82 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 an equipment residual life prediction method based on a physical-data model, and the method comprises the steps: building a degradation model based on a physical degradation mechanism and a standard Wiener process model; obtaining fixed parameters in the degradation model by using a maximum likelihood estimation method in combination with a nonlinear regression method; acquiring an equipment state observation value in real time, and updating random parameters in the degradation model by using a weight optimization particle filter algorithm; and utilizing the degradation model, the fixed parameters in the degradation model and the random parameters in the degradation model to obtain a probability density function of the residual life of the equipment under a random failure threshold value, and performing numerical integration on the probability density function of the residual life of the equipment to obtain an expected value of the residual life of the equipment, and outputting the expected value as the residual life of the equipment. Compared with the prior art, high-precision and high-reliability residual life prediction is realized by fusing a physical mechanism and a data driving method.

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 discloses an MIQP problem optimization processing method and system based on an improved random branch strategy, and the method comprises the steps: constructing a graph network model according to a multi-source historical data set, and traversing a path from a power node to each load node in a target power distribution network based on a graph search algorithm, generating a branch strategy candidate set of target power distribution network reconstruction according to the path; reconstructing a target power distribution network in the typical power distribution network operation scene, and calculating expected values of different branch strategies in the branch strategy candidate set in the reconstruction process; inputting the real-time load characteristic data, the real-time line characteristic data and the real-time topological characteristic data into a pre-trained decision tree model to obtain an initial weight parameter of each branch strategy in a target power distribution network reconstruction process; and generating a strategy weight parameter of the branch strategy based on the initial weight parameter and the expected value, and optimizing a solution process of target power distribution network reconstruction according to the strategy weight parameter. According to the method provided by the invention, the efficiency of the reconstruction process is improved.

The invention discloses a medical treatment risk detection method, device and equipment and a computer storage medium, and relates to the technical field of data processing. The method comprises the following steps: acquiring medical-seeing communication behavior data of a user; inputting the medical-seeing communication behavior data of the user into a medical-seeing risk detection model, and determining a medical-seeing risk classification result corresponding to the medical-seeing communication behavior data of the user according to a pre-trained target expected value table; the target expected value table comprises a corresponding relation between the doctor-seeing communication behavior data and the doctor-seeing risk classification result, the target expected value table is obtained by training the doctor-seeing communication behavior data and a target user tag, and the target user tag is determined according to a relative distance among a user clustering tag, a user prediction tag and a data point; the user clustering label is obtained by clustering according to the local density and the minimum distance of the user data points, and the user prediction label is obtained through a pre-trained classifier. According to the embodiment of the invention, the optimal decision is made according to the target expected value table, and the risk identification accuracy is improved.

The invention discloses a primary frequency modulation optimization system and method for a new energy station and a medium. The method comprises the following steps: performing principal component analysis on to-be-measured historical data to obtain dimension-reduced historical data; training a plurality of XGBoost models by using a sample set formed by the historical data after dimension reduction, obtaining data to be measured at the current moment, inputting the data to be measured at the current moment into the trained XGBoost models for net load prediction, and obtaining a predicted net load at the next moment; obtaining an expected value and a standard deviation of the predicted net load at the next moment according to the distribution of the predicted net load at the next moment, calculating a net load variable quantity at the next moment according to the expected value of the predicted net load at the next moment and the net load at the current moment, and calculating a frequency deviation according to the net load variable quantity at the next moment and an equivalent inertia constant; and performing frequency modulation control according to the frequency deviation and the standard deviation of the predicted net load at the next moment. According to the invention, high-precision prediction is carried out on the load by using the extreme gradient lifting algorithm, the frequency modulation response speed of the new energy generator set is improved, and the accuracy of frequency regulation is also enhanced.

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 present invention relates to the technical field of computer data processing, and in particular to a training method and system for a multi-model collaborative distillation and dynamic fine-tuning model; the method comprises: collecting data, inputting unlabeled data into a model to make labeled data, integrating the data with labeled data into a data set, and then dividing the data into a training set, a validation set and a test set; inputting the training set into the model, using prompt words to guide the model to generate extended data, and using the extended training set to train a student model; evaluating the student model on the validation set, and if the evaluation result shows that the evaluation index of the student model does not reach the expected value, then inputting error information into the model for analysis and data generation, and then adjusting the student model until the evaluation index of the student model reaches the expected value; the method has the advantages of using multiple large language models for collaborative distillation, making the training data richer and more diverse, and the student model more accurate, and then dynamically fine-tuning the student model through a cycle, so that the model has higher accuracy and stronger adaptability.

There are provided systems and methods for a machine learning model for probabilistic anomaly detection in streaming device data. A server obtains a plurality of features associated with a user interaction between a user device and the server. The server selects a combination of features from the plurality of features, where the combination of features comprises features having a variance of expected values that exceeds a threshold variance. The server selects a prediction engine to process the combination of features with a corresponding non-parametric statistical model of a plurality of non-parametric statistical models and to generate a prediction indicating a likelihood that the combination of features represents an anomaly corresponding to fraudulent activity. The server issues a remedial action to the user device through an application programming interface with a remedial action engine based on the prediction for applying the remedial action on the user interaction.

The embodiment of the invention relates to the field of artificial intelligence, and discloses a model training method, a video generation method, devices, equipment and a medium, and the model training method comprises the steps: obtaining a first segmentation model, a sample video and a reference segmentation point; training the first segmentation model according to the reference segmentation points to generate a second segmentation model; constructing a first score model for endowing segmentation scores for statements in the sample video based on the second segmentation model; training the first score model according to the reference segmentation points to generate a second score model; generating a statement data set according to the training data set; an objective function is constructed, and the output of the objective function is matched with a segmentation score expected value and the updating step length of the second segmentation model; and training the second segmentation model according to the statement data set until the output of the target function is converged to obtain a target segmentation model for segmenting the sample video based on the content theme of the video, so that the model accuracy, the content theme recognition accuracy and the editing effect are improved, and the segmentation point output by the model is closer to the actually required segmentation point.

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.

The present invention discloses an industrial process control method and system based on an expected setting error neural network, comprising: establishing a control output network model and a target prediction network model, determining a specific control target and a corresponding control object in the industrial process, selecting a control variable according to the control target, and setting a corresponding expected value; collecting time series data in the industrial process, and performing data processing on the collected time series data; training a target prediction network using the time series data after data processing, obtaining a prediction result, and calculating and integrating a plurality of error types to optimize the target prediction network; training a control output network according to the prediction result and time series data provided by the target prediction network, and calculating and integrating a plurality of error types to optimize the control output network; applying the trained network model to industrial process control, and continuously performing additional training according to environmental changes. The present invention can improve the control effect of the industrial process.

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 present invention discloses a water pump operation energy-saving control method and system based on multi-source data analysis, which relates to the technical field of water pump energy saving. By obtaining the measured state parameters and the corresponding water pump energy consumption value data at the same moment, abnormal data points in the sampled data are eliminated, and the neural network is trained using the sampled data until the expected value of the mean square error of its prediction result is less than the set expected threshold, and the mapping relationship between the state parameters and the water pump energy consumption value is output. Using the mapping relationship, the adjustable range of the active state parameters and the range of the water pump energy consumption value corresponding to the measured values of the passive state parameters are obtained at the current moment. Abnormal detection is performed on each water pump energy consumption value in the range with λ as the detection step length to obtain the reasonable range of the water pump energy consumption value at the current moment. The value of the active state parameter corresponding to the upper limit of the reasonable range of the water pump energy consumption value at the current moment is taken as the optimal state value of the state parameter, and the active state parameter is adjusted.

The invention provides a time slot ALOHA adaptive access method which allows an age gain threshold value and a sending probability to dynamically change along with a time slot under a multi-packet receiving mechanism. According to the method, age gain initial distribution is given, and the following steps are executed in each time slot: each time slot initial access point sets a sending probability and an age gain threshold by taking maximization of a current time slot AoI reduction amount expected value as a target according to the estimated age gain distribution; the users whose real-time age gains are greater than or equal to a set threshold in each time slot are accessed to a channel according to a set sending probability, if the number of the users simultaneously transmitted in the time slot is less than or equal to the packet acceptance gamma of the receiving end, the transmission is successful, otherwise, all the users fail; the last access point of each time slot records the observation information of the number of users transmitted at the same time in the time slot; and each time slot end access point estimates the age gain distribution of the next time slot beginning based on the Bayesian update theory according to the age gain distribution estimated by the time slot beginning, the observation information in the time slot and the data arrival probability.

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 present application discloses an optimization processing method and system for MIQP problems based on an improved random branching strategy. The method includes constructing a graph network model according to a multi-source historical data set, traversing the paths from power nodes to each load node in the target distribution network based on a graph search algorithm, and generating a candidate set of branching strategies for the reconstruction of the target distribution network according to the paths; reconstructing the target distribution network under typical distribution network operation scenarios, and calculating the expected values of different branching strategies in the candidate set of branching strategies during the reconstruction process; inputting real-time load characteristic data, real-time line characteristic data, and real-time topology characteristic data into a pre-trained decision tree model to obtain the initial weight parameters of each branching strategy during the reconstruction of the target distribution network; generating strategy weight parameters of the branching strategies based on the initial weight parameters and the expected values, and optimizing the solution process of the reconstruction of the target distribution network according to the strategy weight parameters. The method provided by the present application improves the efficiency of the reconstruction process.