97 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 relates to the technical field of agricultural data dynamic perception, in particular to an agricultural data dynamic perception scheduling method based on the Internet of Things, and the method is realized based on an Internet of Things sensor, an edge computing platform and an edge server. Comprising the following steps: deploying Internet of Things sensors including a meteorological station, a soil humidity monitor and an unmanned aerial vehicle inspection system, and deploying a real-time data processing module on an edge server through an edge computing platform. According to the method, a dynamic cost-benefit analysis model is constructed, various factors and dynamic changes in agricultural production are comprehensively considered, a multi-objective optimization algorithm and a dynamic planning technology are applied, optimization of resource allocation and maximization of long-term benefits are achieved, in addition, uncertainty factors are processed by introducing a fuzzy mathematical method, and the method is suitable for large-scale popularization and application. The method improves the robustness and reliability of analysis, and solves the problems of lack of dynamics, incomplete consideration factors and limited optimization means in a traditional scheme.

The invention discloses a multi-energy load prediction method considering multiple uncertainties and suitable for an integrated energy system, and belongs to the technical field of energy load prediction. According to the method, a multi-task learning framework is used for multi-load prediction, the complex coupling relation among electricity, heat and hydrogen loads is captured, and the prediction precision of each prediction task is enhanced; considering the influence of uncertainty factors on prediction, specifically including input uncertainty and model parameter uncertainty, respectively performing uncertainty modeling on the input uncertainty and the model parameter uncertainty, and quantifying the uncertainty risk of the uncertainty factors on load prediction; and in combination with an attention mechanism, the model is helped to pay attention to important time step feature information, and unnecessary information interference is reduced. According to the multi-load prediction method provided by the invention, the coupling relationship between the loads can be captured under the condition that uncertainty factors are considered, and accurate prediction of the multi-energy loads is realized.

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 invention relates to a flexibility demand modeling method based on a probability box, and belongs to the field of flexibility demands of power systems. According to the method, a probability box theory P-Box is introduced to describe random uncertainty and cognitive uncertainty of wind power output, photovoltaic output and load demand prediction errors; introducing a conditional value-at-risk theory CVaR to a confidence interval boundary of the probability box, and intercepting an uncertainty factor boundary; and establishing a source-load double-side flexibility quantification model based on a prediction error of intercepting an uncertain factor boundary.

The invention discloses a historical data driving method and system for power load prediction, and relates to the technical field of load prediction. Compared with a previous load prediction model, the problem that a common single power load prediction model is difficult to adapt to power load prediction requirements of different time lengths in different power utilization areas is solved; through a historical data driving method, short-term and long-term power load prediction of a region or a grid can be effectively carried out, and a short-term load prediction model further optimizes an LSTM network which carries out parameter optimization based on an improved particle swarm algorithm through an A3C algorithm, so that the overall prediction performance can be improved. And important support is provided for operation and planning of a power system. The short-term load prediction model is further adjusted to be a long-term load prediction model based on an improved Monte Carlo simulation method, various possible scenes and uncertainty factors can be considered, the prediction performance of long-term load prediction is improved, and important data support is provided for efficient operation and optimization of a power system.

The invention discloses a new energy-energy storage combined operation strategy optimization method based on uncertainty modeling and distributed robust optimization. The method comprises the following steps: a) carrying out data acquisition and preprocessing on power generation data and power market prices of a new energy plant station; b) using a fuzzy set to express uncertainty factors in the data; c) based on the probability distribution of the uncertainty data, establishing an optimization model of new energy-energy storage joint operation through a distribution robust optimization method; d) modeling and solving the optimization problem by using a deep neural network or a convolutional neural network; and e) adjusting a scheduling strategy of the energy storage facility according to an optimization result, ensuring maximum consumption of new energy in an uncertain environment, reducing wind and light abandoning phenomena, and improving robustness of power grid scheduling. The method has the advantages that the intelligent level of scheduling is remarkably improved, and the risk and cost of a traditional scheduling method are reduced.

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 provides a collaborative interval management conversion program optimization method based on flight demand uncertainty. The method comprises the following steps: determining an uncertainty variable of a collaborative interval management conversion program CMCP optimization problem; estimating probability density function key parameters of normal distribution of the uncertainty variables according to historical data; constructing a CMCP opportunity constrained programming model and equivalent transformation thereof; and solving the converted CMCP opportunity constraint programming model based on a particle swarm algorithm. According to the method, the flight demand uncertainty factor, namely the uncertainty of the flight point passing time, is considered. Under the condition that the actual flight passing time is uncertain and interfered, the flight operation based on the optimization result can keep good airspace performance, so that the actual execution effect of the flight plan cannot be seriously fluctuated due to the uncertainty of the flight passing time.

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 discloses an uncertain factor quantitative analysis method for active access of a grid-connected main body to a power distribution system. A sensor network is deployed to collect data in real time and preliminarily analyze the data, and the state of a power distribution system is monitored. The device defects are then detected using a multi-physical imaging technique based on the real-time operational data. Relevant data are integrated, risk assessment including data cleaning, time synchronization, space integration and other operations is carried out in combination with user-side power use data, and a three-dimensional visualization model is further established to assist assessment. Then, data are integrated, a risk matrix tool is used for sorting and priority division, a state space model is established for simulation, and the influence of uncertain factors on stability is analyzed; and finally, analyzing and evaluating factor influence degrees by using global sensitivity, updating a risk matrix and formulating a risk management strategy so as to provide effective support for ensuring stable operation of the power distribution system.

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.

The invention provides a micro-service dynamic governance method and system for a distributed computing power network call chain, and the method comprises the steps: initializing, namely configuring a service guarantee level and setting a service governance triggering condition; collecting data, and preprocessing the data; performing risk prediction according to the preprocessed data; performing service management according to a risk prediction result; after the service management is completed, service recovery is carried out; the system adopts the method to realize micro-service dynamic management of the distributed computing power network call chain. According to the invention, the real data of the system is fully used to carry out positive feedback correction on the algorithm, so that the accuracy of service quality is improved; a data analysis algorithm is adopted for accurate calculation, and step-by-step and gradient governance services are provided within a period of time. In other words, system misjudgment caused by uncertain factors of business scenes and client actions is compatible, and the target of smooth transition is achieved.

An information processing apparatus capable of improving accuracy of search results. The information processing apparatus includes: a management unit that manages at least one object, an attribute by which the object is classified, and a certainty factor indicating a probability that the object has the attribute in association with each other; a calculation unit that, using a certainty factor of an attribute designated as a search condition and a certainty factor being managed in association with an attribute identical or similar to the attribute designated as the search condition, calculates a score indicating a matching degree of the object with the search condition; and a display unit that displays the object based on the calculated score.

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