707 results about "Feature modeling" patented technology

The invention discloses a data annotation method and system of a collaborative computing architecture based on quantum computing, and belongs to the field of data annotation. The method comprises the steps that S1, multi-modal data are input and preprocessed; s2, extracting features of each mode after preprocessing; s3, coding the features of each mode into a quantum state, and carrying out mode fusion; s4, performing label reasoning on the quantum state after modal fusion, and performing label constraint optimization by using a quantum approximate optimization algorithm; s5, based on a quantum Bayesian network or an approximate causal graph generation method, generating explanation according to a modal contribution causal path, and deducing marginal contribution of each modal to final label prediction by using a joint probability measurement result; and S6, outputting a labeling result. According to the method, a quantum-classical cooperative computing architecture is designed, the efficiency and accuracy of multi-modal data labeling are remarkably improved, the interpretability, the distributed processing capacity and the high-dimensional feature modeling capacity of the system are enhanced, and a brand new solution thought is provided for development of the multi-modal labeling technology.

The invention discloses a multi-modal fusion rumor detection method and system based on a dynamic graph convolutional neural network. According to the method, a dynamic feature graph of a language propagation path is constructed, and potential features in the language propagation process are extracted and analyzed by utilizing time sequence changes and key node relations between nodes in a propagation graph. A neural network is adopted to extract and enhance image data, text semantic features are extracted in combination with a text feature modeling network, text feature vectorization expression is achieved based on a BERT model, and rich semantic information is obtained. And a gating mechanism is introduced to dynamically adjust fusion weights of different modal features, and an information fusion strategy is optimized. A collaborative attention mechanism is further adopted for deep fusion, interactive learning of text, image and propagation path features is enhanced, and the relevance of cross-modal and time series data is improved. And finally, inputting the fused feature vectors into a classifier for accurate classification, thereby realizing accurate detection of the social media rumors. According to the method, the multi-modal features are effectively integrated, and the false information identification efficiency is remarkably improved.

The invention relates to the technical field of computers, and discloses a computing power resource multi-dimensional scheduling method and system based on dynamic weight, and the method comprises a data perception step, a weight generation step, an intelligent decision-making step and a scheduling optimization step. The system corresponds to the method. The method comprises the following steps: a data sensing step: collecting multi-dimensional state parameters of computing power nodes and carrying out feature modeling to construct a global feature space; a weight generation step: dynamically adjusting the weight of each dimension based on a machine learning model and a rule engine; an intelligent decision-making step of screening candidate nodes in the global feature space and evaluating priorities, generating an optimal node cluster and performing resource dynamic slice distribution; and a scheduling optimization step: monitoring an execution effect and performing closed-loop feedback so as to iteratively optimize a weight strategy and decision logic. The problems that in the prior art, the sensing dimension is single, and decision-making weight is rigid are solved, and multi-dimensional accurate sensing, dynamic weight decision making and elastic resource allocation of computing power resources are achieved.

The invention relates to the technical field of video behavior recognition, and discloses a behavior recognition and early warning system for a real-time video stream. The system comprises a spatio-temporal feature modeling module, a behavior fragment extraction module, an anomaly propagation modeling module, a risk area positioning module and an early warning strategy generation module. The spatial-temporal feature modeling module builds a dynamic model based on historical data, captures a skeleton key point three-dimensional coordinate sequence, a motion optical flow vector field and a micro-expression intensity spectrum, and outputs a theoretical behavior mode vector; the behavior fragment extraction module generates a multi-modal difference feature tensor through cross-modal difference analysis; the exception propagation modeling module generates an exception propagation path risk probability distribution cloud picture in combination with spatial constraint and trajectory information; the risk area positioning module identifies a high-risk area and marks a boundary; and the early warning strategy generation module dynamically configures monitoring parameters, starts high-frame-rate micro-expression capture for a high-risk area, and performs a track disturbance test on an adjacent area.

The invention relates to the technical field of airport safety monitoring, and discloses an airport video data real-time analysis system. The system comprises a video stream spatial-temporal feature modeling module, a behavior trajectory map construction module, an abnormal region association analysis module, a risk level semantic judgment module and a situation structure visualization module. According to the method, multi-scale spatial-temporal feature analysis is carried out on an airport monitoring video stream, a multi-dimensional behavior trajectory map is established, abnormal behavior region association is analyzed, risk level semantics are judged, and finally an airport global risk situation thermodynamic distribution map is generated. According to the system, the whole process processing from video data acquisition to risk situation visualization is realized, the abnormal behavior area can be accurately identified, the risk level and category are clear, comprehensive and visual situation information is provided for airport safety management, and the intelligent level of airport safety management is improved.

The invention relates to an underwater image enhancement method based on wavelet Mama, which aims at the problems of color shift, low contrast ratio and fuzzy details of an underwater image, extracts shallow layer features of an underwater low-quality image, inputs the shallow layer features into a multi-scale coding network, and performs joint enhancement on low-frequency color information and high-frequency detail features by using a wavelet Mama unit. Global semantic features are obtained by combining down-sampling layer-by-layer compression, and global modeling of color offset correction and contrast enhancement is completed; the spatial resolution is recovered through up-sampling, and reconstruction and enhancement of texture details and color information are realized in combination with jump connection and a wavelet Mama unit; and finally, the features are mapped to an image domain and fused with input image residual errors, and an enhanced underwater image with natural color, clear texture and balanced contrast is generated. According to the method, frequency domain-space domain joint feature modeling is realized through the wavelet Mama unit, and the color authenticity, the structural definition and the detail perceptibility of the underwater image are remarkably improved.

The invention relates to the field of government affair work order intelligent processing, in particular to a government affair work order intelligent processing method and system based on space-time semantic clustering and a large language model. According to the scheme, unified data feature modeling is conducted on a work order to be processed, an improved DBSCAN clustering algorithm is executed on the work order through a weighted space-time semantic three-dimensional distance measurement formula, and combined clustering of space, time and semantic features is achieved; calculating priority scores of the work orders, and dynamically allocating scheduling resources according to the clustering scale and the priority of the work orders; based on a retrieval enhancement generation technology of an RAG framework and an FAISS vector retrieval library, historical similar work orders are matched, a few-sample learning case is generated, and two sets of differential treatment schemes are generated by controlling temperature parameters of a large language model; visual display and interactive analysis of work order clustering are realized through an interactive GIS platform; and establishing a quality feedback closed loop of work order reconstruction. The method is suitable for intelligent government affair work order processing.

The invention relates to the technical field of architectural design drawing recognition, in particular to an architectural drawing multi-dimensional defect feature extraction and automatic prompting method and system. The method comprises the following steps: preprocessing collected architectural design drawing data; carrying out primitive recognition and semantic tag extraction on the preprocessed drawing based on deep learning; carrying out multi-dimensional defect feature modeling based on the semantic tags identified and extracted by the primitives; defect identification and intelligent prompting are carried out based on the modeled defect features; and generating a defect report. According to the method, full-dimensional automatic identification and accurate prompt of building design drawing defects are realized, the limitation of traditional manual examination on efficiency and coverage range is broken through, dominant problems such as geometry, layers and annotation can be quickly positioned, hidden defects such as standard conflicts and semantic contradictions can be deeply mined, and the comprehensiveness of drawing quality control is improved.

The invention relates to an industrial Internet of Things anomaly detection method based on time sequence and text joint modeling, and belongs to the technical field of industrial Internet of Things anomaly detection. The method comprises the following steps: constructing text prompt information based on collected industrial Internet of Things time sequence data, and respectively taking the text prompt information as inputs of a time sequence channel and a text prompt channel; a sensor association graph is constructed by using a multi-hop GCN, and on the basis of the association graph, time feature modeling from local to global is completed by using multi-scale expansion convolution and combining a differential attention mechanism; performing word segmentation processing on the text prompt information through a word segmentation device, and encoding the text prompt information into vector representation; and calculating attention weight between time sequence embedding and text prompt embedding, fusing to obtain joint embedding representation, enhancing the joint embedding representation, inputting the enhanced joint embedding representation into MLP for reconstruction, calculating an abnormal score through a reconstruction error, and carrying out industrial Internet of Things anomaly detection according to the abnormal score. The method is high in anomaly detection accuracy, and can improve the equipment anomaly perception and risk early warning capability.

The invention relates to the technical field of retrieval enhancement generation, in particular to a method for automatically adjusting retrieval enhancement generation parameters based on content feature modeling. The method comprises the following steps: receiving an original query text of a user, performing component analysis, identifying terminologies, general vocabularies and question entities, and quantifying to form query fingerprints; acquiring a historical behavior sequence of the user, and constructing a score reflecting the level of the user by combining the query fingerprints and adopting a time decay weighting algorithm; the user level score is converted into specific retrieval parameter configuration, and a retrieval strategy blueprint is formed; guiding document library retrieval according to the retrieval strategy blueprint, and screening out a candidate knowledge set which is most matched with the professional level of the user; and according to the user level score, a preset instruction template is intelligently filled, and a situational generation instruction is constructed. According to the method, the problem of non-uniform cognitive load caused by a traditional system is solved through a retrieval enhancement generation technology, and the technical knowledge transmission efficiency and the user satisfaction are remarkably improved.

The invention provides a remote intelligent operation monitoring method and system for an intelligent substation, relates to the technical field of intelligent operation and maintenance of substations, and relates to multi-source heterogeneous sensing, depth feature modeling, fault prediction evaluation and model self-optimization. According to the method, electrical, environmental and meteorological data are collected through heterogeneous sensors, a structured original data set is constructed, time sequence prediction is carried out in combination with a convolution-LSTM model, a Transform fusion network is utilized to output a fault probability and a confidence interval, online early warning and response control are realized, and the method has a federated learning driven adaptive updating capability.

The invention discloses a long-time pedestrian re-identification method based on dual-path cooperation and key frame guided reconstruction. The method comprises the steps of firstly collecting a pedestrian video to be recognized, and extracting a video feature sequence; space and time position coding is introduced into the video feature sequence; capturing local fine-grained dynamic features through a local dynamic feature capturing path, and modeling long-range time sequence association through a cross-frame global feature modeling path; then, dual-path feature complementation is realized through bidirectional gating interaction; further screening out key frames, and realizing feature reconstruction through a full-frame attention propagation mechanism; and finally fusing the dual-path fusion features, the key frame guide reconstruction features and the refined features to generate pedestrian identity features. And processing pedestrian identity features to obtain standardized feature vectors, performing similarity comparison on the standardized feature vectors and pedestrian features in an image library, and returning a matching list. According to the method, video time sequence information is fully utilized, and the problem of insufficient robustness caused by appearance change in long-time pedestrian re-identification is effectively solved.

The invention discloses a marketing strategy optimization management system based on six elements of order transaction, and belongs to the field of communication management systems, in terms of data acquisition, multi-source comprehensive information collection enables enterprises to perceive consumer demands in all directions and no longer blindly grope, a dynamic feature modeling unit fuses various kinds of data into six-dimensional feature vectors, and the six-dimensional feature vectors are integrated into a database; multiple factors of commodities, users and festivals are balanced and considered, a solid foundation is laid for a marketing strategy, a commodity-festival-user ternary association graph constructed by a festival graph generation engine enables commodities to be pushed in a targeted manner, a personalized strategy is generated by an intelligent decision module, the matching degree of the commodities and consumers is improved, and the marketing efficiency is improved. The overall continuous feedback optimization mechanism of the system enables the marketing strategy to be like a continuously evolved life entity, can better adapt to the market change, and improves the sales probability of commodities.

The invention provides a bridge settlement monitoring method and system, and relates to the technical field of bridge engineering monitoring, and the method comprises the steps: firstly selecting a component surface region directly associated with settlement conduction in a bridge structure as a visual clue node, and constructing a bridge structure visual clue network; setting shooting parameters of image acquisition equipment according to spatial distance parameters in the bridge structure visual clue network to form a multi-clue collaborative image set; then, carrying out settlement conduction characteristic modeling on the multi-clue collaborative image set, extracting a visual characteristic variable quantity, establishing a corresponding relation with a settlement conduction path, and generating a settlement conduction characteristic matrix; inputting the settlement conduction characteristic matrix into a preset model, converting the settlement conduction characteristic matrix into settlement displacement parameters of corresponding components, and integrating the settlement displacement parameters to form bridge settlement state information; and finally, based on the settlement state information, generating a monitoring instruction containing a settlement conduction path identifier and settlement parameters of each component, and transmitting the monitoring instruction to a monitoring terminal. The method is comprehensive, accurate, economical and efficient, and can effectively guarantee the safety of the bridge structure.

The invention belongs to the field of environment monitoring and computer vision, and particularly relates to a water body color recognition regression method and system based on space-time causality and manifold learning, and the method mainly comprises the steps: carrying out the detection of a current target water body video sequence, extracting a water body region, carrying out the high-dimensional feature dimension reduction of the water body region, and obtaining a water body color recognition result; and performing feature extraction on the water body region through a space-time causal feature learning model, fusing the flow shape learning features and the space-time causal features to obtain fused features, and outputting a finally predicted water body color value. According to the method, end-to-end assembly line design of preprocessing-segmentation-feature modeling-regression is adopted, manual intervention is not needed from video input to color prediction, and through cascade cooperation of five core modules (video preprocessing, water body segmentation, manifold learning, time sequence causal modeling and color recognition), the real-time performance of the system is improved. Full-link automation from environmental interference suppression, feature extraction to result output is realized, information loss of intermediate links is avoided, and recognition efficiency and robustness are improved.

The invention relates to a light-weight pest image detection method based on dynamic adaptive scanning and attention mechanism joint optimization, and solves the problems that a light-weight model sacrifices a feature modeling capability during parameter compression, so that missing detection is more, small-scale pest information is difficult to extract, and the detection precision is low. And although small-scale features can be extracted by a high-parameter-quantity scheme, the calculation complexity is high, the high-parameter-quantity scheme is difficult to deploy to edge equipment, and the unification of high detection precision and low calculation power requirements cannot be realized. The method comprises the following steps: constructing a multi-category crop pest data set; constructing a lightweight pest image detection network; training a lightweight pest image detection network; acquiring a pest image to be detected; and obtaining a pest image detection result. Target features are extracted through the dynamic self-adaptive scanning module, long-distance dependency relationships in different directions are captured by utilizing the features of a state space model, and meanwhile, the calculation efficiency is kept; meanwhile, the detection network is optimized based on the attention mechanism, the detection precision and robustness of the pest target are remarkably improved, and the real-time performance of pest detection is achieved through light-weight design.

The invention discloses a method, a device and equipment for identifying illegal actions in a video and a storage medium, and relates to the technical field of computers. The method comprises the following steps: carrying out joint point detection on a target video stream to determine a joint point coordinate of each video frame, and generating a joint point coordinate sequence based on the joint point coordinates; inputting the articulation point coordinate sequence and the target video stream into a double-flow feature modeling network, and extracting articulation point spatial-temporal features of the articulation point coordinate sequence and image semantic features of the target video stream; and performing cross attention processing on the joint point spatial-temporal features and the image semantic features, and performing illegal action recognition according to the obtained fused features. It can be seen that the double-flow feature modeling network is utilized to extract the joint point spatio-temporal features and the image semantic features at the same time, the fused features rich in the context relation are generated through cross attention processing, illegal action recognition is conducted according to the fused features, and the accuracy of illegal action recognition in the video is improved through the multi-modal features.

The invention discloses a microseismic source positioning method, device and system, and a storage medium. The method comprises the following steps: dividing a microseismic data sample data set into a training set and a test set; according to the training set, constructing a microseism inversion subnet containing a Swin Transform encoder, and according to the training set, constructing a microseism inversion subnet containing a Swin Transform encoder; the micro-seismic forward modeling subnet realizes seismic wave field continuation by inputting a micro-seismic source position and a speed model and utilizing a recurrent neural network structure and a convolution operator, and establishes a forward modeling subnet based on a wave equation; constructing an inversion-forward modeling closed-loop neural network according to the micro-seismic inversion subnet and the forward modeling subnet based on the wave equation; and inputting the test set into the inversion-forward closed-loop neural network to carry out micro-seismic source positioning. By adopting the technical scheme of the invention, the limitations on physical constraint, feature modeling and anti-noise capability in the prior art are overcome.

The invention belongs to the technical field of computer vision and image recognition, and particularly relates to a fine-grained image block embedded multi-task emotion recognition method, which comprises the following steps of: constructing a golden snub monkey multi-modal emotion data set for wild primate animals, and covering emotion, individual and gender multi-dimensional labels; the method comprises the following steps: preprocessing an input wild primate image, dividing the input wild primate image into non-overlapping local image blocks with fixed sizes through blocking and feature extraction, and mapping the non-overlapping local image blocks to a high-dimensional feature space through linear projection to form a series of image block embedding vectors; performing local feature modeling on the image block embedded vector based on a fine-grained local scanning module to enhance fine-grained perception of local features such as facial expression and hair texture of the golden snub monkey, and performing global feature modeling on the image block feature vector by a global scanning module to enhance global semantic representation; according to the invention, the performance and generalization ability of multi-task identification of golden snub monkeys are improved.

The invention relates to the technical field of deep learning and computer vision, and discloses a histogram interactive attention-based pavement crack segmentation network processing method and system, so as to enhance the edge detail fidelity and improve the crack segmentation precision. The method comprises the steps of image preprocessing, up-sampling, down-sampling, feature fusion and image reconstruction processing. Wherein global feature modeling in the intensity sub-boxes and among the sub-boxes is realized by constructing a histogram interactive attention module (HIA); a double-branch detail enhancement feedforward module (DDEF) is introduced to enhance spatial detail and high-frequency edge information expression; meanwhile, a Fourier jump enhancement module (FFSM) is adopted to jointly refine jump connection features in a spatial domain and a frequency domain. Through the synergistic effect of the modules, the network can realize continuous recovery and structural consistency modeling of a crack boundary in a complex pavement environment, so that the accuracy and the stability of a segmentation result are remarkably improved.

The invention discloses a multi-organ medical image segmentation method based on a multi-feature fusion Swindow-Unet architecture, and belongs to the field of medical image processing. The core of the method is that CT and MRI images are input into a pre-trained CMFSA-UNet model for segmentation, and the model comprises an encoder, an MAFR module, an MFDF module, a decoder and a jump connection layer. CNN-Swin Transform double branches are adopted by the encoder, local details and long-range semantics are extracted, and Attention Gate reinforcement is carried out; the MAFR module widens a receptive field through double branches, combines an attention mechanism with residual connection, reduces the calculated amount and gives consideration to local and global features; and the MFDF module fuses multi-scale dense connection and frequency domain processing, so that feature loss is reduced. The decoder extracts features through Swin Transform Block, resolutions are recovered through 4 times of up-sampling, and the segmentation precision is optimized in combination with depth supervision and a mixed loss function. According to the method, local and long-range feature modeling is efficiently cooperated, precision and efficiency are balanced, segmentation global consistency, boundary accuracy and training stability are improved, the method is suitable for multi-modal multi-organ segmentation, and reliable support is provided for clinical diagnosis and the like.

The invention provides an AGC hydropower station intelligent control method based on multi-source data fusion. Constructing a control feature vector of the multi-dimensional feature; performing spatial-temporal feature modeling on the control feature vector, and extracting a time sequence dependency relationship between power grid load change and hydraulic dynamic response and a spatial coupling effect between units; establishing a multi-objective optimization function, and dynamically adjusting the weight coefficient of each objective through fuzzy logic according to the current working condition; a self-adaptive differential evolution algorithm is adopted to carry out on-line optimization on an active power distribution coefficient of a unit and PID parameters of a speed regulator, and the requirements of guide vane opening change rate constraint and water hammer effect avoidance are met. According to the method, multi-source heterogeneous data such as power grid, hydraulic engineering and equipment states can be effectively fused, multi-target dynamic optimization control is realized through the space-time attention model and the adaptive differential evolution algorithm, and the control precision, the response speed and the equipment operation safety of the hydropower station AGC system are remarkably improved.

The invention discloses a bionic walking feature fused quadruped robot gait reinforcement learning training method, which comprises the steps of S1, bionic gait feature modeling for extracting key features from a motion mode of a natural quadruped animal and constructing a bionic template capable of directly guiding robot gait control; and S2, constructing a reinforcement learning training environment, and simulating diversified actual scenes by constructing a high-fidelity simulation platform. According to the method, key features are extracted from natural four-footed animal gaits, a bionic template library is constructed, and natural features such as nonlinear rhythm and dynamic symmetry of animal movement are fused into robot gait control, so that the problem of action mechanical stiffness caused by dependence on manual design of a track in a traditional method is effectively solved; movement of the robot is closer to natural biological gaits, impact generated when the robot interacts with the environment is reduced while movement energy consumption is reduced, and movement smoothness is improved.

The invention discloses a power switch abnormity monitoring method and system based on artificial intelligence, and relates to the technical field of power communication network monitoring, and the method comprises the steps: collecting multi-source operation data of a switch and a port in a power communication network, and constructing a high-dimensional feature tensor through time synchronization, data alignment, missing compensation and residual extraction; outputting a node residual error based on a self-supervised prediction model, extracting a topological consistency feature in combination with a dynamic threshold and a graph attention mechanism, and constructing a multi-source feature through a comprehensive abnormal sub-model in combination with residual error intensity, a prototype distance and an extreme value tail risk; and setting a grading alarm threshold value based on dynamic distribution, and outputting an alarm optimization strategy through abnormal aggregation and topological consistency analysis. According to the method, through multi-source feature modeling, self-supervision prediction and comprehensive anomaly division, anomaly accurate identification, topology consistency analysis and dynamic alarm optimization are realized, the multi-domain data fusion capability and anomaly detection precision are improved, and the method has innovativeness and engineering application value.

The invention discloses a dynamic attention multi-scale remote sensing target detection method. A space-channel attention enhancement mechanism, a scale dynamic fusion strategy and a lightweight efficient detection structure are fused. According to the method, a high-coupling path from feature modeling to detection output is constructed based on a multi-dimensional feature regulation and control framework, so that the target detection performance in a scene with a complex background, violent scale change and serious shielding is improved. The system has the advantages of modular structure, flexible deployment, strong compatibility and the like, is suitable for rapid and accurate detection tasks of key targets such as airplanes, vehicles, ships and the like in multi-source remote sensing images, and has good engineering suitability and industrialization potential.

The invention relates to the technical field of audio signal processing, and discloses a sound box and sound effect intelligent adjusting method and system based on data analysis and a storage medium, and the sound box and sound effect intelligent adjusting method based on data analysis comprises the steps: constructing a user feature modeling engine, and extracting user auditory characteristic data; constructing an environment characteristic analysis engine, and collecting environment acoustic characteristic data; constructing a content feature extraction engine, and analyzing audio content semantic data; constructing a three-dimensional fusion optimizer, inputting the three feature vectors into an auditory scene fusion model, calculating an auditory experience score through tensor fusion operation, and solving an optimal sound effect parameter by applying a multi-objective optimization algorithm; constructing a parameter generation controller, and converting the optimal sound effect parameter into a specific audio processing parameter; according to the invention, the problem of mutual interference caused by traditional separated processing is solved, and the accuracy of sound effect adjustment and the user satisfaction are improved.

The invention provides an equipment operation state anomaly detection method based on edge computing. According to the method, the accuracy, the robustness and the real-time response capability of equipment anomaly detection are improved. The method comprises the following steps: firstly, setting a channel confidence coefficient screening mechanism at an edge calculation end, and eliminating an abnormal transmission channel with drifting or fluctuation to guarantee data quality; then, fusing and modeling a structural dependency relationship among multiple channels by utilizing a graph neural network and a self-attention mechanism, and extracting a global context sensing vector; and finally, constructing a dynamic criterion system based on a multi-task state index, and realizing efficient anomaly judgment in combination with state difference scoring. According to the invention, through the integration of edge-side high-quality data screening, depth feature modeling and a fusion type detection mechanism, the intelligent level and field adaptability of equipment operation state abnormity identification are significantly improved.

The invention discloses a free curve feature design optimization method, which comprises the following steps of: constructing a free curve feature region containing a central bus, and setting the direction and the position of the central bus to obtain a free curve feature region; a two-dimensional coordinate transformation formula is adopted to transform the free curve characteristic area from a global coordinate system to a local coordinate system with a central bus as an X axis, and bending control of the structure in any direction is achieved; a height function is utilized to describe changes of a longitudinal boundary, an implicit modeling mode is adopted to construct a region boundary with a longitudinal coordinate smaller than a height function value, and adjustability of the longitudinal form of a feature region is achieved by controlling parameters of the height function. The method has the advantages that through free curve feature modeling, flexible expression of a complex structure form and precise boundary control are achieved; meanwhile, by taking control parameters as design variables and combining an iterative optimization algorithm, the structural layout is optimized, the performance is improved, the boundary is ensured to be clear, and dual optimization of the structural performance and the design freedom degree is achieved.

The invention discloses a casting production line energy consumption optimization control method, which comprises the following steps of: deploying intelligent sensing nodes in each process unit, acquiring and marking various process data in real time, and establishing a multi-target feature modeling and optimization framework based on layered multi-agent by adopting data preprocessing means such as time sequence synchronization, exception elimination and normalization; balanced optimization of key indexes such as energy consumption, productivity, quality and safety is achieved, the system has the capabilities of parameter self-adaptive switching, real-time response, rolling optimization and continuous self-lifting, and the operation efficiency of a production line, energy consumption control and robustness under complex working conditions are effectively improved.

The invention discloses a target identification method and device in an infrared image, equipment and a storage medium. The method for identifying the target in the infrared image comprises the steps of training a deep learning identification model fusing vision and track features based on a marked target data set; time sequence visual features of the target are extracted from the continuous multiple frames of infrared images; based on the position change of the multi-frame detection frame, reversely deducing the motion trail of the target in the physical space, and extracting the time sequence change characteristics of the trail; and fusing the time sequence visual features of the target and the track time sequence change features to form a joint feature vector, and identifying and classifying the joint features of the target through a deep learning identification model. According to the method, a target trajectory feature modeling mechanism is introduced, continuous multi-frame infrared image feature extraction is combined, and visual appearance features and physical space motion features are fused, so that the distinguishing capability of targets with similar appearances such as an unmanned aerial vehicle and a flying bird in a motion behavior dimension is effectively enhanced, and the accuracy of recognizing a complex target in an infrared image is improved.