706 results about "Sequence Feature" patented technology

The invention discloses a drug target affinity prediction method and system based on a multi-scale protein attention mechanism, and belongs to the crossing field of bioinformatics and artificial intelligence. The method comprises the following steps: firstly, extracting protein sequence features through an ESM2 pre-training model, predicting that a three-dimensional structure is converted into a two-dimensional contact graph, and extracting spatial topological information in combination with a graph convolutional network; a two-dimensional attention mechanism is innovatively designed, structural features are taken as query vectors, sequence features are taken as key value pairs, and cross-modal feature fusion is realized by dynamically associating sequence semantics and spatial proximity relationships through multiple attention. Drug molecules are characterized by adopting MACCS fingerprints, are spliced with protein multi-modal features and then are optimized through a deep network, and finally an affinity value is output through a regression prediction module. According to the technology, the problem of protein heterogeneous data fusion is effectively solved, the generalization ability to unknown targets is remarkably improved, an efficient calculation tool is provided for new drug research and development and drug relocation, and the drug research and development cost can be reduced.

The invention discloses a fault diagnosis method and device, computer equipment and a computer readable storage medium, relates to the technical field of power systems and automation thereof, and solves the problem of fault misjudgment caused by important fault characterization of a method which is easy to lose when single modal information is used for fault diagnosis at present. The method comprises the following steps: converting a bus voltage signal into a two-dimensional time-frequency diagram based on a continuous wavelet transform model, and converting the bus voltage signal into a one-dimensional frequency spectrum sequence based on a fast Fourier transform model; performing feature extraction on the two-dimensional time-frequency graph based on a time-frequency image feature extraction model to obtain a first feature vector, and performing feature extraction on the one-dimensional frequency spectrum sequence based on a frequency spectrum sequence feature extraction model to obtain a second feature vector; performing feature fusion on the first feature vector and the second feature vector based on a feature fusion model, and obtaining a category probability vector by using a fault diagnosis model based on the fused feature vector; and determining the highest probability value in the category probability vector, and taking the corresponding fault type as a target fault type.

The invention discloses a text abstract generation method and system based on sparse attention acceleration, and the method comprises the steps: reading long text data, carrying out word segmentation and embedded coding processing, extracting a sequence feature vector, and mapping the sequence feature vector into a query matrix Q, a key matrix K and a value matrix V; constructing an abstract generation network, wherein the abstract generation network comprises a sparse attention calculation module, a feedforward calculation module, a prediction head module and a key value cache module; inputting the query matrix Q, the key matrix K and the value matrix V into an abstract generation network, passing through a backbone network formed by stacking a sparse attention calculation module and a feedforward calculation module for multiple times, processing by a prediction header module, and caching a historical decoding state in real time through a key value caching module to obtain an initial text feature vector; and based on the initial text feature vector, executing an autoregressive decoding process through an abstract generation network, and outputting a final abstract result. According to the method, the decoding process can be accelerated, and the semantic integrity and coherence of the generated abstract are ensured.

The invention provides a multi-modal characterization molecular property prediction method based on hierarchical bidirectional cross attention, and relates to the technical field of machine learning assisted organic chemistry, and the method comprises the following steps: S10, generating same-molecule multiple sequences for data enhancement; s20, coding the sequence features through a pre-trained molecular language model MolBERT; s30, performing multi-modal feature fusion through a layered bidirectional cross attention mechanism; s40, establishing a prediction head; s50, in the reasoning stage, only the feature extraction and fusion steps are executed, and a molecular property prediction result is output through the trained prediction head. According to the method, the molecular sequence, the topological graph structure and the fingerprint features are effectively integrated, so that the prediction precision of the model on a plurality of MoleculeNet (molecular network benchmark) public data sets is superior to that of an existing method.

The invention provides a distribution network traveling wave fault point intelligent positioning method and system based on deep time sequence feature learning, and belongs to the technical field of intelligent power distribution detection based on deep learning. Firstly, a high-speed traveling wave sensor is arranged on a distribution line, multi-dimensional three-phase voltage and current signals are collected, and a data set of fault types, phases, grades and positions is constructed; then, fault state features are extracted through topology perception normalization, symmetric component mapping and two-channel time sequence modeling, and accurate recognition of fault types, related phases and grades is achieved through an attention mechanism; furthermore, a fault intelligent positioning model based on a topological graph is constructed, a tower sensing weight and a line information bearing weight are introduced, and space-time embedding is extracted through a graph attention network and an echo state network, so that line fault classification and accurate positioning are realized. Finally, through combination of off-line model training and on-line system deployment, real-time identification and positioning of power distribution network faults are realized, and accuracy, robustness and response speed of fault diagnosis are effectively improved.

The invention relates to the technical field of data processing, and discloses a child autism behavior recognition method and system based on data analysis and a medium. The method comprises the steps of collecting and preprocessing child multi-modal behavior data; extracting nonlinear features to obtain a time sequence feature matrix; identifying a repeated behavior mode through a three-dimensional convolutional network; analyzing behavior time sequence change by using a time convolutional network; fusing a plurality of feature representations to obtain a comprehensive feature vector; a multi-classifier system is applied to identify autism behavior types and evaluate severity. By extracting the nonlinear time sequence features, constructing the deep spatial-temporal feature extraction network and designing a feature fusion mechanism and a multi-classifier integration system, the method can overcome the limitations of a single mode, a linear feature and a single algorithm in the prior art, and improves the accuracy and interpretability of autism behavior recognition.

The invention belongs to the field of bioinformatics, and relates to an improved integrated deep learning cell communication ligand-receptor interaction prediction method. The method comprises the following steps: firstly, carrying out extraction and dimensionality reduction on biological sequence features of a ligand and a receptor, and constructing multi-modal feature input; secondly, constructing an improved deep neural network branch, introducing a batch normalization layer and a Leaky ReLU activation function, solving the problems of gradient disappearance and neuronal necrosis, and improving regularization strength to prevent overfitting; meanwhile, an enhanced heterogeneous graph auto-encoder branch is constructed, the graph embedding dimension is remarkably expanded to improve the feature capacity, and full convergence of the model is ensured by increasing training rounds; thirdly, fusing the improved deep network with the prediction probability of a heterogeneous graph auto-encoder by adopting a weighted integration strategy; and finally, outputting a potential interaction relationship based on the fusion probability. By optimizing the architecture and the strategy, the prediction accuracy and robustness are remarkably improved, and a reliable tool is provided for analyzing a complex cell communication network.

The invention relates to a method for classifying antihypertensive peptides by fusing sequence and structure multi-modal features and combining contrast-generative combined optimization. The method comprises the following steps: extracting sequence feature representation and structure feature representation of peptide fragments; performing multi-modal feature enhancement of comparison-generative joint optimization on the sequence feature representation and the structural feature representation; and performing peptide classification on the enhanced feature representation by using a preset Kan-Conv structure and tag smooth joint optimization classification model. According to the method, high-precision recognition of the functional activity of the antihypertensive peptide is achieved, information in the three aspects of the sequence, the structure and the generative potential space is creatively and comprehensively utilized, the blank that the structure-sequence synergistic effect is ignored in the existing peptide function prediction field is filled, and the screening efficiency and prediction reliability of the antihypertensive peptide are remarkably improved.

The invention discloses a diopter detection data fusion processing system based on deep learning, and relates to the technical field of medical detection. The system comprises a multi-modal data acquisition module, a data preprocessing and aligning module, a dynamic sequence analysis module, a deep learning regression core module, a personalized calibration module and a result output and visualization module which are connected in sequence. The system obtains objective detection data through a multi-modal data acquisition module, and multi-modal data is formed after preprocessing and space-time alignment; the dynamic sequence analysis module extracts adjustment response time sequence features; the deep learning regression core module calculates an initial diopter parameter; the personalized calibration module performs parameter optimization in combination with the biological characteristics of the user; and finally, a visual quality simulation result is generated through a result output and visualization module. According to the method, the multi-modal data fusion and deep learning technology is utilized, rapid and accurate diopter automatic detection is realized, and the detection efficiency and the individuation level are remarkably improved.

The invention discloses a gas recognition method based on multi-source information fusion and environmental perception, and the method comprises the steps: constructing a deep feature learning framework of multi-source fusion through combining the spatial response features, time sequence features and external environmental information of gas; the method comprises the following specific steps: preprocessing collected gas data, and respectively extracting features of an image mode, a sequence mode and an environment mode; fusing the image features and the sequence features through a cross attention fusion module, and capturing the space-time correlation of the data; a cross-modal attention compensation module is introduced, so that main-modal gas data adaptively gathers key information in an auxiliary-modal environment, and effective compensation of environmental factors on gas recognition performance is realized; and finally, gas category prediction is performed through a classification decision head. According to the method, the problems that the detection is easily interfered by environmental factors, the stability is poor or the qualification is inaccurate due to the fact that modeling depends on single modal data in the existing gas identification technology are solved.

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 shaft operation state intelligent prediction method based on a hybrid deep learning framework, and belongs to the field of petroleum engineering oil-gas field development engineering, and the method comprises the following steps: building a geological sequestration shaft transient temperature-pressure coupling mathematical model, and carrying out the numerical solution; constructing a double sequence feature database covering CO2 injection and leakage working conditions; adopting a parallel architecture to process the double sequence features to construct an improved double attention network; the method comprises the following steps: establishing a hybrid deep learning framework DT-DANet by coupling an improved double attention network and a time fusion Transform; and carrying out collaborative optimization training on the hybrid deep learning framework by adopting a composite loss function, wherein the output of the framework is a wellbore pressure profile, a temperature profile and phase state distribution under different time steps. According to the method, high-precision prediction of shaft multi-parameter dynamic evolution in the CO2 geological storage process is realized.

The invention discloses a large language model battery evaluation method and device based on multi-modal fusion, and relates to the technical field of battery intelligent management. The method comprises the steps that according to time sequence operation data, an improved multi-scale gating time sequence model is used for time sequence feature extraction, and operation time sequence features are obtained; according to the log text data and the monitoring image data, performing semantic feature extraction by using a multi-modal large language model to obtain fused semantic features; performing medium-term fusion reasoning by using an improved large language model according to the operation time sequence features and the fusion semantic features to obtain a battery evaluation result; generating a battery risk detection result and a corresponding explanatory text according to a preset alarm threshold value and a battery evaluation result based on the post-masking time sequence attention flow and the post-masking semantic attention flow; and carrying out risk visualization according to the battery risk detection result and the corresponding explanatory text. The invention relates to a high-reliability battery evaluation method based on a multi-modal large language model.

The invention discloses a protein palmitoyl transferase prediction method and system based on a multi-branch deep convolutional neural network, and belongs to the technical field of bioinformatics and artificial intelligence. The method comprises the following steps: S1, obtaining a to-be-detected protein sequence; s2, inputting the protein sequence into a pre-trained iPalmT model; and S3, judging whether the target protein is palmitoyl transferase or not according to a model output result. The iPalmT model comprises a coding module, two paths of parallel convolution branches, a feature fusion module and a classification module; and after the convolution layers of each convolution branch are stacked, an SE module is arranged and is used for channel weighting and feature re-calibration. The model extracts multi-level sequence features through convolution kernels of different scales, realizes high-precision prediction through feature fusion and a residual structure, can automatically learn multi-scale features from large-scale data, realizes end-to-end palmitoyl transferase recognition, and has high accuracy and good universality.

The invention discloses a millimeter-wave radar fusion system and method for electrocardiograph monitoring false alarm recognition, and the method comprises the steps: carrying out the multi-scale time-frequency feature extraction of a millimeter-wave radar signal and an electrocardiograph signal, obtaining a radar feature sequence and an electrocardiograph feature sequence, carrying out the time alignment of the radar feature sequence and the electrocardiograph feature sequence, and inputting a multi-modal fusion architecture; outputting cross-modal joint embedding features based on the multi-modal fusion architecture; a cross-modal consistency judgment model obtained based on self-supervised contrast learning optimization training is constructed, whether a cross-modal feature inconsistent state exists in the joint embedded features or not is judged, and if yes, it is judged that a potential false alarm event exists; and based on the potential false alarm event, extracting sequence feature fragments before and after alarm triggering in the joint embedded feature, inputting the sequence feature fragments into an anomaly classification network, outputting a final judgment result about whether false alarm is formed, and when the final judgment result is false alarm, generating an alarm adjustment instruction and sending the alarm adjustment instruction to a monitoring terminal.

The invention discloses a behavior recognition method and device based on dynamic context, equipment and a medium, and the method comprises the steps: obtaining a dynamic context graph through cross-modal modeling among multi-modal data, fusing long / short-term events in a monitoring video through dynamic upper and lower graphs, carrying out the cross-modal semantic connection, carrying out the multi-modal feature fusion, and carrying out the multi-modal feature fusion. Determining a gating strategy vector and a gating weight matrix according to a splicing result; and performing multi-modal fusion according to the gating strategy vector, the gating weight matrix and the multi-modal sequence feature to obtain a multi-modal fusion feature, and performing abnormal behavior identification on the monitoring video based on the multi-modal fusion feature. Richer information support is provided for decision making through feature fusion, so that the decision making accuracy in a complex scene is improved. The method can be applied to security and protection monitoring scenes in the financial field or the medical field, so that the abnormal behavior recognition accuracy in the security and protection monitoring scenes is improved.

The invention relates to a logistics order processing efficiency analysis method based on cloud computing, and the method comprises the steps: constructing a multi-dimensional and multi-node behavior log and environment variable synchronous collection system, and obtaining standardized structured data through timestamp calibration, abnormal value elimination, semantic normalization and desensitization processing; and based on node features, sequence features and environment embedding, generating a multi-node multi-scene feature vector, and inputting the multi-node multi-scene feature vector to a multi-order causal nested graph neural network for dynamic causal modeling among nodes, events and constraints. Through combination of anti-fact inference and multi-source evidence fusion, hidden bottleneck automatic identification, attribution report output and optimization suggestion generation are realized; through expert and user feedback mechanisms and working condition and rule change adaptive network structure adjustment, long-term accurate optimization and dynamic closed-loop updating of the bottleneck diagnosis model are realized. According to the invention, the intelligence and adaptive level of order flow bottleneck identification can be improved, and a high-quality data basis and intelligent decision support are provided for flow optimization in a complex logistics scene.

The invention provides a time sequence prediction method and system based on frequency filtering and multi-view modeling, and relates to the technical field of time sequence prediction. Comprising the following steps: acquiring historical time sequence data, filtering to obtain a filtered seasonal component, and performing multi-scale division to obtain a multi-scale sequence; in each scale sequence, introducing an embedding amount, performing cyclic translation in a time dimension, generating fragments of multiple view angles, and calculating cyclic attention of the multi-scale sequence; calculating the inter-segment attention of the current scale sequence and the lag differential attention between adjacent segments to obtain the residual attention of the multi-scale sequence; and further obtaining a feature representation corresponding to each scale sequence, and aggregating the feature representations of the multi-scale sequences to obtain a multi-scale comprehensive feature representation. According to the method, effective high-frequency details are reserved while noise is removed, periodic features and local dynamic changes in time sequence fragments are extracted, and the long-term dependency relationship between different time fragments is modeled.

The invention relates to the technical field, in particular to an unmanned inspection method and system for a switch cabinet, and the method comprises the following steps: building an incidence relation between signal nodes through a graph neural network, improving the time sequence consistency of multi-source signals, enabling instrument reading and temperature and humidity signals to form a hierarchical structure under a unified time frame, and improving the accuracy of inspection. The logic association of state layering is enhanced, continuous sampling sequence features are extracted through a long-short-term memory network, the response sensitivity and time prediction capability to abnormal signal abrupt change are improved, amplitude matching of temperature difference and current signals is achieved under the same time index, early abnormal nodes are accurately recognized, and a dynamic positioning sequence is established. Through joint tradeoff of distance and energy consumption, path lengthens and energy peak values are reduced, task throughput and equipment availability are improved, timing sequence capture and trend judgment capabilities are enhanced in an anomaly recognition layer, and accuracy and continuity of inspection decision and reliability of safety assessment are integrally improved.

The invention discloses a sound authenticity identification method based on multi-modal feature deep interactive fusion. According to the method, a double-flow architecture is adopted, and a pre-trained BEATs model and a CNN14 network are respectively utilized to extract Transform sequence features and convolution time-frequency embedding features of an audio; an adaptive MobileFormer fusion device is innovatively proposed, an original MobileFormer structure used in the image field is transformed into a bidirectional cross-modal interaction module suitable for one-dimensional time sequence audio features, dynamic complementary modeling of local details and global semantic features is achieved through a cross attention mechanism, and dynamic nonlinearity is introduced to activate and enhance the expression ability; and the fused enhanced features are input into a hierarchical graph attention network ASSIST, and high-order semantic modeling and authenticity classification are completed by combining spectrogram and time sequence double-flow reasoning. Experimental results show that the performance of the method on an ASVspoof2021LA data set is superior to that of an existing baseline model. The method can effectively detect AI generation voice, replay attack and other forged voice, and is suitable for a voice authentication system.

The invention discloses a vibration event early warning diffusion method based on edge calculation, and relates to the technical field of computer application, and the method comprises the following steps: constructing a topological phase coordinate under a unified time baseline, playing back a historical track of early warning diffusion based on the topological phase coordinate, recognizing a continuous path segment with a closed trend in the track, and carrying out the early warning diffusion. Outputting a loop suspicious region; a fingerprint code of early warning information is constructed on the basis of a loop suspicious area, a three-dimensional feature set containing time sequence features, path sequence features and phase sequence features is extracted, and a corresponding loop suspicion degree spectrum is generated. According to the method, through space-time modeling, path fingerprint construction, causal discrimination and duplicate removal mechanisms, accurate identification and repeated suppression of a vibration early warning information diffusion path are realized, dynamic threshold regulation and reverse intervention are combined, loop closed-loop treatment and topology self-healing are realized, and early warning accuracy, timeliness and system stability are remarkably improved.

The invention discloses a universal HLA antigen presentation prediction method based on a protein language model and a multi-modal neural network, which comprises the following steps: firstly, extracting verified HLA binding peptide fragment sequences from an immune epitope database, generating equivalent non-epitope peptide fragment sequences in combination with a protein database, and constructing an HLA-I class and HLA-II class balanced data set; then, extracting sequence embedding characteristics and contact graph structure information of the peptide fragment sequence by utilizing a protein language model; the method comprises the following steps: processing a protein map constructed by a contact map through a map neural network to obtain global structure features, and meanwhile, carrying out regional convolution and residual convolution processing on sequence embedding by adopting a one-dimensional convolutional neural network (1DCNN) to extract local context sequence features; the method effectively fuses sequence semantics and space structure information, improves the accuracy and generalization ability of HLA antigen presentation prediction, and is suitable for immune recognition modeling tasks under various HLA subtypes.

The invention relates to the technical field of ocean detection, in particular to a DBN-based underwater target tracking system, which comprises a multi-source data processing module, an environment monitoring module, a feature fusion module, a target feature extraction module and a dynamic prediction recognition module. According to the invention, through real-time signal-to-noise ratio evaluation of sonar and visual data and dynamic adjustment of modal weight, data reliability under the condition of insufficient illumination or complex water quality is ensured, and a data fusion strategy is optimized in combination with illumination intensity, turbid concentration and visible distance, so that the system has good environmental adaptability. Time sequence features and image edge gradients are utilized, key geometric differences are extracted, the feature recognition distinction degree is improved, significant edge features are screened and optimized, a target area is highlighted, the recognition precision is effectively improved, and target tracking conditions can be judged in real time based on migration analysis of DBN node response; and the underwater detection efficiency and the applicability in a complex environment are improved.

The invention relates to the technical field of optical communication, and discloses a multi-modal optical device production line risk identification and prediction method and system, and the method comprises the steps: collecting multi-modal data in real time from each process of an optical device production line, carrying out the preprocessing of the multi-modal data, carrying out the feature extraction, constructing a unified feature matrix, respectively extracting depth time sequence features and depth space features, and carrying out the recognition and prediction of the risk of the multi-modal optical device production line. The depth time sequence features and the spatial features are fused, a risk identification prediction decision model carries out risk prediction through the fused features, process nodes are obtained, target features of the process nodes are screened through an SHAP method, abnormal areas in the target features are positioned through gradient mapping, and an early warning threshold value is dynamically set. According to the method and the device, the technical problems that risk early warning and identification cannot be carried out in time and decisions cannot be taken in time during production of the optical device production line are solved.

The invention relates to a powder metallurgy defect detection method based on pattern recognition. The method comprises the following steps: collecting process environment parameter data of each key position on a powder metallurgy production line and defect images and signal data of a detection object in real time; performing denoising and normalization processing on the original data set; extracting spatial features of the defect image and the signal data, and process environment parameter dynamic sequence features of each production unit; constructing a process parameter distribution model based on an adaptive time sequence; inputting the joint vector and the parameter distribution description into a depth time sequence neural network model; calculating an occurrence probability trend of each defect type in a future preset time window; and judging whether the defect trend early warning signal meets a process safety limit or an industry quality threshold. According to the method, the process change of a production field can be sensed in real time, the model can still automatically compensate characteristic drift even under the condition of raw material batch replacement or equipment characteristic fluctuation, and the high precision of defect type trend early warning judgment is kept.

The invention discloses an AI-based gene data analysis system. The system comprises a plurality of omics data matrixes; local association pattern mining is performed on the multi-omics data matrix through a 1D-CNN one-dimensional convolutional neural network, a topological structure of a gene network is identified through continuous coherence analysis, dynamic weights are allocated to sequence features and a topological feature matrix by using a dynamic attention mechanism, and weighted multi-scale feature vectors are output; establishing a multi-modal fusion model based on a Transform architecture to fuse the multi-scale feature vectors, performing fine adjustment on the adaptive disease data set by using the general genome feature of a pre-training model, and outputting a fused feature vector; and inputting the fusion feature vector into an MLP multilayer perceptron for disease risk prediction, generating a disease risk prediction index in combination with an SHAP algorithm, and generating an auxiliary decision scheme according to the prediction index. And the accuracy and generalization ability of disease risk classification are effectively improved.

The invention provides an intelligent curriculum recommendation method and device based on a knowledge graph and a storage medium, and the method comprises the steps: constructing a multi-dimensional knowledge graph which comprises a curriculum skeleton knowledge sub-graph, a user capability sub-graph and a scene demand sub-graph; generating a knowledge dependence matrix based on the course skeleton knowledge sub-graph, and generating user learning sequence features based on historical learning data of the user; acquiring a learning target, a learning time constraint and a learning scene input by a user, and determining a first knowledge learning path and task sequence set and a second knowledge learning path and task sequence set of the user; and based on the user learning sequence features, the course skeleton knowledge sub-atlas, the user capability sub-atlas and the scene demand sub-atlas, using a fusion model to perform fusion processing on the first and second knowledge learning paths and the task sequence set to obtain a course meeting the user demand. The accuracy of course recommendation is improved.

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 provides an intention recognition response method and system based on forest farmer question and answer data, and the method comprises the steps: firstly obtaining an initial question and answer data set of forest farmer users, the initial question and answer data set comprises a plurality of question and answer interaction records, and then carrying out the semantic standardization processing of the initial question and answer data to obtain a structured question and answer data set; then semantic association features and interaction time sequence features are extracted from the structured question and answer data, the semantic association features reflect the semantic matching degree of user questions and system responses, the interaction time sequence features reflect the context dependency relationship of adjacent question and answer records, and dynamic intention mapping processing is conducted based on the semantic association features and the interaction time sequence features; and finally, according to the target intention recognition result, matching a preset response knowledge base, generating optimized response data, and sending the optimized response data to the forest farmer user terminal to update the response, so that the accuracy of forest farmer question and answer intention recognition and the accuracy of the response are improved.

The invention discloses a micro-grid cooperative scheduling control method, system, equipment and medium based on edge calculation, and relates to the technical field of computer platform load balancing, and the method comprises the steps: collecting power grid operation data through a micro-grid unit, carrying out the short-time power prediction of the micro-grid unit according to the power grid operation data, and obtaining a load prediction value; deploying a genetic algorithm at an edge node, and optimizing a load prediction value to obtain a scheduling strategy parameter; the scheduling strategy parameters are input into the micro-grid units, a game model is constructed to carry out multi-micro-grid cooperative game, and optimal scheduling strategy parameters are output; and performing power output scheduling through a multi-stage control mechanism. According to the method, through multi-dimensional data acquisition and sequence feature learning of micro-grid units, high-precision short-time power prediction is realized, a rapid convergence local optimization strategy is obtained, it is ensured that multiple micro-grids reach Nash equilibrium under autonomous conditions, and rapid response to sudden disturbance and effective correction of steady-state errors are realized.