60 results about "Expected value of sample information" patented technology

The invention discloses a power generation equipment state fault diagnosis method and system based on artificial intelligence, and the method comprises the steps: actively injecting a mechanical excitation signal of a preset frequency spectrum into a key part according to a physical topological structure of power generation equipment, and carrying out the fusion to generate a time-space-frequency three-dimensional data volume; inputting the three-dimensional data volume into a physical embedded variational auto-encoder, and outputting an equipment state pure feature tensor; inputting the pure feature tensor into a graph space-time causal reasoning network to generate a fault propagation causal graph with probability weight; performing multi-agent diagnosis on the fault propagation causal atlas, and outputting a fault diagnosis report which has a credibility interval and comprises fault positioning and root cause analysis; and mapping the fault diagnosis report to the digital twin of the equipment in real time, and outputting a self-adaptive maintenance strategy sequence which minimizes the expected value of the whole life cycle operation and maintenance cost. According to the embodiment of the invention, the accuracy and anti-interference capability of fault diagnosis can be improved, and the operation and maintenance cost can be effectively reduced.

The invention relates to the technical field of tunnel engineering automation and intelligent control, and discloses a TBM tunneling parameter intelligent optimization decision-making system based on an LSTM network, and the system comprises a data collection and preprocessing module which obtains external data and outputs a tunneling parameter sequence; the probabilistic tunneling trend prediction module is used for outputting a prediction expected value and prediction uncertainty; the prospective geological precursor sensing module is used for matching and identifying known risks and outputting alarm events; and determining an optimal tunneling mode by the dynamic risk avoidance decision matrix. When the prediction uncertainty is too high, activating the prospective template driven by the uncertainty to excavate a new precursor template and update the template library; meanwhile, the decision-efficiency relevance evaluation and strategy self-optimization engine optimizes the decision rule according to the actual tunneling efficiency. According to the method, decision making is carried out through quantitative risk prediction and fusion of multi-source information, and a double learning closed loop of knowledge discovery and strategy optimization is established, so that the reliability, the adaptability and the long-term efficiency of system decision making are remarkably improved.

The invention discloses a time delay constrained Internet of Vehicles multi-mode cooperative sensing method and system. The method comprises the following steps: collecting point cloud data and image data, and carrying out feature extraction, fusion and space projection; constructing a two-dimensional convolution neural network, and extracting a position-level importance probability graph and a mask used for screening key information; constructing a benefit estimation neural network and a constraint evaluation neural network, and predicting expected values of system benefits and constraint conditions; meanwhile, an overall strategy selection module is established; the motorcade carries out target identification and calculates rewards based on information interaction so as to obtain a perception experience sample; and updating network parameters based on the perception experience sample to obtain a final overall strategy selection module. According to the method, the transmitting power, the transmission channel and the sensing data area of the vehicle are optimized based on reinforcement learning, a strategy correction mechanism is introduced, a risk strategy of high time delay or high packet loss is dynamically corrected, and target identification deviation and decision errors caused by sensing information delay and wrong transmission are avoided.

The invention relates to a multi-source scene-driven power distribution network probabilistic load flow calculation method and system, and the method comprises the steps: obtaining the historical data of 24 hours per day in a complete year of load and photovoltaic, and carrying out the clustering division based on a comprehensive similarity index, thereby obtaining a plurality of optical load typical daily output scene sets and the probability distribution thereof; processing data of each scene set to obtain an expected value and a variance of optical charge power; combining the network topology of the power distribution network, taking an expected value as a node injection amount, and performing probabilistic load flow calculation by adopting a two-point estimation method; and carrying out statistical analysis on a calculation result to obtain related statistical data of branch power and node voltage in each typical scene. According to the method, the typical scene probability distribution set is generated by using historical data, the estimation point selection precision and the calculation result reliability are improved, the calculation times are reduced, the efficiency is improved, the actual power grid operation behavior can be better reflected, decision support is provided for a power grid operator, resource allocation is optimized, the cost is reduced, and renewable energy utilization is promoted.

The application relates to the technical field of automatic testing, and discloses an interface assertion method, a system, a terminal and a storage medium. The method comprises the following steps: when an automatic testing process corresponding to a target business scenario is executed for the first time, if it is detected that a target interface call return state is consistent with an expectation, valid data returned from the target interface is acquired as expected response data; based on a preset business process association rule, corresponding data records are extracted from a plurality of database tables related to the target business scenario as expected business data; the expected response data and the expected business data are integrated into an expected data set, and after the expected data set is associated with a use case identifier, the expected data set is stored in an expected value database to form a data snapshot; when a testing process of a same business scenario as the target business scenario is executed subsequently, actual response data and actual business data are acquired, and the actual response data and the actual business data are compared with corresponding data snapshots in the expected value database to generate an interface automatic assertion result.

The application provides a traffic prediction method and device, electronic equipment and storage medium, wherein the method comprises the following steps: determining historical traffic data and a missing position matrix of the historical traffic data; inputting the historical traffic data and the missing position matrix into a prediction completion model to obtain a prediction result output by the prediction completion model; the prediction completion model is obtained by training an initial model on the basis of weight parameters of a data completion module in the initial model, and the training is performed on the initial model by applying sample data, sample missing data, sample data expected values and a sample missing position matrix; and the weight parameters of the data completion module in the initial model are obtained by pre-training based on the sample data, the sample missing data and the sample missing position matrix. The method provided by the application realizes information interaction between a prediction module and a data completion module in a prediction model, completes end-to-end prediction, improves instantaneity, and improves the accuracy of a prediction result.

The embodiment of the present application provides a screening method and a screening system, the screening method comprises the following steps: obtaining historical flow data of a plurality of target objects from a storage system, and extracting historical values of target variables of the plurality of target objects from the historical flow data; generating a predicted selection probability corresponding to the target objects according to the historical values of the target variables; inputting the predicted selection probability and the historical values of the target variables into an established expectation value model, performing screening analysis on the target objects, and generating expected values of the target variables of the plurality of target objects; and screening the target objects whose expected values of the target variables are greater than a preset threshold value, and storing the corresponding identification information of the target objects into a database. According to the predicted selection probability of the target objects, the expected values of the target variables are obtained by using the expectation value model, and the target objects are screened according to the expected values of the target variables, so that the required target objects can be quickly and accurately screened, and the screening quality is better.

This application provides a reward data analysis method, apparatus, device, storage medium, and program product. The method includes: collecting target data generated by a model executing a game; wherein the target data includes: reward data obtained from the robot's action data under the corresponding state data at each moment in the game, and the mapping relationship between the state data, action data, and reward data; using a neural network model to infer reward data corresponding to the state data and action data; determining a first SHAP value for the state data relative to the expected value and a second SHAP value for the action data relative to the expected value; the first SHAP value characterizes the degree of influence of the state data on the expected value, and the second SHAP value characterizes the degree of influence of the action data on the expected value; adjusting the reward data corresponding to the robot's state data according to the first SHAP value, and adjusting the reward data corresponding to the robot's action data according to the second SHAP value.

The application belongs to the technical field of knowledge graph, and particularly relates to a knowledge reasoning method and system based on an agent dynamic path completion strategy, which extracts entities and the relationships between the entities in a target knowledge graph, and mines rules in the target knowledge graph and confidence score corresponding to the rules; a reinforcement learning agent is constructed, the agent is dynamically guided to complete the path of the knowledge graph according to the current entity state and historical path information through rules, and the total reward of the agent is calculated according to the hit reward when the agent hits a target entity and the rule reward when the reasoning path of the agent conforms to the rules, the strategy network of the agent is trained by maximizing the expected value of the total reward of the agent, and the corresponding knowledge reasoning result is obtained by using the trained agent to perform path reasoning in the knowledge graph for a given target condition to be queried. The application dynamically completes the most likely path in the reasoning process by using the dynamic path completion strategy to obtain a complete reasoning path, and solves the reasoning truncation problem caused by the missing path of a sparse knowledge graph.

This invention provides a blockchain-based data verification method and apparatus, relating to the field of blockchain technology and applicable to the financial technology field or other technical fields. The blockchain-based data verification method includes: receiving data verification information from a client; wherein the data verification information includes data to be tested, a data model, an expected value, and an error value; determining the actual data based on the data to be tested and the data model; determining the verification result based on the actual data, the expected value, and the error value; and sending the verification result to the client. This invention can achieve data validity evaluation while protecting the privacy of the data holder's data.

The application discloses a method and system for strengthening main grid elements considering renewable energy under extreme weather, and mainly relates to the technical field of renewable energy research. The method comprises the following steps: collecting an expected value of system loss load caused by extreme weather, and establishing a first objective function with the minimum expected value; modeling an operation mode of a main grid; establishing a linear programming model for calculating a worst probability distribution of uncertain variables; establishing a three-layer mixed integer linear programming model according to the first objective function and the modeling of the operation mode of the main grid; and solving the three-layer mixed integer linear programming model and the linear programming model of the worst probability distribution and outputting a result. The application has the beneficial effects of solving the problem of difficult information acquisition, reducing the difficulty of information provided by a power grid operator, and improving the efficiency of power transmission grid restoration.

One example method includes receiving a Quadratic Unconstrained Binary Optimization (QUBO) problem that comprises a matrix that includes various cells having data. It is then determined that one or more of cells is missing data or has corrupted data. A machine learning (ML) model performs a denoising process that removes random noise from the one or more cells having the missing data or corrupted data. This results in data being imputed to the one or more cells having the missing data or the corrupted data. The imputed data approximates the missing data or approximates an expected value of the corrupted data before the corrupted data was corrupted.

The present invention discloses a self-supervised behavior recognition method and system based on adversarial training. The self-supervised behavior recognition method based on adversarial training includes: extracting video data and inputting it into a standardized flow model; performing attack processing on the video data to obtain an adversarial video; calculating the adversarial loss of the video data and the adversarial video in a learning mode; updating the expected value parameters of the standardized flow model based on the adversarial loss, and judging whether the update is completed; if so, generating adversarial samples based on the standardized flow model and the expected value parameters; and inputting the adversarial samples into comparative learning. The present invention attacks samples based on the adversarial attack of the standardized flow, and also treats the attacked samples as positive samples and adds them to the training of comparative learning for adversarial training, thereby improving the accuracy and robustness of the model.

The invention discloses a cloud model-based tailing dam rainfall stability risk assessment method. The method comprises the following steps of S1, establishing a complete index system; s2, carrying out denoising preprocessing on the monitoring data by adopting a variational mode decomposition (VMD) algorithm; s3, carrying out weight analysis, and determining a combined weight; and S4, constructing a cloud model, determining a standard cloud of the cloud model, and judging a risk level. According to the method, the VMD algorithm is innovatively introduced to carry out denoising reconstruction on the monitoring data, original noisy data does not need to be stored, only effective signal components are reserved, risk level distribution characteristics can be described only through expected value-entropy-hyper-entropy parameterized representation of the cloud model and by using three groups of core parameters, dependence of a traditional method on full-amount historical data is replaced, and the method is high in reliability and high in reliability. The problem that the size of the buffer area is difficult to match the data size is thoroughly solved; under the mechanism, the risk evolution key information can be completely reserved with lower storage cost, and risk misjudgment caused by data loss can be avoided.

The present application belongs to the technical field of health state evaluation and prediction of mechanical transmission system, and proposes a kind of parallel misalignment state mechanical transmission component wear amount probability prediction method.It includes the following steps: step S1: build the three-dimensional model of mechanical transmission component containing parallel misalignment error;Step S2: considering the random uncertainty of load profile, carry out wear simulation analysis based on grid node iteration;Step S3: considering the cognitive uncertainty of the model itself, train the Bayesian long short-term memory network model;Step S4: predict the expected value of wear and its uncertainty interval.The present application improves the description accuracy of traditional finite element method on mechanical transmission component wear behavior, breaks through the limitation of wear amount deterministic numerical prediction, provides high robustness and high reliability of probability prediction result, and provides scientific basis and data support for transmission system condition-based maintenance decision.

The invention discloses a Bayesian probability fatigue strength evaluation method for a structural member with defects, and relates to the technical field of fatigue strength prediction of aerospace metal structural members. The method comprises the following steps: firstly, drawing a fatigue limit curve based on test data of a defective material sample, embedding a physical rule of an El Hadad curve through a synthetic data set, and establishing physical priori knowledge of a defect size and a fatigue strength behavior; training model parameters by using test data of the structural member, constructing a Bernoulli likelihood function, and matching an actual failure tag of the structural member; and finally, fusing the prior distribution and the likelihood function through the Bayesian theorem to obtain probability distribution after parameter updating, and outputting a predicted failure probability expected value and uncertainty. According to the method, material sample data containing defects are fused as physical prior based on the Bayesian theory, and the probability fatigue strength is evaluated through experimental data of a small amount of structural parts.

This invention discloses a time-delay-constrained multimodal cooperative perception method and system for vehicle-to-everything (V2X) networks, comprising: acquiring point cloud data and image data and performing feature extraction, fusion, and spatial projection; constructing a two-dimensional convolutional neural network to extract a position-level importance probability map and a mask for filtering key information; constructing a benefit estimation neural network and a constraint evaluation neural network to predict the expected values ​​of system benefits and constraint conditions; simultaneously establishing an overall strategy selection module; the vehicle fleet performing target recognition and calculating rewards based on information interaction to obtain perception experience samples; updating network parameters based on perception experience samples to obtain the final overall strategy selection module. This invention optimizes vehicle transmission power, transmission channels, and perception data areas based on reinforcement learning, and introduces a strategy correction mechanism to dynamically correct risky strategies with high latency or high packet loss, avoiding target recognition deviations and decision-making errors caused by perception information delays and erroneous transmission.

The invention provides a collaborative guidance law design method based on a time-varying gain disturbance observer, and the method comprises the steps: carrying out the modeling of a collaborative guidance problem that a plurality of aircrafts jointly intercept a target, obtaining a visual line angle dynamic system containing external disturbance, and abstracting the visual line angle dynamic system into a multi-body nonlinear system with a general strict feedback form; thirdly, designing a time-varying gain interference observer with time-varying gain for the multi-body nonlinear system; an improved Luenberger observer structure is adopted, the difference between system output and an estimated value is processed through a nonlinear function, and related information of an observed system is introduced to reduce the burden of the observer, so that the peak phenomenon in the observation process is effectively avoided. Then, on the basis of a ring communication topology structure, the terminal expected value of each nonlinear system is provided by a neighbor node. Reference value information is transmitted through a network, the current state of each system is adjusted to an expected state through a controller, and the output of a plurality of nonlinear systems is ensured to be consistent at the same time. And finally, designing a cooperative guidance law based on the time-varying gain interference observer, namely transmitting an expected terminal angle value, and adjusting the sight angle from the current state to the expected terminal angle by using a distributed guidance algorithm, thereby improving the interception efficiency of space cooperative guidance.

The application discloses a data filtering method and system, an electronic device and a storage medium. The method comprises the following steps: receiving data to be filtered, and determining a label value of the data; inputting the data into a first filter, and determining an expected value and a tail value; the expected value is used for representing the label value determined based on data continuity, and the tail value is used for representing the earliest effective label value in a second filter; if the label value is equal to the expected value, filtering the data through the first filter to determine target data after filtering; the first filter is used for filtering data with continuous label values; or, if the label value is not equal to the expected value and the label value is greater than or equal to the tail value, inputting the data into the second filter, filtering the data through the second filter to determine the target data after filtering; and the second filter is used for filtering data corresponding to non-continuous label values. The embodiment of the application is beneficial to improving the data processing efficiency. The application can be widely applied in the field of network communication technology.

The application relates to a design method of a granule-based quantum graph neural network for graph classification, and belongs to the field of quantum machine learning. The method comprises the following steps: S1: a classical granule generation method is used to generate granule samples from data samples; S2: a node data set and an edge data set are acquired, and encoding and preparation are completed; S3: a variational quantum wire is designed to enhance node features; S4: a quantum graph convolution circuit is designed to realize information transmission and parameter sharing between nodes; S5: a quantum graph pooling circuit is designed to extract multiple quantum bit information onto one quantum bit, so that feature dimension reduction is realized; S6: training set is input to train parameters of the whole model; and S7: a to-be-tested data set is input to the model, a Pauli Z measurement is performed on a specified quantum bit to obtain an expected value, and finally, graph classification is completed. The application reduces the number of data sample points, overcomes the defect that a large amount of data cannot be prepared in a quantum experiment, and optimizes the quantum graph convolution circuit and the quantum graph pooling circuit.

The invention discloses a dissolved oxygen prediction correction method fusing a graph neural network and extreme value distribution. The method comprises the following steps: preprocessing collected data; constructing a space diagram structure; carrying out numerical value forward translation on the preprocessed DO data, and then carrying out logarithmic transformation to obtain a converted DO sequence; inputting the preprocessed dynamic weather and water quality characteristics and the converted DO sequence into an LSTM model, and outputting a basic prediction sequence of DO in a future time period; based on a prediction result of the LSTM model and in combination with a spatial graph structure, performing spatial dependency modeling by using a graph neural network, and constructing sectional probability distribution at the same time; and outputting a parameter set through a graph neural network, and carrying out loss optimization through CRPS. Compared with a single-point prediction model, the method can obtain an expected value, distribution uncertainty and tail risk information at the same time, achieves the quantitative evaluation of the probability of hypoxia and hyperoxia events, and remarkably improves the reliability and stability of dissolved oxygen prediction.

The application claims a resource sensing and scheduling method of a computing power network, and belongs to the field of computing power network resource scheduling, and specifically comprises the following steps: collecting historical resource state sequences of each node as endogenous variables, and obtaining associated exogenous variable sequences; using a multi-source information fusion model to asymmetrically encode the two, fusing information through a cross-attention and adaptive gating mechanism, and introducing a structure prior to perform conditional normalization; outputting expected values and prediction variances of each resource index in a future time window, and aggregating them into a node resource state vector. In the scheduling stage, a double-time-scale framework is used, an adaptive resource reservation and candidate path scoring are periodically performed in a slow decision layer, and the reservation amount is adaptively adjusted according to uncertainty; a fast decision layer is event-driven, and a reinforcement learning sequence decision is used to complete routing and deployment, and a reward function fuses service quality and uncertainty penalty. The application improves the computing power network scheduling acceptance rate and robustness while ensuring service quality.

The invention discloses an intelligent analysis method for multivariate data fusion mining of an electric power energy system, and the method comprises the steps: collecting and obtaining environment data parameters of the electric power energy system, and carrying out the analysis to obtain renewable energy power generation parameters; acquiring influence factor parameters of the electric quantity load demand to obtain electric quantity load parameters; analyzing the renewable energy power generation parameters and the power load parameters to obtain an analysis result; setting an expected value, and judging whether the analysis result meets the expected value or not; if the analysis result meets the expected value, the analysis result is output, and if the analysis result does not meet the expected value, the analysis result is analyzed based on the expected value, and an optimization report is generated. According to the output analysis result and the optimization report, the overall operation efficiency of the power system can be improved, the adaptability and flexibility of the system to high-proportion renewable energy access are enhanced, the user experience is optimized, and the power supply service quality is improved.

To reduce labor for creating a test case.SOLUTION: A program for causing a computer including a processor and a memory to execute: The program causes a processor to execute a step of inputting information related to test content to a first trained model trained to output an abstract expected value and causing the first trained model to output the abstract expected value, a step of inputting the abstract expected value and information related to a specification to a second trained model trained to output a specific expected value and causing the second trained model to output the specific expected value, and a step of presenting the specific expected value output from the second trained model or both the abstract expected value and the specific expected value to a user.SELECTED DRAWING: Figure 16

This application relates to the field of tunnel engineering automation and intelligent control technology, and discloses an intelligent optimization decision-making system for TBM tunneling parameters based on LSTM networks. The system includes: a data acquisition and preprocessing module to acquire external data and output tunneling parameter sequences; a probabilistic tunneling trend prediction module to output predicted expected values ​​and prediction uncertainties; a forward-looking geological precursor sensing module to match and identify known risks and output alarm events; and a dynamic risk avoidance decision matrix to determine the optimal tunneling mode. When the prediction uncertainty is too high, an uncertainty-driven forward-looking template is activated to discover new precursor templates and update the template library. Simultaneously, a decision-performance correlation evaluation and strategy self-optimization engine optimizes the decision rules based on actual tunneling performance. This invention significantly improves the reliability, adaptability, and long-term effectiveness of the system's decision-making by quantifying predicted risks, integrating multi-source information for decision-making, and establishing a dual learning loop of knowledge discovery and strategy optimization.

The application discloses a dissolved oxygen prediction correction method fusing a graph neural network and an extreme value distribution, and comprises the following steps: pre-processing collected data; constructing a spatial graph structure; for the pre-processed DO data, first performing numerical forward translation, and then performing logarithmic transformation to obtain a converted DO sequence; inputting the pre-processed dynamic meteorological and water quality characteristics and the converted DO sequence into an LSTM model to output a basic prediction sequence of the DO in a future period; based on the prediction result of the LSTM model, combining the spatial graph structure, using a graph neural network to model spatial dependence, and simultaneously constructing a segmented probability distribution; outputting a parameter set through the graph neural network, and optimizing through a CRPS loss. Compared with a single point prediction model, the application can simultaneously obtain expected value, distribution uncertainty and tail risk information, realizes quantitative evaluation of an anoxic and hyperoxic event probability, and significantly improves the reliability and stability of dissolved oxygen prediction.

The present invention provides a dynamic system probabilistic risk assessment method based on Bayesian theorem and event trees. Based on the event tree, the method first gives the prior distribution parameters for the probability of occurrence of initial events and basic events. These prior distribution parameters are updated using Bayesian theorem, and the failure probability and failure rate are updated in real time. These updated parameters are then used to calculate the expected value of the probability of occurrence of each outcome event, and then the system risk probability is further calculated. Based on Bayesian theorem, the method uses statistically generated sample data to update prior knowledge, so that the final calculation results can change over time and have the ability to truly reflect the changes in system risk over time.

A method for improving learning under distribution approaches to AI agent alignment using active inference, wherein an observation method is used to index the likelihood matrix of a Partially Observable Markov Decision Process implemented by an agent, and wherein an action method is used to infer the expected free energy of each possible policy, and wherein an intention method is used to compute the expected value of the expected free energies for each policy, and wherein the policy that affords the least expected value of expected free energies is enacted by the agent.

Various systems and methods are described for correlating technology choices with the risk of system vulnerabilities. A system captures and quantifies both observations of technology choices as well as the outputs certain outputs of internal choices and processes across a number of different organizations. A Bayesian estimate of vulnerability is imputed from the choices and observed use of vulnerable technology, further segmented by business type, revenue, and size. Differences between the observation of a particular organization and Bayesian expected value are measured and converted to vulnerability score, the vulnerability score embodying a point-in-time and longitudinal measure of organizational performance, including the likelihood of future compromise due to software vulnerabilities. The vulnerability score can then be further used to price risk, for example in a cyber insurance context.

The present invention discloses a method for selecting and ranking industrial computer products based on a multi-scale language evaluation scale. The method comprises the following steps: aggregating t evaluation matrices of industrial computer products into an initial probabilistic language decision matrix and calculating the expected value of each probabilistic language term set; utilizing a probabilistic language ratio system to calculate the weighted average of the expected values ​​of all probabilistic language term sets as the ranking value of the probabilistic language ratio system; utilizing a probabilistic language reference point system to obtain the reference point of a criterion using a univariate quadratic score function, and calculating the distance between the probabilistic language term set of each criterion and the reference point using a distance measure, thereby obtaining the ranking value of the probabilistic language reference point system; utilizing a probabilistic language full multiplication model to calculate the weighted geometric mean of the expected values ​​of all criteria as the ranking value of each probabilistic language full multiplication model; and respectively calculating the comprehensive ranking score of each industrial computer product and ranking them. The present invention integrates multiple evaluation systems to obtain a more reasonable final ranking.