1034 results about "Feature transformation" patented technology

The present invention provides a multimodal data closed-loop management method and system based on government affairs, which relates to the field of data processing technology. The method comprises: acquiring multimodal data from multiple information channels; performing modal classification, and extracting features of text information, voice information and image information respectively; performing feature conversion and normalization processing, and generating a cross-modal unified feature vector set; identifying cross-channel complaint information of the same event; matching with preset complaint category labels, and identifying complaint categories; determining responsible departments, and generating task assignment instructions; acquiring the processing status corresponding to the task assignment instructions, constructing task progress records, and generating early warning information when the task times out or the processing progress is abnormal; generating feedback information, and returning the feedback information through a communication channel corresponding to the complainant, and updating the processing status corresponding to the task assignment instructions at the same time. The present invention improves the autonomy and accuracy of multimodal data closed-loop management.

The invention relates to an electric hand drill wear state prediction and health management system, which belongs to the technical field of intelligent fault diagnosis and predictive maintenance of industrial equipment, and comprises a data acquisition and preprocessing unit used for acquiring and processing a multi-modal physical signal to generate a standardized data frame; the multi-domain feature transformation unit is used for receiving the standardized data frame and transforming the standardized data frame into a health feature vector and a load feature vector; the dynamic health baseline construction unit is used for reconstructing and generating a dynamic health baseline through a depth generation model according to the time sequence of the health feature vector and the load feature vector; and the residual error sequence generation and statistical monitoring unit is used for calculating the distance between the health feature vector and the dynamic health baseline, generating a residual error sequence, and performing statistical processing on the residual error sequence to obtain a statistical magnitude. According to the invention, the interference of working condition change on health state assessment is eliminated, and pure and reliable data input is provided for subsequent accurate monitoring.

The invention provides a cluster network flow prediction method based on multi-scale time feature fusion, and belongs to the technical field of computer network flow prediction. The method comprises the following steps: determining a multi-index prediction sequence based on traffic load characteristics of cluster IP instances, and constructing a high-quality time sequence data set; fourier transform and discrete wavelet transform are used for time-frequency feature analysis, and noise filtering and data dimension reduction are completed; projecting sequences of different time granularities to a unified model dimension, performing one-dimensional channel convolution merging, inputting the merged sequences into a time encoder and a cross-channel encoder, and capturing cross-scale long-term time dependence and a coupling relationship between variables; in the loss function design, time domain and frequency domain loss are fused, double-domain error calculation is carried out on a prediction result and a label through Fourier transform, and the robustness of a model to non-stationary fluctuation is enhanced; and through linear layer decoding and reverse normalization processing, the abstract feature is converted into an actual flow prediction value. According to the invention, the precision and reliability of cluster network flow prediction are significantly improved.

The invention relates to the technical field of industrial equipment state monitoring and intelligent fault diagnosis, and discloses an industrial fault diagnosis method and system based on natural language fault features and large model knowledge enhanced reasoning, and the method comprises the steps: obtaining a multi-source monitoring and state analysis result from detected equipment in an equipment operation stage; converting the features into a fault feature text of a structured natural language; carrying out vectorization coding on the fault feature text, executing similarity retrieval in a pre-constructed industrial fault knowledge base, and recalling knowledge fragments; and based on the fault feature text and the recall knowledge fragment, constructing a reasoning prompt word, inputting the reasoning prompt word into a large language model for knowledge enhanced reasoning, and generating a diagnosis result. According to the method, the problems that numerical evidence and semantic knowledge are difficult to unify, the knowledge coverage and updating cost is high, and cross-working-condition migration and conclusion consistency are insufficient in an existing intelligent diagnosis technology based on a rule base or a knowledge graph are effectively solved.

The invention discloses an environmental protection facility state monitoring and diagnosing method and system based on vibration and infrared detection, and the method comprises the following steps: collecting vibration and temperature data of environmental protection facility equipment, removing noise and environment interference through a filtering algorithm, and carrying out the standardization processing; extracting features of the data through a space-time convolutional network; performing deep fusion on the features by using an adaptive feature transformation network; performing abnormal mode detection; analyzing potential reasons of the abnormal mode by using a causal inference model, and generating a fault diagnosis report; a real-time processing strategy is generated, and the operation state of the environmental protection facility equipment is controlled in real time; and the fault diagnosis result and the instant processing strategy are uploaded to a cloud management platform, a notification is sent to related maintenance personnel, and remote monitoring and collaborative maintenance are realized. According to the method, the space-time convolutional network and the causal inference model are adopted, the faults of the environmental protection facility equipment are effectively identified, a proper emergency processing strategy is generated according to the real-time monitoring data, and the accuracy and response efficiency of equipment fault diagnosis are improved.

The invention discloses an injection molding process fault diagnosis model training method and system based on a large language model and a fault diagnosis method. The model training method comprises the following steps: collecting and cleaning process parameters under the fault working condition of the injection molding machine, converting the process parameters into a natural language text, combining the natural language text with a fault label to construct a textualized data set, and dividing the textualized data set into a training set and a verification set according to a proportion; and in combination with the text data dimension and the fault category number, loading the pre-trained large language model and configuring a diagnosis model structure in a quantitative mode. And inputting the training set into a model to extract semantic features, processing the semantic features by a feature conversion module to generate a high-order feature vector, and inputting a classification head to output a fault category probability. And back propagation is carried out by using a loss function, and model parameters are efficiently and finely tuned in combination with low-rank adaptation and a layered freezing strategy. And repeating training until the performance reaches the standard, and outputting a final diagnosis model. The method is efficient in training, and can effectively reduce the maintenance and use cost of the model.

The invention relates to the technical field of speech processing, can be applied to business scenes of medical health, financial science and technology and the like, and discloses a speech enhancement method based on multi-scale feature learning, which comprises the following steps: framing an input audio signal, extracting a Mel-frequency spectrum feature, extracting a frequency domain feature by using a multi-scale convolutional neural network, and carrying out multi-scale feature learning on the frequency domain feature; carrying out coding dimension reduction on the image; noise is suppressed through a deep residual network, enhanced audio features are generated, a non-autoregression generative model is adopted for feature conversion, and finally a generative adversarial network is used for reconstructing a target voice waveform. Voice frequency domain features are extracted through the multi-scale convolutional neural network, and the feature expression ability of different frequency bands is improved; noise suppression is carried out through a deep residual network, and the purity of the voice signals is enhanced; feature conversion is optimized through a non-autoregression generation model, and the modeling efficiency of speech enhancement is improved; the target voice waveform is reconstructed through the generative adversarial network, and the naturalness and definition of the generated voice are improved.

The invention relates to the technical field of wireless communication, in particular to a wireless communication anti-interference enhancement system based on multi-mode signal fusion. Comprising an intelligent signal transceiving module, a signal sensing and preprocessing module, an interference feature coding and classification module, an interference suppression decision module, an interference suppression module, a signal fusion module, an interference suppression performance evaluation module, an interference feature library and an interference suppression strategy library, the interference feature coding and classification module adopts a three-layer progressive architecture of multi-dimensional manifold feature extraction, feature transformation and coding inspired by a group theory, and deep learning classification based on chaotic dynamics, extracts interference features from three dimensions of time domain, frequency domain and nonlinearity, and realizes accurate interference identification; the signal fusion module adopts a multi-level fusion strategy, and dynamically adjusts the fusion weight according to the signal quality; the interference suppression module selects an optimal suppression strategy according to the interference type, and executes frequency domain cancellation, space domain cancellation and time domain cancellation; and performance evaluation feedback forms a closed-loop optimization mechanism.

The invention discloses a cross-modal target detection method based on learnable Fourier transform, and mainly solves the problem of insufficient fusion of a visible light image and an infrared image in a complex scene due to inter-domain difference in the prior art. According to the implementation scheme, the method comprises the steps that bimodal features are extracted through a double-flow CSPDarknet53 network; a target position guiding module is utilized to enhance target area representation and suppress background interference; the features are converted to a frequency domain, and amplitude texture information of the visible light image and phase contour information of the infrared image are adaptively enhanced through a learnable frequency domain feature enhancement module; suppressing noise through global filtering and then inversely transforming back to a spatial domain; and finally, outputting a target detection result of the multi-modal image by the detection head. According to the method, frequency domain physical characteristics are fully utilized, full complementation and adaptive fusion of cross-modal features are realized, the detection precision and robustness of vehicles, pedestrians and other targets under low-illumination and complex backgrounds are remarkably improved, meanwhile, high calculation efficiency is kept, and the method can be applied to the fields of automatic driving, intelligent monitoring and the like.

The present invention relates to the technical field of expression recognition, and in particular, to an expression recognition method and system based on multi-scale features and spatial attention. The method includes: performing feature extraction on acquired facial image data by using an HNFER neural network model to obtain an original input feature map; performing pooling and concatenation on extracted features based on a CoordAtt attention mechanism to obtain a feature map; performing deep convolution processing on the feature map to obtain an attention map, and then performing element-by-element multiplication to obtain a final feature map; and performing feature transformation and normalization on the final feature map to obtain an expression category probability and output the expression category probability. In the present invention, by integrating scale perception and spatial attention technologies, the model can recognize and classify different emotional states more accurately and maintain high performance even under complex environmental conditions.

The invention provides an intelligent classification method for Beidou positioning data, and relates to the technical field of Beidou positioning data classification, and the method comprises the steps: obtaining the Beidou positioning data of a monitoring target; performing standardization processing, abnormal value processing and time sequence alignment processing on the acquired Beidou positioning data to generate a preprocessed time sequence data matrix; constructing a classification model; performing feature extraction, including time feature extraction and spatial feature extraction, on the preprocessed time sequence data matrix by using a spatial-temporal feature extraction module to obtain a time feature vector and a spatial feature vector respectively; carrying out fusion processing on the time feature vector and the space feature vector by adopting a spatial-temporal feature fusion module, and constructing a spatial-temporal joint feature; and inputting the spatio-temporal joint features into a classification module, and obtaining a classification result through feature conversion, attention weight calculation and feature aggregation. According to the invention, accurate identification and classification of the motion state of the monitored target are realized by establishing a data processing and classification framework of the system.

The invention relates to a turbine starter appearance defect detection method based on multi-scale feature extraction, and the method comprises the steps: collecting appearance defect data of a turbine starter, and defining defect types to obtain an appearance defect feature map; the image is input into a data enhancement module, parameters are adjusted according to image types, and a multi-attribute defect feature map is output; constructing a feature extraction module, extracting defect data features in the multi-attribute defect feature map, converting the defect data features into multilayer feature mapping, and generating a multi-scale defect feature map; inputting the multi-scale defect feature map into an enhanced feature extraction network, obtaining and aggregating high-dimensional multi-scale defect features, and generating a multi-scale fusion defect feature map; constructing a defect detection module, and detecting bounding box prediction and classification results of multi-scale defects; and carrying out training and weight updating on the model by adopting similarity and union set intersection loss to obtain a final defect detection model. According to the invention, accurate positioning and classification of defects of various targets with different sizes on the appearance of the turbine starter are realized.

The invention belongs to the technical field of agricultural machinery track classification, and particularly relates to an agricultural machinery track data classification method and device, electronic equipment and a storage medium. The method comprises the following steps: carrying out preprocessing and feature conversion operation on original agricultural machine GNSS trajectory data to obtain continuous and uniform time sequence trajectory data; the original agricultural machine GNSS trajectory data comprises a plurality of discrete trajectory points, and the category of the trajectory points comprises a field trajectory and a road trajectory; a trajectory classification model comprising a feature extraction module and a classification head is constructed, the time sequence trajectory data serve as training samples to train the trajectory classification model, the feature extraction module is trained in a comparative learning mode, and the classification head is trained through cross entropy loss; and deploying the trained trajectory classification model, and classifying agricultural machinery trajectory data by using the trained trajectory classification model. According to the invention, a contrast learning framework fused with the dynamic negative sample queue is designed, the classification accuracy and generalization ability are improved, and the method is suitable for track classification tasks in various complex scenes.

The invention discloses a data knowledge base management method and device for the egg industry and a medium, and relates to the technical field of intelligent breeding informatization, and the method comprises the steps: inputting a multi-modal fusion data set into a single deep learning framework, carrying out the multi-scale feature coupling of each modal feature through a self-adaptive nonlinear interaction function, and carrying out the multi-scale feature coupling; the feature weight is dynamically adjusted in combination with an enhanced feedback mechanism, and a multi-dimensional health assessment index set is generated by utilizing end-to-end joint feature extraction and nonlinear reasoning; performing entity recognition and relation extraction on the multi-dimensional health assessment index set, constructing an egg industry knowledge graph in combination with the breeding environment and the individual feature information, and performing structure optimization and dynamic evolution to obtain a dynamic evolution knowledge graph; and performing rule reasoning and data analysis on the dynamic evolution knowledge graph to generate a health management decision. Fine modeling and high-quality unified expression of multi-source data are achieved through piecewise nonlinear feature transformation and weighted fusion, and the accuracy of health state analysis input is ensured.

The invention provides a karst area pile foundation intelligent positioning and precise construction method which comprises the steps that a karst development characteristic data set of a target engineering area is obtained, and the karst development characteristic data set comprises a three-dimensional geological exploration data set and a karst attribute data set; and preprocessing the data set to obtain a karst preprocessed data set. And based on a feature extraction model, performing feature extraction on the preprocessed three-dimensional geological exploration data set to generate a three-dimensional geological feature map. And identifying the map through a karst cavity identification model to generate a karst cavity distribution thermodynamic diagram. And karst cave feature conversion processing is conducted on the karst preprocessing data set, and a pile foundation construction feature data set is obtained. And according to the karst cavity distribution thermodynamic diagram, constraint processing is conducted on the pile foundation construction characteristic data set, and a pile foundation constraint boundary diagram is generated. And outputting a pile foundation end hole positioning coordinate set according to a pile foundation positioning optimization algorithm and the pile foundation constraint boundary diagram. The construction safety and accuracy of the pile foundation in the karst area can be improved, and the construction risk and cost are reduced.

The invention discloses a lightweight underwater sonar target detection model based on Transform architecture, which comprises a backbone network, a lightweight hybrid encoder and a decoder, and is characterized in that the backbone network inputs the features of the last three stages of an input image into the encoder; the encoder converts the multi-scale features into a series of image features; the decoder fuses the loss functions WCIoU and NWDLoss while capturing the category and position information of the object, and generates the category and bounding box by iteratively optimizing the object query. According to the method, the backbone network adopts the depth separable convolution TConv to reduce the calculation complexity, and the feature processing and characterization capability of the backbone network is remarkably improved by fusing the index moving average and the gating attention mechanism; a query selection mechanism based on WCIoU perception is combined with an NWD loss function, collaborative optimization of classification information and position information is achieved, and the detection capacity for small targets is particularly enhanced.

The invention relates to the technical field of geological survey, in particular to a potential landslide hidden danger identification method based on rock mass deterioration characteristics. The technical problems that in the prior art, in landslide monitoring, the monitoring range is limited, data collection is not comprehensive, and early warning timeliness is insufficient are solved; according to the technical scheme, the potential landslide hidden danger identification method based on the rock mass degradation characteristics comprises the steps that feature extraction is conducted on a preprocessed image through a convolutional neural network, the rock mass degradation characteristics are identified, and the identified degradation characteristics are converted into numerical value characteristics; compared with the problems of limited monitoring range, incomplete data collection and insufficient early warning timeliness in landslide monitoring in the prior art, the intelligent machine vision technology is used for identifying the rock mass degradation characteristics, and the sensor network is deployed in combination with the Internet of Things technology to monitor the rock mass state in real time; the cloud computing platform is used for data analysis and processing, and finally intelligent early warning of the landslide hidden danger is achieved.

The invention discloses a multi-modal time sequence hybrid query system and method. The hybrid query method comprises the steps of collecting multi-modal time sequence data, performing normalization, denoising and feature extraction on a numerical time sequence, performing cleaning, conversion and classification feature extraction on text data, and performing enhancement and feature extraction on picture data; the statistical features of the numerical time series are converted into vectors, similarities of different modes are calculated, comprehensive similarity measurement is constructed, and a neighbor graph index and a multi-layer index structure are constructed based on the measurement; the method comprises the following steps: analyzing a mixed query statement input by a user, extracting a multi-modal keyword and a natural language description, and converting the natural language description into a multi-modal feature vector; according to the method, a comprehensive similarity measurement and multi-layer index structure is constructed, so that related data can be quickly positioned and matched in the query process, the problems of low multi-modal search efficiency and poor accuracy are solved, the combination of a time sequence and multi-modal query is realized, and the query efficiency is improved.

The invention relates to the technical field of image optimization, and discloses an image lossless fusion method and system based on depth feature extraction, and the method comprises the following steps: collecting multi-modal input data from different sensors, carrying out the preprocessing of each modal in the multi-modal input data, and carrying out the preprocessing of each modal in the multi-modal input data; the multi-modal input data comprises a visible light image, an infrared image and a depth map, a multi-layer pulse neural network is used for simulating pulse emission characteristics of biological neurons, time sequence feature extraction is performed on the multi-modal input data, and feature representations of different levels are captured. According to the method, deep feature extraction is carried out through the SNN algorithm to carry out image fusion, the problems of feature mutual exclusion and modal conflict when infrared and visible light images are processed through a traditional method can be effectively solved, a concrete image can be converted into an abstract feature, and then the abstract feature is converted into an end-to-end conversion process of the fused image. And the real-time and fidelity requirements in a dynamic scene are met.

According to the motor anomaly detection model training method and the motor anomaly detection method provided by the invention, a plurality of audio samples and category labels thereof during motor operation are acquired, a plurality of disturbance audios are generated through speed disturbance for each audio sample, the disturbance audios are input into a frequency domain feature extraction sub-model and a time domain feature extraction sub-model, and the frequency domain feature extraction sub-model and the time domain feature extraction sub-model are extracted. Performing linear transformation and weighted summation on the enhanced wavelet time-frequency spectrum and the enhanced time-domain features through a full connection layer to generate fusion enhanced features, performing feature transformation on the fusion enhanced features through a deep neural network, inputting the fusion enhanced features into a classifier to obtain a classification probability, and calculating triple loss according to a category label and the classification probability; and updating parameters of the frequency domain feature extraction sub-model, the time domain feature extraction sub-model, the full connection layer, the deep neural network and the classifier through loss back propagation and a stochastic gradient descent algorithm to obtain a trained motor anomaly detection model. Therefore, the accuracy of motor state recognition of the model in a high-noise environment is improved.

The invention discloses an accurate analysis method for Internet user behaviors based on artificial intelligence, and the method belongs to the technical field of Internet, and comprises the steps: collecting user behaviors and basic attribute information through a multi-source data collection technology, and integrating data through a data fusion algorithm after data cleaning and preprocessing; designing and extracting a plurality of behavior features, screening key features by using a feature selection algorithm, and performing feature transformation processing at the same time; constructing a deep learning model fusing a recurrent neural network (such as LSTM and GRU), a convolutional neural network and the like, training the model by using a large-scale data set, and optimizing parameters; establishing a real-time analysis system, inputting new data into the model in real time, providing personalized service and recommendation according to an analysis result, and collecting feedback data to update the model online; and finally, evaluating the model by adopting multiple evaluation indexes, verifying the generalization ability through methods such as cross validation and the like, and further optimizing and adjusting the model.

The invention discloses a time-space pixel feature progressive fusion remote sensing change detection method based on ViT, and the method comprises the steps: employing a pre-trained Vision Transform (ViT) as a backbone network, carrying out the fine adjustment of a model parameter through introducing a low-rank matrix, and extracting the features of a dual-time-phase remote sensing image through the fine-adjusted model; global-local feature interaction of the dual-temporal remote sensing image is enhanced through a global context branch and a time sequence dependent branch, and three-stage fusion (shallow layer, middle layer and high layer) is performed on features of the front and rear temporal remote sensing images by combining a multi-level progressive fusion mechanism to obtain enhanced features of the dual-temporal remote sensing image; after the features of the dual-time-phase remote sensing image are connected according to channels, convolution fusion is carried out, and a dichotomy prediction change image is generated through up-sampling, nonlinear feature transformation and convolution. According to the method, the target boundary definition and detection precision in a complex scene can be remarkably improved, the model generalization ability is effectively enhanced, and information loss is reduced.

The invention discloses a method and equipment for improving data nonstop efficiency based on a large model, and a medium, belongs to the technical field of large models and data processing, and aims to solve the technical problem of how to reduce time delay of data transmission and processing in a large data processing process, ensure integrity and consistency of data, and improve data nonstop efficiency. According to the technical scheme, the method comprises the steps that multi-source data collection and processing are conducted, specifically, real-time data streams are integrated and synchronized in batches, data are efficiently collected from a database, an API, a file and Internet of Things equipment through a data source adapter, then preprocessing and optimization preprocessing are conducted on the data, and an original data set is obtained; feature engineering: performing feature extraction and feature conversion on the original data set to obtain a pre-training data set; training and optimizing a large model; model deployment and integration: the trained model is exported as a SavedModel format of deployable TensorFlow or a TorchScript format of PyTorch, and real-time data pushing is realized through an API (Application Program Interface) according to a data direct requirement; and a real-time analysis engine.

The invention relates to a method for realizing BEV local map real-time perception by fusing SD Map, which comprises the following steps: encoding a navigation map to obtain a broken line sequence of the navigation map, and processing the broken line sequence of the navigation map by using a Transform encoder to obtain features of the navigation map; extracting features from the vehicle-mounted sensor data by using a map encoder, and converting the features into uniform BEV features; and based on a preset local map feature fusion network, fusing the features of the navigation map with the BEV features by using a multi-head cross attention mechanism to obtain fused BEV features, inputting the fused BEV features into a preset lane topology model decoder, and predicting a lane center line, traffic elements and an affinity matrix associated with the lane center line and the traffic elements. Therefore, the problems that in the prior art, a high-precision map is high in cost and difficult to maintain, and local map generation stability is poor are solved, and robustness and accuracy of a sensing system are improved.

The invention relates to the technical field of information security, in particular to a security baseline setting method and system equipment based on clustering decision and a medium, and the method comprises the steps: collecting original data, carrying out the construction of a feature vector through feature conversion, generating a sample, and marking and extracting the sample; constructing a classification model by using manifold learning, inputting the extracted feature vectors into the constructed classification model, optimizing the model through training, and outputting classification labels; processing the decision boundary by using constraint optimization and feature scaling to obtain a standardized feature vector converted by a decision boundary model, grouping based on the standardized feature vector, and extracting a baseline; the security policy is generated through data analysis, and the data is dynamically updated and continuously monitored, so that the unified configuration and centralized management of the security policy are realized, and the policy implementation efficiency and the system expansibility are remarkably improved.

A method for performing a computing task includes: extracting one or more features from a user content; converting the features to a floating point query vector; quantizing the floating point query vector; obtaining a database vector including one or more floating point feature vectors; determining a compensation vector based on a data distribution of the floating point query vector; quantizing the floating point feature vectors; determining an error function based on a difference between data distributions of i) the quantized query vector compensated with the compensation vector, and ii) the floating point query vector; determining, based on the error function, values of the compensation vector corresponding to the quantized feature vectors; combining the quantized query vectors and the values of the compensation vector to obtain one or more compensated query vectors; and performing the computing task using the compensated query vectors and the quantized feature vectors to obtain an output.

The invention discloses a multi-sensor feature level fusion target detection method and system, and the method comprises the steps: continuously collecting spatial data information and image data information in a vehicle operation process; superposing first data information collected at a plurality of previous historical moments and the current moment, extracting first feature information based on the superposed first data information, and extracting second feature information based on second data information collected at the current moment; performing feature transformation on the first feature information and the second feature information to obtain a first feature aerial view and a second feature aerial view, and performing sampling, channel splicing and feature fusion on the first feature aerial view and the second feature aerial view to obtain a final fused feature aerial view; and performing analysis according to the final fusion feature aerial view to obtain a target detection result. According to the invention, the detection precision and the detection stability of a long-distance target can be improved.

The invention relates to the technical field of image processing, in particular to a multi-modal remote sensing image semantic segmentation method and system, and the method comprises the steps: building a data set which comprises a plurality of image pairs, and each image pair comprises an SAR image and an optical image; constructing a multi-modal semantic segmentation model, wherein the multi-modal semantic segmentation model comprises a basic feature transformation layer, a multi-scale semantic acquisition layer, a significant feature cross fusion layer, a feature deep coding layer and a feature decoding layer which are connected in sequence; training and optimizing a multi-modal semantic segmentation model based on the data set to obtain a target multi-modal semantic segmentation model; and performing joint semantic segmentation on the SAR remote sensing image and the optical remote sensing image by adopting the target multi-mode semantic segmentation model to obtain a semantic segmentation result graph. The embodiment of the invention is at least beneficial to reducing the parameter quantity and calculation quantity of semantic segmentation of the multi-modal remote sensing image.

A multimodal metadata retrieval-augmented generation method and system, relating to the technical field of artificial intelligence. In the present invention, the method comprises: determining data modalities of query content, performing feature extraction on data of each modality on the basis of the determined data modalities, converting extracted features into feature vectors, and fusing the feature vectors of the modalities to generate a multimodal feature vector; extracting, from data of each modality in the query content, key metadata of the data of each modality, converting the key metadata into a key metadata vector and connecting same to the multimodal feature vector, and constructing an augmented vector; and performing retrieval by means of the augmented vector. The method helps improve knowledge coverage and information utilization efficiency, and enhances the accuracy, relevance, and credibility of generated content.

The invention relates to the technical field of artificial intelligence, and provides a quantum-enhanced multi-scale network intrusion detection method, which comprises the following steps: calculating a covariance matrix for an original traffic feature matrix, and obtaining a feature value and a feature vector through feature decomposition, mapping each sample xi to a quantum Hilbert space to generate an enhanced feature matrix, executing complex field transformation on the enhanced feature matrix to generate an entangled feature tensor, and realizing dynamic feature enhancement through a multi-head attention mechanism based on a quantum probability amplitude; performing space-time attention calculation and gating fusion on the feature tensor after dynamic feature enhancement to obtain a space-time fusion feature; converting the space-time fusion features into a time sequence form, extracting behavior features through a multi-scale convolution branch, and fusing the behavior features to obtain a three-dimensional feature tensor; and calculating a mean value of the three-dimensional feature tensor in a sequence dimension, generating a two-dimensional feature matrix, and performing classification prediction, uncertainty quantification and threat grading evaluation based on a classification network, an uncertainty network and a threat grading network.