69 results about "Certainty factor" patented technology

The invention provides a harbor district energy double-layer coordination optimization method oriented to multiple interest subjects. The method comprises the steps of performing fusion processing and data cleaning on real-time operation data, historical operation data and environmental parameter data of a harbor district energy management system; performing multi-scale decomposition and feature importance evaluation by adopting a time sequence decomposition algorithm; probability modeling and multi-scale prediction are carried out on the basis of feature recognition uncertainty factors; constructing an upper-layer government regulation and control model and a lower-layer harbor district-ship east game model, and calculating a balance coefficient; and performing monthly strategy optimization, weekly scheduling optimization and hour-level real-time control based on the balance coefficient. By constructing a complete technical chain of data processing, feature extraction, prediction modeling, game optimization and real-time control, dynamic balance of benefits of the government, the harbor district and the ship eastern is realized, and the operation efficiency and the control precision of the harbor district energy system are remarkably improved.

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.

The present invention provides a method for calculating the credible capacity of flexible loads for power grid planning. According to the historical power consumption load data of the regional distribution network obtained, the typical daily loads in different seasons are determined; according to the uncertain factors affecting the flexible load response, the probability distribution model corresponding to the uncertain factors is selected, and within the corresponding confidence interval, the uncertain factors are converted into deterministic quantitative correction parameters to realize the correction of the flexible load response quantity; within the dispatching period, with the goal of minimizing the operation cost of the regional distribution network, an optimization model of the regional distribution network considering flexible loads is established, the constraint conditions of the model are configured, and iterative solution is carried out to obtain the typical daily demand response value; according to the calculated typical daily demand response value, combined with the flexible load response quantity, the response quantity and certainty of the user under the corresponding incentive level are calculated; the present invention can determine the credible capacity of system operation and provide a basis for load assessment during power grid planning.

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 combustion experiments, and relates to a self-adaptive adjustment control system and method for a NOx generation mechanism combustion experiment, and the method comprises the steps: inputting current experiment data, historical experiment data, a preset knowledge base and an experiment scheme inputted by a user; historical experimental data, experimental schemes and a preset knowledge base are input into a pre-constructed experimental element recognition model, key fuzzy variables, a fuzzy range set, regulation and control quantities and fuzzy rules are output, and an initialized fuzzy rule base is established; inputting current experimental data and an experimental scheme to a fuzzy optimization model output weight set and an activation sequence; determining an activated rule and an adjustment variable set according to the activation sequence; according to the initialized fuzzy rule base, the weight set and the adjustment variable set, performing demodeling to obtain an adjustment value of each adjustment variable; uncertain factors are processed through a fuzzy algorithm, a machine learning model is combined to optimize a rule base and a de-modeling method, and the adjustment precision of experimental parameters and the reliability of experimental results are improved.

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 discloses a flexible job shop scheduling method considering fuzzy factors. The method comprises the following steps: constructing a flexible production process model with fuzzy features; initializing a differential evolution algorithm; defining a differential evolution algorithm as a Markov decision process; and strategy optimization is carried out by using deep reinforcement learning, and flexible job shop scheduling is realized. According to the method, the fuzzy set theory and the flexible job shop scheduling problem are combined, uncertain factors in the production process are represented by fuzzy numbers, scheduling imbalance caused by changes of production and processing parameters can be prevented, the anti-interference performance of a scheduling scheme is improved, an optimization algorithm can better cope with a complex and changeable flexible production environment, and the production efficiency is improved. And a more reasonable and effective production scheduling scheme is obtained. And deep reinforcement learning is introduced to learn the variation strategy and the variation rate, and the algorithm is guided to be optimized in a more promising direction, so that the optimization process is more efficient, the solution performance is stronger, the solution result distribution is more concentrated, and the solution quality is higher.

The invention discloses a virtual power plant optimal scheduling method and system considering multiple uncertainties, and belongs to the field of virtual power plant optimal scheduling. The method comprises the steps of firstly obtaining historical data of a wind turbine generator, a photovoltaic unit, purchase and sale electricity price and demand response load capacity, determining a day-ahead scheduling predicted value of an uncertain factor by using a Monte Carlo method and the like; a reliable basis is provided for subsequent scheduling, and the scheduling strategy accuracy is improved. Then, a staged optimization strategy is adopted, from day-ahead to day-intra-day to real-time scheduling, an objective function and constraint condition solving are constructed in each stage on the basis of a previous stage result and real-time information, multi-time scale differences are fully considered, a scheduling decision is made to fit the actual operation condition, deviation is corrected in time through real-time scheduling, and the real-time scheduling efficiency is improved. The accuracy of the virtual power plant scheduling strategy is improved, and the problem that the accuracy of the virtual power plant scheduling strategy is low due to the fact that uncertainty factors and multi-time scale differences are ignored in an existing virtual power plant optimal scheduling technology is solved.

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.

The invention discloses a complex system control decision method and system fusing WRT and SPT, and the method comprises two layers of architectures: an upper layer carries out the coarse graining classification of uncertain factors according to the structural information and language and other non-structural information of the related fields inside and outside the system, and provides the information needed by the generation of a training sample for a causal algorithm of a lower layer; comprising prediction working conditions corresponding to each learning sample example, disturbance events, control measures which can be called at that time and the like, and a training process of deep reinforcement learning in a high-dimensional decision space is efficiently organized; a lower layer adopts a cause and effect driven deterministic analysis algorithm based on WRT to complete mapping from a system state to a control decision; the two layers cooperate to realize pre-training of decision, and a pre-decision table is periodically updated according to the result. Once the actual disturbance is detected and recognized in the objective system, the actual disturbance is matched with the pre-decision table, and the control decision is quickly completed.

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.

A data conversion learning apparatus includes a data conversion unit that performs data conversion of source data and target data, a first deduction unit that deduces data of a non-appearing class on the basis of a domain certainty factor acquired by a domain identification using converted data, a second deduction unit that deduces data of a non-appearing class on the basis of a class certainty factor acquired by a class identification using converted data, a class identification learning unit that performs machine learning for class identification using the data of the non-appearing class deduced by the first deduction unit and the source data and the target data which are inputs, and a domain identification learning unit that performs machine learning for domain identification using the data of the non-appearing class deduced by the second deduction unit and the source data and the target data which are inputs.

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.

To provide a label generation method and device, a learned model generation method, a machine learning device, an image processing method and device, and a program for providing information with a certainty factor along the position and seriousness of a disease in a medical image.SOLUTION: A label generation method is such that one or more first processors execute the steps of: acquiring one or more candidate positions of a disease in a first division unit from a first medical image; acquiring diagnostic information in which the position of the disease is uncertain or the position of the disease is specified in a second division unit; converting the diagnostic information into a certainty factor label according to the seriousness of the disease; associating the certainty factor of the disease according to the certainty factor label with the candidate positions of the disease acquired from the first medical image; and acquiring the position of the disease and a correct answer label of the certainty factor generated through the association.SELECTED DRAWING: Figure 3

The present invention proposes a random scenario construction method that considers temporal autocorrelation and mutual correlation. The method comprises the following steps: constructing a probability distribution model f for each of q uncertain factors, using Monte Carlo random sampling to obtain N initial random scenarios for each of the q uncertain factors; calculating the N random scenarios with temporal autocorrelation corresponding to each uncertain factor; and using a particle swarm algorithm with linearly decreasing inertia weights to select the random scenario with the minimum mutual correlation error from the N random scenarios with temporal autocorrelation obtained. Finally, a random scenario that comprehensively considers temporal autocorrelation and mutual correlation and conforms to real-world conditions is constructed. By analyzing the mutual correlations between multiple uncertain factors and the temporal autocorrelation within each uncertainty factor, the method further refines uncertainty modeling, making the random scenarios generated using the construction method more consistent with real-world conditions.

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.

The invention provides a user side energy storage charging and discharging strategy based on an electric power spot price prediction model, and belongs to the technical field of electric power system automation, and the strategy comprises the steps: collecting and preprocessing electricity price sequence data and related market feature data, guaranteeing the data integrity and consistency, and carrying out the data cleaning and standardization processing; constructing an inner-layer framework of the double-layer prediction model, constructing a multi-model prediction algorithm, and realizing preferential prediction; an outer layer frame of the double-layer prediction model is constructed, an outer layer algorithm is calibration window integrated prediction based on Bayesian model averaging, and an interval prediction result of the electric power spot price is obtained; constructing a deterministic optimization scheduling problem model based on complete information; a two-stage minimum and maximum regret value optimization scheduling problem based on uncertainty factors is constructed, a system scheduling objective function and constraint conditions are included, and an efficient energy storage charging and discharging plan is formulated, so that the income is improved, and the reliability of the system is enhanced at the same time.

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 invention relates to a micro-grid optimization scheduling method and system, and belongs to the technical field of power grid scheduling. The method comprises the following steps: establishing a micro-grid optimization scheduling model which takes the minimum new energy abandoned energy in a certain typical scene or the minimum new energy abandoned energy sum in two or more typical scenes in a prediction interval of prediction quantity as a target and takes energy storage charging and discharging power as a decision variable, and solving the model to obtain a micro-grid optimization scheduling plan. According to the invention, the uncertainty of the uncertainty factors during the operation of the micro-grid is quantified through interval prediction, and the micro-grid optimization scheduling model considering the uncertainty of the uncertainty factors is established and solved. The solved microgrid optimization scheduling plan can guarantee the consumption level of new energy and the operation efficiency of the system under the uncertain situation of the uncertain factors in the prediction interval, and the operation stability of the microgrid is improved. According to the method, quantification of uncertainty relates to a mechanism level, and compared with existing micro-grid optimization scheduling, the method is more refined.

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.