the design efficiency and quality of the box-type transformer substation.

The invention relates to the technical field of data processing, in particular to a box-type substation intelligent integrated design method based on cloud data, and the method comprises the steps: carrying out the integrated storage of historical system diagram data, component data, cabinet type data and heat dissipation scheme data; through a machine learning algorithm, the relevance between the classified and sorted data is learned, and the machine learning algorithm is applied to the prediction model; training the prediction model, and outputting corresponding component data, cabinet type data and a heat dissipation scheme according to newly input system diagram data; in the data transmission process, encryption processing is carried out, and a transport layer security protocol is adopted for encryption processing. According to the method, the design process is simplified, the design accuracy and speed are improved, the matching and heat dissipation scheme of each component is optimized, the problem of parameter complex relevance is solved through systematic data analysis, meanwhile, the safety of data in the transmission and storage process can be ensured, and the reliability of the system is improved. Therefore, the design efficiency and quality of the box-type substation are comprehensively improved.

An example method for controlling an electronic device includes detecting at least one user and acquiring user information of the detected at least one user; determining a user mode based on the acquired user information; determining a service to be provided to the detected at least one user, by inputting the user information and the determined user mode as input data to a model learned by an artificial intelligence algorithm; and providing the determined service corresponding to the user mode. A method for providing the service by the electronic device may at least partially use an artificial intelligence model learned according to at least one of machine learning, neural network, and deep learning algorithms.

The invention discloses a navigation method, device and equipment based on a GRPO algorithm and a medium, and relates to the technical field of reinforcement learning, and the method comprises the steps: calculating the average similarity between a current strategy and a plurality of previous iteration strategies based on KL divergence; updating a step length factor through an average reward change rate and an average similarity determined based on a plurality of iterated rewards; determining a gradient estimation correction item based on the gradient estimation of the sampling trajectory, determining target gradient estimation according to the gradient estimation correction item and the original gradient estimation, and updating the current strategy through the target gradient estimation and the updated step length factor; when the current strategy is updated, the importance weight is cut, the target function of the GRPO algorithm is corrected according to the cut weight, the GRPO algorithm is trained based on the corrected function and the updated strategy, so that the intelligent agent learns the optimal strategy based on the trained GRPO algorithm, and the outlet of the labyrinth is determined according to the optimal strategy. Therefore, the stability of the algorithm is improved.

This invention discloses a device type identification method, terminal device, and storage medium based on traffic sampling. It eliminates the need to collect complete device traffic data; device identification is achieved simply by sampling traffic at the gateway. To address the issue of missing traffic features, traditional tensor imputation algorithms learn the embeddings corresponding to tensor rows, columns, and depths, but cannot generalize to unknown rows, columns, or depths. This results in repeated retraining, time-consuming and costly feature imputation. This invention proposes an inductive tensor imputation method that utilizes historical information to learn and generate embedding functions, enabling fast and effective device traffic feature imputation.