1575 results about "Online learning" patented technology

The invention provides an industrial environment monitoring and accident prediction method fusing multi-modal data, and relates to the technical field of data processing, and the method comprises the steps: carrying out the semantic collection and causal association preprocessing of multi-modal heterogeneous data collected in real time through constructing a dynamic industrial knowledge graph; a customized deep learning model is adopted to extract deep abstract features of each mode, and weak signals and potential risks are accurately represented and uncertainty is quantified; a high-fidelity digital twin model is utilized to drive a deep reinforcement learning algorithm, and dynamic optimization and verification are performed to generate a multi-level and multi-target preventive intervention strategy combination; an intervention strategy is executed through an edge-end-cloud three-layer collaborative intelligent architecture, and online learning and system sustainable evolution are realized by using a closed-loop data feedback mechanism. According to the method, the sensing and early warning capability of the early weak and complex abnormal state of the industrial environment can be remarkably improved, the accident evolution path is accurately predicted, and credible explanation is provided.

The invention discloses an aerospace intelligent manufacturing large model construction method, which comprises the steps of collecting original data, performing preprocessing and data association, and constructing an aerospace intelligent manufacturing database; establishing a knowledge acquisition and structured conversion assembly line, a multi-dimensional associated domain knowledge graph, a knowledge quality control system and a dynamic updating mechanism, and constructing a professional knowledge base; aligning the cross-modal manufacturing data to generate a corpus; combining base general large model pre-training, injecting terminology semantics and multi-modal association capability, and completing knowledge migration; based on the pre-trained aerospace intelligent manufacturing large model, constructing an aerospace manufacturing cognitive agent, and forming a complex engineering problem solving framework; professional ability is optimized through a two-stage progressive multi-task training strategy, and dynamic adaptation of a production environment is realized in combination with an online learning and incremental updating mechanism. The intelligent level of aerospace intelligent manufacturing is remarkably improved.

The invention relates to the technical field of artificial intelligence, and discloses a bidding document generation method based on retrieval enhancement generation and a large language model, and the method comprises the steps: analyzing technical parameters, legal terms and score weights in a bidding demand document; retrieving matched historical bidding document fragments and technical specifications from the industry knowledge base, and generating a retrieval enhanced data set; fusing the data through a dynamic weight distribution algorithm and generating an initial bidding document draft; checking conflict terms based on the legal semantic knowledge graph and marking correction suggestions; and optimizing the bidding document structure and the key content according to the score weight. According to the method, an online learning mechanism driven by sentence vector matching, timeliness weight calculation and user feedback is adopted, so that the problems of inaccurate technical parameter extraction, low legal conflict detection efficiency and non-optimized scoring rules in the bidding document generation process are solved.

The invention relates to the technical field of program control systems, in particular to a pentahedron machining center precision calibration method and system based on multi-sensor fusion, and the method comprises the steps: a sensor system construction and calibration module is used for field calibration, drift correction and redundancy deployment to ensure data precision, and achieves the whole-course traceability of a calibration process through a block chain technology; the multi-source data preprocessing and fusion module is used for time-space synchronization of heterogeneous data and dynamic fusion of multi-source information; the intelligent modeling and state prediction module is used for performing real-time and multi-task prediction on key states such as tool wear and thermal deformation; based on the prediction result, the adaptive compensation and path optimization module is used for dynamically optimizing the tool path; meanwhile, through an online learning mechanism, the calibration model is continuously updated by utilizing a processing result; the distributed cooperative control module executes data processing and calibration algorithms locally and makes a cooperative decision with a numerical control system, and low-delay and intelligent response to machining abnormity is achieved.

The invention relates to the technical field of tunnel engineering, in particular to a tunnel surrounding rock grading method and system, comprising intelligent sensing and data acquisition, multi-source data fusion and modeling, hybrid model dynamic grading, real-time decision and support optimization, online learning and dynamic feedback, and risk early warning and emergency response. Compared with the prior art that a geological data acquisition mode combining manual drilling coring and low-resolution geophysical prospecting is adopted, efficiency is low, subjective errors are large, and a complex geological structure is difficult to cover, unmanned aerial vehicle LiDAR scanning, intelligent rock core image analysis and a high-density IoT sensor network work cooperatively, and the working efficiency is greatly improved. Real-time dynamic acquisition of full-section geological information is achieved, manual intervention errors are eliminated in combination with a multi-source data fusion algorithm, the automation level and three-dimensional space representation precision of data acquisition are remarkably improved, and a high-resolution holographic data base is provided for surrounding rock classification.

The invention discloses an industrial equipment fault prediction method based on multi-modal data, and belongs to the technical field of specific calculation models, and the method comprises the steps: carrying out the preprocessing according to the collected multi-modal data of the operation of industrial equipment, so as to unify the format of the multi-modal data, and obtaining the structural data; extracting features of the structured data one by one according to data categories, and obtaining a multi-modal fusion feature through a dynamic fusion mechanism; according to the multi-modal fusion features, a fault prediction classification score is obtained through a deep neural network model to perform fault prediction; and when the drift parameter of the multi-modal data is greater than a preset threshold value, performing incremental training on the deep neural network model through the multi-modal data collected in real time to update parameters of the deep neural network model. Through multi-modal data unified processing, dynamic feature fusion, deep neural network modeling and an online learning mechanism, the problems of insufficient multi-modal data fusion, prediction uncertainty quantization deficiency, poor model adaptability and the like are solved.

The invention discloses a memory access optimization method based on intelligent cache management, and relates to the technical field of computer storage. According to the method, spatial-temporal characteristics and semantic association data of memory access requests are collected in real time, a dynamic heat matrix is constructed, and a multi-dimensional access rule is fused to improve modeling precision. And inputting the dynamic popularity matrix into a hybrid prediction model, predicting a future access probability by using a time convolutional network, analyzing a competition relationship between data blocks through a graph attention network, generating a conflict pre-judgment weight and a corrected popularity ranking, and effectively reducing the cache jitter risk. On the basis of popularity ranking and conflict weight, a fragmented reinforcement learning algorithm is adopted to divide logic sub-regions, a differential reward function is designed to dynamically decide cache operation, and performance and energy efficiency requirements are balanced; and finally, through an online learning mechanism, combining real-time feedback to dynamically adjust a prediction model weight and strategy parameters, forming a closed-loop optimization link, and realizing adaptive stability under long-term load fluctuation.

The invention provides a thermal power plant boiler combustion visual monitoring and early warning method and system, a storage medium and electronic equipment. According to the method, obtained multi-measuring-point temperature data are input into a pre-constructed temperature field reconstruction model, the model comprehensively considers the geometric structure and combustion thermodynamic characteristic factors of a hearth, model parameters are dynamically adjusted through a particle swarm optimization algorithm, the temperature field reconstruction precision is continuously improved, and a three-dimensional real-time temperature distribution diagram of the hearth is generated; fusing and presenting the diagnosed combustion abnormal information and the visual image of the hearth temperature field, generating a visual in-furnace combustion state monitoring interface, and meanwhile, pushing the abnormal information to a mobile terminal to realize remote monitoring and early warning; the combustion state data in the long-period operation process of the boiler are mined and analyzed, typical combustion modes are extracted, a combustion mode library is continuously enriched and optimized, an online learning algorithm is used for adaptively updating a combustion abnormity diagnosis model, and the intelligent level of combustion state sensing and abnormity early warning is continuously improved.

The invention provides a crane remote instruction response delay detection and in-advance compensation method and system, and relates to the technical field of cranes, and the crane remote instruction response delay detection and in-advance compensation method comprises the following steps: adopting an adaptive space-time alignment algorithm to map real-time operation data to a dynamic knowledge graph, and generating a feature vector; inputting the feature vector into a depth map neural network integrated with a causal reasoning mechanism to generate an incidence matrix; a multi-head attention network with a residual structure is adopted to extract time sequence features; constructing a hybrid decision system based on the delay prediction tensor, and outputting an optimal compensation strategy; and establishing a double-closed-loop evolution system with an online learning capability, and dynamically optimizing a prediction and compensation strategy according to a compensation effect. Through the dynamic knowledge graph, causal reasoning, the multi-head attention network and the double-closed-loop evolution system, the remote instruction response delay can be accurately predicted, effective compensation is carried out, and the real-time performance and safety of remote control of the crane are improved.

The invention provides a multi-heat-source multi-material additive manufacturing optimization method and system based on machine learning, and relates to the field of additive manufacturing, and the method comprises the following steps: collecting process parameters, material physical properties and target response data in real time, and constructing a multi-source data set; on the basis, a multi-task learning model is trained, common features are extracted, target responses are synchronously predicted, a proxy target function is constructed through predicted values, if errors exceed a threshold value, experimental verification is conducted, and new data is fed back to update the model. And applying the weight coefficient to a multi-objective evolutionary algorithm, iteratively screening a Pareto candidate solution set, verifying a high-uncertainty solution, and updating the model to error convergence. And selecting and deploying an optimal parameter combination by using an entropy weight-TOPSIS method, and embedding an online learning mechanism to form closed-loop optimization. According to the method, multi-task learning and a self-adaptive evolutionary algorithm are combined, and efficient optimization of multi-heat-source multi-material additive manufacturing process parameters can be achieved.

The invention discloses an interdisciplinary scientific research potential assessment method based on a dynamic multi-modal knowledge graph, relates to the technical field of scientific research management and intelligence analysis, and aims at the dynamic requirements of college scientific research ecology by relying on an incremental assessment framework. Comprising the steps of multi-source data acquisition and incremental knowledge graph construction, time sequence trend identification, online and transfer learning optimization, scene simulation and multi-scene strategy verification and multi-modal data fusion. The map structure is updated in real time through concept drift detection and ontology extension, and emerging hotspots are identified by combining time sequence difference indexes such as citation acceleration and a cooperative network; and the accuracy and continuous iteration capability of the evaluation model are kept in multiple environments by means of online learning and a cross-domain migration strategy. Finally, potential evolution is quantified through scene simulation, strategy suggestions are output, the sensitivity to the cross domain is further enhanced with the assistance of multi-mode embedding, a closed-loop evaluation system from data to decision is formed, and college subject cross layout and resource allocation are assisted.

The invention relates to the technical field of power transformer discharge fault diagnosis in a power supply system, in particular to a power transformer discharge fault diagnosis method based on a neural network, and the method comprises the steps: collecting a partial discharge signal, characteristic gas data in oil and power supply system operation parameters in real time through a sensor, and unifying timestamps; statistical aggregation is performed on the partial discharge signals, and standardization processing is performed on the electrical quantity parameters and the discharge characteristic parameters to generate fusion characteristic vectors; constructing an expansion mapping relation library containing laboratory simulation data and actual operation data; training a diagnosis model by adopting a mixed structure of a convolutional neural network and a long-short-term memory network; and deploying the model in an edge computing device, and dynamically verifying and optimizing the performance of the model through online learning. According to the method, the problems of low multi-source data fusion efficiency and insufficient generalization ability of a neural network model to a complex discharge mode are solved, and the operation stability of a power supply system is improved.

The invention discloses a dynamic leakage prediction method and system for an old oil and gas pipeline, and the method comprises the steps: collecting pressure, flow and temperature parameters in real time through a multi-source sensor, and recognizing abnormal fluctuation through the combination of time sequence analysis and frequency domain feature extraction; calculating a pipeline state evaluation result based on the material degradation model; establishing a leakage prediction model fusing a wall thickness degradation kinetic equation and an LSTM neural network, calculating a leakage probability by adopting a Monte Carlo method, and generating a diffusion velocity and a concentration gradient through CFD numerical simulation; when the diffusion prediction exceeds a safety threshold value, a control strategy is optimized through fuzzy logic and a genetic algorithm; the verification model is fed back after real-time adjustment, and online learning is carried out through Bayesian optimization; and finally, calibrating the model by using experimental data, and deploying and generating risk early warning. The system comprises a multi-source sensor array, a data processing platform and other modules, and full-chain closed-loop control from sensing to early warning is achieved.

The invention relates to the technical field of data processing, in particular to a standardized detection result calibration method based on multi-modal fusion, which comprises the following steps of: performing high-precision space-time synchronization and standardization on different modal data, performing dynamic compensation by utilizing timestamp alignment, a cross-correlation function and an IMU (Inertial Measurement Unit), and performing dynamic parameter adjustment by adopting a local abnormal factor algorithm. Carrying out cross validation by utilizing inherent relevance of multi-modal data, constructing and continuously optimizing a high-confidence system state representation model, and realizing real-time identification and self-adaptive calibration of model parameters by adopting a Bayesian online learning framework; the method further comprises the steps that closed-loop recalibration is conducted through multi-sensor cross validation and residual analysis, an optimal action sequence is generated through a reinforcement learning agent, accurate prediction of key indexes of a monitored object is achieved, instant calibration and situational prediction can be conducted according to a specific event, and the intelligent level and maintenance efficiency of system monitoring are comprehensively improved.

The invention relates to a man-machine hybrid autonomous navigation system in an unknown dynamic environment, which comprises an environment sensing module for acquiring environment information by using sensors such as a laser radar, a camera and an inertial measurement unit and performing data preprocessing; the local target point selection module selects a local target point set meeting requirements in the environment according to the real-time information provided by the environment sensing module; the decision planning module is used for generating an optimal path and a navigation strategy based on mixed training of a deep reinforcement learning algorithm and artificial experience; and the learning optimization module improves the generalization ability of the algorithm through online learning and transfer learning technologies, and realizes effective introduction and strategy adjustment of human experience in combination with the man-machine interaction module. The man-machine interaction module can work in cooperation with the local target point selection module so as to dynamically adjust target point selection according to external input and optimize path planning. According to the method, high decision stability can be kept in an unseen environment, the training cost is reduced, and the method is more suitable for autonomous navigation application in the real world.

The invention provides an intelligent recommendation method and system for an e-commerce platform, and the method comprises the steps: collecting user interaction behaviors and time-space context data in real time, and constructing a user behavior multi-modal feature matrix; extracting commodity multi-level features, and generating a commodity comprehensive feature matrix; identifying and predicting a user intention based on the user behavior feature matrix, and generating an intention distribution vector; a recommendation candidate set is obtained by combining the commodity feature matrix and utilizing a context awareness collaborative filtering enhancement technology; a multi-objective optimization function is constructed, and after the user intention vector is input, a personalized recommendation sequence is generated in combination with an optimization result and the candidate set; and user feedback is monitored in real time, online learning and reinforcement learning algorithms are adopted, and a recommendation strategy is continuously optimized based on user instant feedback and long-term satisfaction. According to the scheme, the recommendation accuracy, the diversity of recommendation results and the user experience can be improved.

The invention discloses a Bayesian learning and piezoelectric ceramic driving numerical control machine tool thermal error compensation system and method. According to the system, a distributed temperature sensor array is arranged in heat sensitive areas such as a machine tool spindle, a ball screw, a guide rail and a bearing seat, whole-field temperature information is collected in combination with a thermal infrared imager, and multi-source thermal field sensing is achieved; meanwhile, a laser interferometer and a capacitive displacement sensor are used for constructing a dynamic pose monitoring network. The intelligent decision-making unit integrates a Bayesian online learning engine, fuses a physical model and a data driving model, dynamically predicts a thermal error and generates a compensation instruction. And the piezoelectric execution mechanism carries out nonlinear pre-compensation on a driving signal through a three-section Prantl-Ishlinskii hysteresis inverse model according to the instruction, so that high-precision pose adjustment is realized. The thermal error compensation precision is remarkably improved, the adaptability of the system to complex working conditions is enhanced, the service life of equipment is prolonged, and the method is suitable for various numerical control machine tools.

The invention discloses a flotation froth dynamic diagnosis and self-adaptive regulation and control system based on multi-mode depth perception and time sequence prediction. The flotation froth dynamic diagnosis and self-adaptive regulation and control system aims at solving the problems that in the prior art, the flotation process is not comprehensive in monitoring perception, dynamic prediction is missing, and regulation and control self-adaptability is poor. According to the invention, by deploying a multi-source heterogeneous sensor array, multi-modal data of vision, spectrum, acoustics and the like of foam are synchronously collected; and generating comprehensive foam comprehensive state characterization by using a cross-modal attention fusion network. Modeling is carried out on dynamic evolution of foam by adopting a hierarchical time sequence prediction and anomaly detection network, the future state trend is accurately predicted, and early warning of anomaly is realized. And finally, an intelligent regulation and control agent based on deep reinforcement learning is constructed, the intelligent regulation and control agent autonomously decides optimal process parameter adjustment according to the current state and future prediction, and online learning and optimization are carried out through continuous interaction with the actual process. The beneficiation recovery rate, the grade and the stability of the production process are remarkably improved, and the operation cost is reduced.

The invention relates to the related technical field of distributed computing power resources, and discloses a distributed computing power resource dynamic fusion and cooperative computing system which comprises a resource sensing module, a task analysis module, a dynamic scheduling module, a virtualization adaptation layer, a cooperative computing engine, a global controller and a hierarchical communication bus. The resource sensing module collects and quantifies the hardware computing power of the heterogeneous computing nodes in real time. According to the invention, a complete closed-loop control system is formed through a five-layer dynamic fusion architecture (a sensing layer, an analysis layer, a scheduling layer, a virtualization layer and a collaboration layer), so that a distributed computing system can adapt to a scene that edge nodes are dynamically added and pushed out; meanwhile, through the purpose of developing virtualization middleware supporting cross-platform instruction real-time conversion and based on a multi-target dynamic weight adjustment mechanism of online learning, the heterogeneous task scheduling success rate can be greatly improved through the overall structure of the system, and the fault recovery time is shortened.

The invention discloses a highway full life cycle carbon emission statistical accounting method, and relates to the technical field of carbon emission accounting, and the method comprises the steps: dividing the highway full life cycle into a plurality of stages, and dynamically calibrating the accounting boundary of each stage based on the space-time dimension; setting all types of carbon emission accounting indexes, and collecting multi-modal data corresponding to each stage; establishing a hierarchical computing architecture based on an edge-cloud collaborative technology, checking the carbon emission of each stage, and verifying a carbon emission checking result by using a virtual road model capable of online learning and updating; and drawing a carbon footprint thermodynamic diagram based on a carbon emission accounting result, and displaying a space-time evolution process. According to the invention, the construction, operation and demolition stages of the whole life cycle of the expressway are covered, and the comprehensiveness and systematicness of an accounting result are ensured; a plurality of data sources and collection methods are adopted, and a corresponding accounting method is combined for calculation, so that the accuracy and reliability of an accounting result are ensured, and a scientific basis is provided for low-carbon construction and operation of the expressway.

The embodiment of the invention provides a vehicle-mounted emotion interaction method and device based on multi-dimensional recognition, and the method and device achieve the precise judgment of the emotion of a driver through innovatively constructing an emotion fusion recognition model and integrating the facial expression, voice emotion, driving behavior and physiological state features. And designing a scene-based self-adaptive interaction strategy, and establishing an interaction triggering threshold value for intelligent matching in combination with external environment data and a danger level. An interaction effect evaluation mechanism is introduced, an interaction strategy model is continuously optimized through an online learning module, and dynamic adjustment of personalized interaction content is achieved. According to the method, the defects of the traditional technology in the aspects of emotion recognition, interaction strategies, effect evaluation and the like are effectively overcome, and the intelligent level and the user experience of vehicle-mounted emotion interaction are remarkably improved.

The invention discloses a vacuum preloading soil settlement prediction and control method based on deep learning, and aims to realize precise prediction and dynamic closed-loop control of soil settlement. According to the method, an encoder-decoder architecture is adopted to construct a model framework, and off-line generation of a proprietary model is completed through multi-measurement-point space-time joint training and a combinatorial optimization strategy; multi-source data of a target monitoring point is collected and processed to generate a sequence sample of an adaptive model; in order to adapt to the time-varying characteristics of the geological attributes, deploying an incremental online learning mechanism, and solidifying and finely tuning model parameters through an elastic weight; and based on a prediction result of the updated model, a PID control unit calculates a vacuum degree adjusting amount, a vacuum pump control system is driven to dynamically adjust the operation vacuum degree, and it is ensured that soil settlement is within a reasonable range. Finally, a cooperation mechanism of high-precision settlement prediction and dynamic vacuum degree regulation and control is constructed, and the settlement control precision, the response speed and the construction safety are remarkably improved.

The invention discloses a private cloud intelligent load balancing method. The method comprises the following steps: S1, establishing an LSTM-Transform hybrid model to predict real-time load data of a private cloud node; s2, calculating a load capacity index according to the load prediction value and the current resource state of the node; s3, dynamically adjusting the weight value of each node based on the calculation result of the load capacity index; s4, sending the weight value to a load balancer, and adjusting a flow distribution strategy in real time; and S5, establishing a closed-loop feedback module for executing model parameter updating and dynamic threshold automatic adjustment operation according to the deviation between the actual load and the predicted value. According to the method, the dynamic weight adjustment mechanism based on the load capacity index is set, the model parameters are updated in real time in combination with the online learning algorithm, and the weight coefficient is optimized according to the index, so that the weight adjustment response delay is shortened from the minute level to the millisecond level, the load variance is reduced, and the resource utilization rate is improved.

The invention discloses a multi-modal electroencephalogram classification method under multi-source interference based on a multi-head attention mechanism, and relates to the technical field of electroencephalogram signal processing. The method comprises the following steps: synchronously acquiring three types of electroencephalogram modal signals of motor imagery, steady-state visual evoked and event-related potentials, and synchronously acquiring multi-source interference data; performing data preprocessing on the multi-mode signal data; time domain, frequency domain and spatial domain three-dimensional features of each electroencephalogram mode are extracted, and meanwhile, physical features of interference data are quantified; the multi-domain features of the three types of electroencephalogram modes serve as parallel query vectors, the multi-source interference features serve as key value vectors, interference suppression weights are dynamically distributed through a layered attention mechanism, and anti-interference enhanced multi-mode fusion features are generated; and the multi-modal fusion features are processed through a gating circulation unit and a time domain attention module, a multi-modal classification result is output, and online learning and weight updating are carried out. According to the method, a robust decoding scheme can be provided for a brain control interaction system in a complex environment.

The invention relates to the technical field of target detection and tracking, and discloses a multi-modal real-time target detection and tracking system, which is characterized in that a multi-modal data acquisition module integrates a high-definition camera, a millimeter-wave radar, a laser radar and an infrared sensor and is used for acquiring target information from multiple dimensions such as visual images, distance, speed and angle, three-dimensional point cloud and thermal radiation; and the data preprocessing module is used for carrying out denoising, enhancement, normalization, filtering, coordinate conversion and temperature correction processing on the original data acquired by the multi-modal data acquisition module. Multiple sensors are integrated to collect multi-dimensional data, after preprocessing, efficient fusion is achieved through hierarchical fusion and an attention mechanism, the detection module is combined with an improved algorithm and a dynamic threshold value, the tracking module fuses multiple features and has the online learning ability, and the system control module achieves intelligent management. The system greatly improves the accuracy, the real-time performance and the stability of detection and tracking, has remarkable advantages of an innovative technology, and provides a new scheme for related fields.

The invention relates to the technical field of power grids, in particular to a metering box remote monitoring method, system and device based on artificial intelligence and a medium. Acquiring electrical parameter data and environmental data of the metering box; performing time domain and frequency domain feature extraction on the data to obtain key features; inputting the processed data into a preset abnormal state detection model; identifying an abnormal state of the metering box based on a model output result; and when the abnormity is identified, alarm information is generated and sent to a monitoring terminal through a remote communication network. Multi-dimensional monitoring data are collected in real time through multiple sensors, feature extraction and anomaly detection are performed by applying an artificial intelligence technology, a model is continuously optimized in combination with an online learning mechanism, and comprehensive sensing of the running state of the metering box and accurate recognition of the abnormal state are achieved. Compared with a traditional monitoring system, the method improves the detection accuracy, reduces the false alarm rate and the missing report rate, improves the fault processing efficiency through the automatic generation of the maintenance suggestions, and provides a powerful guarantee for the operation of a power system.

The invention provides an industrial equipment energy efficiency evaluation and maintenance decision-making method and system based on reinforcement learning, and relates to the technical field of reinforcement learning, and the method comprises the steps: collecting equipment operation data and extracting features, constructing a maintenance time sequence decision-making model based on deep reinforcement learning, and achieving the model optimization through the combination of transfer learning; and generating a maintenance decision scheme according to the energy efficiency index analysis and the time sequence causal relationship, executing maintenance, and recording process data for online learning and updating of the model. The energy efficiency management level of industrial equipment is improved, the service life of the equipment is prolonged, and the operation and maintenance cost is reduced.

The invention relates to the field of recommendation systems, and particularly discloses a personalized recommendation method based on multi-modal behavior sequence modeling, which comprises the following steps of: acquiring multi-modal behavior data such as user text, image, time and place, preprocessing, and realizing dynamic fusion of the data by utilizing a multi-modal self-attention mechanism (MMSA) to obtain a multi-modal behavior sequence model; and the interest evolution of the user is accurately captured. An independent RNN module is adopted to model long-term and short-term interests of a user, and the long-term and short-term interests are combined through a self-learning weight coefficient, so that the change of the user interests is reflected more accurately. In addition, by introducing an online learning and incremental learning mechanism, model parameters are dynamically adjusted according to real-time feedback of the user, and it is ensured that a recommendation result can respond to user interest changes in time. According to the method, the defects of an existing recommendation system in the aspects of data fusion, time sequence modeling and real-time adaptability are effectively overcome, recommendation individuation and accuracy are improved, and the real-time updating capacity and scene adaptability of the system are enhanced.

The invention discloses a hospital project intelligent management and collaborative operation optimization system based on an AI algorithm technology, and the system comprises a multi-modal data collection module which is used for obtaining hospital operation data, medical equipment data, patient vital sign data and medical staff operation records in real time; the data fusion engine, the dynamic resource scheduling module, the risk prediction unit and the collaborative work platform provide intelligent task distribution, conflict detection and emergency plan generation functions; the self-adaptive optimization controller is used for continuously updating system parameters through an online learning mechanism; the method breaks through the limitation of traditional single-mode data processing, constructs a cross-department data association model through a graph neural network, integrates heterogeneous data sources such as radiation images, inspection reports and equipment logs, realizes dynamic mapping of diagnosis and treatment paths and resource states, introduces a space-time attention mechanism, and improves the diagnosis and treatment efficiency. Equipment operation cycle characteristics and patient movement tracks are subjected to combined modeling, so that the sensing precision of the system is improved to a minute level.

The invention discloses an intelligent port operation vehicle scheduling system and a scheduling robot, and relates to the technical field of intelligent port operation. The problems that in traditional port vehicle scheduling, the manual scheduling response is slow, the error rate is high, resource distribution is uneven, the labor cost is too high, and the efficient and intelligent requirements of modern ports are difficult to meet are solved. The vehicle no-load distance is reduced and the heavy load rate is improved by using a bipartite graph matching strategy, the prediction scheduling module plans in advance, task overstock and vehicle idleness are reduced, the operation efficiency is effectively improved, the constraint processing module optimizes task allocation according to task and vehicle conditions, and the multi-target optimization module gives consideration to the heavy load rate and order dispatching fairness. The scheduling robot integrates system functions, and can adapt to different working environments and realize intelligent scheduling through data acquisition and continuous optimization of an online learning technology, so that the scheduling accuracy and efficiency are improved, the labor cost is reduced, and the overall competitiveness of a port is improved.