246 results about "Optimal weight" patented technology

The invention discloses a modeling method for inverting surface high-precision three-dimensional deformation through InSAR and GNSS data fusion, and belongs to the technical field of surface deformation monitoring. The method includes the steps of resolving InSAR line-of-sight deformation, unifying InSAR and GNSS data reference systems, establishing a stress model based on an elastic theory, calculating a three-dimensional deformation component for the first time based on a least square theory, obtaining an optimal weight matrix by applying a variance component estimation method, calculating final three-dimensional deformation and the like. According to the method, three-dimensional deformation and strain parameters are estimated by fusing InSAR measurement data and GNSS measurement data based on the elastic theory, the stress relation between adjacent points is considered, and unnecessary spatial interpolation of GNSS measurement values is avoided. The variance component estimation method solves the problem of unreasonable weight ratio of different types of observation values, and according to the adjusted optimal weight matrix, a function model is solved based on the least square method, and high-precision and high-spatial-resolution earth surface three-dimensional deformation information within the research range is obtained.

The invention belongs to the technical field of production quality management, and provides a production quality online prediction method for a cotton carding process. The method comprises the following steps: constructing and operating a fiber movement mechanism model based on a physical law and a data driving model based on machine learning; the first quality prediction result and the second quality prediction result are input into a dynamic weight fusion mechanism, and the dynamic weight fusion mechanism executes the following operations to generate a high-precision digital twinborn model: deploying the digital twinborn model integrated with optimal weight configuration on an industrial operation and maintenance platform, and driving the digital twinborn model by utilizing cotton carding process production data acquired in real time, so as to realize online dynamic prediction of the production quality. According to the method, the physical interpretability of the fiber motion mechanism model is reserved, the advantage of high precision of the data driving model is fully played, and the prediction precision and robustness of the digital twin model under different working conditions are remarkably improved through adaptive weight adjustment.

The invention provides an optical proximity correction method and system based on mask error model optimization, and a terminal, and the method comprises the steps: building a data sample set containing a plurality of process conditions through obtaining a large amount of mask manufacturing data under a specific photoetching process; and based on the sample set, constructing a physical optical sub-model and a machine learning sub-model to form a mask error model for outputting a final mask key size value. And then, according to a plurality of preset alternative weight coefficient combinations, reconstructing an objective function which introduces a mask error term, calculating corresponding objective function values, and correcting the photoetching pattern by using an OPC algorithm corresponding to each objective function value to obtain performance index data so as to determine an optimal weight coefficient combination. And finally, optimizing the OPC algorithm by using the target function of the optimal combination, thereby realizing the accurate correction of the photoetching pattern, and improving the precision and yield of semiconductor manufacturing. According to the method, the mask error model comprehensively considering physical optics and machine learning is established, and the OPC process is introduced, so that the influence of the mask error can be predicted and compensated more accurately, the critical dimension deviation and the like are reduced, and the device performance and the yield are improved. The method can adapt to different process conditions, and parameters and weight coefficients can be dynamically adjusted. And moreover, rework can be reduced, automatic adjustment is realized through closed-loop feedback control, the production stability and efficiency are improved, and the cost is reduced.

The invention combines the advantages of priori knowledge fusion, provides an accidental explosion concrete penetration depth prediction method fused with experience algorithm knowledge, and aims to improve the prediction precision and physical rationality in a small sample scene. The method comprises the following steps: firstly, constructing a combined parameter Z through a physical parameter, a target attribute and a Forrest formula, and taking the combined parameter Z as an input feature of a model to represent a physical rule; then, a multi-objective loss function is designed, the multi-objective loss function comprises a data fitting item, a physical constraint item and a boundary constraint item, the physical constraint item verifies the physical consistency of predicted values through trace disturbance input parameters, and the boundary constraint item ensures that the penetration depth is non-negative. And further constructing a dynamic weight adjustment mechanism, and automatically balancing the optimal weight of the physical law and data fitting according to the training process. And based on a back propagation neural network framework, performing model training in combination with the constraints. According to the method, the mean square error is remarkably reduced in a concrete penetration task, and meanwhile, the physical interpretability of a data-driven model is expanded.

The invention discloses a multi-model weight optimization fusion wind power prediction method under different wind speed scenes, and belongs to the technical field of wind power prediction, and the method comprises the steps: obtaining historical wind power data and historical meteorological data; performing wind speed scene recognition on the historical meteorological data, then performing clustering processing to obtain historical meteorological data under different wind speed scenes, and performing data set construction; constructing a multi-algorithm hybrid model based on three prediction models, namely a random forest prediction model, a bidirectional long-short-term memory neural network prediction model and a support vector regression prediction model; constructing a weight optimization function by using the target function and the constraint condition, and solving an optimal weight based on a KKT condition to obtain a trained multi-algorithm hybrid model under different wind speed scenes; and after wind speed scene recognition is carried out on the predicted meteorological data, a multi-algorithm hybrid model in a corresponding wind speed scene is input, and a wind power prediction result can be output. Therefore, wind power prediction can be realized.

The invention belongs to the technical field of energy storage, and particularly relates to a hybrid energy storage system energy management strategy based on adaptive weight and a related device. The invention provides a self-adaptive weight model based on a multi-layer perceptron by adopting a model prediction control method, and the self-adaptive weight model is used for real-time control of a hybrid energy storage system. Specifically, firstly, terminal constraint is applied to the state of charge of the electric energy storage equipment in an optimization function, then optimal weights corresponding to different initial state variables are calculated off line, and the multi-layer perceptron is trained according to the optimal weights. After training is completed, the multi-layer perceptron calculates the weight of the model prediction control module in real time based on the current state variable. And finally, the model predictive control module outputs an input variable of the system as a control reference value based on the obtained weight so as to control the system to operate. Compared with a traditional fixed weight model prediction control method, the weight can be adaptively adjusted according to the real-time state of the hybrid energy storage system, so that the control performance of the hybrid energy storage system is effectively improved.

The invention discloses a multi-vehicle cooperative positioning method based on progressive non-convex factor graph optimization, and the method comprises the steps: firstly calculating a GNSS pseudo-range error factor, an inter-vehicle distance measurement error factor and an inter-epoch constraint factor according to the collection data of a vehicle-mounted terminal GNSS sensor and a UWB distance measurement sensor; then a progressive non-convex welsch cost function is constructed, and an equivalent objective function is constructed in combination with a GNSS pseudo-range error factor, a workshop distance measurement error factor and an inter-epoch constraint factor; and finally, carrying out factor graph optimization on the equivalent objective function, solving an optimal weight and state, and outputting a global state and final weights of a GNSS pseudo-range error factor, a workshop distance measurement error factor and an inter-epoch constraint factor. According to the method, abnormal measurement values in cooperative positioning are suppressed by constructing a progressive non-convex welsch cost function, and meanwhile, multi-epoch and multi-node measurement information is fully fused by adopting a factor graph optimization architecture, so that the reliability of cooperative positioning is improved.

The invention relates to the technical field of process automation and artificial intelligence technologies, in particular to a multi-modal-based agent collaborative auditing method and system, and the method comprises the following steps: obtaining file information,..., carrying out the similarity analysis of a feature vector set and a decision vector set, obtaining a weight set corresponding to the historical file with the highest similarity from a database; setting the weight set as an initial weight set or a final weight set based on a similarity result; performing decision conflict analysis based on the decision vector set, and optimizing the initial weight set based on an analysis result to obtain a final weight set; and performing weighted fusion based on the final weight set and the decision vector set to obtain a fusion decision, and the method has the advantages of effectively solving decision conflicts in a multi-modal data fusion process, accurately positioning a problem root and improving man-machine cooperation efficiency.

The invention relates to the field of oil and gas drilling and production engineering, and discloses a crack complex index construction method and device, which can obtain normalized parameter value distribution of a plurality of crack basic parameters in a fractured stratum and a relative importance scale between every two crack basic parameters. Based on the normalized parameter value distribution of each fracture basic parameter, generating an objective weight coefficient of each fracture basic parameter; and based on the relative importance scale between every two fracture basic parameters, generating a subjective weight coefficient of each fracture basic parameter. And according to the objective weight coefficient and the subjective weight coefficient of each fracture basic parameter, performing combined weighting on each fracture basic parameter to generate an optimal weight coefficient of each fracture basic parameter. And performing linear weighting on each fracture basic parameter based on the optimal weight coefficient of each fracture basic parameter so as to construct a fracture complex index. The crack complexity index constructed by the method can be used for comprehensively representing the crack complexity of the fractured stratum.

The embodiment of the invention provides a vector map construction model training method and device based on knowledge distillation, and relates to an automatic driving perception technology method.The method comprises the steps that a teacher model is constructed by constructing a first vector map, and a student model is constructed by constructing a second vector map; determining Bayesian loss of the first vector map construction teacher model for training, determining intermediate layer feature loss and optimal weight loss of the trained first vector map construction teacher model and the second vector map construction student model, and determining first loss of each intermediate layer feature of the second vector map construction student model, and adjusting parameters of the second vector map construction student model based on the intermediate layer feature loss, the optimal weight loss and the first loss so as to train the second vector map construction student model, and constructing a map based on the second vector map construction student model. The cost of map construction can be reduced.

The invention relates to the technical field of marketing data analysis, and discloses a marketing analysis control method and system. The method comprises the steps of collecting multi-dimensional interaction data of a target user group; performing time sequence alignment processing on the multi-dimensional interaction data, eliminating asynchronous interference caused by acquisition time offset, and generating a synchronization behavior matrix; potential mode mining is carried out based on the synchronization behavior matrix, behavior clusters with relevance are recognized, and each behavior cluster corresponds to one user decision mode; constructing a dynamic weight model according to the user decision-making mode, calculating the contribution degree weight of the data of each dimension in the current marketing scene, and updating the model by adopting an incremental learning mechanism in combination with real-time feedback data; establishing a marketing strategy mapping table, and selecting an adaptive strategy group according to a current optimal weight combination output by the model; when the strategy is executed, a responsivity index is monitored synchronously, if the responsivity index is lower than a dynamic threshold value, re-matching is triggered, incremental data is fed back to a potential mode mining link, and a closed-loop optimization mechanism is formed.

The invention belongs to the field of ocean internal temperature detection, and particularly relates to a method for optimizing a multi-source constraint weight in ocean thermohaline profile variational inversion, which comprises the following steps of: acquiring a historical actually measured thermohaline profile and matched satellite remote sensing sea surface data, constructing a training set and counting a climate state background field; constructing a weighted variational cost function containing multiple constraint terms, and introducing an adjustable weight parameter for a key constraint term; based on the training set, constructing an external optimization function taking inversion error minimization as a target; weight parameters are iteratively learned through a double-layer optimization framework, the weight is updated by adopting methods such as Bayesian optimization in outer-layer optimization, and the optimal profile is solved under the given weight in inner-layer optimization; and finally, applying the optimal weight obtained by learning to variation inversion of new observation data to realize high-precision reconstruction of the thermohaline profile. According to the method, objective and adaptive optimization of the weight is realized, the inversion precision and robustness are remarkably improved, and the method has good physical interpretability and system universality.

The invention discloses a rotating machine cross-domain fault diagnosis method based on transfer learning. The method comprises the steps that a sensor is used for collecting source domain data and target domain data of a rotating machine; based on a feature extraction module, improving a local maximum mean value function, a weighting factor and a classifier, and constructing a fault diagnosis model; inputting the source domain data into a feature extraction module and a classifier, and obtaining an optimal weight and source domain classification loss through back propagation; inputting target domain data into the feature extraction module and the classifier after weight migration through weight migration to obtain a pseudo tag and classification loss of a target domain; obtaining domain alignment loss by using an improved local maximum mean value function; adjusting by using a weighting factor to obtain total loss; and inputting the target domain test data into the trained network model to obtain a fault result. According to the method, the fault features of the signals can be effectively extracted, and higher fault diagnosis accuracy and better generalization performance are shown under the cross-working condition.

The invention discloses a multi-user and multi-service resource sensing type scheduling method suitable for a low-orbit satellite system, and belongs to the field of satellite communication. The method comprises the following steps of: firstly, for a low-orbit satellite multi-beam communication scene, calculating the priority of each service in an initial time slot of a current scheduling period, and scheduling and distributing unoccupied PRBs (Physical Resource Blocks) in sequence after sequencing each user; calculating the throughput of each user; and entering the next time slot, and recalculating the throughput of each user until the cycle of the scheduling is ended. Constructing an optimization target to maximize the system throughput, and recording the total system throughput under the current weight combination; and finally, setting a learning rate and the maximum number of iterations, updating the weight, entering the next scheduling cycle, and recalculating the system throughput under the current weight combination. Traversing and screening out an optimal weight group which maximizes the throughput of the system, and combining and outputting the optimal weight group; according to the invention, the utilization efficiency of satellite resources is maximized, and the fairness of user services is ensured.

The invention relates to the technical field of enterprise financial management and risk control, in particular to a financial data risk prediction method and system based on big data and artificial intelligence. The method comprises the following steps: constructing an internal data set and an external data set, and preprocessing the internal data set and the external data set to obtain a preprocessed data set; constructing an enterprise financial risk scoring model composed of a random forest model and an XGBoost model, inputting the preprocessed data into the model to obtain a prediction result, calculating an optimal weight vector of the model through a Lasso method according to the prediction result and an actual result, and embedding the obtained optimal weight vector into the model; training and optimizing the enterprise financial risk scoring model, and predicting a final result; and carrying out risk grade division according to the final prediction result of the enterprise, generating risk early warning according to the risk grade, and generating a detailed risk analysis report. The method can improve the precision and stability of enterprise financial risk prediction.

The invention relates to the technical field of microbial fermentation, in particular to a biological fermentation monitoring method and system based on multi-source data fusion. The method comprises the following steps: acquiring multi-source monitoring data in a fermentation tank and constructing a multi-dimensional state vector; on the basis of data of the multi-dimensional state vector in a preset time window, constructing a metabolic entropy of the current fermentation process; a simulated annealing algorithm is adopted to optimize control parameters, the temperature of the simulated annealing algorithm is positively correlated with the metabolic entropy, and minimization of a second energy function is taken as a target; a catastrophe reignition mechanism is set in the optimization process, fermentation abnormity is diagnosed based on triggering of the catastrophe reignition mechanism, and abnormal state characteristics are matched with a preset abnormity database through iterative search of an optimal weight distribution scheme so as to determine the abnormity type with the highest matching degree. By introducing the metabolic entropy and the catastrophe reignition mechanism, the sensitivity, the accuracy and the robustness of monitoring the biological fermentation process are remarkably improved.

The invention discloses a power distribution network load access point dynamic weight preferential determination method considering new energy access, and belongs to the technical field of electric power. The method solves the problems that an existing load access point determination method depends on expert experience or static empowerment and is difficult to adapt to multi-source time-varying data of the power distribution network under new energy access, weight distribution lacks dynamic response, and consequently the deviation between a decision and actual operation is large. According to the technical scheme, the method comprises the steps of collecting multi-source data and preprocessing; constructing an evaluation index system, and performing dimension alignment based on the standardized data to form a feature tensor; generating an initial dynamic weight of a load access point by using a physical information space-time diagram network; optimizing the initial dynamic weight by adopting a constrained multi-objective Bayesian optimization method to obtain an optimal dynamic weight; and calculating a comprehensive score of each load access point based on the optimal dynamic weight and determining an optimal access point. According to the invention, the safety and operation efficiency of the power distribution network under new energy access are improved.

The invention discloses a multi-algorithm fusion cyclone similarity identification method and a storm surge forecasting method based on weight optimization, relates to the field of cyclone similarity identification, and establishes a similarity calculation function by constructing a multi-dimensional time sequence containing a plurality of key factors and combining a weighted dynamic time warping algorithm. In this way, multi-dimensional comprehensive characterization of the cyclonic space-time evolution process is achieved, and the identification deviation caused by the fact that a traditional method only depends on a single feature is overcome. On this basis, a genetic algorithm, a particle swarm optimization algorithm and a differential evolution algorithm are respectively adopted to carry out iterative optimization on the weight vectors, minimization of a deviation coefficient of a candidate similar cyclone set is taken as a target, and a global optimal weight vector corresponding to each algorithm is searched; and based on a preset fusion strategy, determining a target optimal weight vector from the plurality of globally optimal solutions. According to the closed-loop recognition process, multi-dimensional feature modeling and scientific weight optimization are effectively fused, and objectivity and accuracy of cyclone similarity recognition are remarkably improved.

The invention discloses a PEMFC residual service life prediction method based on multi-feature fusion, and the method comprises the steps: carrying out the related feature screening of an original data set through employing a Pearson's correlation coefficient method, carrying out the denoising of the screened features through employing adaptive noise complete set empirical mode decomposition, and carrying out the reconstruction of each denoised feature data based on statistical features; introducing Kent chaotic mapping and a linear adjustment strategy to improve a northern eagle algorithm (NGO) to construct an INGO; the weight and the offset of a channel attention mechanism (CAM) are optimized by using INGO, and the optimal weight and the optimal offset are found by using information entropy to perform feature fusion; the method comprises the following steps: introducing Logistic-Tent chaotic mapping and an index adaptive strategy to improve a GKSO (Genetic Kery Shark Algorithm) so as to construct IGKSO; the IGKSO is used to optimize the weight and bias of the bidirectional long short-term memory network (BiLSTM), and the mean square error (MSE) is used to find the optimal weight and bias to predict the remaining service life (RUL).

Systems and methods for interpreting one or more borehole features are provided herein. The method can include deploying an azimuthal borehole measurement tool into a borehole, obtaining at least one azimuthal borehole image, generating a synthetic image by sparse convolution of a weight function and a plurality of feature kernels, determining an optimal weight function that minimizes a difference between the synthetic image and the at least one azimuthal borehole image, and determining one or more geological characteristics of the borehole based on the optimal weight function and the feature functional representation.

The invention discloses a multi-modal large model parameter fusion method, device, equipment and medium, and the method comprises the steps: firstly dividing a universal multi-modal large model into a visual encoder, a language model and a projection layer, and then carrying out the fine adjustment of the universal multi-modal large model through vertical domain data, so as to obtain a vertical domain professional model; then visual encoder parameters, language model parameters and projection layer parameters are extracted from the general multi-modal large model and the vertical domain professional model respectively, and corresponding parameters of the general multi-modal large model and the vertical domain professional model are fused according to a preset weight ratio based on module parameters. And determining an optimal weight ratio combination of each module according to a performance evaluation result of the target data set, and finally generating a fused multi-modal model based on the optimal weight ratio combination. The fused multi-modal model can ensure the universality of the model under the condition of reducing the fine tuning cost of the model, and meanwhile, the performance of the model in the vertical field is remarkably improved, so that the application breadth and depth of the model are improved.

The invention discloses a bearing fault diagnosis method and system based on a simulated physical neural network. The method comprises the following steps: S1, preprocessing a collected vibration signal to obtain a to-be-diagnosed signal; s2, executing a hybrid optimization algorithm to obtain an optimal transmission band parameter and an optimal weight parameter; s3, configuring a feature extraction module according to the optimal transmission band parameter, and writing the optimal weight parameter into a classification module; s4, inputting the to-be-diagnosed signal into a feature extraction module to obtain a simulation feature vector; s5, inputting the simulation feature vector into a classification module, and outputting a classification voltage signal; and S6, determining a fault type, and outputting a result. According to the method, a hybrid optimization algorithm is adopted, the ratio of the inter-class dispersion degree to the intra-class polymerization degree of the feature vectors is calculated, optimal adaptation of feature extraction and classification tasks is achieved, and the diagnosis precision and generalization ability are improved; by constructing a full analog domain signal processing architecture, analog-to-digital conversion and digital calculation are not needed, and microwatt-level power consumption and microsecond-level real-time response are realized.

The invention provides a massive financial and accounting data fusion auditing feature extraction method, medium and system, and belongs to the technical field of digital data feature extraction. After a basic time window is determined through time sequence clustering, a self-changing time window function is constructed, and the window boundary is dynamically adjusted according to parameters such as a service fluctuation coefficient; extracting time dimension features by using a finance and accounting time sequence feature recognition model, extracting spatial dimension features based on geographical distribution and organizational structure information, extracting service dimension features according to service types and transaction attributes, calculating an outlier degree through statistical analysis to form outlier degree features, and establishing a second time window for verification; and a multi-objective optimization weight determination model is adopted to solve an optimal weight coefficient, and the four types of features are subjected to weighted fusion to form a comprehensive audit feature vector, so that the technical problem of insufficient audit feature extraction precision caused by incapability of dynamically adapting to internal change features of the data during feature extraction of the massive financial and accounting data is solved.

The invention discloses an electric spindle thermal error prediction method based on an improved particle swarm optimization BP neural network. The method comprises the following steps: collecting temperature rise and thermal error data of the motorized spindle through temperature and displacement sensors, and dividing the data into a training set and a prediction set; normalizing the data by using a grey correlation method and a Pearson correlation method, analyzing the correlation degree between the temperature rise and the thermal error, and further determining the weight coefficients of the stator, the rotor, the front bearing and the rear bearing; dynamic learning factors C1 and C2, dynamic weights W1 and W2 and weight coefficients rho 1, rho 2, rho 3 and rho 4 are introduced into the particle swarm optimization to optimize particle initialization position parameters, and then a GRA-APSO algorithm is constructed to find the optimal weight and threshold value of each layer of the BP neural network; and finally, predicting the thermal error condition of the motorized spindle by using the optimized model, and analyzing the prediction performance of the motorized spindle. By accurately predicting the thermal error of the main shaft, the performance and reliability of the electric main shaft system can be effectively improved, and the method has remarkable engineering application value.

The application discloses a kind of multi-feature enhanced deep learning clothes classification method based on auxiliary information, including determining clothes and user information needing classification;Mu-Cloth Net model is constructed based on convolutional neural network;Mu-Cloth Net model is trained, optimal weight is saved, and Mu-Cloth Net model is determined;Actual clothes image and user information are input into Mu-Cloth Net model, and clothes are classified.This scheme greatly improves the classification efficiency and improves the accuracy of classification by integrating auxiliary information, reduces the harmful effects of clothes deformation on the recognition result.And this scheme is respectively convolved to two information, and different scale features are extracted, so that the network can more effectively utilize auxiliary information to establish effective constraints.

This invention provides a sound source localization method, apparatus, and product based on incremental learning and imbalance correction. The method includes: acquiring a dataset for a new task, a parameter-frozen feature extractor, and regularization parameters; enhancing the tail category of the new task dataset; initializing an autocorrelation matrix, a cross-correlation matrix, and a category count based on the number of categories in the new task dataset; iterating through each sample in the new task dataset, updating the autocorrelation matrix, cross-correlation matrix, and category count; calculating the category weight for each category and calculating the Gini coefficient describing the category distribution; adaptively adjusting the regularization parameters based on the Gini coefficient; and solving for and outputting the optimal weight matrix based on the adjusted regularization parameters and aggregating the global autocorrelation matrix and global cross-correlation matrix of different categories. The optimal weight matrix is ​​used to update the sound source localization model. This invention addresses the problem of intra-task and inter-task imbalance in sound source localization, improving the sound source localization effect.

The present invention discloses a variable weight combination prediction method for battery manufacturing capacity based on triple reinforcement learning, which belongs to the field of power battery manufacturing prediction. This method targets the periodicity, mutation and seasonality of lithium battery manufacturing capacity, selects corresponding models to respectively mine the linear and nonlinear features in the time series to form a new combination prediction model; in view of the problem that the existing methods are difficult to obtain the optimal combination prediction weight, a double-layer reinforcement learning algorithm is designed to solve the optimal weight matrix under this time to reduce the prediction error; in addition, since the prediction effect of each single prediction model is different at different time points, this application integrates reinforcement learning and entropy ideas, explores the optimal sliding window length, uses sliding windows to divide the prediction sequence, and uses a double-layer reinforcement learning algorithm in each window to determine the optimal combination weight, and finally constructs a triple reinforcement learning battery manufacturing capacity combination prediction model, which further improves the prediction accuracy and reliability of battery manufacturing capacity.

The invention belongs to the technical field of virtual power plant operation evaluation, and particularly relates to a virtual power plant operation comprehensive evaluation method and system.The method comprises the steps that a three-level multi-dimensional operation risk evaluation index system is constructed, and three types of weight vectors are obtained through an analytic hierarchy process, an entropy weight method and an inter-index correlation and conflict objective weighting method; carrying out collaborative correction through a non-cooperative game model to obtain an optimal comprehensive weight vector; a standard cloud model of each risk level is constructed based on an extension cloud model, an actual evaluation cloud model is constructed in combination with the score of a professional scoring system and an optimal weight, the score weight of the professional scoring system is determined through a grey correlation degree, an evaluation value is corrected, the actual cloud model is updated, and finally the digital characteristics of the actual and standard cloud models are compared. And quantitatively judging the operation risk level of the virtual power plant. According to the method, the comprehensiveness, reliability and scientificity of evaluation are improved, and support is provided for risk prevention and control of the virtual power plant.

The application provides a broadband array electronic detection anti-interference method and device and a storage medium, and relates to the technical field of array signal processing.The method comprises the following steps: defining a signal receiving carrier core parameter and constructing an interference signal model, arranging the interference signal in a space-time two-dimensional manner to form a data matrix, and decomposing to obtain an interference characteristic vector set; constructing a target signal model and fusing the interference model to form a mixed signal matrix, and estimating a covariance matrix thereof; constructing a target direction vector based on a search direction, establishing an optimization constraint in combination with the interference characteristic and the covariance matrix, and solving to obtain an optimal weight vector; and performing weighted processing on the mixed signal through the optimal weight vector, and outputting a target signal after interference suppression.The application constructs a data adaptive anti-interference logic, the core of which is to separate the interference and the target signal characteristics, and the fixed filtering parameter is not needed, so that the complex electromagnetic interference can be adaptively adapted, the interference can be suppressed, and the target signal can be completely reserved.