139 results about "Sample space" patented technology

The invention belongs to the field of intelligent construction, and discloses a deep foundation pit displacement prediction method combining finite element and data driving, which comprises the following steps: calculating a theoretical elastic modulus according to an investigation report, and constructing a sample space by adopting a Latin hypercube sampling method and an orthogonal experiment design method; a proper constitutive model is selected, and corresponding lateral displacement is calculated through finite elements; dimensionality reduction processing is carried out on the data set based on global sensitivity analysis of variance so as to accelerate convergence during subsequent inversion; training an SKRR-LSBOOST model through an ensemble learning method to replace a traditional model; combining an improved Hemma optimization algorithm with a substitution model, and applying the combined model to parameter inversion; and performing forward analysis based on the finite element model defined by the inversion parameters to predict the deformation of the deep foundation pit in the next excavation stage. According to the method, the advantages of numerical simulation and data driving are fully exerted, the calculation cost is reduced according to the rock and soil parameters, higher prediction precision is achieved, and the accuracy of foundation pit deformation prediction is improved.

The invention discloses a small sample modeling stability evaluation method and system based on Bootstrap resampling, and particularly relates to the technical field of computers and data intellectualization, the method comprises the following steps: completing data preprocessing and stable stage identification under a unified time base, and forming a segment set; an average block length is used as a decision quantity, an SBB is optimized to construct a sample space, and an optimal block length is determined in combination with variance consistency and nominal coverage rate consistency criteria; carrying out re-sampling training around short / medium / long scales, and collecting layered indexes such as prediction, parameters and features; stability calculation and empirical coverage rate calibration are completed based on intra-scale statistics and inter-scale weighted mixing, and a pseudo-stationary diagnosis score is output; and finally, engineering judgment and backspacing optimization are carried out according to the coverage rate, the correlation maintenance and the risk threshold. The system records random seeds, block metadata and model version generation evidence pointers, has the characteristics of traceability and reverifiability, and is suitable for scenes of production lines, network traffic, finance and the like.

The invention discloses a flow field prediction-oriented full-process automatic joint reduced-order modeling method, which comprises the following steps of: specifying a target physical field parameter space, and randomly generating a sample space according to a Latin hypercube sampling method; constructing a full-process automatic simulation tool chain, driving target physical field numerical calculation and generating a training data set; carrying out singular value decomposition-based intrinsic orthogonal decomposition on the output physical field data, and only retaining first r main feature components to construct a reduced-order data set; constructing a multi-input multi-output full-connection feedforward neural network, and modeling and training a nonlinear mapping relation between input parameters and reduced-order features; new working condition parameters are input, reduced-order features are predicted through the trained neural network, distribution of a target physical field is reconstructed according to a singular value decomposition reduction matrix, and more flexible and reliable technical support is provided for reducing the training cost of a reduced-order model and improving simulation efficiency.

The invention belongs to the technical field of water conservancy and hydropower engineering, and particularly relates to a computer vision-based dam seepage field rapid calculation method, which comprises the following steps of: acquiring geological attribute parameters and upstream and downstream water level working condition parameters, and constructing a sample space; constructing a data set by using a dam seepage numerical model considering the spatial variability of the permeability coefficient based on the sample space; and establishing an improved conditional deep convolutional generative adversarial network model based on the data set to realize rapid calculation of the dam seepage field. The problems that in an existing dam seepage behavior analysis and research method, a numerical simulation method occupies too many computing resources and consumes long time, most agent models only pay attention to local scattered measuring points for modeling, and overall seepage field modeling at a key section cannot be achieved are solved. However, most of the current image generation researches based on computer vision neglect the generation precision problem of high-frequency details of the image, and the high-frequency edge information of the image cannot be accurately generated.

The invention relates to a harmonic reducer fault diagnosis method based on double-branch feature fusion. The harmonic reducer fault diagnosis method comprises the following steps: synchronously acquiring multi-sensor data of a harmonic reducer acquired by vibration, acoustic emission and current sensors; processing the multi-sensor data through a one-dimensional convolutional neural network to obtain data in a two-dimensional matrix form; the data in the two-dimensional matrix form is input into a pre-trained model, and the processing process of the model is as follows: feature extraction is performed on the data in the two-dimensional matrix form through an MCWGraphKAN branch feature extraction module and a GBiMama branch feature extraction module to obtain a first feature and a second feature, the first feature and the second feature are fused and then mapped to a sample space through a full-connection layer, and the data in the two-dimensional matrix form are obtained; and fault classification is realized. The method achieves the fault diagnosis of the harmonic reducer through a deep learning method, and is used for solving the problems that a single sensor cannot fully express the operation state of the harmonic reducer, and the fault feature extraction of a single-branch network is insufficient.

The invention discloses a micro-channel cooling turbine blade inner cavity anti-deposition bionic structure design method and system, and relates to the field of turbine blade cooling of aero-engines. The problems of optimizing the wall cooling effect, flow resistance and calculation efficiency of the micro-channel structure are solved. The method comprises the steps that parameterization design is conducted on a bionic turbulent flow structure, and characteristic geometric parameters are obtained; establishing a micro-channel three-dimensional model of the bionic fish scale structure; screening the characteristic geometric parameters as design variables, and designing upper and lower intervals of the variables; taking heat exchange, pressure drop and particle deposition of the micro-channel as optimization targets, adjusting a design variable interval, and constructing a test sample space; performing numerical calculation by using the channel model to obtain a multi-objective optimization parameter value; establishing an agent model between the optimization parameter values and the geometric characteristic parameters; and optimizing by utilizing an agent model and a multi-objective optimization algorithm to obtain the optimal geometric parameters of the bionic fish scale turbulent flow structure, so that the efficient design of the micro-channel structure and the efficient utilization of the cold air consumption are realized.

The invention discloses a self-adaptive parameter identification method for an SST turbulence model in shock wave boundary layer interference, and the method comprises the steps: S1, constructing a high-dimensional sample space according to a closed parameter interval range of the SST turbulence model under a determined working condition, carrying out the optimal Latin hypercube sampling, and uniformly obtaining the sample information in the whole space, and enabling the sample information to serve as an input parameter sample; s2, performing numerical simulation based on an input parameter sample and a determined working condition to form a model prior sample set; s3, according to the model prior sample set, constructing a flow field wall surface pressure prediction model under shock wave boundary layer interference based on an improved Bayesian neural network; and S4, based on the flow field wall surface pressure prediction model under the interference of the shock wave boundary layer, performing adaptive parameter identification on the SST turbulence model by adopting a particle swarm optimization algorithm to obtain the optimal closing parameter of the SST turbulence model for realizing the high-precision simulation of the flow field. According to the method, the numerical simulation precision and speed are improved, and technical support is provided for safe and reliable work of the scramjet engine in a complex environment.

The invention discloses a scenario simulation experiment design method and device, and the method comprises the steps: processing experiment demand information, and obtaining a simulation scenario; processing the simulation scenario by using an experiment design model to obtain a simulation experiment sample and experiment sample space description information; processing the simulation experiment sample and the experiment sample space description information by using an integrated operation model to obtain an experiment simulation result; performing situation monitoring on the simulation scenario by using a situation monitoring model to obtain simulation process data; and comprehensively evaluating the simulation process data and the experiment simulation result to obtain a comprehensive evaluation result. According to the method, the simulation scenario is subjected to experimental design, and the scenario simulation experiment efficiency is improved. The simulation process data and the experiment simulation result are comprehensively evaluated to obtain a comprehensive evaluation result, and rapid and efficient evaluation of the scenario simulation result is realized.

The invention discloses a CKM-CNN-based power system transient stability assessment method, system, equipment and medium, and relates to the technical field of power system assessment, and the method comprises the steps: collecting time sequence electrical quantity data after power system fault disturbance, constructing a transient sample set, carrying out the standardization processing and marking of a sample, and carrying out the calculation of the transient stability of the power system; a transient sample set is divided through a clustering algorithm, based on a sample cluster division result, an adaptive oversampling technology is adopted to carry out directional enhancement on an unstable sample, an equalized sample space is generated, a multilayer convolutional neural network is utilized to carry out hierarchical feature extraction on the equalized sample space, a high-dimensional feature vector is generated, and according to the high-dimensional feature vector, a high-dimensional feature vector is generated. And outputting a transient stable state probability evaluation result through the classification decision-making layer. According to the method, a generalization sample set is constructed through standardization and labeling, the scarce instability sample identification capability is improved by combining two-stage clustering and ADASYN oversampling, and the system dynamic state is accurately captured by utilizing 1D-CNN hierarchical feature extraction, so that efficient and robust evaluation of the transient stability of the power system is realized.

The invention relates to the technical field of power data processing, in particular to a power data management planning method based on source network load storage coordination interaction. The method comprises the following steps: constructing a power data clustering sample space; obtaining a one-dimensional cluster corresponding to each moment and a first local optimal risk degree of the one-dimensional cluster, and screening the one-dimensional cluster to obtain an abnormal risk cluster; analyzing the spatial features of the data points in the abnormal risk cluster to obtain a second local optimal risk degree of the data points; obtaining the clustering weight of the data points in the abnormal risk cluster according to the second local optimal risk degree of the data points in the abnormal risk cluster and the first local optimal risk degree corresponding to the abnormal risk cluster; clustering is carried out in the power data clustering sample space based on the clustering weights of the data points in the abnormal risk clustering clusters, and a clustering result is obtained; and predicting the power data by using a clustering result. According to the method, the robustness of power data clustering and prediction can be improved.

The invention relates to a frozen coal train uniform temperature rise optimization method based on Morris-CNN fusion. The method comprises the steps that model structure parameters and electromagnetic parameters are determined; a Morris method is adopted, and parameters are selected; obtaining a sample space; obtaining a plurality of grid units; obtaining the temperature value of the temperature measuring point; calculating temperature rise efficiency and uniformity indexes; forming a sample data set to train a convolutional neural network; searching values of five parameters corresponding to the optimal temperature rise efficiency and the optimal uniformity; and the final optimal temperature rise efficiency and the optimal uniformity are obtained. According to the method, a structure optimization design based on the combination of Morris global sensitivity analysis and the convolutional neural network is adopted, the method combines a mathematical method and a statistical method, modeling and analysis are carried out on the influence of multiple variables, and the efficiency and the effect are improved; according to the method, the temperature rise uniformity and the temperature rise efficiency are optimized by applying a multi-objective optimization method, the gradient descent characteristic of the convolutional neural network is utilized, the uniformity and the temperature rise efficiency are balanced, and the optimal parameters are obtained to guide the quick and uniform temperature rise design of the frozen coal train.

The invention discloses a power system probabilistic power flow method and system based on polynomial proxy and graph parallelism, and belongs to the technical field of power system planning and operation optimization. The method comprises the following steps: establishing a probability distribution model of a bus load prediction error to obtain sample distribution of predicted power; representing the output response quantity of the system as an input polynomial function, and establishing an initial polynomial agent model; determining an optimal configuration point set; mapping all the selected configuration points to an input sample space to obtain a small number of sample points of an input variable; performing deterministic load flow calculation on the sample points to obtain a sample vector of output response; calculating an undetermined coefficient in the proxy model according to the configuration point matrix and the sample vector; and solving the statistical characteristic quantity of the power flow response to obtain the probability distribution of the response. According to the calculation method provided by the invention, the probability distribution of the power flow response can be accurately and efficiently estimated, and convenience is provided for probabilistic power flow calculation of a novel power system under the access of a distributed power supply.

The invention provides a gear wind resistance power loss prediction method and system, computer equipment and a medium, and belongs to the field of aero-engine power transmission, and the method comprises the steps: obtaining physical parameters of a to-be-predicted gear; according to the physical parameters of the to-be-predicted gear, gear structure parameters are extracted, tooth surface torque parameters and fluid mechanics parameters are calculated, and the gear structure parameters, the tooth surface torque parameters and the fluid mechanics parameters of the to-be-predicted gear are consistent with the same type of parameters in the sample space in sequence and dimension; the gear structure parameters, the tooth surface torque parameters and the fluid mechanics parameters of the to-be-predicted gear are input into a pre-trained power loss prediction model, the wind resistance power loss of the to-be-predicted gear is obtained, and the power loss prediction model is obtained through a sample space training deep learning model. Compared with numerical simulation, the method has the advantage that the power loss prediction efficiency is remarkably improved.

The invention discloses a machine learning-assisted on-chip transformer automatic synthesis method. The method comprises the following steps: firstly, setting an on-chip transformer electrical parameter target and a process constraint; quickly predicting by using a reverse artificial neural network to obtain a group of structure parameters as an initial solution; then structure parameters are sampled randomly, a part of sample space is obtained by floating around an initial solution, and full-wave electromagnetic simulation is carried out to obtain samples; sample full-wave simulation data is compressed, and then on-chip transformer priori knowledge is introduced to train a GPR proxy model, so that the dimension of the model is reduced, and the prediction accuracy is higher; and then global optimization is carried out, the optimization range is constrained through an initial solution, and the convergence and exploratory performance of the algorithm are enhanced through multi-path optimization brought by different lower confidence limit constants. According to the method, an algorithm combining a reverse network and forward optimization is adopted, so that the accuracy is ensured, the calculation cost is lower, the optimization speed is higher, and the automation and intelligence of the design of the on-chip passive device are promoted.

The invention provides a building energy consumption multi-area cooperative control method, device and equipment and a storage medium, and can be applied to the technical field of artificial intelligence and building energy consumption control. The method comprises the steps that temperature evaluation information and energy consumption evaluation information of a sample space area in a sample building are processed, an initial evaluation result of a sample environment temperature in a preset time period is obtained, and the temperature evaluation information is determined according to sample temperature information of the sample space area; the energy consumption evaluation information is determined by sample power information, sample energy consumption information and cost information of the sample space region; obtaining a control agent based on the sample information, the initial evaluation result, and loss functions and fusion strategies of respective strategy models of the sample space region and the sample association region; and on the basis of the state information of the space area in the building and the associated area information, action information of the valve opening degree of the control equipment is obtained, control operation is executed on the valve opening degree on the basis of the action information, and energy consumption control information of the space area is obtained.

The invention provides an MPC grinding machine thermal management control method based on a finite element, and the method comprises the steps: building a physical model related to the thermal management of a grinding machine system, and taking finite element simulation data as a prediction sample space; according to the temperature delay of the grinding machine system, a target transfer function controlled by a prediction model is established to serve as a control equation of model prediction; performing rolling optimization and feedback correction on the prediction model through an MPC control algorithm, and fusing the predicted temperature change trend with real data measured by a temperature sensor to obtain a fused optimal output variable quantity; and the input flow is determined according to the optimal output variable quantity, so that the real-time temperature control effect of the grinding machine system is achieved. While the control effect is improved, predictive control under different working condition requirements can be achieved, combined control over multiple grinding machines is achieved, and more reasonable heat management control is achieved by combining the particle swarm optimization algorithm with coupling adjustment of the Gaussian process.

The invention discloses a small sample space target key component segmentation method and equipment based on meta-learning, and the method comprises the steps: obtaining a to-be-segmented image, and carrying out the preprocessing of the to-be-segmented image, and obtaining a preprocessed image; performing segmentation processing on the preprocessed image by adopting a trained improved double-branch Swindow-UNet network to obtain a key component segmentation result of the target in the to-be-segmented image; wherein the trained improved double-branch Swindow-UNet network is obtained by training based on a model independent element learning method; the improved double-branch Swindow-UNet network has a multi-scale feature extraction capability and a global feature modeling capability. According to the method, rapid training and adaptive parameter adjustment of the model under a small number of samples can be realized, and the generalization ability and segmentation precision of the model in a small sample environment are remarkably improved.

The invention discloses an oil-immersed current transformer data enhancement method based on transfer learning. The method comprises the following steps: constructing a data preprocessing model, and carrying out standardization processing on oil chromatographic data of a transformer and a current transformer by utilizing a quantile transformation method; constructing an encoder to map original signal data into a potential variable sample space, and proposing a noise enhancement mechanism with multi-distribution fusion; the generative network performs random sampling from known probability distribution to obtain hidden variables, and calculates a preliminary enhanced signal obeying Gaussian distribution; the generative adversarial module adopts a generator and a discriminator, the generator performs incremental generation on the preliminary enhanced signal and outputs a sample conforming to the characteristics of the current transformer, and the discriminator distinguishes the difference between a real current transformer sample and a generated sample according to physical knowledge constraints in the oil chromatography field; and finally, carrying out reverse quantization on the generated sample, and outputting high-quality oil chromatographic data of the current transformer. According to the invention, the problem of rare data of the current transformer is effectively solved.

The invention belongs to the technical field of network security, and discloses an intrusion detection method and system based on metric element learning, and a storage medium. The intrusion detection method comprises the following steps: S1, acquiring flow data and preprocessing the flow data; s2, dividing a training set, a verification set and a test set, and generating a task composed of a support set and a query set; s3, constructing an intrusion detection module by adopting an autocorrelation representation module and a cross-correlation attention module, and embedding the intrusion detection module into the backbone network to form an intrusion detection model; s4, constructing a meta learning framework, using a training set to optimize sample space mapping function parameters of the detection model through a meta learning mode, searching an optimal meta learner under the detection model, and performing fine tuning to achieve an optimal state of new task detection; and S5, applying the intrusion detection model to actual intrusion detection. According to the method, the model generalization is high, a small number of sample categories can be trained, high accuracy can be kept in evaluation, and the classification effect of small sample intrusion detection is improved.

The invention discloses a small sample action recognition method based on multiple tasks and local parallel attention, which comprises the following steps: S1, initializing an SSL-LPA network model, modeling a human skeleton sequence into a space-time diagram, dividing the space-time diagram into a training set and a test set, and constructing a support set and a query set; s2, inputting a training sample, and executing a feature extraction task to extract a support sample and a query sample; s3, performing data enhancement on the support sample, and then executing a self-supervision auxiliary task; s4, executing a supervised classification task, expanding a sample space, and enhancing action uniqueness between a prototype vector and a query sample; s5, calculating the similarity distance between the prototype vector and the query sample to determine the category of the query sample; s6, the supervised classification task and the self-supervised auxiliary task have a synergistic effect, and the SSL-LPA network performance is optimized through mutual feedback; the robustness and generalization ability of the model are improved, and the ability of accurately identifying the action can be learned under the condition of limited label samples.

The invention relates to the technical field of mechanism reliability analysis, and particularly discloses a mechanism reliability calculation method based on a multi-fidelity active learning agent model. Comprising the steps of determining a sample space, performing initial test design, constructing a multi-fidelity model, adding sample points based on an active learning function, updating the model, calculating a failure probability and a variable coefficient, obtaining a mechanism performance function and a reliability analysis result and the like. Compared with the prior art, the method has the advantages that the multi-fidelity agent model is constructed, so that the calculation cost for constructing the model is reduced on the premise of ensuring the fitting precision of the model; an active learning function is applied to screen and add an optimal sample point, and a multi-fidelity Kriging model is updated, so that the precision of the constructed proxy model is improved; by setting the TAEA convergence criterion, the calculation precision and the calculation efficiency of the model are balanced, and the calculation efficiency of the proxy model is improved.

The invention provides an optimized design method for an impact type rotating wheel bucket blade structure based on entropy production analysis. The method comprises the following steps: firstly, generating a sample space through parametric modeling and optimal Latin hypercube experimental design of a rotating wheel; then, acquiring internal flow field data through numerical simulation; identifying a key loss area based on entropy yield flow loss; then, a Gaussian process regression agent model is constructed by taking the geometric dimension parameters of the bucket blade as input and hydraulic efficiency and entropy production as output, multi-objective optimization is carried out by adopting an improved NSGA-II algorithm, and the accuracy of the agent model is improved through a dynamic updating strategy; and finally, carrying out numerical simulation verification on the Pareto optimal solution, and screening out a runner design scheme with excellent comprehensive performance. The flow loss can be effectively reduced, the hydraulic efficiency and the operation stability of the impact type runner are improved, and technical support is provided for optimization of the hydraulic performance of the impact type runner.

The invention relates to a coal rock material mesoscopic modeling and multi-scale simulation method based on active deep learning. The method comprises the following steps: firstly, drawing up a complete label-free data sample space, and establishing a labeled data sample set based on the complete label-free data sample space; constructing a multi-scale analysis offline proxy model based on a deep learning model, and training the multi-scale analysis offline proxy model based on the labeled data sample set to obtain a basic predictor; inputting an unlabeled data sample into the basic predictor to obtain a sample false label, and determining an unlabeled data information measure by adopting an improved active learning strategy based on the sample false label; expanding the annotated data sample set based on the unannotated data information measurement, and performing iterative training to obtain a final offline agent meta-model; and finally, embedding the final offline agent element model into a finite element, and obtaining a strain-stress response under coal rock loading multi-scale simulation. And the multi-scale simulation speed is obviously improved.

The present invention discloses a user operation trajectory analysis method, apparatus, device, and storage medium, which are applied to the field of computer interaction technology. The method comprises: obtaining operational behavior information of users accessing system pages; the operational behavior information includes at least: page information, information about operational components corresponding to the page interactions, and information about regions corresponding to the operational components; constructing a dynamic trajectory sample space based on the operational behavior information; characterizing and stratifying the dynamic trajectory sample space to obtain stratification results; fitting the user operation trajectory based on the stratification results to obtain a target operation trajectory. The present invention characterizes and stratifies the dynamic trajectory sample space and fits the user operation trajectory based on the stratification results to extract high-frequency operational behavior trajectories of different types of users, thereby greatly improving the accuracy of data analysis.

A method for EMT image reconstruction based on the sequential Monte Carlo principle, comprising the following steps: step one, modeling the EMT image reconstruction process; step two, initializing the state space; step three, state space sampling and sample weight update: after obtaining the initial sample space, importance sampling is performed on it, and the corresponding weight is set according to the observation distance of the sample; step four, optimal state estimation: based on the obtained particles and their weights, the state space estimation of the system is calculated, and the optimal solution is searched by iterative updating, and finally the image is reconstructed as the global optimal estimation output. The present invention is superior to other existing methods in terms of image error and correlation coefficient, and has better imaging quality. Therefore, it is an efficient and accurate EMT image reconstruction method, and also provides a new method and means for the research of EMT technology.

The invention provides a balance calibration modeling method and system based on sample errors and space optimization. The method comprises the steps of obtaining a calibration data set and dividing the calibration data set into a sample set and a verification set; calculating a load space weight and a load complexity weight of the sample set, and obtaining a comprehensive weight based on the load space weight and the load complexity weight; and constructing a polynomial model of the sample set, and solving a coefficient matrix in the polynomial model based on the comprehensive weight to obtain a final balance calibration model. The method further comprises the following steps: verifying and evaluating the balance calibration model by using the sample set and the verification set, and optimizing the balance calibration model according to an evaluation result. According to the balance calibration modeling method cooperating with the sample space distance and the load complexity weight provided by the invention, nonlinear system errors introduced by calibration load complexity are fully considered, so that the influence of error distribution of calibration samples under various load combinations on modeling is more uniform and reasonable; and meanwhile, the problem of sample space occupation ratio is also solved.

The present invention relates to a probabilistic power flow method and system for power systems based on polynomial surrogate and graph parallelism, belonging to the technical field of power system planning and operation optimization. It includes establishing a probability distribution model of the bus load prediction error to obtain the sample distribution of the predicted power; representing the output response quantity of the system as a polynomial function of the input and establishing an initial polynomial surrogate model; determining the optimal set of collocation points; mapping all selected collocation points to the input sample space to obtain a small number of sample points of the input variables; performing deterministic power flow calculations on the sample points to obtain the sample vector of the output response; calculating the undetermined coefficients in the surrogate model according to the collocation point matrix and the sample vector; and solving the statistical characteristic quantities of the power flow response to obtain the probability distribution of the response. The calculation method provided by the present invention can accurately and efficiently estimate the probability distribution of the power flow response, facilitating the probabilistic power flow calculation of a new power system under the access of distributed power sources.

This invention discloses a method for constructing a multiscale geological model based on a convolutional conditional neural process. The method comprises: building and training a convolutional conditional neural process model; loading conditional data and generating a grid to be simulated; inputting the grid to be simulated and the conditional data into the trained convolutional conditional neural process model; obtaining a current spatial probability distribution map based on the conditional data and its spatial distribution; and, based on the conditional data and the spatial probability distribution map, converging the variable range of the generated result to a sample space using statistical methods; and saving the final result to complete the simulation. The invention has the beneficial effect of significantly improving the reconstruction capability and efficiency of geological heterogeneous models.

The application discloses a kind of federal learning adversarial sample detection methods based on variational bayesian network, comprising the following steps: training model is issued to each participant of federal learning;Each participant initiates private intersection operation two by two, so that each participant obtains respective sample intersection;The sample intersection obtained by each participant is intersected, to obtain common sample space;Each participant filters target local sample from respective local sample according to common sample space, uses respective target local sample of each participant to train model, to obtain respective gradient information of each participant;Gradient information sent by each participant is received, and the uncertainty of gradient information distribution density is calculated;Adversarial sample in the local sample of each participant is determined according to the uncertainty of gradient information distribution density.The application can complete the task of adversarial sample detection in federal learning system without directly accessing private training data.

The application provides a building energy consumption multi-region collaborative control method, device, equipment and storage medium, which can be applied to the fields of artificial intelligence and building energy consumption control technology. The method comprises the following steps: processing temperature evaluation information and energy consumption evaluation information of a sample space region in a sample building to obtain an initial evaluation result of a sample environment temperature within a preset time period, the temperature evaluation information being determined by sample temperature information of the sample space region, and the energy consumption evaluation information being determined by sample power information, sample energy consumption information and cost information of the sample space region; obtaining a control intelligent agent based on sample information, the initial evaluation result, a loss function of a respective strategy model of the sample space region and a sample associated region, and a fusion strategy; obtaining action information of a valve opening degree of a control device based on state information and associated region information of a space region in the building, performing a control operation on the valve opening degree based on the action information, and obtaining energy consumption control information of the space region.