49 results about "Certainty factor" patented technology

The invention belongs to the technical field of multi-energy micro-grid cooperative regulation and control. The invention discloses a multi-energy micro-grid coordinated regulation and control system based on cross-layer knowledge injection and federated distillation. The system is characterized by comprising a cross-layer knowledge injection network; a federal knowledge distillation module; and the uncertainty map regulation and control module is used for constructing an MEMG uncertainty association map, learning the influence weight of each uncertainty factor on a regulation and control decision through a map attention mechanism, and realizing a dynamic risk avoidance strategy. The invention discloses a multi-energy micro-grid cooperative regulation and control method based on cross-layer knowledge injection and federated distillation. The method is characterized by comprising the following steps: step 1, MEMG topology knowledge coding; 2, cross-layer knowledge injection training is carried out; step 3, federal knowledge distillation optimization; and step 4, performing uncertainty map regulation and control. According to the system and the method, the precision, the robustness and the data privacy protection capability of MEMG regulation and control are improved, and the system and the method are suitable for efficient collaborative optimization of the park-level multi-energy microgrid.

The invention belongs to the technical field of automatic driving, and particularly relates to an automatic driving decision-making method and system with dynamic risk perception and attention focusing and a vehicle, and the method comprises the steps: predicting the track of a surrounding vehicle in real time through a multi-feature Gaussian weighted particle filtering algorithm, and improving the prediction precision through combining a vehicle kinematic model and resampling optimization; constructing a comprehensive evaluation model fusing transverse and longitudinal risks, and dynamically quantifying the collision risk of the vehicle and surrounding vehicles; and inputting the risk value as a key state feature into a double-depth Q network based on attention mechanism enhancement, focusing key information through a feature attention distribution mechanism, and generating an optimal driving decision in combination with a multi-target reward function. Compared with the prior art, the method solves the problems of insufficient quantification of uncertainty factors, incomplete risk assessment and low decision-making efficiency of automatic driving in a complex dynamic environment, and significantly improves the risk perception capability and decision-making safety of the automatic driving vehicle.

A fingerprint information processing apparatus includes: an output unit that outputs a certainty factor that is an index indicating probability in which fingerprints indicated by a fingerprint image correspond to at least one of a plurality of pattern types, by using the fingerprint image and a learning model constructed by machine learning using learning data including a sample image indicating fingerprints; and a processing unit that performs processing based on the certainty factor.

The invention relates to the technical field of data processing, and discloses an engineering construction whole process digital management method and system. The method comprises the following steps: collecting and standardizing engineering construction full-period multi-source data; constructing a stacked integrated prediction system by using the standardized data to obtain a three-dimensional index prediction value; allocating resources based on the predicted value in combination with the ergonomics risk index to obtain a configuration scheme; uncertainty factors are quantified for the configuration scheme, an optimal scheme set is calculated through a multi-target symbiotic algorithm, and a management strategy is formed. According to the method, high-precision prediction of the engineering three-dimensional indexes is realized through the stacking integration algorithm, ergonomics risk assessment and resource allocation optimization are combined, and the technical defect that human factor engineering is neglected in a traditional method is overcome.

The invention relates to the technical field of electricity price prediction, in particular to an electricity market long-period simulation-based medium and long-term price prediction method, which comprises the following steps of S1, market basic data acquisition: acquiring whole market basic data information; s2, unit commitment model construction and day-ahead simulation: constructing a unit commitment model considering power grid security constraints; s3, economic dispatching model construction: constructing an economic dispatching model considering power grid security constraints; s4, node marginal electricity price and load flow calculation: node marginal electricity price calculation and load flow calculation are carried out; s5, uncertainty factor evaluation: the uncertainty factors existing in the electricity market are evaluated; s6, analyzing technical performance and economic indexes: analyzing statistics of various technical performance and economic indexes of the system; according to the method, the stability and the reliability of an enterprise under an emergency situation are enhanced, so that the enterprise can better adapt to market changes, and the scientificity and the accuracy of decision making are improved.

The application discloses a full-symmetrical polyhedral set-membership state estimation method based on coding and quantization mechanism, which comprises the following steps: a corresponding linear repetitive process system model is established; an augmented technology is applied to convert a time-varying linear multi-rate system model into a corresponding time-varying linear single-rate system model; the range of uncertainty factors such as disturbance and measurement noise in the system is expressed by a full-symmetrical polyhedron; a coding mechanism is introduced in the information transmission process of two types of sensors with different sampling rates, and the quantization effect is considered; the properties of the full-symmetrical polyhedron are used to design an estimator for the system, and the estimation value is obtained; and finally, the optimal full-symmetrical polyhedron in the F norm sense is obtained through the design of the parameters of the estimator. The application can process the information transmission security problem under a 2D system, introduces the coding mechanism to reduce the network communication burden, and solves the set-membership state estimation problem considering the coding mechanism under the linear repetitive process.

The invention discloses a method for analyzing the strength and rigidity related degradation failure reliability of an automobile crane boom. The method comprises the following steps: firstly, establishing a load-stress equation of the boom; constructing a single strength degradation model and a rigidity degradation model based on the equation; then quantizing uncertain factors influencing the reliability to obtain a failure probability function corresponding to single degradation; and finally, establishing a strength and rigidity related degradation failure model based on the functions, and analyzing the reliability. According to the method, an automobile crane boom system is used as a research object, uncertainty factors and strength and rigidity degradation laws in actual service are deeply explored, and the system reliability is evaluated through a reliability analysis method; aiming at the limitation that a traditional method only considers single degradation failure, a related failure analysis theory is introduced, and a related degradation failure model is established on the basis of a traditional single degradation model in combination with the working condition characteristics of the boom, so that a reliability analysis method considering strength and rigidity related degradation failure is formed.

The present invention optimizes the amount of input information with respect to a language model for determining the confidentiality of information, and enables identification of confidential information with excellent efficiency and accuracy. This information processing system 100 is configured to comprise: an auxiliary storage device 203 for retaining information about sentences; and a processor 201 that calculates, on the basis of the information about the sentences, an anticipated computational cost and certainty factor during confidentiality identification with regard to respective sets of a specific sentence and a peripheral sentence, that generates a peripheral information list indicating peripheral sentences selected on the basis of results thereof, and that inputs specific sentences and peripheral sentences to a language model on the basis of the peripheral information list to thereby identify whether the specific sentences are confidential information.

The invention discloses a monthly provincial level electricity sale quantity prediction method and system considering multiple uncertainties. The method comprises the following steps: acquiring historical monthly electricity sales data, meteorological data, economic factor data and time factor data, and preprocessing the acquired data; based on the meteorological data, the economic factor data and the time factor data, calculating a meteorological fluctuation index, an economic growth deviation degree and a workday adjustment factor respectively to obtain a meteorological data uncertainty factor, an economic uncertainty factor and a time uncertainty factor; constructing a time sequence sub-model, a regression analysis sub-model and a machine learning sub-model, respectively predicting the electricity sales quantity, and dynamically calculating and adjusting the weight of each sub-model according to the sensitivities of the meteorological data uncertainty factor, the economic uncertainty factor and the time uncertainty factor, and carrying out weighted fusion on the electricity sale quantity prediction values output by the sub-models to obtain a monthly electricity sale quantity prediction result. According to the scheme, the adaptive capacity of a prediction system to a complex environment is improved.

The invention discloses a natural circulation system reliability robust optimization design method based on active learning. The method comprises the following steps: determining a system limit state function and screening key parameters; establishing a mean value sensitivity model and constructing a probability optimization model; adopting multi-objective optimization to obtain a Pareto solution set; quantizing parameter uncertainty and constructing an initial proxy model; performing reliability evaluation based on the initial agent model and generating a candidate sample pool; iteratively updating the proxy model through an active learning function; reliability analysis is carried out again based on the updated model, and the reliability of the solution set is judged; and performing optimization decision on the solution set meeting the requirements to output an optimal solution. According to the method, the agent model is adaptively updated through the active learning strategy, the evaluation efficiency is remarkably improved while the calculation precision is ensured, collaborative optimization of the system weight and the robustness under the reliability constraint is realized, and the anti-interference capability of the natural circulation system facing uncertain factors is effectively improved.

This information processing device comprises: an acquisition means for acquiring a fingerprint image including a fingerprint; a calculation means for calculating, for each finger, a certainty factor indicating the fingerprint likeness of the fingerprint included in the fingerprint image; a fingerprint frame determination means for determining, for each finger on the basis of the certainty factor, the size of a fingerprint frame corresponding to a region where the fingerprint is present in the fingerprint image; and a superimposition means for superimposing the fingerprint frame on the corresponding position on the fingerprint image. According to such an information processing device, it is possible to appropriately determine a region in which a fingerprint is present in a fingerprint image.

The invention provides a day-ahead multi-grid transaction method considering multiple uncertainty factors, provides a joint modeling method comprehensively considering interval inflow and load multiple uncertainty, and accurately describes spatial correlation among multi-basin inflow through a copula function; a conditional value-at-risk-based regret degree minimization decision model is constructed, and the problem that the risk of a traditional expected value criterion is out of control in an extreme scene is solved; a complete two-stage random optimization framework is established, and effective coordination of a day-ahead plan and real-time adjustment is realized; an adjustable risk control mechanism is developed, and economy and safety are flexibly balanced through risk coefficients. Compared with a traditional method, the method has the advantages that the robustness and practicability of multi-power-grid transaction decision making are remarkably improved, and an effective risk management tool is provided for multi-power-grid collaborative operation in an electricity market environment.

The invention relates to a Bayesian theory-based virtual-real fusion life and reliability evaluation method, which introduces a Bayesian theory to carry out life and reliability evaluation research aiming at the problems of limited sample size, incomplete test data, more uncertainty factors and the like in life evaluation of electronic products. According to the technical scheme, the method comprises the following steps: firstly, dividing prior distribution and sample data according to obtained simulation data and test data; secondly, combining prior distribution with sample information according to a Bayesian theory, and deriving posterior probability distribution of parameters; and finally, comparing and verifying the precision of the fusion evaluation method according to actual test data. The patent mainly realizes effective fusion of electronic product simulation priori knowledge and newly acquired test data, and improves the evaluation accuracy and robustness under the conditions of small samples and high uncertainty.

The application discloses a kind of decision method and system of power capacity market demand response based on scene method, determine the basic assumption of the participation capacity market of demand side response supplier, based on basic assumption, determine the uncertainty factor of the demand side response supplier decision model to be established;Determine the probability distribution of uncertainty factor, extract the different scene of the demand side response supplier decision model to be established;Determine the number of scenes that need to be removed, remove scene based on preset rule;Based on the scene after removal, establish demand side response supplier decision model;Establishing deterministic decision model, random decision model as contrast model;To demand side response supplier decision model, deterministic decision model and random decision model are solved, obtain the participation capacity of demand side response supplier, income and loss;Calculate the probability of violation, based on the probability of violation and the income and loss of demand side response supplier, determine the optimal number of removed scenes.

The application discloses a kind of multi-source random cause's deep sea pipeline ultimate bearing capacity evaluation method, it is related to deep sea pipeline system technical field, method includes: obtaining the uncertainty factor of deep sea pipeline ultimate bearing capacity evaluation, including the uncertainty factor of composite defect and the uncertainty factor of pipe material size strength;Based on the sample point of the uncertainty factor of composite defect, establish finite element analysis theoretical numerical model and solve, obtain the structure ultimate bearing capacity of each sample point;And the probability characteristic analysis is carried out to structure ultimate bearing capacity, obtains first structure failure probability;Estimate the second structure failure probability under the condition of pipe material size strength uncertainty factor;Based on first structure failure probability and second structure failure probability, calculate the structure failure probability under the coupling action of multi-source random cause.This application solves the problem that the nonlinear high of containing random composite defect deep sea pipeline structure response under the action of external pressure load, uncertainty is strong.

A generation method includes acquiring a first measure and a second measure configured from graph data including a plurality of conditional branches coupled by a directed edge and nodes coupled to destinations to which each of the plurality of conditional branches is branched, first generating information regarding a first difference between the acquired first and second measures, the first difference being at least one of a difference in structure of graph data and a difference in branch probability of an object assigned to a node of the conditional branch, and second generating correspondence information in which the generated information regarding the first difference is associated with information regarding a second difference between the acquired first and second measures, the second difference being at least one of a difference in effect value and a difference in certainty factor, by a processor.

The invention belongs to the technical field of power system load prediction, and particularly relates to a regional long-range and recent-mid-term comprehensive load prediction method, which comprises the following specific steps: widely collecting various load-related data including but not limited to historical power load data, economic data, meteorological data and policy documents, and carrying out load prediction for long-range prediction; social data such as macroeconomic development, long-term energy strategy policy information, population growth trend and urbanization process are focused on; for recent-middle-stage prediction, real-time economic fluctuation data tracking, short-term weather forecast and recent industrial policy adjustment details, uncontrollable interference is converted into computable variables through a closed-loop design of uncertainty factor quantification, time-phased model adaptation, data full-life-cycle treatment and dynamic updating iteration, and therefore the accuracy and the reliability of the system are improved. Data defects are made up through technical means, and finally high-precision and anti-interference prediction of long-range and near-and-medium-term loads is achieved.

The application relates to a new energy station contract replacement method, system, equipment and medium, wherein the method constructs an uncertainty original scene set of a new energy station according to new energy station operation data, covering uncertainty factors of a market environment. High-dimensional time series data in the uncertainty original scene set is reduced in dimension to obtain a low-dimensional uncertainty scene set. The low-dimensional time series data in the low-dimensional uncertainty scene set is increased in dimension to obtain an uncertainty target scene set. The method combining dimension reduction and dimension increase significantly reduces the calculation complexity while retaining the key features of the data. Based on the uncertainty target scene set, a contract replacement model is constructed. The replacement result generated in combination with real-time parameters realizes the maximization of the overall expected income of each new energy station under the uncertainty scene, and solves the problem of low data processing efficiency when the new energy station replaces the contract.

The invention provides a test case dynamic scheduling method and system based on an ant colony algorithm, and relates to the technical field of software test optimization. The method comprises the steps that core variables and parameters are initialized; analyzing the dependency relationship of the test cases; estimating the execution time of the test case according to the dependency relationship of the test case; selecting a to-be-executed test node according to the test case execution time; selecting a test case to be executed according to the task-node distribution table; according to the to-be-executed test case, updating a node operation state; according to the task execution result, the total completion time and the pheromone matrix, updating a test case execution state and the pheromone matrix; and obtaining an optimal scheduling scheme according to the current number of iterations or the total completion time fluctuation of continuous multiple iterations. According to the method, a load balancing problem is converted into an integer programming problem through mathematical modeling, optimal task allocation is realized by utilizing a heuristic search mechanism of the ant colony algorithm, and the efficiency and stability of a test system facing uncertain factors are remarkably improved.

The invention relates to the technical field of power system engineering project management, in particular to a power distribution network project construction period evaluation method based on chance-constrained programming, which comprises the following steps: firstly, collecting historical data of a power distribution network engineering project, and preprocessing the collected historical data to obtain a standardized data sequence; establishing an opportunity constraint construction period evaluation model, describing a task dependency relationship through a directed acyclic graph, and introducing opportunity constraint; then, mixed solution is carried out, the influence of each uncertainty factor on the total construction period is calculated, a sensitivity index is obtained, which factors have the largest influence on the total construction period is recognized, the resource utilization rate is monitored in real time, potential bottlenecks are early warned in advance, the calculation efficiency is high, risk control is accurate, and robustness is high; high-sensitivity uncertainty factors are simulated through a scene method, the response ability of the model to extreme conditions is enhanced, the excessive conservative or underestimated risk is avoided, and the method is suitable for construction period evaluation and resource allocation of a power distribution network project containing multiple construction links.

The invention discloses a road infrastructure service performance big data integration analysis method, and belongs to the technical field of road data analysis. Data fusion is realized through a federated learning architecture, digital twin bodies constructed by multi-source data are fused, and an accurate data basis is provided for subsequent scene deduction and performance prediction through low-delay data transmission and a state correction algorithm; through combination of deep reinforcement learning and a random process, an adaptive random process model is adopted for different types of uncertainty factors, probability distribution of a future scene can be quantified, and through matching combination of a scene deduction result and a digital twinborn performance degradation model and entropy item design of a reward function, the probability distribution of the future scene can be quantified. The limitation that the decision depends on single optimistic and pessimistic scenes is avoided; the dynamic interaction effect of damage factors is accurately captured through a space-time attention mechanism, the method can adapt to road performance prediction under different climate and traffic conditions, and the generalization ability of the model can be effectively enhanced.

This invention provides a dynamic risk assessment method and system for risk control in the electricity spot market, relating to the field of hydropower station management technology. It constructs a unified database by collecting and integrating heterogeneous data from multiple sources, including meteorology, power grid, and the market. A multi-timescale dynamic risk assessment model is used to predict key uncertainty factors, and a multi-objective optimization scheduling model is constructed using mixed-integer linear programming to achieve cascade linkage and risk-return balance. Simultaneously, it generates position optimization and robust pricing strategies, and introduces a CVaR (Continuous Value Assurance) pricing model to dynamically adjust risk hedging strategies. The system comprises a data layer, a model layer, an application layer, and a presentation layer, enabling data management, model calculation, interactive decision-making, and visualization, effectively improving the risk resilience and economic benefits of large hydropower stations in the spot market.

The invention relates to a mutual information and Shapley value fused power system operation reliability diagnosis method and equipment. On the basis of the power system operation reliability agent model, the mutual information and Shapley value fused power system operation reliability diagnosis method is provided, and the influence degree of uncertain factors on operation reliability indexes can be effectively quantified. According to the scheme, firstly, a normalized mutual information index is applied, the interpretation ability of each uncertainty factor for a reliability index is quantitatively evaluated, and key factors are screened out; and calculating the contribution of each key factor to the reliability index through a Shapley value to realize interpretable decomposition and responsibility attribution of the operation reliability of the power system. According to the scheme, interpretable decomposition and quantitative attribution of the influence of the uncertainty factors on the reliability indexes are achieved, an innovative analysis framework is provided for power grid operation risk identification and scheduling decision making, and the method can be used for assisting in further diagnostic analysis of the operation reliability risk of a power system.

The application discloses a multi-energy load forecasting method suitable for a comprehensive energy system and considering multiple uncertainties, and belongs to the technical field of energy load forecasting. The application uses a multi-task learning framework to perform multi-load forecasting, captures complex coupling relationships among electric, heat and hydrogen loads, and enhances the prediction accuracy of each prediction task. Uncertainty factors affecting prediction are considered, specifically including input uncertainty and model parameter uncertainty, and uncertainty modeling is performed on both, so as to quantify the uncertainty risk of load forecasting. In combination with an attention mechanism, the model can focus on important time step feature information and reduce unnecessary information interference. The multi-load forecasting method provided by the application can capture the coupling relationships among loads and realize accurate prediction of multi-energy loads under the condition of considering uncertainty factors.

The invention discloses a green power demand prediction method and system based on dynamic coupling and self-iterative optimization. The method comprises the following steps: acquiring green power demand multi-dimensional influence index data; obtaining text information representing green electricity related policies and technical standards, and fusing the text information into comprehensive feature vectors representing policy orientation and technical development situations; constructing an improved Logistic core model, reconstructing a dynamic saturation capacity function and a hierarchical growth rate function, and fusing a spatial-temporal heterogeneity correction factor to form a basic prediction model; introducing a Monte Carlo random disturbance term; a self-iteration optimization mechanism is constructed based on real-time data feedback, and model parameters are dynamically adjusted through residual error calibration; outputting green electricity demand curves in different scenes according to the prediction model, and judging a demand growth trend, a saturation boundary and a risk interval; according to the method, the problems of insufficient prediction precision and poor engineering adaptability caused by neglecting dynamic engineering constraints, spatial-temporal heterogeneity and uncertainty factors of a traditional model are solved.

A complex system control decision method and system fusing WRT and SPT, the method comprising two-layer architecture: the upper layer classifies the rough granulation of uncertain factors according to the structural information and language and other non-structural information of each related field inside and outside the system, and provides the information required for generating training samples to the causal algorithm of the lower layer, including the corresponding prediction working condition, disturbance event and control measures available at the time of each learning sample example, and efficiently organizes the training process of deep reinforcement learning in high-dimensional decision space; the lower layer adopts a causal-driven deterministic analysis algorithm based on WRT to complete the mapping from system state to control decision; the two layers cooperate to realize the pre-training of decision, and periodically update the pre-decision table according to the results. Once the actual disturbance is detected and identified in the objective system, it is matched with the pre-decision table to quickly complete the control decision.

This explanation support device includes a specification unit and an explanation basis acquisition unit. The specification unit receives, with respect to a first image, a first identification result and a first certainty factor output by a black-box type first inference engine, and, with respect to the first image, a second identification result and a second certainty factor output by a white-box type second inference engine. Furthermore, the specification unit receives at least one identification result and certainty factor from among the following: a third identification result and a third certainty factor output by a white-box type third inference engine different from the second inference engine, with respect to the first image; a fourth identification result and a fourth certainty factor output by the second inference engine, with respect to a second image separated by a predetermined number of frames from the first image; and a fifth identification result and a fifth certainty factor output by the second inference engine, with respect to a third image obtained by processing the first image. The specification unit identifies, from the certainty factors other than the received first certainty factor, a certainty factor closest to the first certainty factor, among the certainty factors associated with the same identification result as the first identification result. The explanation basis acquisition unit acquires identification basis information associated with the identified certainty factor and outputs the identification basis information to a monitor.

The invention discloses a dynamic model fuzzy adaptive control method fusing visual feedback, which comprises the following steps of: firstly, constructing an actuating mechanism dynamic model containing external interference and model uncertainty, and accurately initializing a system state and parameters to lay a foundation for high-precision control; according to the modeling mode, actual dynamic characteristics and uncertain factors are fused, so that the model is closer to a real system; meanwhile, a preset time controller and an updating law are designed, it is ensured that tracking errors converge to zero within preset time, and control rapidity and accuracy are improved; by utilizing a high-speed and high-resolution image acquisition and processing technology, accurate extraction and real-time analysis of moving parameters are realized, and the automation and intelligence level of the system is enhanced; besides, according to the scheme, the motion data, including position, speed and other key parameters and timestamps, of the execution mechanism are comprehensively recorded, rich information is provided for data analysis and performance evaluation, the motion law can be deeply understood, and the stability and reliability of the system are improved.

Provided is technology related to active learning whereby high performance (in other words, high accuracy of a model) can be achieved regardless of the number of possible annotations, and annotation cost can be reduced. A computer of an active learning system includes: a model inference unit (301) that acquires a result of inference by a machine learning model on the basis of unlabeled data that is input data; a certainty factor calculation unit (304) that calculates a certainty factor related to the unlabeled data on the basis of the result of inference; a prototypicality calculation unit (302) that calculates a prototypicality related to the unlabeled data on the basis of the result of inference; a low certainty factor data selection unit (306) that selects low certainty factor data on the basis of the certainty factor related to the unlabeled data; a high prototypicality data selection unit (305) that selects high prototypicality data on the basis of the prototypicality related to the unlabeled data; and a data determination unit (307) that selects annotation target data from the selected low certainty factor data.

This application relates to the field of data processing technology and discloses a method and system for digital management of the entire engineering construction process. The method includes: collecting and standardizing multi-source data throughout the entire engineering construction cycle; constructing a stacked integrated prediction system using the standardized data to obtain predicted values ​​for three-dimensional indicators; allocating resources based on the predicted values ​​and an ergonomic risk index to obtain a configuration plan; quantifying the uncertainty factors of the configuration plan; calculating the optimal solution set through a multi-objective symbiotic algorithm to form a management strategy. This application achieves high-precision prediction of three-dimensional indicators for engineering projects through a stacked integrated algorithm, combining ergonomic risk assessment with resource allocation optimization, and solving the technical shortcomings of traditional methods that neglect human factors engineering.