7 results about "Algorithmics" patented technology

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.

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 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 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 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 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.