3811 results about "Feature generation" patented technology

The invention provides an intelligent real-time interactive question-answering system based on a virtual digital human, and belongs to the technical field of voice signal processing and voice recognition, and the system comprises a data acquisition module which receives a voice or text interaction request input by a user, collects the expression dynamic parameter sequence and limb movement sequence data of the user in real time, and transmits the data to a user interaction module; obtaining a standardized voice feature vector and structured text data; the cross-modal fusion module is used for constructing an interactive feature matrix; the behavior decision module outputs a decision instruction set; the knowledge retrieval module is used for generating an answer text with emotional adaptability and voice features; and the voice generation module is used for generating a mouth shape animation key frame, a micro expression parameter sequence and a limb action track of the virtual digital human, generating a voice response in combination with the answer text and the voice characteristics, and pushing the voice response to the user terminal. According to the method, the interaction experience and adaptability of the virtual digital human are remarkably improved.

The invention discloses a cross-modal information fusion and retrieval method and system driven by semantic comprehension, and the method comprises the steps: obtaining text, image and audio original data, and extracting an initial feature set of each modal through a deep neural network; dynamically distributing each modal weight coefficient based on an attention mechanism, and performing weighted fusion on the initial feature set to obtain cross-modal fusion feature representation; through a cross-modal semantic association analysis model, high-dimensional semantic association features are extracted from the fusion feature representation, and semantic enhancement feature vectors are generated; constructing a cross-modal semantic graph network based on the vector, complementing missing modal features, and generating an optimized multi-modal feature set; and inputting the optimized feature set and the query sample into a contrast learning model, calculating a semantic similarity score, and generating a cross-modal retrieval result sorting list according to the score.

The invention discloses a turbofan engine operation monitoring method and system based on digital twinning, belongs to the technical field of turbofan engine monitoring, and aims to solve the problems that weak fault signals such as early cracks and abrasion are difficult to extract and the prediction precision of a multi-source fault propagation path is low under a strong noise background. An original operation signal is collected through a sensing array, and is processed by an adaptive resonance demodulation chain to generate a demodulation signal. The method comprises the following steps: carrying out time-frequency transformation on a demodulation signal, constructing an initial candidate feature set by combining feature frequency prior matching actual measurement and theoretical feature frequency, and generating an independent feature set by fusing multi-scale decoupling network separation features of digital twin constraints; for independent features, effective causal pairs are screened by adopting a physical coupling relationship combining Granger causal analysis and digital twinborn simulation, a dynamic Bayesian network is constructed to simulate fault propagation, a posterior probability is calculated through digital twinborn verification and Monte Carlo simulation, early warning is triggered, and a maintenance decision is generated. And weak signal extraction and accurate fault prediction under strong noise are realized.

The invention discloses a water turbine state intelligent prediction method which comprises the following steps: collecting multi-source sensor data of a water turbine, including vibration, temperature, pressure, flow and electrical parameters; performing space-time alignment preprocessing on the multi-source sensor data to generate a space-time associated data set; extracting and fusing the features of the space-time associated data through a multi-modal space-time diagram network, and generating a joint feature vector; performing health state prediction on the joint feature vector based on a dynamic digital twinborn model, and outputting a health score, a fault probability and a confidence interval; analyzing a fault propagation path by using a causal reasoning module, positioning a fault root cause and generating an interpretable report; and triggering an early warning or maintenance decision according to the prediction result. According to the technical scheme, the technical problems that multi-source heterogeneous data fusion is difficult, fault coupling and propagation are uncertain, real-time performance and computing resources are contradictory, and interpretability and reliability are insufficient are solved.

The present disclosure relates to the technical field of artificial intelligence, and provides a multimodal large language model training method, a correlation calculation method, and a label generation method. The multimodal large language model training method comprises: on the basis of a sample text feature vector, a sample image feature vector, and a sample first multimodal feature vector which are obtained by processing image information and text description information of a sample commodity by a pre-trained multimodal large language model, training the pre-trained multimodal large language model to obtain a multimodal large language model; and processing a sample search word, the image information and the text description information on the basis of the multimodal large language model to obtain a sample search word feature vector, a first multimodal feature vector and a sample second multimodal feature vector, and training the multimodal large language model to obtain a trained multimodal large language model. The trained multimodal large language model of the present disclosure can simultaneously learn image features related to the search word and the text description information, and thus the generated second multimodal feature vector is more accurate.

The invention discloses a multi-modal bill processing method based on dynamic knowledge enhancement. The multi-modal bill processing method comprises the following steps: S1, constructing a dynamic knowledge base containing an aging weight; s2, synchronously processing text, image and format features of the bill by adopting a multi-modal feature fusion network to generate a composite feature vector; s3, semantic-level, format-level and timeliness three-level fusion retrieval is carried out based on the composite feature vector, and a three-level fusion retrieval engine comprises dynamic weighted sorting with timeliness attenuation, a difference degree triggered artificial review mechanism and a policy sensitive slope adjustment algorithm; s4, setting a multi-expert cooperative verification system, wherein the multi-expert cooperative verification system comprises cooperative work of a rule engine, a large language model and a logical reasoning module; s5, implementing a dynamic knowledge updating mechanism, and automatically triggering incremental learning of the knowledge base when policy change or format update is detected; and S6, outputting structured data, and synchronously generating an auditing traceability chain containing a decision path. According to the method, the key field identification accuracy can be improved, and auditing traceability and non-perceptual increment updating in the whole process are realized.

The embodiment of the invention discloses an outer wall thermal insulation defect diagnosis method and system based on artificial intelligence, and the method comprises the steps: firstly obtaining an infrared thermal imaging and visible light image sequence of a target building outer wall, the former comprising continuous temperature distribution data, and the latter comprising textural feature data in time-space alignment with the latter; performing dynamic temperature gradient analysis on the infrared thermal imaging image sequence to generate a three-dimensional heat conduction abnormal map, extracting surface deformation characteristics from the visible light image sequence to generate a structure deformation distribution map, and performing multi-modal characteristic fusion on the two to obtain a joint defect characteristic matrix; performing defect type classification and region positioning on the matrix based on a pre-trained deep residual neural network model, outputting a defect type identifier and a corresponding region boundary coordinate, and finally generating a diagnosis report containing a repair priority score and a material matching suggestion according to the defect type identifier and the corresponding region boundary coordinate, and sending the diagnosis report to a user terminal for visual display. And efficient and accurate external wall thermal insulation defect diagnosis is realized.

The invention relates to a remote sensing target detection method and device and a medium, and the method comprises the steps: inputting a feature map to a backbone network for feature extraction, inputting an extracted feature tensor into a multi-branch expansion convolution structure, and extracting multi-scale features through convolution kernels with different expansion rates. Then, multi-scale features are fused through a space and channel double-path attention mechanism, and enhanced features are generated; and the enhanced features are further input into a cascade pooling module to generate multi-level reconstruction features, and weight coefficients are calculated through a gating fusion network to carry out weighted fusion, so that multi-scale fusion features are obtained. Next, these features are input into an asymmetric decomposition convolutional layer for downsampling, and dynamic channel attention calibration is performed to generate channel enhanced features. And finally, inputting the feature map processed by the backbone network and the neck network into a detection head network, and outputting a target bounding box and category prediction. According to the method, high-precision detection of multi-scale rotating targets and high-density small targets is realized in a complex remote sensing scene.

The present disclosure relates to a multimodal computing-based early intelligent graded screening system for a brain disease. The system comprises: a multimodal data acquisition unit, configured to acquire multimodal data of a target patient under a specified screening grade to form a screening data set; a multimodal feature generation, completion, fusion and calculation unit, configured to generate and complete feature data of modal features in the screening data set to obtain complete modal features, and extract pathological features for calculation to obtain a first screening result; a knowledge-based intelligent screening unit, configured to encode the feature data of the modal features in the screening data set into corresponding graph structure data features, use a multimodal association graph optimized by expert knowledge to match the graph structure data features to obtain knowledge-based association features, and obtain a second screening result on the basis of the knowledge-based association features; and an intelligent graded screening unit, configured to carry out weighted calculation on the first screening result and the second screening result to obtain a final screening result. The present disclosure achieves accurate early screening of brain diseases of patients.

The invention discloses an adaptive teaching real-time feedback method based on multi-modal fusion, and the method comprises the following steps: synchronously collecting text modal information, voice modal information and image modal information generated by students in a teaching process, forming multi-modal original data information, and extracting historical student interaction behavior data; preprocessing the multi-modal original data information, and respectively generating corresponding text, voice and image sequence features; a visual feature encoder and a sequence feature encoder are adopted to encode each modal sequence feature to obtain a high-dimensional feature; inputting the modal high-dimensional features into a cross-modal fusion network for deep fusion; parameters of the feedback model are optimized through a model-independent element learning feedback regulation and control algorithm, and a personalized feedback strategy is generated; generating comprehensive feature representation according to the fusion features, and outputting personalized teaching feedback; and the interaction information is updated based on the feedback behavior data to realize closed-loop optimization.

The invention relates to the technical field of wind turbine generator data analysis, and discloses an offshore wind turbine generator operation data analysis and prediction system. The system comprises a marine environment data integration module for collecting data to generate a multi-source time-space synchronization data set; the multi-modal feature fusion module is used for extracting cross-modal correlation features to generate a high-dimensional fusion feature tensor; the dynamic fault prediction module is used for constructing a two-way gating circulation network model to predict the degradation probability and the residual life of key components of the equipment; and the self-adaptive optimization control module is used for constructing a multi-target dynamic programming model to optimize a fan operation strategy. In addition, the system is further provided with a feedback correction module for correcting prediction model parameters, and a virtual sensor module based on a physical information neural network is used for monitoring tower stress and diagnosing sensor faults. According to the system, comprehensive monitoring, accurate fault prediction and optimal control of the offshore wind turbine generator are realized, the operation efficiency, reliability and safety of the wind turbine generator are effectively improved, and the operation and maintenance cost is reduced.

The invention discloses a multi-screen voice interaction system and method applied in an automobile cabin, and relates to the technical field of vehicle-mounted intelligent interaction, and the system comprises a voice collection unit which is used for carrying out sound source positioning and collection, carrying out the noise reduction processing of a collected voice signal, extracting features from the processed voice signal, and generating a voice feature vector; the multi-modal sensing unit is used for collecting behavior information and physiological state data of a driver and passengers through multi-sensor fusion, so as to extract multi-modal information; the man-machine interaction unit is used for carrying out space-time modeling on the multi-modal information to generate a scene state vector; and the cooperative scheduling unit is used for realizing multi-screen intelligent distribution and cooperative control. According to the invention, voice instructions of a driver and passengers can be accurately identified, the safety and experience of the driver are improved, changes in different driving environments are dynamically adapted, the operation efficiency and user experience of a vehicle-mounted system are improved, and the intelligence and adaptability of the system are improved.

The invention discloses an access anomaly analysis method and system based on multi-dimensional features and user behaviors, and relates to the technical field of dynamic access anomaly detection, and the method comprises the steps: based on a dynamic hypergraph structure, extracting high-order correlation features of the user behaviors through a multilayer hypergraph convolutional network, and generating a high-order feature matrix; based on the high-order feature matrix, generating an authority approval threshold through a causal reinforcement learning framework, constructing a user behavior causal graph to generate strategy network parameters, and storing the strategy network parameters to distributed nodes of a regional data center; based on strategy network parameters stored by distributed nodes, security multi-party computing is adopted, cross-node collaborative optimization is carried out, and global defense strategy parameters are generated through a security aggregation algorithm. According to the method, security multi-party computing is adopted, cross-node collaborative optimization is performed, and the global defense strategy parameters are generated in combination with homomorphic encryption and a block chain fragmentation technology, so that the collaboration efficiency and strategy consistency among distributed nodes are improved on the premise of ensuring data privacy.

The invention discloses a network security analysis early warning system based on artificial intelligence, and the system comprises a data collection layer which captures full flow based on DPI, aggregates firewall logs, terminal behaviors and threat intelligence, and constructs a structured data pool; through TLS fingerprint identification of AI driving, the encrypted traffic is penetrated, and a sampling strategy is dynamically adjusted in combination with reinforcement learning. The intelligent analysis layer is used for carrying out cross validation on known threats and abnormal behaviors; the time sequence CNN extracts encrypted traffic features, and a novel threat detector is rapidly generated by using historical attack fragments in combination with a meta-learning framework; sHAP value driving dynamic feature selection and optimization feature vector input; the decision-making early warning layer is used for fusing multi-source features through a Bayesian network and generating 0-100 score risk scores; a self-adaptive threshold module is combined to adjust a score threshold in real time, and a high-risk event is pushed; the collaborative response layer is used for triggering a preset decision tree, deploying a GAN dynamic honeypot to trap an attacker and reversely tracing; the Neo4j visually restores the attack path, and blocking is executed after the threat is confirmed by a progressive response mechanism.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses an enhanced decision method, device, equipment and medium based on thinking chain annotation, which comprises the following steps: extracting core information features to generate a structured data set, loading a basic language model and executing supervision fine tuning to generate a fine-tuned model, fusing multi-modal input to generate fusion features, constructing a state observation space to receive the fusion features as input, generating reward signals based on a double reward mechanism and optimizing model parameters to generate an optimized model, deploying a monitoring module to dynamically adjust parameter configuration to generate an adaptive decision model, and outputting a decision response result. According to the method, multi-source data extraction, structured expression, multi-modal fusion, reinforcement learning optimization and dynamic adaptive mechanism fusion are carried out, so that the understanding ability of the model to complex data, reasoning transparency and the adaptive ability of the model to coping with environmental changes are remarkably improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of medical health, financial science and technology and the like, and discloses a voice generation method which comprises the following steps: constructing a multi-language voice synthesis model, obtaining plain text data and paired voice text data, and constructing an expansion vocabulary; updating a language perception embedding layer and model parameters, and converting an input text into a mark sequence; and the encoder extracts context semantic features, extracts pronunciation rule features, and the decoder fuses the features to generate an acoustic feature sequence, and converts the acoustic feature sequence into target voice data. According to the invention, the multi-language speech synthesis model is combined with the language perception embedding layer, so that the speech generation capability of a low-resource language is improved; the text conversion accuracy is improved by expanding the vocabulary, the target language learning ability is enhanced by unsupervised training, the low data environment adaptability is optimized by supervised training, and the speech naturalness and fluency are improved by feature fusion.

The invention discloses a mechanical arm motion control method based on multi-agent cooperation, and the method comprises the steps: firstly, receiving an RGB image through a sub-task generation agent, and generating a structured sub-task sequence according to a natural language task instruction of the RGB image; secondly, performing joint modeling on a task text and a scene image through a 3D sensing intelligent body, positioning specific coordinates of a target object in a three-dimensional space, reasoning dynamic characteristics of a current environment based on historical state information of a robot by combining an environment sensor, and generating an environment sensing vector; and finally, the action generation agent performs fusion modeling according to the subtask text, the subtask target coordinates, the current state of the robot and the environment perception vector, generates a continuous action vector, drives a mechanical arm to complete each subtask action, and constructs closed-loop feedback by a controller and a discriminator to realize task execution state judgment and automatic circulation. The precise action control instruction can be effectively generated, and the task execution fineness of the mechanical arm is remarkably improved.

The invention provides a remote sensing strip mine area detection method based on a double-branch structure and a feature fusion mechanism, and the method comprises the steps: obtaining multi-source high-resolution remote sensing image data of a strip mine mining area, carrying out the preprocessing of atmospheric correction, radiation calibration, color correction, image registration, cutting operation and the like, and obtaining time sequence remote sensing image data; a deep learning algorithm is adopted to construct a strip mine area detection model based on a double-branch structure and a feature fusion mechanism, training is carried out through the time sequence remote sensing image data, a remote sensing image detection model is obtained, the double-branch structure comprises a feature extraction branch and a feature generation branch, and the feature extraction branch comprises a feature extraction branch and a feature fusion branch; the feature fusion mechanism comprises a cross attention fusion module and a feature adaptive fusion module; inputting to-be-detected remote sensing image data into the remote sensing image detection model to obtain a detection result of the strip mine mining area. According to the method, the recognition precision of small target details and mining area boundaries of low-resolution images is improved, and the calculation efficiency and the detection precision are both considered.

The invention provides an unmanned cluster brain-like navigation map fusion construction and cooperative positioning method. The method comprises the following steps: acquiring multi-modal perception data, respectively acquiring vision, sound wave and pose information through a binocular camera, a radar and an inertial sensor, simulating a human brain nerve coding mechanism, and generating a pulse sequence and a feature vector in combination with spatio-temporal information; constructing a multi-modal coding unit into a hypergraph node, and based on a dynamic hyperedge connection topological relation, aggregating spatial-temporal characteristics through a heterogeneous hypergraph convolutional network to generate a high-order brain-like semantic map of a single agent; sharing a local brain-like map by multiple agents through distributed communication, detecting geometric and semantic conflicts of an overlapped region, performing space-time alignment based on an anchor point reference, eliminating feature contradictions by utilizing probability distribution matching, and generating a global consistent high-confidence brain-like map; and outputting the optimal position estimation. Through bionic neural coding, heterogeneous hypergraph modeling and multi-agent collaborative optimization, establishment and positioning of a high-order brain-like map in a dynamic unknown environment are realized.

The invention relates to the technical field of sensing data security, and discloses a sensing data chip-level dynamic key negotiation method, which comprises the following steps of: acquiring a unique hardware identifier and key parameters of a sensor node, and generating a dynamic key seed matrix; after acquisition is completed, randomly intercepting data segments, performing median filtering and normalization preprocessing, extracting local statistical features and global features to generate a data feature sequence, and splicing the data feature sequence to a seed matrix to obtain a dynamic key generation matrix; a dynamic negotiation key is generated through standardization and SM3 Hash algorithm encryption, and is stored in a cloud and node security unit; during verification, dual verification is realized through hash comparison and plaintext bit-by-bit matching; and setting an environment parameter exception triggering mechanism, and updating the key if accumulative exception exceeds the limit. According to the method, hardware and dynamic data features are fused, and the key security and adaptability are improved.

The invention relates to the technical field of spectral signal processing, in particular to a high-precision spectral signal peak detection method and system. According to the technical scheme, the method comprises the following steps: denoising and smoothing spectral data; dividing a spectrum curve by adopting a dynamic partitioning strategy; generating candidate partitioning points based on various local features, and generating feature blocks through optimization processing; according to the method, the influence of feature differences of different regions on a detection algorithm is reduced through a dynamic partitioning strategy, a multi-scale morphological feature system is constructed to comprehensively describe spectral curve morphological characteristics, potential peaks are gradually mined by applying a layered iteration peak detection algorithm, and local characteristics of spectral signals are adapted by means of an adaptive parameter adjustment mechanism, so that the spectral curve morphological characteristics are accurately detected. The peak value correction and fusion mechanism is utilized to improve the quality and reliability of a detection result, spectral data with a complex structure and a high noise level can be effectively dealt with, an efficient and reliable solution is provided for the field of spectral analysis, and the accuracy and robustness of peak value detection and the conciseness and reliability of the result are improved.

The invention discloses a short-time rainfall prediction method based on radar image and reanalysis data fusion, and the method comprises the following steps: collecting radar images and reanalysis data at continuous times, and generating input data in a unified grid format through spatial interpolation, time alignment and standardization processing; respectively extracting spatial and temporal features of the radar image and the reanalysis data by using a dual-channel encoder, and carrying out weighted fusion through a channel attention mechanism to generate a fusion feature tensor; inputting the fusion features into a ConvLSTM (Convolutional Long Short-Term Memory Neural Network), modeling a spatio-temporal evolution process of a rainfall system, and outputting a preliminary rainfall prediction image in 0-3 hours in the future; constructing a residual learning network, and performing deviation correction on the preliminary prediction result based on historical residual and observation information; when the radar image input is missing, the completeness of the input structure is maintained through the replacement feature generation module; generating a rainfall intensity image or a probability graph in 0-3 hours in the future; the method supports visual output.

The invention relates to the field of robot control, and provides a humanoid robot control system and method based on reinforcement learning, and the humanoid robot control system comprises a first control subsystem and a second control subsystem. The first control subsystem comprises a strategy reasoning module, a state conversion module and a robot control module; the second control subsystem comprises a data acquisition module and a driving control module; the data acquisition module is used for finishing timestamp alignment and abnormal value filtering of sensor data and transmitting the data to the state conversion module; the state conversion module is used for fusing multi-source sensor data and constructing a time sequence state feature containing a real-time measurement value and historical time sequence information; the strategy reasoning module is used for generating a multi-joint angle target value of the robot according to the time sequence state characteristics provided by the state conversion module; and the robot control module is used for analyzing the multi-joint angle target value output by the strategy reasoning module, selecting a control mode and generating a control command comprising a parameter adjustment instruction.

The invention discloses an advertisement putting real-time optimization method and system based on user behavior prediction, and particularly relates to the technical field of advertisement putting. Real-time behavior data and historical behavior data of a target user group are collected, a dynamic behavior sequence is generated through time window division, the historical behavior data are classified to construct a user behavior feature library, potential behavior prediction features of users are extracted, and a unified prediction feature vector set is generated; comparing and analyzing with a preset advertisement content label library, and outputting a user interest association degree data set in combination with an advertisement inventory state; a dynamic optimization parameter set is generated in combination with putting strategy constraint conditions; finally, the advertisement putting content, the advertisement putting frequency and the display position are adjusted in real time according to the dynamic optimization parameter set, an optimization result is output, and an advertisement display queue is updated, so that accurate capture and intelligent matching of user interests are achieved, and the advertisement putting effect and the user experience are improved.

The invention relates to the technical field of power equipment fault diagnosis, in particular to a power equipment fault intelligent diagnosis method and system based on deep learning. The method comprises the following steps: automatically learning high-dimensional space-time correlation features in original time series data through a deep feature extraction network, and generating feature vectors representing potential abnormal modes of equipment; performing adaptive weight distribution on the high-dimensional space-time correlation features by using an attention enhancement mechanism, and marking a fault sensitive area to form enhanced fault features; inputting the enhanced fault features into a multi-level classifier for joint fault mode recognition and severity evaluation, and outputting a diagnosis result tensor containing a fault type and confidence; and an equipment maintenance decision signal is triggered based on the diagnosis result tensor, and the feature extraction network and classifier parameters are iteratively optimized according to feedback data, so that the intelligent level of operation and maintenance of the power equipment can be comprehensively improved.

The invention relates to the technical field of PC (Personal Computer) terminal AI (Artificial Intelligence) computing, and discloses a method and a system for dynamically scheduling generative AI heterogeneous computing resources of a PC terminal. The system comprises a resource state acquisition module, a scheduling graph generation module, a resource fluctuation entropy analysis module and a scheduling decision engine module. The resource state acquisition module captures running state parameters of a GPU kernel, a CPU thread and a memory block in real time, and generates a resource state feature tensor through normalization processing; the scheduling atlas generation module analyzes and computes the node connection topology, extracts the correlation between the devices, and constructs a multi-dimensional scheduling atlas; the resource fluctuation entropy analysis module separates the load feature vectors, calculates the entropy of each calculation unit and generates a heterogeneous resource entropy matrix; and the scheduling decision engine module jointly analyzes the atlas and the matrix, identifies bottleneck node resource competition characteristics, generates a dynamic scheduling instruction set, adapts to generative AI task requirements, and ensures efficient and stable operation of the task.

The invention provides a machine vision defect real-time detection and classification method and system based on deep learning, and relates to the field of machine vision detection.The method comprises the steps that regional enhancement weights are determined by calculating local entropy and gradient direction consistency, and regional self-adaptive enhancement is carried out; establishing a feature transfer sequence and progressively fusing features; generating and correcting a defect area probability distribution diagram; and constructing a dynamic decision matrix to calculate a comprehensive score for defect grading. According to the method, the defect detection accuracy under a complex background can be improved, false detection and missing detection are reduced, and real-time defect positioning and accurate classification are realized.

The invention provides an intelligent recommendation method and system for an e-commerce platform, and the method comprises the steps: collecting user interaction behaviors and time-space context data in real time, and constructing a user behavior multi-modal feature matrix; extracting commodity multi-level features, and generating a commodity comprehensive feature matrix; identifying and predicting a user intention based on the user behavior feature matrix, and generating an intention distribution vector; a recommendation candidate set is obtained by combining the commodity feature matrix and utilizing a context awareness collaborative filtering enhancement technology; a multi-objective optimization function is constructed, and after the user intention vector is input, a personalized recommendation sequence is generated in combination with an optimization result and the candidate set; and user feedback is monitored in real time, online learning and reinforcement learning algorithms are adopted, and a recommendation strategy is continuously optimized based on user instant feedback and long-term satisfaction. According to the scheme, the recommendation accuracy, the diversity of recommendation results and the user experience can be improved.

The invention relates to the technical field of artificial intelligence and emotion calculation, in particular to an audio and video dual-mode emotion recognition method and system based on adapter fusion. The method comprises the following steps: acquiring a video frame sequence and an audio signal, and preprocessing the video frame sequence and the audio signal; constructing an emotion recognition model; based on a bimodal feature extraction module, a space adapter and a global adapter are embedded in sequence, and corresponding modal enhanced space features and global features are obtained in sequence; generating intermediate representations of the corresponding modes based on the global features, and performing feature fusion according to the intermediate representations to obtain fusion features of the corresponding modes; the fusion features are spliced, time sequence features are extracted, and final features are obtained; inputting the final features into a classifier to obtain a predicted emotion category, training an emotion recognition model by adopting a loss function, and determining an optimal emotion recognition model; and inputting a to-be-recognized video frame sequence and an audio signal into the emotion recognition model, and outputting a recognition result.

The invention relates to the technical field of manufacturing process control, and discloses a multi-dimensional process data co-simulation control method and system and a storage medium. The method comprises the following steps: firstly, collecting a multi-dimensional process parameter set of a plurality of process equipment in a manufacturing production line, carrying out cross-dimensional feature extraction, and generating a comprehensive feature matrix containing a time sequence feature, a spatial distribution feature and an energy consumption feature; according to feature relevance of different dimensions in the comprehensive feature matrix, a process parameter dynamic coupling model is constructed, co-simulation is carried out on interaction between process equipment, and a process state evolution sequence is output; extracting abnormal fluctuation characteristics in the sequence, and generating a process parameter adjustment instruction set; and finally, according to the adjustment instruction set and real-time process feedback data, dynamically correcting model simulation parameters, generating an optimized process control strategy, and executing the optimized process control strategy. According to the method, collaborative analysis and dynamic control of multi-dimensional process parameters are realized, and the accuracy and adaptability of process control are improved.