8446 results about "Feature based" patented technology

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 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 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.

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.

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 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 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 relates to the field of distributed machine learning, provides a big data-oriented distributed model training optimization method, and solves the problems of load imbalance, low resource utilization rate, large communication overhead, insufficient fault-tolerant efficiency and the like caused by data fragmentation staticization in the prior art. Load balancing is realized through intelligent clustering and overlapping control; the multi-dimensional heterogeneous resource evaluation model monitors calculation / storage / network indexes in real time, and realizes adaptive scheduling in combination with a task prediction and optimization algorithm; the hierarchical gradient synchronization mechanism adopts a tree-shaped parameter server and a dynamic compression technology, so that the communication traffic is reduced by 50%, and the precision loss is less than 0.8%; the incremental checkpoint system uses erasure code coding and parallel recovery to shorten the fault recovery time from 15 minutes to within 2 minutes, the resource utilization rate reaches 85% or above, the convergence speed is improved by 30%-40% in ResNet, BERT and other model training, and the large-scale training efficiency and the system stability are remarkably optimized.

The invention belongs to the technical field of smart home control, and particularly relates to a smart home generation strategy optimization system based on adaptive learning, and the system comprises the steps: collecting the operation history, sensor data and equipment use modes of a user in a smart home system, and determining an evaluation standard and an optimization direction; extracting features according to user behaviors, environment changes and equipment states, and constructing a feature matrix; selecting a reinforcement learning method for modeling according to an optimization problem, designing an adaptive learning mechanism, and enabling the model to be continuously adjusted according to real-time data in an operation process; according to the method, historical data is used for training the model, a reward mechanism in reinforcement learning is used for guiding the implementation of an optimization target, a control strategy adapting to the current environment and demand is generated based on model prediction, and the method has the effects of better meeting the use demand of a user for the smart home and improving the intelligent level of the system and the user experience.

The invention belongs to the field of data processing, and discloses an archive arrangement multi-mode intelligent AI classification system and method. Comprising the following steps: introducing a cross-modal consistency loss function and structured constraint loss into a deployed dual-channel generator, and carrying out facies evaluation on original multi-modal data and evolutionary multi-modal data based on a modal quality evaluator; through conflict identification and digestion mechanism loop optimization, features are extracted based on the enhanced multi-modal data to obtain a quality scoring triple; a PPO algorithm is used for performing dynamic routing decision, quality evaluation closed-loop optimization is introduced, path rules are executed according to decision actions, a multi-modal feature matrix is constructed based on comprehensive features, a multi-modal classifier is designed based on Attention-Transformer, a modal weight adaptive adjustment module is introduced, a classification result verification mechanism is added, and closed-loop optimization is formed; and intelligent AI classification of the multi-modal archives is realized.

The invention relates to the technical field of remote sensing monitoring, in particular to a remote sensing target detection method and system for a low-visibility image. The method comprises the following steps: acquiring multi-modal remote sensing image data; carrying out defogging enhancement processing on the low-visibility input image; normalizing the defogged RGB image and the defogged IR image, and then splicing and fusing the RGB image and the IR image; carrying out layer-by-layer coding on the multi-modal fusion image by adopting a mixed trunk structure fusing Transform, Mamba and CNN (Convolutional Neural Network); performing frequency domain decomposition on the trunk output features based on two-dimensional wavelet transform; generating an HR feature map by adaptively selecting a key region; and carrying out cross-scale aggregation on the HR feature map to obtain a detection target frame. Through the multi-modal image defogging enhancement and feature distillation mechanism, the definition and contrast of the remote sensing image in severe weather such as haze and rainy days are effectively enhanced, the shielding interference of environmental degradation on small target detection is weakened, and the stability and adaptability of the model in complex weather scenes are enhanced.

The invention provides an interaction method and system based on deep learning, and relates to the technical field of computer vision, and the method comprises the steps: collecting a continuous image sequence, extracting feature points, calculating the position variation and gradient variation of the feature points, constructing a two-dimensional feature vector, and carrying out the clustering through a Gaussian mixture model, thereby obtaining a stable feature class. And weighting the feature points based on a feature weight distribution function, calculating feature similarity and constructing a matching relationship. And calculating a dynamic parallax threshold in combination with the vehicle movement speed, screening reliable matching feature pairs, and solving pose transformation parameters. A feature change sequence is extracted through a feature detection network, a motion feature mapping matrix is constructed, and a compensation depth map is generated in combination with depth information. And scene reconstruction is carried out based on the compensation depth map and the pose transformation parameters, the scene confidence score is calculated, the pose parameters are calibrated, a high-precision pose parameter sequence is obtained, and the robustness of feature matching and the accuracy of pose estimation can be improved.

The invention provides a multi-modal document understanding model, a training method, a reasoning method and equipment, global visual features are extracted by using a weight-frozen first visual encoder, the understanding ability of the model to natural scene images is enhanced, a second visual encoder extracts fine-grained features based on high-resolution document images and region-of-interest information, and the understanding ability of the model to natural scene images is enhanced. And the analysis precision of the complex document is improved. And the information interaction module improves the intelligent understanding ability of a specific area in combination with the position of the region of interest input by the user. The feature fusion module splices multi-modal features in a channel dimension, so that visual information from different sources is efficiently integrated. The linear layer converts feature dimensions, so that the visual features are adaptive to the input requirements of the large language model, and the large language model combines visual and text information to generate a text understanding result conforming to semantic logic. According to the model, the capability of analyzing and extracting the fine granularity of the document information is improved by combining the two-way visual encoder with the selection of the region of interest of the user on the document image of the image-text structure.

The invention discloses an electric power marketing management information platform daily electric power fitting method and related equipment, and relates to the technical field of electric power data management, and the method comprises the steps: obtaining historical load data, external environment data and equipment operation state data of a target region; constructing an initial load fitting model based on the historical load data; extracting a date characteristic factor according to a preset time classification rule, and dynamically correcting the initial load fitting model based on the date characteristic factor to generate a corrected load model; generating a multi-source fusion feature based on the external environment data and the equipment operation state data; and inputting the multi-source fusion features into the corrected load model, and outputting a target load prediction result.

The invention provides an improved underwater target detection system and method based on RT-DETR, and belongs to the technical field of underwater target detection based on computer vision. The system comprises a backbone network module, a hybrid encoder module, an InerMPDIOU sensing module and a decoder module, the backbone network module performs large separable kernel attention calculation after extracting features based on the large separable kernel attention, and outputs a multi-layer feature map; the hybrid encoder module design comprises a deformable large kernel attention-based in-scale feature interaction unit and an improved cross-scale feature fusion unit; the sensing module introduces a scale factor and an auxiliary bounding box into IoU calculation of an original IoU sensing module, and target query initialization is improved by using query selection sensed by InerMPDIOU. According to the model improvement, adaptation is carried out aiming at the particularity of the underwater environment, the underwater operation efficiency is improved, and the accuracy is improved while the parameter lightweight is realized.

The invention discloses an intelligent definition method and system for industrial edge data acquisition, a medium and equipment. The method comprises the following steps: acquiring operation data of field equipment in real time; performing protocol analysis and cleaning on the data to generate standardized data points, dynamically detecting the communication state and adaptively adjusting the acquisition frequency; classifying and aggregating the standardized data points into a running state feature set and calculating feature indexes; edge side anomaly detection is carried out based on the feature set, and a protocol container library is synchronously called to match a device communication protocol to generate matching information; an acquisition strategy optimization instruction is generated in combination with the abnormal result and the matching information, and transmission characteristic indexes and instructions are packaged; and structuring storage feature indexes according to equipment types and time dimensions to form a historical operation database, and managing a storage period by adopting a sliding window mechanism. According to the invention, protocol adaptive analysis, dynamic acquisition adjustment and edge intelligent analysis are realized through software definition, the hardware dependence and field debugging risk are reduced, and the data acquisition efficiency and the intelligent level are improved.

The invention discloses a voice interaction method and device based on lip language enhancement, equipment and a storage medium, and the method comprises the steps: extracting lip language features based on an image sequence of a lip region, and carrying out the feature extraction of a voice signal, and obtaining an audio feature; performing cross-modal fusion coding on the lip language features and the audio features to generate mixed features containing audio-visual information; inputting the mixed features into a large language model, understanding the intention of the interaction object and generating a corresponding semantic reply; and finally, synthesizing into voice and / or converting into characters. According to the invention, by introducing the lip features, additional visual clues are provided for speech recognition, and the robustness and accuracy of speech recognition can be significantly improved; effective fusion coding is carried out on the lip language features and the sound features, and semantic information splitting caused by simple and independent recognition is avoided; and the capability of the large model is fully utilized, so that more natural and more intelligent interaction experience is realized.

The invention relates to the technical field of industrial Internet of Things, in particular to a machine equipment online state monitoring and fault diagnosis system, which comprises the following steps of: acquiring multi-source heterogeneous sensing data through an edge computing node deployed on an equipment body, performing adaptive noise filtering and feature dimension reduction processing on original data, and acquiring multi-source heterogeneous sensing data; outputting a standardized equipment state vector set; inputting the equipment state vector set into a dynamic knowledge graph engine, constructing a fault evolution network comprising space-time correlation characteristics based on an equipment operation entropy change quantification model, and generating a graph node connection relationship with a weight coefficient; and inputting the fault evolution network into a migration reinforcement learning module, and outputting a diagnosis decision set comprising a fault type, a severity degree and an evolution path through knowledge migration of a cross-device fault mode. According to the method, the problems of edge redundancy and single feature expression in traditional rule-based atlas construction are effectively avoided, and the structuring ability and physical traceability of fault recognition are improved.

The invention discloses an industrial surface defect detection method based on multi-scale feature fusion, and the method comprises the steps: collecting the multi-source data of a detected surface in real time through a multi-modal sensor array, and forming a structured data set through time-space synchronization and denoising; constructing an adaptive geometric correction model to realize spatial transformation and scale normalization of multi-scale features, and cooperatively realizing cross-modal alignment and preliminary fusion through texture and physical attribute branches of a double-flow decoding network; dynamically reweighting the fusion features based on a defect physical model, strengthening physical mechanism defect characterization and suppressing interference; combining optical flow compensation and three-dimensional convolution to extract spatio-temporal evolution characteristics, and forming dynamic defect characterization; and finally outputting defect type and severity evaluation through the classification model in combination with the process parameter library. Therefore, the adaptability of the method to a complex industrial environment is enhanced, and the detection stability can be maintained under different materials, illumination conditions and dynamic interference.

The invention relates to the field of re-recognition model processing, and discloses a re-recognition model training method and system based on a noise robust prompt learning framework, and the method comprises the steps: generating a global visual feature based on a pre-trained CLIP model, and calculating a context visual feature based on sample neighborhood information; a learnable mapping network is converted into a pseudo-language cue word to construct a self-adaptive cue, and the self-adaptive cue is integrated into a comprehensive text for embedding; through cross-attention module optimization prompting, generalized cross entropy loss and symmetric contrast loss are used to improve robustness to noise labels; through prompt-driven knowledge distillation, learned text embedding is used as a category vector to guide model optimization, and student model output is aligned with a teacher model; training the re-recognition model through identity loss, triple loss and knowledge distillation loss functions; according to the method, the problem that the performance of an existing pedestrian re-identification model is reduced under a noise label is solved, and the identification precision and robustness in a noise environment are improved.

The invention relates to the technical field of agent construction, and discloses an agent construction method and system based on agentive workflow, and the method comprises the following steps: defining an agent core function module and distributing a unique identifier, building an inter-module communication protocol standard, and determining a message format and a priority rule; the method comprises the following steps: distributing computing resources for each module, setting an elastic capacity expansion and contraction strategy, generating a module dependency graph and an architecture metadata file, detecting the loss and delay of multi-modal input data, generating compensation characteristics based on historical context, calculating the quality confidence of each modal, and carrying out dynamic weighted fusion. According to the invention, a three-level alignment architecture is provided to realize cross-modal semantic consistency, and the generated content is ensured to accord with real scene logic; a double-layer decision-making mechanism is designed to drive workflow dynamic optimization, and the system agility under a complex task is remarkably enhanced; a privacy-efficiency balanced federated architecture is constructed, and differential content generation is supported while enterprise data sovereignty is protected.