85 results about "Simultaneous learning" patented technology

The invention discloses a multi-label electrocardiogram classification method based on self-supervised pre-training and multi-modal semantic alignment, which belongs to the technical field of artificial intelligence, and comprises the following steps: realizing self-supervised pre-training of unlabeled data through a single-modal contrast enhancement network, generating global and local contrast views by adopting a multi-scale random cutting strategy, and classifying the global and local contrast views in a multi-scale random cutting mode; in combination with a teacher-student network architecture, the potential invariance features of the ECG signals are learned while negative sample dependence is avoided, the problem of annotation data scarcity is effectively relieved, and the feature robustness is improved. A multi-modal fusion mechanism based on label semantic guidance is provided, a time domain signal and a frequency domain time-frequency graph are mapped to a unified semantic space through fine-grained semantic alignment, local feature enhancement and cross-modal complementary information fusion are realized by using a cross attention mechanism, and the problem of semantic difference caused by modal heterogeneity in a traditional method is overcome. A multi-label comparison loss function based on a disease co-occurrence relation is proposed, a category discrimination boundary is dynamically optimized by modeling a label co-occurrence probability, the feature separability of a tail category is improved while the head category discrimination ability is enhanced, and the problem of sample category imbalance in a multi-label scene is remarkably relieved.

The invention discloses an artificial intelligence speech recognition system, and the system comprises a multi-modal feature extraction module which employs an improved Conformer architecture to synchronously extract the time-frequency features and text embedding vectors of speech signals; the joint training module is used for performing joint optimization on ASR and NMT loss functions through an adversarial training strategy, learning voice recognition and machine translation tasks at the same time through joint training, and completing direct mapping from voice features to a target language; the context perception translation engine is used for integrating an attention mechanism of a pre-training language model, carrying out deep coding on the extracted speech features and generating cross-language semantic representation; the self-adaptive post-processing module is used for dynamically optimizing an output result by adopting a reinforcement learning framework, dynamically adjusting the output result according to a reward function, and optimizing translation quality and a speech synthesis effect; the dynamic language recognition module is a real-time language classifier based on a Wave2Vec 2.0 framework and is used for recognizing the language of the input voice in real time; and the incremental field adaptation module is used for quickly updating a field term library by using a LoRA fine tuning technology.

The invention discloses a multi-mode emotion continuous recognition method for medical treatment, belongs to the technical field of artificial intelligence and medical treatment information, and mainly aims to simulate the dynamic change process of emotion by establishing a Neural ODEs framework and overcome the static property and discreteness of emotion modeling in a traditional method. Through a causal inference technology, emotional features are separated from individual-independent physiological differences, and the generalization ability across individuals is improved. A self-supervised learning method is utilized, the synergistic effect between the EEG and the eye movement signal is improved through cross-modal contrast learning, and the emotion recognition precision is enhanced. The calculation complexity is reduced through a dynamic sparse attention mechanism, and meanwhile, focusing is performed on a key time slice in emotion recognition. Through multi-task joint learning, the model learns multiple tasks such as emotion intensity regression and tested identity recognition during emotion classification, and the personalized emotion recognition capability is improved.

The invention discloses a pipeline defect magnetic flux leakage detection method and device based on multi-scale data driving deep learning, and relates to the technical field of pipeline defect detection. A multi-scale magnetic flux leakage signal data set containing defect global distribution and local details is generated, and multi-level primary features are automatically extracted by using a convolutional neural network; and the defects of poor generalization and easy key information omission of a manual method are overcome. And then dynamically enhancing and performing weighted fusion on multi-scale features by means of a multi-scale convolution branch and an attention mechanism, so that the model can learn global and local features of the defect at the same time, the problem that the global and local features of the defect are difficult to consider in traditional deep learning is solved, and through transverse connection and up-sampling fusion of a feature pyramid, a multi-scale feature is obtained. And the features have high semantic information and high spatial details. And finally, independently carrying out multi-task prediction by virtue of a task decoupling module, and calibrating result consistency by virtue of a task alignment module, so that the detection accuracy in a complex scene is improved, and more accurate and robust pipeline defect magnetic flux leakage detection is realized.

The invention provides a wind power plant power prediction method based on data distribution fitting, which comprises the following steps of: when a bidirectional GRU neural network model of a weighted triple attention mechanism is constructed, firstly fitting Gaussian mixture distribution of errors through maximum likelihood estimation, embedding an error distribution parameter theta into an error loss function of a PINN neural network model, and then fitting the error distribution parameter theta into the error loss function of the PINN neural network model; a PINN neural network model can learn data rules and error distribution characteristics at the same time, the problem that distribution hypothesis is not matched with actual data characteristics of the wind power plant in a traditional method is solved, and the accuracy of wind power plant power prediction is improved; and then connecting an output layer of the triple attention mechanism model with a bidirectional GRU layer of a PINN neural network, and realizing dynamic feature enhancement of input fan feature data based on triple attention-bidirectional GRU dynamic feature selection. The wind power plant power predicted by using the bidirectional GRU neural network model of the weighted triple attention mechanism constructed in the invention is high in precision, and the problem of model response lag when the wind speed suddenly changes in a traditional mode is solved.

The invention discloses a coal mine disaster early warning method based on multi-source data fusion and a time-space diagram network. The method comprises the following steps: (a) collecting multi-source heterogeneous monitoring data; (b) constructing a geologic-physical dual constraint anisotropic space-time diagram; (c) training the graph by using a geologic-physical anisotropic space-time graph neural network, wherein a graph convolutional layer of the network is designed into a propagation mode which is learned and defined at the same time; and (d) predicting a disaster risk by using the trained model and generating an early warning. According to the method, geological and physical priori knowledge is taken as constraints to be incorporated into the topological structure of the GNN model, so that the model can follow physical laws, disaster propagation under anisotropic geological conditions is predicted, and the accuracy and timeliness of early warning can be improved.

The invention discloses a multi-view self-representation and clustering joint optimization deep clustering method, which comprises the following steps of: firstly, acquiring data samples of a plurality of views, and selecting a most representative sample from each view as an anchor point by adopting a VDA algorithm; constructing and pre-training an auto-encoder network; on this basis, a self-representation module is introduced, shared self-representation and view unique self-representation are learned at the same time, and self-representation of each view is constructed through the similarity between an anchor point and a sample; constructing comprehensive self-representation based on sharing and unique self-representation, constructing a bipartite graph affinity matrix, and obtaining an initial clustering result of samples and anchor points by adopting a bipartite graph clustering algorithm; a clustering result is fed back to the self-representation module, and the representation is iteratively corrected; and finally, sharing and view unique self-representation are fused, comprehensive self-representation is obtained and used for spectral clustering, and a final clustering result is obtained. According to the method, the consistency and diversity characteristics between the views are effectively combined, and the problems that a traditional method is insufficient in structure modeling and low in optimization efficiency are solved.

A method for learning a shared machine learning model while preserving privacy of individual participants is provided. The method includes: receiving, from each of a group of users, an encrypted user input; when a number of user inputs is greater than or equal to a threshold, transmitting, to each user, a list of the group of users; receiving, from each user, a message indicating a mutual agreement regarding a shared secret among the group; and when a number of received messages indicating the mutual agreement is greater than or equal to the threshold, determining information about the shared machine learning model by combining the received encrypted user inputs. The shared machine learning model facilitates a secure multi-party computation of a function that generates an updated version of the shared machine learning model.

The invention discloses a cross-modal Hash learning method oriented to high-order structure similarity and label correlation, which comprises the following steps of: inputting cross-modal information into a joint learning framework, and learning high-order similarity structure information and label correlation between samples, consistency potential representation and Hash codes at the same time; and finally generating a discrete hash code capable of fully retaining high-order similarity structure information and label correlation between samples. The learning framework uses a graph diffusion-fusion mechanism to adaptively learn the high-order structure similarity between samples, and uses an asymmetric embedding mechanism to migrate the consistent similarity and semantic relationship between the samples to a to-be-learned hash code; and obtaining high-order label correlation through label correlation adaptive learning, integrating the high-order label correlation into a framework, and obtaining high-level semantic information of a multi-modal sample to guide learning of a hash code. The variables of the Hash code learning model are subjected to interactive optimization through an iterative optimization algorithm, and the iterative optimization algorithm adopts an augmented Lagrangian multiplier method to iteratively update the variables to solve the problem.

The invention provides a general information extraction method based on contrast supervision and cross-stage distillation. The general information extraction method comprises the following steps: selecting a pre-trained model as a base model and performing initialization; initializing a first low-rank decomposition matrix and a second low-rank decomposition matrix for the weight matrix of the base model; training the first low-rank decomposition matrix and the second low-rank decomposition matrix; combining the weight of the base model, the first low-rank decomposition matrix and the second low-rank decomposition matrix to obtain a reasoning model; inputting natural language text information into the inference model; and assigning a task type and an output format through a natural language instruction, and outputting a structured information extraction result. The method has the beneficial effects that the LoRA matrix is trained in stages and the parameters are compounded, so that the target task is learned while the auxiliary task knowledge is reserved, and the loss of cross-stage knowledge migration is remarkably reduced; the natural language instruction drives the same model to process different tasks, and model architectures do not need to be switched.

The invention discloses a building apparent disease detection and evaluation method based on a visual language model, and the method comprises the steps: receiving a high-resolution image and related text description of a building surface through an input module, and converting the high-resolution image and related text description into a format for subsequent processing, so as to guarantee that the model fully obtains visual and language information; the encoder module extracts the multi-scale features of the building image and the semantic features in the text description, thereby enhancing the feature expression capability. The double-branch multi-mode fusion device effectively integrates visual features and text features so as to improve the performance of defect detection. The prior experience learning module optimizes the model performance by storing historical experiences and generating dynamic soft tags. According to the multi-task training mechanism, the comprehensive performance and robustness of the model are improved by learning a plurality of related tasks at the same time, training is carried out in two stages, and effective feature extraction and further optimization are ensured. The efficiency and accuracy of the disease detection method are improved, and the method is suitable for industrial large-scale application and popularization.

The invention provides a micro-service anomaly detection method based on multi-modal feature fusion, and belongs to the technical field of computers. The method comprises the following steps: firstly, performing semantic feature extraction on a log template by combining BERT and a knowledge distillation technology, and learning category features of the log template while enhancing log semantic information; secondly, performing time sequence feature extraction on the log template sequence and the KPIs data by using LSTM and TCN respectively; and then, designing a double-path cross-modal Transform module, respectively extracting collaborative information and redundant shared information between the log and the KPIs, and carrying out weighted fusion through an improved gating unit to obtain a final fusion feature vector. And finally, reconstructing the fusion feature vector by using a diffusion model, calculating a reconstruction error, and comparing the reconstruction error with an abnormal threshold value to realize abnormal detection. Experimental results show that the micro-service anomaly detection method is superior to the existing mainstream method in various indexes of micro-service anomaly detection, and has relatively high detection precision and robustness.

The application discloses an image clustering method based on a composite subspace, and is implemented according to the following steps: step 1, pre-training a convolutional autoencoder; step 2, training a composite subspace image clustering network; and step 3, obtaining a clustering result by applying spectral clustering. The application realizes image clustering in a manner of unsupervised learning, obtains an explicit nonlinear mapping by simultaneously learning self-expression coefficients in an input space and a latent space, embeds input samples into corresponding deep representations, and thus better captures a subspace structure and improves clustering precision.

A method for training a student model based on a pre-trained language model (PLM) and a text classification system are disclosed. The method includes: constructing cue-based training samples; adjusting the pre-trained language model using the cue-based training samples to obtain a cue-adjusted teacher model; and training the student model using the processed training samples, wherein during training, the student model simultaneously learns the classification probability vectors output by the cue-adjusted teacher model and the original teacher model. This invention requires the student model to learn from two teacher models simultaneously, thereby alleviating the overfitting problem of the student model in small-sample scenarios by adding a distillation path that learns from the original PLM teacher model with unsupervised data. Furthermore, by transferring the intermediate layer representation of the PLM through knowledge probes and stabilizing the performance of knowledge distillation through comparative learning, the student model can learn higher-order dependencies from the intermediate layer representation of the teacher model, improving the accuracy and efficiency of knowledge distillation.

The application discloses a sintering endpoint intelligent sensing method based on a 3D convolution network and belongs to the field of industrial process soft measurement modeling. The prediction model utilizes a space-time 3D convolution network to simultaneously learn space-time features hidden in data, and adopts an encoding-decoding network to perform multi-step prediction on a present sintering endpoint. Firstly, a random permutation and combination method is used to convert a two-dimensional data structure into a three-dimensional format. Then, a 3D convolution is used to extract time features and space features in the data. Next, a space-time feature calibration module is proposed to learn the importance of different features and finely process the features. Finally, the fine space-time features are input into the encoding-decoding network, and a dynamic multi-step prediction loss function is used to realize multi-step prediction on the sintering endpoint, so that the purpose of intelligent sensing is achieved. Related data collected in a sintering plant in South China verifies the effectiveness and feasibility of the method.

The present application relates to an online map construction method and system based on inverse perspective mapping and synchronous learning, a perspective image is obtained and preprocessed, a multi-scale inverse perspective mapping image is obtained through transformation, multi-scale strip convolution decoding is performed, different direction strip convolution residual connections are used, layer-by-layer decoding and feature fusion are performed, and global bird's eye view features are obtained; the preprocessed perspective image is subjected to a synchronous learning sequence and a semantic segmentation network to obtain perspective features; the perspective features are converted to a bird's eye view coordinate system, and are spatially aligned with the global bird's eye view features to obtain enhanced perspective features; the global bird's eye view features and the enhanced perspective features are fused through convolution operation; and the fused features are output by a decoder to construct a current frame of a map. In combination with inverse perspective mapping and synchronous learning, the bird's eye view features and the perspective features of multiple frames are fused, the road structure under the bird's eye view is retained, and the perspective image is supplemented with environmental details.

A method of performing target detection during autonomous driving, comprising: performing 3D target detection in a 3D target detection segment; uploading outputs of a plurality of sensors in communication with the 3D target detection segment into a plurality of point clouds; transmitting point cloud data of the plurality of point clouds to a region proposal network (RPN); independently performing 2D target detection in a 2D target detector, the 3D target detection being performed in parallel in the 3D target detection segment; and obtaining a given input image and simultaneously learning bounding box coordinates and class label probabilities in a 2D target detection network that operates to treat target detection as a regression problem.

The application discloses an image classification method based on adaptive local ordinal preserving analytic dictionary learning and belongs to the technical field of computer vision. The method comprises the following steps: performing feature extraction on images in a data set, dividing the images into a training set and a test set, adopting a support adaptive local ordinal preserving analytic dictionary learning model to simultaneously learn an analytic dictionary and a classifier based on the training set, then calculating coding coefficients of the test set based on the learned analytic dictionary, and finally obtaining class labels of the test set through the classifier and the coding coefficients of the test set. The method introduces an adaptive ordinal local preserving term based on a discriminative convolution analytic dictionary learning model, simultaneously preserves neighborhood correlation between dictionary atoms and distance ordering information of atoms in the neighborhood in the learning process, optimizes the analytic dictionary learning model, enhances the discriminativeness of the dictionary, and improves the image classification accuracy of the model in general scenarios such as face recognition, object recognition and scene recognition.

The invention discloses an industrial chain financial risk prediction method based on RGNN-Crossform fusion architecture. The method comprises a data preprocessing module, a relation extraction module, a feature embedding module and a final prediction module. Firstly, aiming at the problems of high noise and serious loss of financial data, a function type data analysis method is used for preprocessing original data, and then an industry chain upstream and downstream enterprise association network is constructed through a data driving method and a text driving method; and then a routing mechanism and a graph neural network are introduced into a feature extraction part of the deep learning prediction model to help the model to learn enterprise risk association structures from different information sources, and finally a financial risk prediction model RGNN-Crossform considering an enterprise association relationship is constructed. Experimental results show that the prediction effect of the constructed RGNN-Crossform model is obviously superior to that of various leading edge financial risk prediction models. Wherein the routing mechanism and the graph neural network module are adopted, so that the model can learn data internal dependence and external correlation characteristics at the same time, and the performance and robustness of the model are improved.

The invention discloses a training method of an unsupervised convolutional neural network model and a clustering method and device thereof, and the method comprises the steps: carrying out the preprocessing of original space transcriptome data, obtaining a space group slice image for training, and enabling the original space transcriptome data to comprise a plurality of bins; the space group slice images for training are input into an unsupervised convolutional neural network model, and the unsupervised convolutional neural network model learns the bin space position information and the gene expression similarity at the same time; and optimizing the unsupervised convolutional neural network model based on a cell space information continuity loss function and a gene expression similarity loss function to obtain a trained unsupervised convolutional neural network model. Compared with the prior art, the method has the advantages that after preprocessing, the clustering result can be denoised, and the compactness and continuous integrity of the tissue anatomical structure can be kept by keeping the clustering result through learning of cell space position information and gene expression similarity.

The invention provides a training method and device of a document processing model, electronic equipment, a storage medium and a program product, and relates to the technical field of artificial intelligence, in particular to the fields of large language models, image processing, document processing, text recognition and the like. Comprising a first sub-data set associated with the identification task, a second sub-data set associated with the question and answer task and a third sub-data set associated with the understanding task, and the data proportion among the sub-data sets is dynamically adjusted in multiple stages of training; and performing multi-task collaborative learning on the document processing model based on the corresponding data proportion in each stage according to the sequence of the multiple stages, so that the document processing model learns an identification task, a question and answer task and an understanding task at the same time. The document processing model can process at least one of an identification task, a question and answer task, and an understanding task by generating a layout inference chain associated with the layout information of the target document.

The invention discloses a life prediction method for an automatic tool change system of a CNC machine tool based on a physical information neural network, which belongs to the technical field of health monitoring and fault prediction of CNC machine tools. The method first uses physical degradation modeling to establish a differential equation for the degradation process of the automatic tool change system of a CNC machine tool; constructs a neural network model 1, which predicts the remaining life by inputting vibration signals and time information; constructs a neural network model 2, which further describes the trend of the remaining life change in combination with the response of the physical equation, and performs a loss function with the prediction result of the neural network model 1; finally, the model is trained by designing a comprehensive loss function, so that the neural network can simultaneously learn the physical degradation law of the system and the data-driven prediction ability. In summary, the present invention has high prediction accuracy and practicality, is suitable for the remaining life prediction of the automatic tool change system of a CNC machine tool, is conducive to reducing maintenance costs, and ensures the safety and stability of the production process.

This invention relates to the field of power distribution technology, providing a power distribution edge intelligent agent, an intelligent fusion terminal, energy storage, and load regulation method. The power distribution edge intelligent agent includes an intelligent algorithm model, which comprises a shared layer and a dedicated layer. The shared layer includes multiple gated recurrent units for acquiring common temporal features among multi-dimensional data. The shared layer simultaneously learns the common temporal features of multiple tasks using a multi-task learning algorithm. The dedicated layer includes multiple task branch modules. The prediction task branch module includes a gated recurrent unit for obtaining the predicted value of the corresponding task based on the common temporal features of multiple tasks. The regulation task branch module includes a gated recurrent unit and a reinforcement learning policy network for obtaining the regulation strategy of the corresponding task based on the common temporal features of multiple tasks. The reinforcement learning policy network is updated using a near-end policy optimization algorithm. This invention enables the coordinated operation of power distribution system sources, grid, load, and storage.

The invention discloses a face analysis method based on multi-task learning and transformer, and the method comprises the following steps: dividing an input image into a plurality of non-overlapping windows, and carrying out the feature transformation through a multi-layer perceptron; tasks are classified according to output types, each prediction head has an independent and unique feature fusion module and a decoding module and can learn a plurality of sub-tasks at the same time, finally, corresponding losses are calculated, and a gradient normalization strategy is adopted to ensure that a model does not tend to each task. The invention provides a face analysis architecture based on multi-task learning and transformer, a plurality of related or unrelated tasks are allowed to be processed in a unified model at the same time, and feature representation is shared among different tasks, so that redundancy of feature extraction is reduced, and the resource utilization efficiency is improved. By dynamically calculating the learning weight for different tasks, the multi-task learning framework can synchronize the learning speed of each sub-task to the greatest extent, and an effective convergence effect is achieved.

The invention discloses an intelligent contract vulnerability detection method based on large model reasoning enhancement, and the method comprises the steps: firstly generating the data of a CoT (Chainof Though, CoT) containing a logic derivation path through employing a Teacher Model, and carrying out the consistency verification and cleaning through combining with a static analysis tool (Slitter); then, a multi-task learning framework is constructed, and supervised fine tuning (SFT) is carried out on a Student Model, so that the Student Model learns vulnerability classification and logical reasoning at the same time; and finally, a direct preference optimization (DPO) technology is adopted, and alignment calibration is carried out on the model strategy according to the constructed preference data set. The method is suitable for security automatic auditing of various blockchain smart contracts, and can improve the detection precision of complex logic vulnerabilities while maintaining high detection efficiency.

The present invention provides a general information extraction method based on contrastive supervision and cross-stage distillation, comprising selecting a pre-trained model as a base model and initializing it; initializing a first low-rank decomposition matrix and a second low-rank decomposition matrix for the weight matrix of the base model; training the first low-rank decomposition matrix and the second low-rank decomposition matrix; merging the weights of the base model, the first low-rank decomposition matrix and the second low-rank decomposition matrix to obtain an inference model; inputting natural language text information into the inference model; specifying the task type and output format through natural language instructions, and outputting structured information extraction results. The beneficial effect of the present invention is that by training the LoRA matrix in stages and compounding parameters, the target task is learned while retaining the knowledge of the auxiliary task, thereby significantly reducing the loss of cross-stage knowledge transfer; natural language instructions drive the same model to process different tasks without switching the model architecture.

This invention provides a wind farm power prediction method based on data distribution fitting. When constructing a weighted triple attention mechanism bidirectional GRU neural network model, the method first estimates the mixture Gaussian distribution of the fitting error using maximum likelihood estimation. The error distribution parameter θ is then embedded into the error loss function of the PINN neural network model, enabling the PINN model to simultaneously learn data patterns and error distribution characteristics. This solves the problem of mismatch between distribution assumptions and actual wind farm data characteristics in traditional methods, improving the accuracy of wind farm power prediction. Then, the output layer of the triple attention mechanism model is connected to the bidirectional GRU layer of the PINN neural network. Based on triple attention-bidirectional GRU dynamic feature selection, dynamic feature enhancement of the input wind turbine characteristic data is achieved. The wind farm power prediction accuracy using the weighted triple attention mechanism bidirectional GRU neural network model constructed in this invention is high, and it solves the problem of model response lag during sudden wind speed changes in traditional methods.

The invention relates to the technical field of radar target detection, in particular to an HFSWR target intelligent detection method based on background subspace projection filtering and a deep learning network, and the method comprises the following steps: S1, carrying out the unbiased covariance estimation and feature decomposition on a multi-channel distance-Doppler spectrum through employing a protection-reference window, and obtaining an HFSWR target; constructing a local clutter main subspace and projecting the whitening snapshot to obtain an energy tensor changing along with a subspace rank k; according to the method, array phase information is effectively utilized by adopting background matching subspace projection, the separation degree of target and non-target components is remarkably improved, projection energy of different ranks is stacked into a three-dimensional tensor according to channels, a frame of distance-Doppler spectrum is expanded in a distance-Doppler-rank three-dimensional space, and the distance-Doppler-rank three-dimensional space is expanded. And the deep network can learn the distance-Doppler spectrum structure features and the cross-channel complementary features under the multi-rank hypothesis at the same time.

A master game board comprising a plurality of 49 cell spaces aligned in vertical columns and horizontal rows that embody color schemes, biblical indicia, definable alphabets, and a game title. The callout markers are detachably affixed to the master game board, and randomly called out until a winner is declared. The game cards mirror the master game board with the exception of the chronologically placement of the biblical indicia, the winning diagrams, and the callout markers. All game cards embody identical biblical indicia randomly designated on cell spaces allowing for concurrent learning engagement among players without a loss of turn during game play. The game cards are affixed with detachable cell space markers that are placed on the surface of individual cell spaces that have answers corresponding to called out entries. Unused callout markers, at the end of a game, are firstly used in a new game.

The present invention belongs to the field of wind power generation, and discloses a collaborative yaw control method for a wind farm based on a multi-layer artificial intelligence system. A numerical simulation wake data set is used to train a single wind turbine yaw wake machine learning model, and combined with a superposition model, an accurate and rapid prediction of the power of the entire field under the yaw control state is achieved. Intelligent control optimization consists of two stages. The first stage intelligently partitions the wind farm based on the wake interference pattern between wind turbines to reduce the dimension of the problem to speed up the optimization efficiency. The second stage uses a Bayesian machine learning network to call intelligent power prediction limited data based on the partition constraint to establish an approximate probabilistic relationship between total power generation and yaw combination, and simultaneously learns and optimizes the power objective function to further accelerate the positioning of the optimal yaw state. The collaborative yaw control method based on the above system can accurately and efficiently determine the optimal yaw control strategy for the wind farm based on real-time incoming flow information, and significantly weaken the wake influence between wind turbines.