306 results about "Short-term memory" patented technology

The invention discloses a power transmission line fault diagnosis and operation and maintenance scheduling method and system based on networking learning, and relates to the technical field of power transmission line fault diagnosis operation and maintenance scheduling. Related data is extracted to construct a high-risk equipment area and a visual high-risk area thermodynamic diagram, a visual risk grading diagram is constructed in combination with electrical quantity data, and meanwhile, an intelligent recognition storage network and a fault type classification recognition model are constructed in combination with a convolutional neural network-long and short-term memory network hybrid model; the model is optimized through networking learning and an attention mechanism, maintenance teams and resources are autonomously allocated in combination with an operation and maintenance management system, then autonomous optimization and closed-loop operation are achieved, full-process coverage of fault sensing, intelligent decision making and efficient response is achieved, the response time after a line fault occurs is remarkably shortened, and the maintenance efficiency is improved. And the fault handling and operation maintenance capabilities of the power grid system are comprehensively enhanced.

The embodiment of the invention provides an elderly emotion recognition method and device based on multi-modal perception, and the method and device achieve the optimization and enhancement of the signal quality through innovatively constructing a multi-modal data preprocessing mechanism and integrating the facial expression, voice and posture features. And designing a personalized feature mapping model based on historical emotion expression data, and establishing an adaptive feature fusion strategy for intelligent matching in combination with a cross-modal attention network. A hierarchical time sequence classification mechanism is introduced, dynamic modeling of the emotional development trend is realized through a long-short term memory network, and accurate prediction of the emotional state is supported. According to the method, the defects of the traditional technology in the aspects of multi-modal processing, personalized modeling, time sequence analysis and the like are effectively overcome, and the accuracy and reliability of sentiment recognition of the old people are remarkably improved.

The invention discloses a Multi-agent-based psychological counseling platform and method. The counseling platform comprises an evaluation agent used for dynamically extracting psychological features based on dialogue behaviors of a visitor agent and updating results to a traits library module; the planning agent is used for constructing a personalized psychological counseling scheme based on the traits library module and dynamically adjusting a counseling process and a strategy according to counseling feedback; the consultant agent is used for carrying out dialogue interaction with the visitor agent according to the personalized consultation scheme and implementing emotion pacification and psychological intervention; the visitor agent is used for expressing psychological troubles, feeding back psychological state changes and promoting the consultation process; the historical dialogue long and short-term memory module is used for storing multiple rounds of psychological counseling interaction contents and comprises historical dialogue short-term memory and historical dialogue long-term memory; and the dynamic probability memory retrieval module is used for generating probability distribution based on the current consultation context and dynamically calling memory nodes from the historical dialogue long and short term memory module or the traits library module.

The invention provides a metallurgical process optimization method and system for a low-oxygen low-nitrogen aluminum-vanadium alloy, and relates to the technical field of process optimizing.The method comprises the steps that correlation characteristics among process parameters are extracted through a three-layer gating map attention network, and a parameter coupling state vector is constructed in combination with a time sequence attention module; constructing a process prediction model based on the state vector, and fusing a thermodynamic equilibrium equation and a long-short term memory network to predict the oxygen and nitrogen content; a deep reinforcement learning network of a Soft Actor-Critic algorithm is adopted, the parameter coupling state vector and the oxygen and nitrogen content prediction value serve as state input, and the technological parameter adjustment amount is output; and planning and iteratively training a future adjustment strategy through Monte Carlo tree search to finally obtain an optimal process parameter combination meeting oxygen and nitrogen content constraints, thereby realizing intelligent optimization control of the metallurgical process.

The invention belongs to the technical field of ship fuel consumption prediction and big data analysis, and relates to an operating ship fuel consumption prediction method based on a deep learning model. The method comprises the following steps: 1, synchronously acquiring multi-source data from a ship automatic identification system, a cabin monitoring system and an ERA5 meteorological database, and preprocessing the multi-source data; step 2, inputting the preprocessed data into a pre-trained CNN-BiLSTM-Attention model to carry out ship fuel consumption prediction; wherein the CNN-BiLSTM-Attention model is composed of a convolutional neural network, a bidirectional long and short term memory network and a time attention module; the model adopts a composite loss function based on navigational speed-power-fuel oil physical constraint. The method is more excellent in the aspects of prediction precision, robustness and fitting effect. Compared with the prior art, the method has higher accuracy and reliability in the aspect of ship fuel consumption prediction.

The invention relates to the field of power system operation and maintenance, in particular to a power transformation equipment operation and maintenance risk online assessment method and system. A power transformation equipment operation and maintenance risk online evaluation system comprises a data acquisition module, a weight configuration module, a sequence risk evaluation module, a collaborative decision game module and a disposal scheme output module. According to the method, a multi-source data fusion and dynamic threshold mechanism is introduced, real-time state quantity, historical maintenance records, operation modes and external weather information are uniformly mapped to a convolution-long and short-term memory network, key features are adaptively amplified in a feature weighting layer, redundant features are weakened, and collaborative recognition of short-term fluctuation and long-term degradation is achieved; compared with a traditional fixed threshold value or single monitoring quantity model, the method can keep sensitive and steady risk early warning capacity under the complex working conditions of severe weather, heavy load operation and the like, the false alarm rate and the missing report rate are greatly reduced, potential faults are locked in advance, and sudden power failure events are avoided.

The invention provides a chemical ship cabin intelligent coating leakage positioning and early warning control method and system, and relates to the technical field of intelligent monitoring, and the method comprises the steps: collecting data through a multi-class sensor array, constructing a parameter distribution map, extracting features through a convolutional neural network, carrying out the time sequence analysis through a long and short term memory network, and carrying out the early warning. The variational auto-encoder constructs a leakage feature fingerprint database, collects data in real time, then carries out feature extraction and fusion processing, matches the data with the fingerprint database, determines a leakage position and a leakage rate, divides a monitoring area, and executes prevention and control. The leakage monitoring precision and the response speed can be improved, and the chemical accident risk is reduced.

The invention belongs to the field of industrial thermal engineering and intelligent modeling, and discloses a heat exchanger heat exchange coefficient prediction method and system based on a physical information neural network. The method comprises the following steps: acquiring multi-dimensional operation data through a signal acquisition system, cleaning abnormal and blank values, standardizing, and segmenting into time sequence samples by adopting a sliding window method; a double-layer physical information long-short-term memory network is constructed, and a time sequence feature and a physical equation residual error are combined to generate a space-time fusion feature matrix. And a composite loss function including data loss, physical equation loss and physical consistency loss is designed, physical and data driving influences are balanced through hyper-parameter tuning, and accurate prediction of the heat exchange coefficient is achieved based on a gradient descent optimization model. The method combines field physical laws and data features, improves the reliability and physical interpretability of prediction, and is suitable for operation optimization of the heat exchanger of the desulfurization wastewater treatment system of the thermal power plant.

The invention discloses a new energy access region power grid balance scheduling method based on artificial intelligence, and relates to the technical field of power grid scheduling, and the method comprises the steps: data collection and preprocessing: obtaining and processing power grid multi-source data and micro-scale meteorological data; meteorological feature coding: extracting dynamic meteorological features by using a long short-term memory network; constructing a dynamic space-time hypergraph and embedding nodes, and generating node dynamic embedding in combination with a graph convolutional network; based on power grid state prediction and pre-fault analysis of causal intervention, accurate prediction and fault identification are realized; and generating and executing a pre-fault scheduling strategy, and generating and executing an optimization strategy through reinforcement learning, so that the method can realize the transformation of the power grid from response type recovery to prospective self-healing, improves the toughness, reliability and economy of the power grid in an extreme scene, and is suitable for the power grid balance scheduling of a new energy access region.

The invention relates to the field of electric power systems, in particular to an electric power facility remote intelligent maintenance method and system. The method comprises the following steps: collecting three kinds of heterogeneous monitoring data of vibration spectrum, infrared thermal imaging and partial discharge signals; mapping the data to a three-dimensional feature fusion space, and calculating a mahalanobis distance to generate a fusion feature matrix; extracting spatio-temporal features by using a convolutional long-short-term memory network, and outputting a triple diagnosis result including a fault type, a severity level and an evolution trend; searching a dynamic maintenance scheme in a maintenance strategy knowledge graph based on the diagnosis result; solving an optimal resource scheduling scheme by adopting an improved Hungary algorithm in combination with the geographic topology and the resource state; the maintenance operation is remotely guided through the augmented reality terminal, and real-time verification is carried out; and collecting the maintained data, carrying out residual analysis, and reversely optimizing the knowledge graph. The method realizes full-process intelligent management, improves timeliness and reliability of operation and maintenance of electric power facilities, and is suitable for a remote intelligent maintenance scene of an electric power system.

The invention discloses an oral cavity data monitoring and early warning method and system based on deep learning, and the method comprises the steps: solving a problem that the focus recognition is inaccurate because the image collection process of an oral cavity endoscope of a patient is affected by various environment and equipment parameters; according to the method, synchronous binding of collected images and equipment parameters and intelligent image preprocessing are proposed, historical time sequence images and variation trend features are combined, lesion space and time features are extracted through combination of a lightweight convolutional neural network and a long-short term memory network, feature weighted reasoning is realized by using a context awareness attention fusion network, and the focus space and time features are extracted. According to the method, the model parameters and the risk threshold value can be dynamically adjusted, the focus recognition accuracy and the early warning individuation are improved, and clinical grading treatment and intelligent health management are effectively assisted.

The invention discloses a method and a device for identifying a risk voice call. The method comprises the following steps: acquiring a time domain signal of a voice call, and determining a first quantum state representation corresponding to the time domain signal; target features are extracted from the first quantum state representation, a self-attention matrix is constructed according to the target features, and the target features at least comprise a frequency spectrum feature, an energy feature and a fundamental frequency feature; performing weighting processing on the target features by using the self-attention matrix to obtain target quantum features; analyzing the target quantum features by using a long short-term memory network to obtain intermediate feature representation; and performing voice classification according to the intermediate feature representation to obtain a classification result, the classification result being used for reflecting whether the voice call is a risk voice call. The technical problem that a traditional voice recognition algorithm is difficult to efficiently and accurately recognize a risk voice call is solved.

The invention belongs to the technical field of gas monitoring, and discloses a limited space gas monitoring method, system and device based on the Internet of Things and a medium, and the method comprises the steps: obtaining gas sensing data transmitted by a pre-constructed gas sensing node network through an optimal transmission path; performing gas concentration prediction on the gas sensing data by using a long short-term memory neural network and a random forest algorithm, comparing a prediction result with a preset early warning threshold value, and triggering an alarm of a corresponding level; and determining a prediction error based on a prediction result, dynamically optimizing an early warning threshold based on the prediction error, and continuously optimizing the plan through a closed-loop feedback mechanism. By introducing the gas monitoring sensor, the wireless Internet of Things ad hoc network technology, the edge computing and the cloud collaborative architecture, the toxic gas real-time monitoring with high sensitivity, low power consumption, flexible deployment and intelligent analysis is realized.

The invention discloses a modal parameter identification method based on a long short-term memory neural network, belongs to the field of artificial intelligence, system identification and structural health monitoring, and aims to improve the precision of modal parameter identification by using the neural network. The method comprises the following steps: firstly, acquiring response data of a structure model after excitation, preprocessing the response data to obtain a sample set, and dividing the sample set into a training set and a test set; then, a neural network based on long and short term memory is constructed, wherein the neural network is mainly composed of an encoder (MLP), an LSTM feature extractor, a decoder (MLP) and a linear reconstructor; training the neural network by using the training set data until the loss function converges, identifying by using the test set data, and finally obtaining the natural frequency of the structure through the power spectral density analysis of the obtained data. According to the method, the modal frequency identification precision is remarkably improved, and an efficient solution is provided for intelligent health monitoring of a complex structure.

The invention belongs to the technical field of mechanical equipment wear state monitoring. The invention provides a gearbox health state stage identification method and system based on oil characteristics, and the method comprises the steps: carrying out the preprocessing of an obtained oil abrasive particle image, and extracting the oil characteristics according to the preprocessed oil abrasive particle image; according to the oil characteristics and a pre-trained long-short-term memory neural network model, obtaining a gearbox health state classification result; training of a long-short-term memory neural network model: fusing the oil characteristics of each oil abrasive particle image sample to obtain a one-dimensional health factor, and obtaining a health index sequence according to the one-dimensional health factor of each oil abrasive particle image sample; and carrying out breakpoint detection on the health index sequence to obtain three breakpoints, and adding health state labels to all the oil abrasive particle image samples according to the obtained breakpoints. According to the method, accurate division of the wear stages and capture of the evolution trend can be automatically completed, and finally decision judgment exceeding artificial experience is formed.

The invention relates to the technical field of dynamic visual target motion tracking control, in particular to a dynamic visual target motion tracking control method and system based on deep learning, and the method comprises the steps: synchronously collecting continuous multi-frame target scene image data through a visual multi-frame collection module; and performing time sequence association and memory fusion on target features in continuous multi-frame target scene image data through a cross-frame feature memory fusion module, and constructing a target feature model. According to the invention, the current and historical stable features are dynamically fused through the cross-frame feature memory fusion module, time sequence association is realized in combination with the long and short-term memory network, and the problem of slow feature model updating under target deformation and shielding is solved; the deformation-shielding bimodal recognition module accurately recognizes a scene state, provides a basis for the multi-branch Kalman filtering prediction module, enables the multi-branch Kalman filtering prediction module to call a corresponding branch, corrects a prediction equation through a compensation factor, and improves the position prediction accuracy.

The invention relates to the technical field of tunnel deformation prediction, in particular to a BPNN parameter inversion and LSTM tunnel deformation prediction method. On the basis of the orthogonal test design principle, multiple parameter combinations are designed for the M-C constitutive model and the HSS constitutive model, numerical calculation is carried out, and vertical displacement data of the tunnel measuring points are obtained through numerical calculation; constructing a BPNN model, and training the BPNN model by taking the vertical displacement data as input and the corresponding constitutive model parameters as output; inputting field actual measurement tunnel settlement data into the trained BPNN model, and performing inversion to obtain optimal constitutive parameters of M-C and HSS constitutive models; performing forward numerical simulation on the whole process of pipe jacking construction by adopting the optimal constitutive parameters obtained by inversion to obtain a tunnel vertical displacement evolution rule; and inputting a vertical displacement monitoring value of the measuring point of the downlink tunnel based on the long-short term memory neural network to obtain a prediction result of future settlement of the tunnel. The accuracy of the predicted value is improved.

The invention relates to the technical field of digital control, and provides a digital prefabricated pump station and a digital control system.Perception data of liquid level height, pressure difference, pump set flow, blockage displacement and pipeline vibration are obtained through a perception judgment module, abnormal working conditions are comprehensively judged, limitation of parameter judgment is overcome, and a reliable basis is provided for follow-up control; the pump-valve cooperation module dynamically adjusts the opening degree of a valve through multiple parameters, optimizes the expansion amount of flexible connection in combination with pipeline vibration and blocking displacement, and cooperatively adjusts the rotating speed of a pump set based on the liquid level height, the flow of the pump set and the blocking displacement, so that linkage control over the pump, the valve and the pipeline is achieved. The energy efficiency optimization module achieves accurate prediction of the liquid level through long and short term memory network processing, optimizes the valve opening degree and the pump set rotating speed by combining a particle swarm optimization algorithm and taking pump efficiency maximization as a target, dynamically generates an optimized pump stopping liquid level, and meanwhile adjusts network parameters through deviation feedback of a predicted value and an optimized value to form a continuously optimized closed loop.

The invention provides an old well reexamination method and system based on a multi-modal digital rock debris virtual well, and the method comprises the steps: obtaining mineralogical data of an old well rock debris sample, optimizing an original well logging curve of an old well through the combination of Bayesian correction, predicting an undrilled layer of the old well through the constraint of a long-short-term memory network neural model and seismic data, and obtaining an optimal well logging curve; and the potential level of the undrilled layer of the old well is quantitatively evaluated through the potential matrix, so that the problems of deep rock debris data, low review efficiency and difficulty in potential layer identification in traditional old well review are solved.

The invention discloses a direct current charging pile detection system and method applied to the field, and relates to the technical field of power equipment on-line monitoring, and the method comprises the steps: inputting a three-dimensional data set into a Toeplitz cyclic measurement matrix for compressed sampling, reconstructing the three-dimensional data set into a high-resolution signal through employing an improved orthogonal matching pursuit algorithm, and carrying out the detection of the high-resolution signal; analyzing and calculating a local variable coefficient through a sliding window, marking abnormal positions, and integrating the abnormal positions into an abnormal position set; inputting the abnormal position set into a pre-trained long and short term memory prediction model, evaluating the insulation residual life and the fault risk level, and calculating the maximum allowable output current of the DC charging pile in real time; the load impedance is dynamically adjusted and detected through the memristor array, the output current of the direct current charging pile is collected in real time, and synchronous compression wavelet transform is adopted to analyze a harmonic ridge line and extract a harmonic component; the accuracy of fault early warning is remarkably improved, rapid dynamic adjustment of the output current is achieved, and the technical problem of multi-physics field coupling monitoring of the rapid charging pile is effectively solved.

The invention discloses an AI-driven endoscope inner pipeline full-process tracing drying monitoring method and system, and relates to the technical field of medical information, and the method comprises the steps: collecting full-process data, including cleaning, disinfection and drying node data, of an endoscope inner pipeline, carrying out data cleaning, and then obtaining preprocessed data; the data and timestamps are analyzed by using a long short-term memory (LSTM) network, and time sequence features are extracted. Based on the characteristics, an AI self-inspection model is configured to evaluate the drying state of the endoscope, a drying state evaluation report is generated, and if the drying state does not meet the requirement, a reminding signal is sent out. The technical problems that in the prior art, the endoscope cleaning, disinfecting and drying process is not accurate enough in monitoring, and the drying state cannot be evaluated in real time are solved, and the technical effect of accurate real-time monitoring and evaluation of the endoscope cleaning, disinfecting and drying process is achieved through AI-driven full-process data collection, time sequence analysis and application of a self-inspection model.

The invention relates to the technical field of infectious disease monitoring and early warning, and discloses an intelligent influenza early warning system based on community multi-modal data fusion. The community-level multi-modal data fusion architecture is constructed, medical health data, environmental data, crowd activity data and network behavior data are integrated, spatial-temporal features are dynamically extracted and fused in combination with a deep learning model, and the problem of community monitoring blind areas caused by a single data source of an existing early warning system is solved; a long short-term memory network and convolutional neural network cascade architecture is utilized to capture a localized propagation rule, and the defect that a region-level prediction model cannot adapt to community heterogeneity is overcome; the risk score is generated in real time, the grading response instruction is triggered, a'monitoring-early warning-intervention 'closed loop is established, the early warning timeliness is remarkably improved, a basic-level response chain scission gap is filled, early prevention and control of flu outbreak are finally achieved, and public health resource consumption is reduced.

The invention provides a flood session division method and device based on deep learning, and belongs to the technical field of flood data analysis, and the method comprises the steps: dividing historical flood data into flood sequence data and non-flood sequence data; and performing data expansion on the flood sequence data to obtain target flood sequence data. And performing data splicing on the target flood sequence data and the non-flood sequence data according to a time sequence to obtain a flow-rainfall time sequence sample. And based on the flow-rainfall time sequence sample, training the multi-layer long and short-term memory network by using the target loss function to obtain a flood prediction model. And performing flood session division on the flow-rainfall time sequence data to be divided by using the flood prediction model to obtain a flood session division result. According to the flood session division method and device based on deep learning provided by the invention, the accuracy of flood session identification and division in a complex flood scene can be improved.

The invention discloses an electrical system load prediction method based on a data center, and the method comprises the steps: obtaining electrical parameters, environmental parameters and historical operation data of each subsystem through a data collection module, carrying out the normalization and time sequence reconstruction of the collected data, and forming standardized time sequence data; a combined neural network model comprising a convolutional neural network (CNN), an improved long-short term memory (LSTM) network and an attention mechanism is utilized to extract local fluctuation, mutation, frequency and statistical characteristics, and capture of data long-term dependence and key time sequence information is realized, so that an electrical load prediction value is output, and finally real-time regulation and control of a data center electrical system are realized. The method can effectively improve the prediction precision and response speed, and provides reliable technical support for the full life cycle management of the data center.

The invention discloses a fault prediction and self-repairing method and device, electronic equipment and a storage medium, and relates to the technical field of computers. According to the method, the function of dynamically monitoring various data of processor hardware for the target node can be realized, the dynamically monitored hardware state data is input, the fault probability is predicted by a method of weighting and combining key indexes through a bidirectional long-short-term memory model and an attention mechanism, the possible faults are intelligently predicted, and the fault prediction efficiency is improved. And when the target node has a test fault in advance, the target node is repaired in a gradual load reduction mode, so that the effects of real-time monitoring, accurate prediction and rapid self-regulation are achieved. Manual intervention is reduced, the intelligent decision-making capability is achieved, operation and maintenance automation and intelligentization are achieved, resource self-adaptive repairing can be integrated, the self-adaptive capability is improved, and the complex scene fault sensing capability is improved.

The invention belongs to the technical field of feed processing, and relates to a feed processing prediction and real-time regulation and control method based on deep learning, and the method comprises the steps: carrying out the feature processing of feed preparation process data, and obtaining a plurality of feed preparation process features and screening features; carrying out feature recognition on the screening features, and predicting a processing abnormal probability and an abnormal result; if the processing is abnormal, classifying and splicing the plurality of material making process features to obtain processing operation features and processing equipment features; processing the processing operation characteristics through a long and short term memory network to obtain operation associated characteristics; processing the processing equipment features through a convolutional neural network to obtain equipment associated features; carrying out attention distribution operation on the operation correlation characteristics and the equipment correlation characteristics, and determining the equipment abnormity probability of the processing equipment based on the attention contribution degree of each material preparation process; the whole-process quality prediction is realized, and the condition of shutdown troubleshooting caused by unqualified feed processing is avoided.

The invention relates to the technical field of power distribution network fault monitoring, in particular to a fault recognition method and system based on an intelligent fusion terminal, and the method comprises the steps: obtaining an instantaneous multi-dimensional electrical data set at each moment, and constructing an input sample with the current moment as an end point, inputting the input sample into the trained long-short-term memory model to calculate a first fault probability of the input sample; inputting the input sample into a trained optimal fuzzy clustering model to calculate a second fault probability of the input sample; and distributing respective weights for an output result of the long and short term memory model and an output result of the optimal fuzzy clustering model, carrying out weighted fusion on the first fault probability and the second fault probability to obtain a comprehensive fault probability of the input sample, and judging whether the power distribution network has a fault according to the comprehensive fault probability. According to the invention, through multi-source information fusion, model collaborative optimization and dynamic weight distribution, the fault identification precision and response speed of the power distribution network under complex conditions are effectively improved.

The invention provides a high-speed train intelligent model prediction control method and system, and belongs to the technical field of train operation control, and the method comprises the steps: carrying out the prediction of the operation state prediction data of a future high-speed train through employing a pre-trained long-short term memory neural network based on historical operation state data; based on the running state prediction data of the future high-speed train, in combination with the current real-time running state data, performing state estimation by using a fixed lag Kalman smoother to obtain an optimized running state estimation result; based on the optimal control input sequence, the traction force and braking force of the train are controlled and adjusted in real time, and it is ensured that the train runs according to the expected trajectory. According to the method, the problems of nonlinear dynamic and non-Gaussian noise interference are solved, and the state estimation precision is improved; by introducing the multi-particle model, the position and speed dynamic state of each particle of the train is accurately described, the physical consistency of the control strategy is improved, the physical authenticity of the model is enhanced, and the high-precision tracking of the position and speed of the train is realized.

The invention discloses an oil chromatography data trend classification method and system based on depth feature enhancement and an attention mechanism, and the method comprises the steps: carrying out numeralization conversion, deletion detection and grouping trend calculation on oil chromatography original gas component data, and generating a basic feature vector; executing multi-scale sliding statistics, change rate and subsequence feature enhancement, and calculating comprehensive similarity and attention weight based on a template library to generate a weighted similarity vector; splicing the enhanced feature and the weighted similarity vector into a time sequence input sequence, and outputting an oil chromatogram trend classification result after attention expansion and long and short term memory network processing. According to the method, structured processing and basic trend extraction of data are realized, adaptive matching and weighted aggregation of historical operation modes are realized, and a multi-dimensional dependency relationship and time sequence dynamic change are captured, so that accurate classification of oil chromatogram trends is realized.

The invention discloses an agent construction method and device based on a large model, a storage medium and equipment, and belongs to the technical field of artificial intelligence. Selecting a dialogue scene according to the current dialogue information and the scene description information; obtaining tool calling data from an intelligent agent corresponding to the dialogue scene; generating a short-term memory context according to the historical dialogue information, generating a tool context according to tool calling data, generating a file context according to a file, generating a long-term memory context according to the current dialogue information and the historical dialogue information, and forming a scene context; generating a candidate tool list sorted according to preferences according to the current dialogue information or the tool calling data; generating a system prompt word according to the scene context and the candidate tool list; and optimizing the intelligent agent according to the system cue word and the candidate tool list to obtain the intelligent agent based on user preference and context awareness. The intention and preference of the user can be recognized, answering is carried out according to the intention and preference of the user, and the accuracy is improved.