30 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 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 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 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.

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 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 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 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.

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.

An information processing apparatus includes: a determination unit that determines whether or not a certainty factor is higher than a predetermined threshold, in a case of obtaining a correspondence between a first element acquired at a first time and a second element acquired at a second time after the first time, by using the first element as a criterion for a correspondence between two elements, and the first and second elements being included in time-series data; and a selection unit that selects the second element as a new criterion for the correspondence between the two elements when it is determined that the certainty factor is higher than the predetermined threshold, and that selects the first element as the criterion for the correspondence between the two elements when it is determined that the certainty factor is lower than the predetermined threshold.

The application discloses a PE pipe microwave detection uncertainty evaluation method and device and equipment thereof, and relates to the technical field of nondestructive testing.The method comprises the following steps: obtaining detection data of uncertainty factors in a PE pipe microwave detection process, wherein the uncertainty factors are labeled with data property identifiers; converting the detection data into membership vectors based on the data property identifiers and corresponding preset conversion rules, wherein the preset conversion rules comprise membership function conversion based on a combination of trapezoidal distribution and triangular distribution, and weight vector direct quantization conversion based on expert scoring and engineering experience adjustment; and performing fuzzy comprehensive evaluation on the uncertainty factors based on the membership vectors and preset weights to obtain a comprehensive evaluation result.Under the premise of not relying on repeated measurements or physical tracing required for uncertainty evaluation, the method realizes structured, operable and engineering interpretable comprehensive evaluation of the uncertainty factors in the PE pipe microwave detection process.

A park integrated energy system optimization operation method considering multiple uncertainties comprises the steps that a fuzzy parameter space of multiple uncertain parameters is constructed, and the fuzzy parameter space comprises a source load equipment random model and a fuzzy parameter space model; establishing a scene slice optimization operation model based on membership function dimension reduction analysis, wherein the model comprises an objective function and constraint conditions composed of power balance constraint, energy purchase constraint, conversion equipment constraint, energy storage constraint and uncertain parameter constraint; and carrying out sensitivity analysis on the fuzzy parameters by using a fuzzy parameter global sensitivity analysis method based on a membership function. According to the park integrated energy system optimization operation method considering multiple uncertainty, economic and robust operation of the park integrated energy system can be realized in multiple uncertainty environments, and key uncertainty factors and influence intensity thereof can be accurately identified.

The invention provides a toughness improvement-oriented multi-energy power distribution system optimization planning method and device. The method comprises the following steps: acquiring a topological structure, equipment parameters, load data and historical operation data of the multi-energy power distribution system, and establishing a multi-energy power distribution system mathematical model comprising a power network, a thermal network and a gas network; the method comprises the following steps: identifying uncertain factors by using a multi-energy power distribution system mathematical model, and constructing a domain randomization sampling distribution function to form a random sample; establishing an uncertainty model of the multi-energy power distribution system according to the random sample, and obtaining system uncertainty characteristics through probability distribution characteristic analysis and statistical characteristic calculation; according to the uncertainty characteristics of the system, constructing a system toughness evaluation index system comprising a reliability index and a recovery capability index, and executing Monte Carlo simulation to form a system toughness evaluation result; and constructing a multi-objective optimization model by adopting a system toughness evaluation result. According to the invention, the toughness level of the multi-energy power distribution system is effectively improved.

The invention relates to a water conservancy resource management method and device based on a cloud platform, equipment and a medium. According to the method, multi-source water conservancy data are integrated through a cloud platform, after data cleaning, a hierarchical Bayesian model is constructed to unify quantitative model parameters, posterior distribution of structural errors is input, on this basis, ensemble Kalman filtering is driven to dynamically assimilate real-time observation data, and forecast distribution fused with multi-source uncertainty is generated; further analyzing a contribution source of a forecast variance in real time through a Sobol'index, identifying dominant uncertainty factors, constructing a coupling error model according to the contribution source, feeding back the coupling error model as a state variable to an assimilation cycle for online correction, and finally forming an improved forecast model capable of dynamically tracking and quantifying an uncertainty coupling propagation mechanism. Therefore, the precision and reliability of hydrological forecasting are remarkably improved, and a risk quantification basis and an optimization scheme are provided for decisions such as flood control dispatching, water resource allocation and urban drainage management.

The invention discloses a water supply network leakage detection data set generation method. According to the method, three uncertain factors including pipeline roughness coefficient random change, node water demand fluctuation and pressure measurement error are integrated in hydraulic simulation; respectively operating leakage-free hydraulic simulation and leakage hydraulic simulation, and calculating pressure responses in a normal state and a leakage state; uniformly intercepting time window data with different lengths from the leakage starting moment by adopting a multi-time window interception mechanism; generating spatial feature data and time sequence feature data for each time window; and generating a spatial feature thermodynamic diagram by adopting a two-layer coordinate mapping system, and sequentially drawing each node according to a sequence of pressure response values from small to large when the thermodynamic diagram is drawn. According to the method, the matching degree of a data set and a real scene is improved through integrated simulation of three uncertain factors; the time consistency of the bimodal data is ensured through a multi-time window unified interception mechanism; leakage characteristics are highlighted through a sorting drawing mechanism; and accurate leakage positioning is supported through a two-layer coordinate mapping system.

The invention discloses a power system power failure risk-oriented key factor extraction method, system and device and a storage medium, and belongs to the technical field of power system safety and stability analysis. The method comprises the following steps: acquiring original fault data of a plurality of power failure events, wherein the original fault data comprises uncertainty factors and power failure loss indexes; probability grading is carried out on the uncertainty factors; calculating the weight of the power failure loss index by adopting an entropy weight method, and calculating a comprehensive power failure loss index based on the weight of the power failure loss index; the correlation degree of the uncertainty factors and the comprehensive outage loss indexes is calculated through a grey correlation analysis method, and loss grading is carried out on the uncertainty factors according to the correlation degree; and according to the probability grading and the loss grading of the uncertainty factors, calculating the risk grading of the uncertainty factors to the comprehensive power failure loss index, and based on the risk grading, determining key factors in the uncertainty factors. According to the method, the problem of accurate extraction of multi-dimensional uncertainty factors under complex coupling features is solved.

In this information processing method, a computer executes processing for: acquiring utterance data of an utterance by a learner in a learning language; detecting a mistake location from the utterance data; estimating, from the utterance data, a degree of fixation of a learning item corresponding to the mistake location and a certainty factor indicating certainty of the degree of fixation when the mistake location is detected; and determining whether it is necessary to intervene in the utterance of the learner on the basis of the degree of certainty.

The invention discloses a power system key factor set influence quantification method and system considering uncertainty coupling and depth. The method comprises the following steps: establishing an uncertainty factor library; screening a potential uncertainty factor combination from an uncertainty factor library according to historical uncertainty factor data and online prediction information of the target area; generating a source-load power sequence corresponding to each potential uncertainty factor combination by using a quantitative mapping relation from the uncertainty factors to the source-load power sequence; calculating a corresponding loss index according to the source-load power sequence, grading the loss index to obtain a loss grading result, and selecting an expected missing power supply quantity as the loss index; grading the probability of occurrence of each potential uncertainty factor combination to obtain a probability grading result; obtaining a risk grading result and a sequence according to the loss grading result and the probability grading result; and acquiring key uncertain factor sets in different seasons and different time periods according to a risk grading result and sorting.

The application discloses a kind of civil engineering construction task automatic scheduling method and system, including the construction of representing construction task element and its associated relationship Heterogeneous graph model;Utilize graph neural network to process heterogeneous graph model, to excavate the interdependence between process and extract the risk associated features related to uncertainty, form construction state atlas;Based on construction state atlas, construct bayesian network;Through bayesian network, probability reasoning is carried out, the influence of uncertain factor is quantified, and the key information representing risk transmission is fed back to graph neural network, to dynamically correct the identification of process interdependence;Fusion graph neural network output dependency constraint and the risk quantification result output by bayesian network, generate construction task scheduling scheme;According to field construction feedback data, heterogeneous graph model, graph neural network and bayesian network are updated synchronously, to realize the closed-loop iterative optimization of scheduling scheme.

The invention discloses a marine route planning method considering uncertain factors, and belongs to the field of marine traffic safety management, and the method comprises the steps: S1, carrying out the preprocessing and feature fusion of multi-source data; s2, carrying out the uncertainty modeling of the marine route, and quantifying the probability distribution of uncertainty parameters; s3, predicting a candidate route trajectory based on PCA hierarchical attention; s4, in combination with the probability distribution of the uncertainty parameters and the predicted candidate route trajectory, calculating the operability super probability of each segment of the route, and generating a probability operability index; and S5, by adopting an approximate MinSumA algorithm, outputting an optimal route by taking the integration of the minimum total navigation time and the maximum probability operability index as targets. By the adoption of the sea route planning method considering the uncertainty factors, a data-modeling-prediction-evaluation-optimization closed loop is constructed, the problems that a traditional method ignores uncertainty, is high in optimization complexity and poor in real-time performance are solved, and safety-efficiency-real-time performance collaborative optimization is achieved.

This invention discloses a weighted prediction-decision integration method, system, device, and medium that considers the impact of uncertainty. Belonging to the interdisciplinary field of power system optimization and artificial intelligence, it includes generating a weighted dataset, training prediction models under different weight parameters and accumulating data; training the prediction model for each weight to obtain the prediction task and calculating the decision loss; constructing and training a fitted surrogate model, and applying the surrogate model to optimize the weight parameters to obtain the optimal value. This invention breaks the traditional paradigm of separating prediction and optimization decision-making. Through a problem-driven weighted mechanism, it directly links the training objective of the prediction model with the quality of downstream decision-making. It achieves the alignment of prediction serving decision-making objectives, thereby automatically identifying and accurately predicting the key uncertainty factors that have the greatest impact on decision-making outcomes in complex power system operation scenarios. Under the same prediction resources, it significantly improves the safety and economy of system operation.