9 results about "Bayesian risk" patented technology

The invention discloses a real-time multi-dimensional sensitivity evaluation and self-adaptive safety prevention and control method and system for natural resource geographic information. The method comprises the following steps: acquiring real-time natural resource geographic information data; obtaining a preset behavior graph model; updating a preset behavior graph model according to the real-time natural resource geographic information data so as to obtain an updated behavior graph model; acquiring an updated node risk vector according to the updated behavior graph model; obtaining a trained Bayesian risk prediction model; inputting the updated node risk vector into a trained Bayesian risk prediction model so as to obtain a real-time abnormal behavior identification result; and generating a personalized prevention and control strategy scheme according to the real-time abnormal behavior recognition result. According to the method, intelligent identification, dynamic evaluation and active protection of natural resource geographic information in a full life cycle are realized by constructing a multi-dimensional sensitivity quantitative model, a dynamic risk perception mechanism based on a graph structure and a safety prevention and control strategy capable of being updated in real time.

The invention relates to the technical field of intelligent fuel supply, in particular to an intelligent optimization method and system for fuel supply of a thermal power plant based on multi-source information fusion. The method comprises the following steps: collecting multi-source heterogeneous data and carrying out space-time alignment and normalization processing; constructing a coal as fired calorific value soft measurement model by adopting a physical constraint corrected LSTM network; generating a transportation delay predicted value and probability distribution thereof through an XGBoost regression model; calculating a comprehensive risk score in combination with a Bayesian risk network; solving a Pareto optimal solution set under the constraint of an inventory safety threshold by adopting a depth deterministic strategy gradient network, and generating a fuel scheduling instruction; and updating the as-fired coal calorific value soft measurement model on line. Through combination of the physical constraint corrected LSTM network and multi-source time sequence data fusion and regularization training, the fire coal calorific value prediction precision is remarkably improved, and the problems that a traditional method depends on off-line assay, response is lagged, and the real-time dynamic state of the combustion process is difficult to reflect are solved.

The invention provides a bidirectional LSTM photovoltaic power generation prediction method based on wavelet decomposition and double attention, and belongs to the technical field of power generation prediction. Selecting an optimal wavelet basis function by using a particle swarm optimization algorithm to carry out adaptive noise complete set empirical mode decomposition on the power sequence to obtain a multi-layer intrinsic mode function component, and carrying out adaptive denoising and power signal reconstruction according to a multi-scale permutation entropy and a Bayesian risk minimization criterion in combination with meteorological conditions; key feature variables are extracted through a maximum information coefficient, a bidirectional long-short-term memory network prediction framework is established, a feature attention mechanism and a double-path time attention structure are introduced, and when power mutation or irradiance mutation is detected, a sparse attention weight rapid reconstruction mechanism is triggered to complete prediction. The technical problem that the prediction precision is reduced when the photovoltaic power generation power changes suddenly under the cloudy weather condition is solved.

The invention discloses a cooperative spectrum prediction method based on minimum Bayesian risk, and relates to the technical field of cognitive radio and wireless communication. According to the method, each secondary user independently completes local spectrum sensing and power prediction; the fusion center performs system perception judgment by using a historical optimal fusion rule, calculates a channel state prior probability, and fits a predicted power condition probability density function of each user in two system states; then, according to a minimum Bayesian risk criterion, through alternative iteration optimization of each user judgment threshold and a system fusion rule, an optimal threshold and a fusion rule are obtained; and finally, obtaining a system-level cooperative spectrum prediction result through threshold comparison and fusion. The method does not depend on prior channel information, has good flexibility and expansibility when the number of users changes dynamically, improves prediction precision and robustness in a complex environment, and can balance main user protection and spectrum access efficiency through a cost factor.

The invention discloses an unknown risk analysis and evaluation method for a power distribution edge computing network. The analysis and evaluation method comprises the following steps: constructing a hierarchical Bayesian risk network HBRN; the hierarchical Bayesian risk network comprises a power grid topological structure, typical working conditions, working parameters, noise nodes and potential risk scenes; the triggering probability of an unknown triggering event is calculated and evaluated by combining the prior probability of the working parameters corresponding to the typical working conditions with the noise nodes; and evaluating a potential risk occurrence probability of the power distribution edge computing network and a corresponding noise node triggering probability by integrating a power grid topological structure and a noise node probability computing method, and taking the potential risk occurrence probability and the corresponding noise node triggering probability as a final risk evaluation score. The invention also discloses an analysis and evaluation system for realizing the analysis and evaluation method, and application of the method or the system in unknown risk analysis and evaluation of the power distribution edge computing network.

The application discloses a real-time multi-dimensional sensitivity evaluation and adaptive security prevention and control method and system for natural resource geographic information. The method comprises the following steps: acquiring real-time natural resource geographic information data; acquiring a preset behavior graph model; updating the preset behavior graph model according to the real-time natural resource geographic information data, thereby obtaining an updated behavior graph model; acquiring an updated node risk vector according to the updated behavior graph model; acquiring a trained Bayesian risk prediction model; inputting the updated node risk vector into the trained Bayesian risk prediction model, thereby obtaining a real-time abnormal behavior recognition result; and generating a personalized prevention and control strategy scheme according to the real-time abnormal behavior recognition result. The application realizes intelligent identification, dynamic evaluation and active protection of natural resource geographic information in the whole life cycle by constructing a multi-dimensional sensitivity quantification model, a dynamic risk perception mechanism based on a graph structure and a real-time updateable security prevention and control strategy.

The invention discloses a block cipher operation mode classification method and system based on a COMC-DBST model, and belongs to the technical field of information security. Comprising the following steps: converting an original ciphertext into a dual-scale grayscale image, and performing CLAHE enhancement on a high-resolution image to amplify texture features; a double-branch Swin Transform architecture is adopted to process double-scale images respectively, a learnable absolute position code and a layered window attention mechanism are integrated, and multi-scale feature fusion is achieved; a hard voting decision mechanism is designed based on Bayesian risk minimization, an ECB mode is recognized preferentially, and the misjudgment risk is reduced. According to the method, the defects of shallow feature modeling, insufficient structural feature utilization and limited generalization ability in password operation mode classification are overcome, block password operation mode classification can be quickly and accurately carried out, and the method is suitable for practical engineering scenes such as ciphertext analysis, network security audit and encryption configuration compliance check.

The application discloses a kind of government affair work order intelligent distribution and efficiency closed loop management method and system, belong to government affair service intelligent processing technical field, the implementation of this method includes: through multimodal fusion classification model processing work order input data, through deep learning model realizes high-precision semantic understanding and entity extraction, generates structured work order entity information;Based on reinforcement learning dynamic decision engine, according to real-time department load state and geographic weight calculation optimal distribution path;Utilize the risk early warning model of bayesian network to monitor the progress of disposal, when the probability threshold triggers when overdue, automatically supervise and deal with.This application realizes the upgrading of the precision, real-time and closed loop of the whole link of work order processing through multimodal fusion classification, reinforcement learning distribution decision and bayesian risk intervention triple technical innovation, solves the problem of insufficient semantic understanding accuracy, lack of dynamic scheduling capability and poor real-time supervision in existing government affair work order system.