10065 results about "Feature based" patented technology

An example operation may include one or more of receiving, via a user interface, a request to test a software program, reading source code of the software program and identifying a vulnerability in the source code based on the reading, generating repair code for fixing the identified vulnerability based on execution of a generative artificial intelligence (GenAI) model on the source code and a repository of repair code used to repair previous vulnerabilities, and displaying information about the repair code via the user interface.

The invention relates to the technical field of servo motor control, and discloses an intelligent servo motor control method and device and computer equipment. The method comprises the following steps: carrying out multi-dimensional data acquisition and preprocessing on a position loop, a speed loop and a current loop of the intelligent servo motor to obtain standard structured parameter data; identifying a working condition characteristic parameter set of the intelligent servo motor; establishing a two-stage optimization architecture comprising a motor parameter layer and a control algorithm layer, and generating a dynamic parameter adjustment mapping table; a depth deterministic strategy gradient network is trained based on the characteristic parameter set and the dynamic parameter adjustment mapping table, and an optimal control strategy model for different working conditions is generated; and the optimal control strategy model is utilized, control parameters are automatically switched according to the working condition types recognized in real time, and smooth transition control of the intelligent servo motor among different working conditions is achieved. Smooth switching among different working conditions is achieved, system oscillation caused by sudden change of control parameters is avoided, and the disturbance influence in the working condition switching process is greatly reduced.

The invention discloses a Transform-based cross-modal fusion multi-modal emotion recognition method and device, which are used for solving the problems of modal isomerism, difficulty in time alignment and insufficient dynamic emotion modeling in a multi-modal emotion recognition task, and the method takes the accuracy and robustness of emotion recognition as performance evaluation indexes. Firstly, feature information of three modes of vision, voice and text is obtained, feature extraction is performed on each mode through a deep learning model, then features of different modes are fused by using a cross-mode Transform module, and a complex dependency relationship between the modes is dynamically modeled through a multi-head self-attention mechanism, so that more accurate emotion recognition is realized, and the emotion recognition efficiency is improved. And finally, performing emotion prediction on the fused features based on time sequence modeling and an emotion classification module. According to the method, the problems of modal isomerism, difficulty in time alignment and insufficient dynamic emotion modeling in multi-modal emotion recognition can be effectively solved.

The invention relates to the technical field of autonomous robot decision-making systems, in particular to an autonomous robot decision-making system and method based on multi-modal sensing fusion, and the system comprises a multi-modal data collection module which is used for obtaining multi-modal sensing data of an environment; the data preprocessing module is connected with the multi-modal data acquisition module and is used for preprocessing the multi-modal sensing data; the feature extraction module is connected with the data preprocessing module and used for extracting features from the preprocessed multi-modal sensing data; the dynamic space-time diagram construction module is connected with the feature extraction module and is used for constructing a dynamic space-time diagram based on the features; the multi-modal data fusion module is connected with the dynamic space-time diagram construction module and is used for fusing the multi-modal data in the dynamic space-time diagram; through adaptive time alignment and a cross-modal space attention mechanism, the system can effectively process space-time inconsistency of data of different sensors, and centimeter-level positioning precision is realized.

The invention discloses a memory retrieval method for enhancing multi-modal long-context dialogue ability of a large language model. The method comprises the following steps: carrying out self-adaptive segmentation based on information density on an original multi-modal dialogue sequence and extracting local features; constructing a three-layer feature pyramid structure to realize multi-scale feature representation; the time sequence expression capability of the features is enhanced through feature compression and time sequence dependency graph modeling; constructing a memory vector sequence based on feature stability evaluation; carrying out structured representation on the query data and carrying out multi-level interaction with the memory vector; efficient retrieval is realized through hierarchical indexing and multi-dimensional similarity calculation; the memory sequence is periodically optimized based on user feedback. According to the method, the multi-modal information understanding and retrieval capability of the model in a long dialogue scene can be effectively improved, and the method has important practical application value.

The invention provides a BIM-based fabricated building component management method and system, and the method comprises the steps: firstly obtaining a building information model data set of a target fabricated building project, a component attribute parameter set covering a prefabricated component unit, and a component geometric feature set, and then carrying out the multi-level component feature analysis of the building information model data set, obtaining a standardized geometric feature sequence and a dynamic construction state feature set containing a transportation positioning offset, a hoisting stress distribution gradient and an installation collision risk coefficient, and then performing cross-stage collaborative analysis on the features based on a preset construction stage matching strategy to generate a component matching strategy set; and the component dynamic optimization module is used for indicating component installation sequence adjustment and connection node precision compensation, finally generating a component dynamic optimization instruction set according to the component matching strategy set, and synchronizing the component dynamic optimization instruction set to the building information model data set to update component attributes, so that efficient management of the fabricated building components is realized.

The invention provides a network topology intelligent generation method and system based on deep learning and topology analysis, and relates to the technical network intelligence field, and the method comprises the steps: obtaining historical topology data, carrying out the feature extraction based on time sequence division, constructing a graph neural network model, training, and evaluating the evolution trend and stability of a network topology structure. And constructing a deep reinforcement learning model to generate an optimization strategy, and carrying out iterative optimization until a network topology structure meeting requirements is generated. The network structure can be adaptively optimized, the network performance is improved, the operation and maintenance cost is reduced, and the network stability is enhanced.

The invention discloses a hierarchical contrast anti-fact learning method and system for a visual question and answer model. The method comprises the steps of obtaining original image data and question text data, constructing multi-modal joint tensor data, calculating probability distribution based on a projection matrix, screening samples and dynamically adjusting weights. Constructing a multi-modal graph structure, performing modal alignment based on the feature enhancement matrix, and executing dynamic feature fusion; establishing a multi-level probability knowledge structure, generating an anti-fact sample, and executing knowledge fusion operation; constructing a multi-level distance measurement system, executing adaptive weight optimization, and performing spectral clustering optimization and boundary adjustment; and constructing a multi-dimensional evaluation index system, executing stability analysis and dynamic performance tracking, and obtaining final performance evaluation index data. According to the method, the performance of the visual question and answer model is improved through a hierarchical comparison anti-fact learning framework, and optimization of feature representation, dynamic adjustment of a knowledge structure and accurate evaluation of the model performance are realized.

The invention provides an urban functional area gridding identification and classification method based on multi-source spatio-temporal data, and relates to the technical field of area identification, and the method comprises the steps: carrying out the dynamic grid division of an urban area through employing a self-adaptive grid division algorithm, obtaining the multi-source spatio-temporal data of a gridding area, carrying out the space-time alignment, and generating a multi-dimensional time sequence feature set; extracting multi-dimensional feature vectors of the grid units through a multi-modal feature fusion algorithm, acquiring long-range time sequence features by using a bidirectional gating recursion unit, calculating spatial correlation features based on a multi-head attention mechanism, and inputting the two types of features into a feature fusion network with jump connection to generate enhanced spatial-temporal features; and an optimization strategy is adopted, a functional area clustering center point is determined through density peak value clustering in sequence to obtain an initial division result, the initial result is optimized by combining spectral clustering of spatial adjacency constraints, and the boundary of the functional area is optimized by using a simulated annealing algorithm, so that accurate identification and classification of the urban functional area are realized.

The invention discloses a power load prediction method based on multi-source heterogeneous data feature fusion, system equipment and a medium, and relates to the technical field of power system load prediction, and the method comprises the steps: obtaining a power load prediction data set, and carrying out the preprocessing; performing input feature processing on the static data, the graph data and the time series data based on a multi-layer perceptron, a graph convolutional network and a gating loop unit network, and fusing the processed features based on a feature splicing method to obtain comprehensive feature information; based on full-connection neural network construction, taking the comprehensive features as input, inputting training set data into a model, and performing iterative optimization according to a model effect to adjust model parameters and hyper-parameters; and based on the parameter optimization adjustment model, using the evaluated optimal model to predict the power load in the future time period. The method is superior to a traditional method in precision and stability, can better adapt to complex and changeable load prediction scenes, and has wide application prospects and remarkable actual value.

An example operation may include one or more of registering a user for a first feature by a software application, wherein the registering of the user comprises receiving information about the user, determining that the received information does not meet a first condition related to a first feature, determining that the received information meets a second condition related to a second feature, generating an offer to the user the second feature based on the information, detecting acceptance of the second feature, by the software application, and enabling the second feature by the software application for the user in response to the acceptance. At least one portion of the example operation: integrates with an artificial intelligence (AI) chatbot, interacts with the AI chatbot, is performed by the AI chatbot, and / or is associated with an AI model.

The invention provides a power grid fault waveform identification and intelligent relay protection rapid control method and system, and relates to the technical field of electric power, and the method comprises the steps: collecting the voltage and current waveform data of a power grid, carrying out the digital filtering and noise elimination processing, carrying out the Fourier transform and wavelet transform, extracting the time domain and frequency domain features, and constructing a combined feature vector. And a deep learning model including a convolutional neural network and a long-short-term memory network is used to identify and evaluate the type, position and amplitude of the power grid fault based on the feature vector database. And then, a multi-target particle swarm optimization algorithm for constructing a target weight matrix based on historical fault data is adopted to optimize fault isolation time, a power supply recovery rate and system stability to obtain a control strategy, and a relay protection device setting value is adjusted according to the control strategy to execute fault isolation and power grid topology reconstruction.

The invention provides a machine room overall electrical scheme configuration method and system for cabinet layout, and relates to the technical field of electrical design, and the method comprises the steps: building a machine room three-dimensional digital model, and carrying out the function partitioning; optimizing the cabinet layout and determining the maximum capacity; constructing an electricity load prediction model; uPS load balancing is carried out by adopting deep reinforcement learning; designing an intelligent bus duct wiring and circuit breaker access scheme; constructing an equipment topological graph and collecting operation parameters; extracting equipment association features based on the graph attention network; and identifying an abnormal power consumption mode and optimizing a regulation and control scheme.

A layout optimization method of water quality monitoring points based on a RF-C-SOM clustering algorithm includes: preprocessing collected water quality data to obtain preprocessed water quality data that are used as data, and using water quality categories as labels to train a random forest model to determine feature importance of water quality indicators; selecting important features based on the feature importance and model training accuracy, performing dimensionality reduction on the preprocessed water quality data to obtain dimension-reduced data; performing a fuzzy clustering on the dimension-reduced data to obtain a water quality section classification result, and based on it, determining initial weight values of a self-organizing mapping algorithm; initializing neurons and training a self-organizing mapping network model with the initial weight values; obtaining a point clustering result through the self-organizing mapping network model; and conducting a water quality index evaluation for the point clustering result before and after screening.

The invention discloses a composite power quality disturbance identification method based on a time-frequency feature fusion classification network. The method comprises the following steps: carrying out noise reduction processing on a noise-containing disturbance signal based on self-adaptive noise complete mode decomposition in combination with a wavelet threshold method; obtaining frequency domain data of the noise reduction signal based on fast Fourier transform; based on a TCN-Transformer deep network, deep feature extraction is carried out on the denoised signals in parallel from a time domain and a frequency domain; and carrying out weighted fusion on the extracted signal time domain and frequency domain features based on a cross attention mechanism to finish classification and identification. The method is simple in principle, the average recognition accuracy rate in a 30dB noise environment can reach 97.86%, and compared with other mainstream deep learning recognition models, the method shows better recognition precision, recognition stability and generalization ability.

The invention relates to the technical field of image recognition, in particular to an image classification system and method based on the image recognition technology, and the system comprises an image collection module which is used for obtaining original image data to be classified; the preprocessing module is used for carrying out denoising, normalization and size standardization processing on the image; the feature extraction module is used for extracting multi-level features of the image by adopting a deep convolutional neural network; the classification decision module is used for weighting fusion features based on an attention mechanism and outputting a classification result; the output module is used for displaying the classification labels and confidence scores; according to the method, the input quality is optimized by dynamically selecting a preprocessing strategy, the multi-scale representation capability is enhanced by adopting a parallel convolution path and a feature pyramid structure, the robustness of the system is improved by integrating an adversarial sample detection and defense mechanism, and the dynamic scheduling and mixing precision acceleration of computing resources are realized by introducing an edge computing optimization technology. And the operation efficiency is obviously improved on the premise of ensuring the classification precision.

A method for structuring the latent space of an autoencoder is provided. The method includes analyzing natural language descriptions related to input data; creating language-guided libraries that categorize and abstract data features based on the analyzed descriptions; mapping input data into the categorized and abstracted features within the latent space of the autoencoder; and training the autoencoder to minimize reconstruction loss while adhering to the structure imposed by the language-guided libraries.

The invention discloses a user behavior prediction system and method based on multi-modal data fusion, and particularly relates to the field of user behavior prediction, and the system comprises a multi-modal data collection module, a preprocessing and feature extraction module, a cross-modal fusion module, a user behavior prediction module, and a model optimization and feedback module. According to the system, multi-dimensional original data such as visual sense, auditory sense, text, physiological signals and environment context of a user are acquired in real time through a multi-modal data acquisition module; then, deep networks such as ResNet, VGGish and BERT are adopted to extract high-dimensional feature vectors of all modals, and contribution weights of features of different modals are dynamically learned through an attention mechanism; and finally, based on a time sequence model of Transform and LSTM, analyzing fusion features, and outputting probability distribution of future behavior intentions. And parameter joint optimization and continuous learning are realized through a multi-objective loss function and end-to-end back propagation.

The invention provides a robust multi-modal image segmentation method and system based on instance perception query, and the method comprises the steps: obtaining original image data and text description data, and generating training data through semantic analysis and causal graph construction; extracting image features and text features by adopting a multi-layer coding network, and establishing a feature dependency relationship through causal reasoning; generating instance clustering features based on attention calculation, and obtaining fusion features in combination with a multi-scale fusion strategy; and calculating query confidence and generating candidate segmentation masks, and outputting a final segmentation result through post-processing optimization. According to the method, by introducing causal reasoning and instance awareness query mechanisms, the cross-modal feature alignment precision and the robustness of the segmentation result are improved, and the method has better segmentation performance and generalization ability in a complex scene.

The embodiment of the invention provides a smart store data processing method and device based on multi-dimensional data analysis, which innovatively realizes alignment and mapping of spatial-temporal feature sequences by collecting sensor, intelligent terminal and social media data. And performing multi-dimensional data fusion based on the feature weight coefficient, and constructing a commodity recommendation model by using a convolutional neural network and a recurrent neural network. The system continuously optimizes the model performance through incremental learning, and realizes personalized commodity recommendation and store operation trend analysis. And meanwhile, a marketing decision optimization model is introduced, intelligent evaluation and dynamic optimization are performed on a marketing strategy, and the model is continuously updated through feedback data. According to the method, the limitation of traditional single data analysis is broken through, and a comprehensive data processing solution is provided for precision marketing of the smart store.

The invention discloses an intelligent software testing method and system based on automatic script generation, and relates to the technical field of software testing, and the method comprises the following steps: deploying a distributed data acquisition probe, dynamically adjusting sampling frequency, collecting software operation data, and extracting data features based on a deep learning model; based on the data features, establishing a behavior pattern classification system, and mapping behavior patterns to realize hierarchical behavior classification; the method comprises the steps of constructing a test scene, generating test data, executing a test task, monitoring a test execution state in real time, calculating a test efficiency index, evaluating and feeding back a test effect, and dynamically adjusting a feature extraction strategy. According to the method, deep learning and reinforcement learning technologies are utilized, accurate recognition and classification of software behavior modes are achieved, and comprehensiveness and effectiveness of testing are guaranteed through multi-dimensional coverage rate analysis and a closed-loop optimization mechanism; and meanwhile, the automation level and the intelligent degree of software testing are remarkably improved, and a powerful guarantee is provided for improving the software quality.

The invention relates to the technical field of database retrieval, in particular to a knowledge database retrieval method based on feature extraction. The method comprises the following steps of obtaining a document to be retrieved, performing local hash de-duplication processing and extracting multi-modal features, performing feature spectrum clustering processing and associated entity analysis on the extracted features, generating a feature associated entity structure, performing document topic modeling and semantic direction positioning, and extracting a semantic topic of the document. The method comprises the following steps: performing word vector retrieval on a to-be-retrieved document and a knowledge database by utilizing a document semantic topic, generating word vector retrieval data, performing sentence vector retrieval, generating a final document retrieval result, integrating word vectors and the document retrieval result, and executing feature retrieval of the knowledge database through an intelligent retrieval engine, thereby realizing efficient knowledge query. The knowledge database retrieval method is more intelligent and more accurate.

The invention relates to a question and answer data processing method and system based on a multi-modal large model, and belongs to the technical field of data processing. Comprising the following steps: capturing a fine-grained feature relationship in multiple modes by constructing a relational graph, and obtaining fused multi-mode features; by calculating the similarity between the multi-modal fusion features and all knowledge vectors in a knowledge base, setting a threshold value, screening the knowledge vectors, and constructing a knowledge subset; inputting the constructed knowledge subset into a machine learning model to perform intention and scene recognition on question and answer input of the user; splicing all the obtained features and then inputting the spliced features into a decoder of the BLIP to generate a prediction answer; user feedback is collected through multiple rounds of dialogues, and excitation signals are generated according to the user feedback to continuously update recognition features. According to the method, cross-modal knowledge retrieval is carried out through the multi-modal fusion features to construct the knowledge subsets, meanwhile, the recognition features are obtained based on the knowledge subsets, expandable guide answers are generated for users, and the accuracy and satisfaction degree of questions and answers are improved.

The invention provides an intelligent data production method and system based on a graph neural network and adaptive learning, and the method comprises the steps: collecting a text, a time sequence, an image and sensor data in real time, and converting unstructured data into a structured JSON format through a format analysis tool; extracting text entity features based on a large language model, modeling a cross-modal entity association relationship in combination with a graph neural network, and fusing multi-source heterogeneous data through a dynamic knowledge graph; the detection threshold is dynamically optimized by adopting a reinforcement learning algorithm, and the false alarm rate is reduced in combination with a double-track verification mechanism; and predicting a data trend by using the space-time diagram convolutional network, and outputting an interpretability analysis report through the generative large model. According to the invention, a multi-protocol adaptation engine and a distributed stream processing architecture are adopted, and a protocol analysis layer automatically identifies a plurality of industrial protocols, so that the manual adaptation time is reduced; and real-time synchronization of production line-level data is realized through edge node parallel acquisition and compression transmission.

The invention relates to the technical field of town sewage pipe network detection, and discloses a town sewage pipe network leakage detection system and method. The system comprises a data acquisition module which uses a multi-source sensor to acquire real-time operation data such as pipe network pressure, flow and the like; the feature extraction module extracts spatio-temporal features based on the multi-scale convolutional neural network, and generates a pipe network state feature matrix; the anomaly detection module inputs the feature matrix into a pre-training model and marks a potential leakage area; the optimization analysis module constructs a multi-constraint dynamic optimization model, and pipe network pressure parameters are optimized by using an adaptive particle swarm algorithm; the hierarchical execution module generates a global regulation and control sequence, dynamically matches local pressure parameters and adjusts the valve opening and the pump station power through a decision layer, a region coordination layer and an execution layer. The system and the method are accurate in detection and reasonable in regulation and control optimization, leakage risks can be effectively reduced, the operation management level of a pipe network is improved, and water resource waste and environmental pollution are reduced.

The invention discloses an insulator defect detecting and positioning method and system based on cross-scale feature fusion, and belongs to the field of intelligent routing inspection of power distribution network lines, and the method comprises the following steps: S1, obtaining image data containing a plurality of insulator defects of the power distribution network lines in real time, and carrying out the preprocessing of the image data; s2, image enhancement; s3, fusing the enhancement results under multiple scales obtained in the step S2 by adopting a pyramid fusion strategy to obtain a fused image; s4, inputting the fused image into a pre-trained cross-scale feature fused defect detection model, and outputting defect features; and S5, based on the detection time and the real-time speed of the defect features, considering the delay time, and predicting the position coordinates of the defect features. By the adoption of the insulator defect detection positioning method and system based on cross-scale feature fusion, high precision, high robustness and self-adaptive processing of insulator defect detection are achieved, and the method and system have important significance on intelligent detection and maintenance of insulator defects of a power distribution network line.

The invention relates to the technical field of artificial intelligence security, and discloses a multi-modal large model confrontation security detection method and system, and the method comprises the steps: obtaining initial multi-modal data from original data sources, such as images, texts and audios, simulating the behavior of an attacker through reinforcement learning to generate a confrontation sample, and generating a cross-domain confrontation sample through transfer learning, extracting a multi-modal feature vector; the modal weight is dynamically adjusted through an attention mechanism based on the feature vector, the vulnerable modal weight is reduced, the credible modal weight is enhanced, inconsistency between abnormal modals is recognized in combination with disturbance analysis, and a weighted feature vector is output; abnormal input is detected by comparing semantic relevance of different modes, if semantic conflicts are found, an alarm is triggered, input is refused, and a corrected feature vector is output; and dynamically closing the untrusted mode and enhancing the trusted mode based on the semantic verification result, and outputting a final security detection result. According to the invention, the security of the multi-modal large model in a complex attack environment can be improved.

The invention relates to the technical field of land surveying and mapping, in particular to a surveying and mapping data intelligent analysis and management method and system based on the Internet of Things. The method comprises the following steps: collecting multi-source surveying and mapping data of a target area through the Internet of Things, and carrying out area perception fusion to obtain geological survey original fusion data; carrying out space-semantic-attribute surveying and mapping feature space reconstruction on the geodesic survey original fusion data to obtain a land semantic unit set; performing significant land element chain right confirmation on each space object in the land semantic unit set to obtain a mapping chain identification block; extracting land element association features according to the identification blocks on the surveying and mapping chain to obtain a land map embedded vector; and acquiring real-time target area multi-source sensing data, and performing land surveying and mapping task intelligent scheduling on the land map embedded vector by using the real-time target area multi-source sensing data so as to obtain a land surveying and mapping instruction network. According to the invention, the response speed, the scheduling efficiency and the task execution quality of land surveying and mapping can be improved.

The invention provides a multi-mode-based man-machine interaction method and system, and relates to the technical field of man-machine interaction. Voice input of a user is received, environment information is collected, facial expressions of the user are captured, and voice features, environment features and facial features are obtained based on the input; utilizing a pre-trained emotion recognition model to generate emotion features of the user, and utilizing a pre-trained user behavior model to generate behavior features according to the current operation information of the user; after the features are obtained, the first weights and the second weights of the features are dynamically adjusted, feature weighted fusion is carried out, and comprehensive features are obtained; the multiple candidate responses are generated based on the comprehensive characteristics, the final response is screened out through a preset scoring mechanism and output, comprehensive understanding of the intention and the emotional state of the user is achieved through comprehensive utilization of multiple data sources, and meanwhile the weight of each characteristic can be dynamically adjusted so that more intelligent and humanized interaction experience can be generated.

The invention discloses a multi-source heterogeneous data fusion processing and intelligent analysis method, device and equipment, and relates to the field of data fusion processing.The method comprises the steps that firstly, data in data sources of corresponding types are collected through different collection strategies; performing data preprocessing on the collected data to obtain standard data in a unified format; performing data fusion on the standard data based on the structural features of the data to obtain a fused data set; and determining a target analysis model according to the data analysis target, and performing data analysis on the fused data set. According to the method, various types of data formats can be processed, multi-source heterogeneous data fusion is realized, and through data semantic understanding and a feature-based fusion algorithm, the accuracy and quality of data fusion are improved, and correct data association is established on the semantic level.