434 results about "Recurrent neural nets" patented technology

The invention relates to the technical field of battery thermal management, in particular to a battery thermal management control method and system, and the method comprises the steps: obtaining the basic operation parameter data of an automobile battery in real time, and carrying out the multi-dimensional data fusion, and obtaining a multi-dimensional data set; based on the thermodynamic model and the multi-dimensional data set, the temperature distribution condition in the battery is calculated in real time, and real-time temperature distribution information is obtained; based on the historical data and the operation state, the future temperature change trend of the battery is predicted through a recurrent neural network model; dynamically adjusting a control strategy of the battery thermal management system according to the real-time temperature distribution information and the prediction result; and analyzing the temperature difference condition of the battery monomers, detecting abnormal temperature rise, and performing temperature difference balance adjustment and triggering a multi-stage protection mechanism when the abnormal temperature rise exceeds a threshold value. According to the invention, the problems of inaccurate temperature prediction and response lag in the prior art are effectively solved, the control strategy is dynamically adjusted through real-time data fusion and recurrent neural network prediction, temperature difference balance and abnormal temperature rise detection are realized, the safety of the battery is improved, and the service life of the battery is prolonged.

The invention relates to a multi-objective optimization scheduling method and system for a multi-AGV flexible job shop. The method comprises the steps of collecting job shop layout data, AGV operation parameters and production task data, and constructing a scheduling system basic model; performing recurrent neural network training and physical constraint model integration to obtain a hybrid scheduling prediction model; subtask decomposition and path search processing are carried out, and a dual-objective optimization model is obtained through constraint linearization conversion; performing particle swarm optimization solution on the dual-objective optimization model, and obtaining a non-dominated solution set through two-stage iterative calculation of an adaptive weight strategy and constraint conversion; performing multi-dimensional evaluation calculation on the non-dominated solution set to obtain an optimal scheduling execution scheme; and performing model feedback updating on the hybrid scheduling prediction model to obtain a target scheduling prediction model. According to the invention, multi-target balance of task completion time and energy consumption is realized, and the real-time response capability and operation stability of the system are improved.

The invention relates to the technical field of artificial intelligence and health management, in particular to a health management method and system based on multi-dimensional data analysis, and the method comprises the steps: collecting the physiological, behavior, environment and psychological data of a user, and carrying out the modeling through a recurrent neural network, and generating health state data; extracting a key causal relationship of the health risk based on a causal analysis model of a Bayesian network; comprehensively analyzing health changes through the short-term time sequence model and the long-term trend prediction model, and generating health risk data; a personalized health intervention scheme is designed by using a conditional generative adversarial network, and safe sharing and dynamic management of the scheme are realized through a block chain technology. According to the method, the health risk can be accurately identified, scientific intervention measures can be designed, the health management process is dynamically optimized, powerful technical support is provided for an intelligent old-age nursing scene, and the health management efficiency and effect are improved.

The invention provides a power plant intelligent early warning method and system based on big data, and relates to the technical field of power systems, and the method comprises the steps: generating a high-dimensional feature map, generating a multi-modal feature tensor through adaptive chaotic mapping, constructing a feature relation network, generating a time sequence multi-dimensional feature vector, and reconstructing a multi-dimensional state vector based on topological entropy. In combination with a topological data analysis method, carrying out high-dimensional topological space mapping on the multi-dimensional state vector, identifying an operation mode of the power plant equipment, calculating a fuzzy region and a mode abrupt change point of the operation state of the equipment, and obtaining a dynamic evaluation result of the health state of the equipment; a multi-level recurrent neural network is combined, a prediction result is obtained through multi-scale time sequence prediction, uncertainty corresponding to the prediction result is quantitatively solved based on a quantum probability graph model, quantum probability distribution of an early warning signal is generated, a real-time operation strategy of equipment is adjusted, and an intelligent early warning signal of the power plant equipment is generated.

The invention provides a marine environment time sequence prediction method and system based on a recurrent neural network, and relates to the technical field of time sequence prediction, and the method comprises the steps: obtaining marine environment historical observation data, carrying out the preprocessing of the marine environment historical observation data, and removing abnormal values and missing values from the marine environment historical observation data; according to the preprocessed marine environment historical observation data, a recurrent neural network model is constructed, and the recurrent neural network model comprises an input layer, two stacked long short-term memory (LSTM) network layers and an output layer; acquiring marine environment observation data at the current moment, and inputting the marine environment observation data into the trained recurrent neural network model; predicting marine environment data of a plurality of time steps in the future through a recurrent neural network model, and obtaining a marine environment prediction result in a preset time; and according to the marine environment prediction result, identifying a hot spot region of marine environment change, and generating a visual result representing a dynamic change rule of the marine environment in the prediction time period.

The invention discloses a multi-dimensional time sequence photovoltaic grid-connected harmonic current prediction control method, and particularly relates to the technical field of power system control. Grid-connected original multi-dimensional electrical signals are collected and decomposed into multi-dimensional frequency signals through Fourier transform, the characteristic components of an independent harmonic source are separated through the blind source separation and non-negative matrix factorization technology, and the frequency and amplitude characteristics of the characteristic components are extracted; then, a multi-dimensional time sequence prediction model is constructed through a recurrent neural network, dynamic behaviors of multiple harmonic sources are predicted at the same time, and frequency domain data are reconstructed and frequency aliasing errors are corrected in combination with high-frequency compensation and multi-scale analysis; and finally, inputting a prediction result into a photovoltaic inverter control system, dynamically generating a compensation current opposite to a target harmonic wave, realizing effective offset of the harmonic wave, accurately processing the multi-source harmonic wave problem in a complex power grid environment, and greatly improving the harmonic wave suppression capability, the electric energy quality and the system operation stability of a photovoltaic grid-connected system.

The invention provides a wireless signal and thermal imaging multi-modal cooperative detection and directional interference method and system, and relates to the technical field of electronic countermeasures, and the method comprises the steps: carrying out the time domain synchronization and spatial domain registration of wireless signal data and infrared thermal imaging data, constructing a multi-modal feature fusion network, and carrying out the wireless signal and thermal imaging multi-modal cooperative detection and directional interference. And an attention mechanism and a recurrent neural network are adopted to extract time sequence features of the target, and position coordinates, motion states and behavior pattern information of the target are output. And then, based on a target state evaluation model and a reinforcement learning algorithm, an intelligent interference strategy is designed, and an interference beam is dynamically adjusted through an adaptive beam forming technology, so that accurate directional interference on the target is finally realized. The target detection precision and the interference efficiency can be effectively improved, the false alarm rate is reduced, and the target identification and interference capability in a complex environment is enhanced.

The invention provides a business vitality prediction and business district evaluation method based on multi-modal feature fusion, and relates to the technical field of spatial data analysis, and the method comprises the steps: obtaining business POI data and time sequence trajectory data of a target area, extracting the score and emotion features of user comments, and calculating the pedestrian flow distribution based on the time sequence trajectory data; then inputting the features into a recurrent neural network to predict a commercial vitality time sequence, constructing POI data into a knowledge graph, and constructing and optimizing a network reachability matrix in combination with position information and human traffic; and finally, dividing a target region by using a Louvain algorithm, fusing multi-dimensional features by edge computing nodes of each sub-region, dynamically adjusting the weight of an evaluation index through reinforcement learning, and outputting a commercial service evaluation result. By fusing multi-source data and a deep learning method, the life circle business service quality can be evaluated more accurately and comprehensively, and decision support is provided for urban planning and business development.

The invention relates to the technical field of production debugging control, and discloses a production debugging control method and system for a plastic container. The method comprises the following steps: arranging a plurality of different sensors in plastic container blow molding equipment, and simultaneously collecting blow molding process parameter data; performing wavelet threshold denoising and anomaly identification on the blow molding process parameter data to obtain a process parameter anomaly identification result; establishing a transfer function matrix between the process parameters and quality indexes according to the process parameter anomaly identification result, and obtaining a target process parameter set through multi-target optimization; and inputting the target process parameter set into a double-integral enhanced recurrent neural network for parameter regulation and control calculation to obtain a process parameter adjustment amount. According to the method, early detection and accurate positioning of process abnormity in the blow molding process are realized, the quality fluctuation risk and the defective product rate are greatly reduced, and the technical problem that different response characteristic parameters are difficult to coordinate is solved.

The invention provides a switch cabinet solid insulating material performance detection method and system based on temperature detection, and relates to the technical field of temperature detection, and the method comprises the steps: constructing a grid detection matrix on the surface of a solid insulating material, collecting real-time temperature data through a passive radio frequency identification temperature sensor, and building an initial state model; adopting a deep learning algorithm to calculate a temperature gradient value and thermal stress distribution, and constructing a performance degradation index; and the recurrent neural network is combined to predict a performance evolution trajectory, and dynamic early warning and diagnosis are realized. The performance degradation state of the solid insulating material can be accurately evaluated, the detection precision and the prediction accuracy are improved, and intelligent monitoring and early warning are achieved.

The invention provides a multi-kitchen-appliance remote control method and system based on context awareness, and relates to the technical field of smart home, and the method comprises the steps: collecting kitchen environment data and user historical use data, and carrying out the wavelet multi-resolution decomposition and density gradient matrix recognition of abnormal data; the method comprises the following steps: extracting data time sequence features by combining a bidirectional recurrent neural network and performing hierarchical coding, inputting a fused feature vector and a density distribution graph into an improved long-short-term memory network, performing feature propagation by combining a Fisher information matrix and a time-varying association graph, and predicting a kitchen appliance use trend by utilizing a prophet model. And based on a risk assessment model and a Monte Carlo tree search method, in combination with an adaptive exploration mechanism and a multi-target evaluation system, generating and assessing a candidate control strategy, and outputting an optimal kitchen electricity cooperative control strategy.

The invention discloses a fatigue driving monitoring and early warning method based on multi-dimensional feature fusion, and relates to the field of intelligent traffic and safe driving, and the method comprises the steps: carrying out the feature extraction of a collected face image of a vehicle driver through a feature extraction model based on a convolutional neural network, and obtaining a face key point; then calculating the eye closing rate, the mouth opening rate, the head posture data and the facial muscle data to finally obtain multi-dimensional features, and fusing the multi-dimensional features by using a multi-layer perceptron to obtain fused facial features; and then, a fatigue state detection model obtained based on recurrent neural network training is utilized to detect the facial feature time sequence at a plurality of continuous sampling moments so as to detect whether a fatigue driving state occurs and trigger alarm feedback. According to the method, the fatigue state of the driver can be identified and judged more accurately through combination of multi-dimensional data fusion and a time sequence analysis algorithm, and the monitoring and early warning accuracy and reliability are improved. And the system can keep high robustness even in an illumination change or complex driving environment.

The invention discloses a power efficiency optimization method based on intelligent load management. The method comprises the following steps: S1, generating an initial load characteristic matrix; s2, decomposing the initial load characteristic matrix into a short-term behavior characteristic matrix and a long-term trend characteristic matrix through pseudo-symmetry decoupling; s3, multi-level load demand prediction is carried out through a fusion structure of the multi-scale convolution and the recurrent neural network; s4, coding a cognitive gene sequence, triggering gene mutation and recombination operation when significant change occurs, and generating a load distribution strategy; s5, a load distribution strategy is transmitted into a causal reasoning and self-supervision feedback control module, and if the deviation between feedback data and a prediction result exceeds a threshold value, a self-learning mechanism is triggered; s6, dynamically distributing the load priority, and generating a decision path of load management; and S7, performing adaptive optimization through a multi-layer feature feedback closed-loop optimization system. The intelligent load management method is adopted, and the power efficiency is optimized through multi-layer feature decoupling and closed loop.

The invention relates to the field of flight training data processing, in particular to a flight training regulation and control method and system, and the method comprises the steps: collecting a physiological signal, an electroencephalogram signal and a visual signal of a pilot, and generating comprehensive feature data through multi-modal feature fusion; time dependence analysis is carried out through a recurrent neural network, a comprehensive pressure trend is generated in combination with historical pressure data, the psychological pressure of the pilot is further evaluated through a deep learning model, and the training intensity is adjusted based on real-time feedback data; in the virtual training, the situation is dynamically adjusted based on reinforcement learning, and a personalized training scheme is generated in combination with personalized analysis, so that the training effect of the pilot is optimized; according to the invention, comprehensive monitoring and personalized training regulation and control of the physiological state and psychological stress of the pilot are realized, and the adaptability and safety of flight training are improved.

The invention discloses a power system false data injection attack detection method and system. Measurement data of a power distribution system are acquired; constructing an attack sequence, and injecting the constructed attack sequence into the obtained measurement data to simulate a false data attack; constructing a false data injection attack detection model based on the expansion causal convolution and the recurrent neural network to analyze the state change of the power distribution system; and judging an attack behavior according to the analyzed state of the power distribution system. According to the method, time sequence data can be processed, the long-term dependency relationship and the complex mode can be captured, and the response capability to the complex attack mode can be improved, so that the false data injection attack can be effectively detected and prevented, and the safety and reliability of the power distribution system can be maintained.

The invention relates to the technical field of data processing, and discloses an intelligent risk early warning method, device and equipment for a power distribution network, and a medium. Historical risk monitoring data of the power distribution network under multiple dimensions are fused through a graph convolutional network and a variational auto-encoder; a variational recurrent neural network and a long-short term memory network are trained in combination with historical risk fault data of the power distribution network, and an attention mechanism is introduced in the training process to generate a risk assessment model; acquiring real-time risk monitoring data of the power distribution network under multiple dimensions to extract multi-dimensional real-time fusion features and inputting the multi-dimensional real-time fusion features into the risk assessment model for processing to obtain a real-time risk assessment level so as to further process the multi-dimensional real-time fusion features through a multi-criterion decision analysis method and a fuzzy inference system; the target risk assessment level is obtained, the level is compared with the risk early warning threshold value, if the level exceeds the threshold value, early warning is triggered, and the accuracy of power distribution network risk assessment is effectively improved.

The invention relates to the field of energy storage frequency modulation prediction, and discloses an energy storage frequency modulation instruction prediction method and system based on a two-stage method, and the method comprises the steps: obtaining an energy storage frequency modulation instruction, and carrying out the variational mode decomposition; judging mutual attraction or mutual repulsion of the subsequences according to a variation mode decomposition result; if it is judged that the two subsequences attract each other, sequence fusion operation is carried out, and if it is judged that the two subsequences repel each other, sequence generation operation is carried out; obtaining a new sub-sequence set through sequence fusion operation and sequence generation operation; and respectively inputting the sequences in the new sub-sequence set into the recurrent neural network to obtain a final prediction result of the energy storage frequency modulation instruction. By optimizing the frequency modulation instruction in advance, the energy storage system can respond to the frequency change requirement more quickly and accurately, the frequency modulation efficiency and economic benefits of the power system are remarkably improved, and meanwhile the stability of the system and the operation efficiency of the power plant are enhanced.

The invention discloses a multi-modal content understanding method and system based on a knowledge graph, and belongs to the technical field of multi-modal content understanding, the method comprises the steps of obtaining multi-modal input data and conducting feature decoupling, semantic information in the multi-modal data and noise and redundant information peculiar to modals can be effectively separated through the feature decoupling technology, and the multi-modal content understanding efficiency is improved. The method comprises the following steps of: establishing a space-time perception graph attention network, improving the purity and semantic expression capability of features, bridging semantic gaps among different modals through the space-time perception graph attention network, realizing cross-modal semantic alignment, enhancing the generalization capability of a model, deeply mining space-time modes, relationships and anomalies in data through space-time association reasoning, and improving the accuracy of the data. The method provides support for multi-modal data analysis in a complex scene, and combines a graph neural network and a recurrent neural network to capture semantic association and spatial-temporal dynamics and optimize the performance of an inference model.

The invention discloses a ship obstacle avoidance and automatic berthing method based on a deep learning algorithm. The ship obstacle avoidance and automatic berthing method comprises the following steps: S1, collecting data and constructing a data set; s2, performing time synchronization and space calibration; s3, carrying out pretreatment; s4, performing feature extraction; s5, generating a unified feature space by using a self-attention mechanism based on Transform; s6, performing environment modeling, and generating a virtual marine environment and a complex scene by using the generative adversarial network; s7, training an obstacle avoidance and path planning decision model of the ship by adopting a method of combining a distributed depth deterministic strategy gradient with a game theory; s8, designing a hybrid power path planning system by adopting meta-reinforcement learning in combination with fuzzy logic control; s9, realizing accurate berthing based on a recurrent neural network control strategy; and S10, performing self-adjustment and optimization according to the new environment and condition by using an online learning technology. According to the invention, deep learning and multi-mode sensor technologies are comprehensively applied, and accurate obstacle avoidance and automatic berthing of the ship are realized.

The invention provides a third-generation semiconductor device multi-physical field coupling effect dynamic monitoring method and system, and relates to the technical field of semiconductors, and the method comprises the steps: collecting the data of a temperature field, an electric field, a magnetic field and a stress field through a complementary sensor array, constructing a feature tensor, inputting the feature tensor into a pre-trained graph neural network, and extracting a coupling effect dynamic evolution rule; on-line analysis and prediction are carried out by using an adaptive time window and a recurrent neural network, a multi-field coupling correlation degree evaluation model is established, an early warning level is determined according to the deviation between the coupling correlation degree and an early warning threshold value, and device risk evaluation and maintenance suggestions are generated, so that early warning of the failure risk of the semiconductor device is realized.

The invention relates to the technical field of the Internet of Things and artificial intelligence, and discloses an equipment maintenance fault intelligent analysis method and system based on the Internet of Things, and the method comprises the steps: representing an entity and a relation as a time function, and constructing a time sequence knowledge graph; monitoring vector change in the knowledge embedding space, detecting a concept drift phenomenon and mining an equipment performance evolution mode; constructing an equipment state transition model by using a recurrent neural network, predicting the future state of the equipment and evaluating the fault risk; collecting feedback processing knowledge conflicts and updating the model; generating multi-time-scale fault risk early warning and maintenance suggestions; according to the method, the knowledge acquisition efficiency is improved, dynamic knowledge support of the full life cycle of the equipment is provided, early recognition of gradient faults is realized, the method has adaptive knowledge updating capability, multi-dimensional fault analysis is provided, maintenance resource configuration is optimized, and the equipment management efficiency is improved.

The invention discloses a lithium battery SOC estimation method based on an ordinary differential equation neural network, and the method comprises the steps: S1, collecting the cycle charging and discharging voltage and current data of a lithium battery, and forming an original data set; s2, establishing a second-order RC equivalent circuit model of the lithium battery, and determining a state equation, an output equation and an ordinary differential equation set; s3, integrating an ordinary differential equation set into a recurrent neural network to parameterize a hidden unit, and establishing an ODE-RNN Net model fused with an equivalent circuit model ordinary differential equation; step S4, initializing parameters of the ODE-RNN Net model, and training the ODE-RNN Net model by using the original data set; and S5, inputting the working current of the lithium battery at the current moment by using the trained ODE-RNN Net model, and predicting the subsequent working state output and the state of charge of the lithium battery. According to the method, the demand of training data volume can be reduced, and the accuracy of battery modeling and battery state-of-charge estimation is improved.

The invention provides a neuromorphic visual target tracking method and system based on image processing, and relates to the technical field of neuromorphic calculation, and the method comprises the steps: fusing the input of an event camera and a conventional image sensor, constructing a combined input tensor, and introducing a multi-scale convolution and synaptic event driving mechanism. Quick response and stable feature extraction of a high-speed moving target are realized, and coding block mistaken deletion and tracking loss caused by quick movement of the target are effectively avoided, so that system delay is reduced. Meanwhile, in combination with significance entropy difference evaluation and a dynamic brightness enhancement mechanism, the target judgment accuracy under low illumination and complex backgrounds is improved; redundant noise blocks are screened out through a significance weight function and confidence calculation, the redundancy calculation burden is relieved, and the lightweight characteristic of the system is guaranteed. A memory trajectory tensor and dynamic template adjustment mechanism based on a recurrent neural network is further introduced, time sequence consistency verification and self-adaptive updating are achieved, and the stability and robustness of the tracking process are enhanced.

The invention relates to the technical field of agricultural information, and discloses a visual analysis method and system based on an agricultural big data model. According to the system, crop growth time sequence data are processed by using a multi-scale feature extraction algorithm combining a recurrent neural network and wavelet transform, and key conversion points of a crop growth stage are automatically identified by using a deep learning architecture combining a variational auto-encoder and a hidden Markov model. A standard growth track model and a multi-dimensional deviation measurement index are constructed to evaluate the abnormal growth condition of crops, a dynamic threshold model based on reinforcement learning is applied to realize early warning of abnormal growth, and a multi-dimensional visual interface based on a clock metaphor is designed to visually display crop growth information. The crop growth stage recognition precision and early warning timeliness are improved, and the agricultural production management efficiency is remarkably enhanced.

The invention discloses a concrete internal defect quantitative detection method and system based on reflected waves, and belongs to the technical field of nondestructive testing. A reflected wave data matrix is obtained through multi-angle excitation and synchronous receiving; calculating energy characteristics of each channel, and constructing an energy response residual field; extracting waveform offset, spectrum jitter and phase change caused by defects by adopting a disturbance comparison algorithm to form a disturbance feature vector set; a defect-response function curved surface is further constructed, a defect topological structure is inversed based on gradient and curvature analysis, and defect geometric parameters are output; and finally, inputting the multi-moment defect parameters into the recurrent neural network, and predicting a defect evolution path and a failure risk. The method has high resolution and trend prediction capability, and is suitable for detection and early warning of concrete structures in bridges, tunnels and nuclear power projects.

An artificial intelligence-driven sentiment analysis system for social media monitoring, comprising: a data collection module configured to retrieve user-generated content from at least one social media platform via application programming interfaces (APIs); a preprocessing module configured to process and clean the collected data by removing irrelevant content, tokenizing the text, removing stop words, and assigning sentiment indicators to emojis, slang, and abbreviations; a sentiment analysis engine comprising one or more deep learning models, wherein the deep learning models include recurrent neural networks (RNNs), long-short-term memory networks (LSTM), and transformer-based models configured to analyze the preprocessed data and classify the content into one or more sentiment categories; an emotion recognition module configured to detect emotional tones within the content and classify them into predefined emotional categories; a real-time data processing unit configured to continuously monitor social media content and provide real-time insights into moods and emotions; a feedback loop module configured to update and refine the deep learning models through reinforcement learning based on new data inputs and sentiment classification results; and a user interface module configured to visualize sentiment and emotion analysis results in real time, including heatmaps, sentiment trend analysis, and emotional breakdowns.

The invention discloses a power battery management method and system based on data driving, particularly relates to the technical field of battery degradation monitoring, and is used for solving the problem that hidden degradation of a battery in a microcirculation state is difficult to monitor in real time in the prior art. According to the invention, microcirculation energy spectrum is analyzed through a transient spectrum clustering algorithm, and risks of spectrum concentration abnormity and structure degradation are identified; the internal resistance deviation trend of the battery is analyzed, and the influence of internal resistance fluctuation on active material granulation is evaluated; judging whether microcirculation abnormity risks exist or not based on spectrum abnormity and internal resistance fluctuation conditions; when an abnormal risk exists, analyzing a stress accumulation condition through tensor decomposition and singular value decomposition, and evaluating an attenuation gradient of an electrode polarization signal in combination with variational mode decomposition and a recurrent neural network so as to determine an overall degradation trend of the battery; management of the power battery can be dynamically optimized, the service life is prolonged, and safety and reliability are improved.

The invention provides a hydro meteorology early warning method for an offshore oil and gas platform, and belongs to the technical field of offshore hydro meteorology. Extreme weather events are identified by adopting minimum probability abnormal event identification vectors to match abnormal characteristic parameters, and abnormal signal characteristic parameters are input into an ocean dynamics prediction model to calculate real-time sea condition parameters; calling a multi-temporal-spatial-scale early warning fusion matrix to combine with a wavelet decomposition technology and a recurrent neural network to realize multi-scale information integration, analyzing an environmental parameter change trend through a sea condition jump identification model and triggering an emergency response, dynamically adjusting system parameters according to a stability evaluation index vector, and optimizing prediction precision by adopting an early warning residual value compensation matrix. And finally, multi-level early warning information is generated and a real-time early warning notification is sent to an operator, so that the technical problem of insufficient early warning precision of an offshore oil and gas platform hydro meteorology early warning system in multi-spatio-temporal scale data fusion processing is solved.

The invention discloses a remote sensing image target detection method based on an accelerated projection recurrent neural network. The method comprises the following steps: acquiring an original remote sensing image and calculating a remote sensing image autocorrelation matrix; constructing a linear constraint optimization mathematical model of filtering output based on the remote sensing image autocorrelation matrix, and obtaining a linear equation mathematical model through conversion; and solving the linear equation mathematical model by adopting an accelerated projection recurrent neural network algorithm, carrying out vector decomposition on a solving result to obtain a filtering coefficient vector, obtaining a remote sensing image after filtering output based on the filtering coefficient vector, and completing remote sensing image target detection. According to the remote sensing image target detection method based on the accelerated projection recurrent neural network, the convergence speed is higher, the calculation time is shorter, the image threshold classification capability is higher and the target detection precision is higher when the optimization problem is solved.

The invention discloses an AI search recommendation method, system and device combined with commodity semantic understanding and a medium, and belongs to the technical field of commodity recommendation. The method comprises the steps that semantic vectors of commodity titles and descriptions are generated; generating a feature vector of the commodity image; fusing the semantic vector of the commodity title and description with the feature vector of the commodity image to generate a comprehensive semantic vector of the commodity; generating a user interest vector; updating the user interest vector by using a recurrent neural network based on the user interest vector; and carrying out joint modeling on the comprehensive semantic vector of the commodity and the updated user interest vector to generate a recommendation result. According to the method, a content semantic driving modeling mode is adopted, recommendation judgment can still be conducted through the deep matching relation between commodity image-text semantics and user behavior preferences even under the condition that user historical behaviors are limited or new commodities are online, good cold start adaptability is achieved, and the method is suitable for popularization and application. And meanwhile, high recommendation difference and content diversity are shown for different user groups.