23 results about "Algorithmics" patented technology

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 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 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.

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.

Provide a system. 【Solution means】 Means for collecting information from a past contract database and learning the characteristics of fraudulent contracts using a machine learning algorithm; Means for analyzing real-time information transmitted from an information processing device during the contract procedure and interpreting this information using natural language processing technology; Means for obtaining image data of personal identification materials and evaluating their authenticity using image recognition technology; Means for comparing the analyzed data with learned abnormal patterns to detect abnormalities; Means for performing a risk assessment on the detected abnormalities and calculating a risk score; Means for sending a warning to the person in charge based on the risk score and proposing additional confirmation procedures; Means for continuously improving the machine learning model upon receiving feedback; Means for analyzing personal information and identification information in real time when opening an e-commerce transaction, and collating with past patterns of unauthorized use to detect abnormalities; Means for instructing additional personal verification when an abnormality is detected; A system including the above.

This invention discloses an autonomous driving reward learning and control method based on ensemble maximum entropy deep inverse reinforcement learning, comprising the following steps: Step 1: Learning the reward function and control operation in a highway autonomous vehicle driving environment and constructing it into a Markov decision process model; Step 2: Constructing a soft Q-learning model based on value pruning to obtain expert demonstrations, and dividing the inverse reinforcement learning task into sub-tasks according to expert preferences; Step 3: Establishing a strong learner ensemble model and recovering the reward function through maximum entropy deep inverse reinforcement learning; Step 4: Achieving the fusion of reward functions of each sub-task through linear combination, thereby improving the learning accuracy of the reward function. This invention considers the problems of gradient explosion, gradient vanishing, and data overflow in soft Q-learning. It learns expert demonstrations through an improved soft Q-learning algorithm and proposes an ensemble maximum entropy deep inverse reinforcement learning algorithm based on the learned expert demonstrations, which can better achieve decision control.

The application provides a manufacturing network maintenance-detection joint optimization method based on deep reinforcement learning, and the steps are as follows: firstly, for the machine level, a machine reliability model considering the influence of feed quality and a processing quality model considering the influence of machine reliability are constructed under the condition that the dynamic production speed caused by machine failure shutdown is considered; secondly, the system evaluation of the manufacturing network state and performance is carried out based on the reliability model and the quality model; and a manufacturing network maintenance and quality detection joint optimization model is built; finally, at the system level, the economic operation of the manufacturing network is taken as the standard of strategy evaluation, and a deep deterministic policy gradient algorithm is designed to learn the optimal strategy of quality detection and maintenance under the given manufacturing network state. The application can well balance the contradiction between the economic benefits and the operation risks of the manufacturing network, and has better adaptability to dynamic and diversified manufacturing scenes.

This invention relates to the field of autonomous driving technology and provides an intelligent quadtree decomposition path planning method for complex dynamic scenarios. The method includes: modeling the quadtree decomposition depth selection problem as a Markov decision process and extracting environmental features as state input; employing an improved Q-learning algorithm to learn the optimal depth selection strategy and constructing a composite reward function that balances planning success rate, path quality, computational efficiency, and depth adaptability; based on the learned strategy, adaptively selecting the quadtree decomposition depth according to environmental features, constructing the quadtree, and performing path planning, while simultaneously combining global planning and local replanning to avoid dynamic obstacles. This invention achieves adaptive matching between quadtree decomposition depth and environmental complexity, solving the problem that a fixed depth cannot adapt to dynamic environmental changes, and significantly improving computational efficiency and robustness in dynamic scenarios while ensuring planning accuracy.

The invention discloses a contract risk retrieval system and method based on intra-group advantages and multi-target distillation, and belongs to the technical field of contract risk retrieval, and the contract risk retrieval system comprises a GRPO contract retrieval strategy optimization module, a large model distillation module and a batch contract processing module. According to the method, corresponding terms of contract signing can be automatically matched, the response speed of law and regulation revision is greatly improved, the lag problem that a traditional rule engine needs long-term reconstruction is avoided, the cross-regional compliance adaptation capability is remarkably enhanced, and the compliance empty window period is effectively eliminated; according to semantic level risk identification, large model semantic understanding and GRPO strategy optimization are cooperated, hidden risks, depending on contexts, of trial period agreed economic compensation calculation and the like can be accurately analyzed, the missed judgment problem caused by lack of semantic understanding of a traditional rule engine is solved, the high-frequency high-risk point identification accuracy is greatly improved, and the labor dispute probability is remarkably reduced.

An artificial intelligence device according to an embodiment of the present disclosure may comprise: a memory for storing an energy prediction model trained through deep learning or a machine learning algorithm; and one or more processors for receiving an energy prediction request including a prediction request time point, and in response to the receiving of the energy prediction request, acquiring, through the energy prediction model, an energy prediction result from the prediction request time point to a time point after a preset time interval.

The application discloses an energy management method and system based on big data, and belongs to the technical field of energy management. The method constructs a digital twin model of an enterprise energy system through a digital twin modeling module, and establishes a real-time mapping relationship between the physical and virtual. Through a multi-source data fusion module, multi-dimensional data such as energy consumption, business operation, environmental parameters and real-time electricity prices are integrated, a unified energy state feature vector is generated based on adaptive fusion weights. Through a reinforcement learning optimization module, a deep Q network algorithm is used to learn the optimal energy configuration strategy. Through a closed-loop feedback adjustment module, the energy configuration effect is evaluated in real time, feedback adjustment parameters are generated and transmitted to the front-end module, and the four modules form a deep-coupled closed-loop collaborative system. The application can realize real-time modeling, adaptive optimization and closed-loop feedback of the energy system.

The application discloses a target detection method based on multi-path component reconstruction residual error, comprising the following steps: step 1: based on the double auto-encoder training algorithm of environmental clutter, learning the clutter structure features in the multi-path channel and reconstructing the multi-path echo; step 2: synthesizing the reconstructed multi-path echo obtained in step 1 into a reconstructed image, based on the target pre-detection algorithm of the reconstruction residual error, comparing the difference between the original image and the reconstructed image, and used for distinguishing whether the to-be-detected region contains target echo. The application fully explores the multi-path characteristics of the clutter, and realizes the weak and small target detection under the condition of no target sample through the multi-path echo reconstruction residual error.

The application discloses a bearing intelligent diagnosis method based on a generalized domain data fusion strategy and kernel sparse representation, designs a generalized domain data fusion strategy for dictionary learning, specifically uses an improved Kalman filter fusion framework to project time domain and frequency domain signals to a generalized domain state space and realizes signal adaptive fusion, and secondly, in order to avoid the influence of time shift characteristics on a dictionary learning model, develops a kernel discriminative sub-dictionary learning method, specifically uses a Gaussian kernel function to map the fused generalized domain signals to a high-dimensional feature space, then learns a specific category kernel discriminative sub-dictionary in a data-driven manner through a kernel K-SVD algorithm, then uses the learned specific category kernel discriminative sub-dictionary to realize sparse representation of unknown bearing signals in a high-dimensional space, and finally realizes intelligent identification of the bearing health state according to a minimum reconstruction error criterion. The application enhances the sparse representation ability and discriminative feature mining ability of the dictionary model for nonlinear data.

The invention provides a dynamic occlusion target complementing and labeling method and system based on BEV time sequence fusion, and is applied to the technical field of data processing. According to the method, BEV time sequence fusion is taken as a core, multi-dimensional sensing data such as a time sequence image and a three-dimensional coordinate are collected firstly according to dynamic shielding target complementation and labeling requirements, and key parameters such as a complementation confidence threshold value are set in combination with detection precision and labeling specifications; the data quality is optimized through integrated processing, and a standardized training data set is constructed and sorted according to completion contribution degrees. A dynamic completion labeling strategy is determined based on scene complexity, hardware computing power and the like, parameters such as sliding window size and the like are adapted, and data sets are split and then imported into training in parallel. A BEV time sequence fusion algorithm is utilized to learn a mapping relation between shielding features and a target form and a motion rule, multi-modal features are dynamically weighted and fused, and accurate completion and labeling are realized by combining adaptive core parameters such as shielding types and target scales.

The invention relates to the field of cloud computing, and particularly provides a method and a device for improving the management capability of a key application in a cloud environment, firstly, collecting basic information and service information of different life cycle nodes of the application, generating an application portrait, and secondly, forming a key application with the application as a core by utilizing numerous association relationships among resources, an application portrait topological graph is generated, meanwhile, total-branch detection nodes are constructed for distributed cloud centers, all the cloud centers collect and detect applications needing service escorting, the applications are reported to a center end for data analysis, and a distributed dial test node network is constructed; and finally, the central end learns and analyzes the nodes generating influence in the application portrait topology through an algorithm, generates a first diagnosis condition of service escort, and forms a service escort report or gives an alarm in time. Compared with the prior art, the method has the advantages that the problem finding capability can be improved on the whole, the customer active fault reporting rate is reduced, the monitoring granularity is improved, the resource utilization rate is improved, and the fault response time is shortened.

The invention discloses an AI-based water purifier user behavior prediction and dynamic heating energy-saving control method, and relates to the field of water purifier control, and the method comprises the following steps: predicting the weighted water consumption of a corresponding single hour period, and further obtaining a water consumption prediction sequence of the next 24 hours; a basic temperature set point of the day is determined according to the relation between the total water consumption of the day and a statistical threshold value; and dynamically adjusting the heating mode based on the predicted water consumption and the actual water consumption. According to the method, the multi-dimensional water consumption behavior mode of the user is learned based on the AI algorithm, then the water consumption demand in the next 24 hours is predicted, a scientific basis is provided for dynamic heating, the temperature maintained by the water tank is dynamically selected according to the water consumption behavior so as to adapt to the current situation of energy waste caused by low water consumption in most scenes, energy consumption is optimized, and the energy consumption is reduced. And real intelligent and personalized energy-saving control is realized.

The present disclosure relates to a technology for generating anomalous time-series data and provides an apparatus and a method for generating anomalous time-series data, the method comprising: receiving time-series data; dividing the time-series data according to a preset criterion; calculating a latent vector including a probability density function for each of the divided time-series data; calculating a probability variable value for each probability density function for a specific probability in an integrated probability density function in which a plurality of probability density functions are integrated; and generating anomalous time-series data on the basis of a modified latent vector obtained by reflecting the probability variable value for each probability density function on the latent vector and an artificial intelligence model trained through a preset first algorithm.

The application discloses a kind of urban safety big data access and dynamic association fusion method, applied to data processing technical field, the present application collects target city multi-source multi-modal heterogeneous data, according to the risk control requirement of disaster chain, sets access standard, collection rule and precision delay threshold value such as accuracy;After data noise reduction, filling, standardization processing and intelligent extraction, the standardized control data set sorted according to emergency correlation degree is formed;Combining cloud edge end cooperation, computing power resource and digital twin modeling demand, determine dynamic association fusion strategy and configure access window and update frequency;According to strategy, data set is split into risk source, disaster body, disaster mitigation and disaster label control batch and orderly transmission;Through multi-modal feature fusion algorithm learning mapping relationship, optimization execution parameter, dynamically adapt access and coding rule and twin model update instruction, finally generate fusion control signal, provide data and algorithm support for urban disaster risk research and emergency decision-making.

The invention discloses an active multi-modal situation fusion deep cognition method and system, and belongs to the technical field of information, and the method comprises the steps: constructing a user portrait, and obtaining portrait feature tags of a user, including a natural tag, a mission tag and a behavior tag; calculating situation element preference scores of the user for different feature tags based on historical scoring behaviors of the user, and forming a user-feature matrix; constructing a situation knowledge graph, extracting situation element entities and semantic relationships thereof, and learning vectorization representation of situation elements by using a knowledge graph embedding algorithm; and performing user similarity calculation on the basis of the user-feature matrix, predicting preference scores of the users on the unscored situation elements, and generating a recommendation list. Through the method and the system, the element recommendation accuracy can be improved.

The application discloses a production process determination method and system based on a machine learning network and electronic equipment, and the method comprises production-related information collection, extraction of load power and historical process information, load power and historical process feature extraction, current production process determination, process state switching broadcast, process energy consumption analysis, and production plan management. The application collects production-related information and extracts power and historical process information therefrom, uses a machine learning algorithm to learn the logical relationship between data, finally determines the current production process, broadcasts a voice when the process switches, and finally compares the current process with a production plan and analyzes process-related energy consumption information. Compared with an artificial analysis method, the production process determination method based on machine learning has the advantages of easy implementation and high determination accuracy due to simple data preprocessing and strong data relationship learning capability.

The invention provides a power equipment operation and maintenance early warning method and system based on AI and Internet of Things big data. The method comprises the following steps: uniformly mapping power equipment operation data to a standardized feature space; an AI algorithm is used for learning boundary features of normal operation of equipment in a standardized feature space and an association rule between heterogeneous equipment, a self-adaptive adjustment mechanism of an early warning trigger threshold value is established, and a multi-dimensional condition combination is dynamically set; constructing a high-dimensional feature vector of a multi-dimensional parameter, and determining an accurate trigger boundary under different parameter combinations by using an AI algorithm; flexible matching and linkage triggering of early warning conditions among heterogeneous devices are achieved through a fuzzy inference rule base, and when a device operation boundary changes, the influence of the device operation boundary on the early warning conditions of the associated heterogeneous devices is automatically analyzed, and a multi-dimensional triggering condition combination is dynamically optimized. According to the invention, high efficiency, accuracy and intelligence of operation and maintenance of the power equipment can be realized, and the overall stability and early warning response capability of the system are improved.

The invention provides a large-scale expressway and connection road network cooperative control method based on multi-agent reinforcement learning, and belongs to the technical field of intelligent traffic signal control. According to the method, on the basis of a road network macroscopic basic diagram theory, an expressway main line and each urban sub road network are regarded as a continuous entity respectively, and a hierarchical control method is constructed; a centralized multi-agent reinforcement learning (such as TD3) algorithm is adopted for upper-layer control to learn a continuous action strategy; outputting the sub-direction boundary total flow between each sub-road network and the expressway and between the sub-road networks; and the lower-layer control distributes the total flow in different directions to a specific boundary control road section or ramp to obtain corresponding traffic flow and convert the traffic flow into green light duration. According to the method, the advantages that a reinforcement learning-based control method adapts to traffic operation remarkable random fluctuation and the online calculation time is short can be played, meanwhile, the calculation cost is greatly reduced without losing high robustness, and support can be provided for scientific research and application practice of urban large-scale mixed road network traffic control.

The application discloses a question recommendation method based on a schema in a question and answer community, and the steps include: 1, collecting data and constructing a question and answer community network; 2, using a schema mining algorithm to mine the schema in the user network, and constructing a schema network based on the schema; 3, using a network embedding learning algorithm to learn the node embedding of the network, and learning the preferences of the answerer for the questioner and the question; 4, fusing the preferences of the answerer in two aspects, using a scoring function to predict the matching degree of the answerer for a new question, arranging the answerer in descending order according to the matching score, and recommending the first N users to answer the question, so as to complete the question recommendation task. The application combines the schema mining algorithm and the embedding learning algorithm of the schema network, fully captures the historical interaction information of the users in the schema network by using the graph neural network, and fully fuses the personal feature information, network structure information and text semantic information of the users, so that more accurate recommendation effect is realized.