29343 results about "Encoder" patented technology

The invention discloses a method for enhancing dynamic knowledge retrieval based on a large language model, belongs to the field of knowledge retrieval, and aims to solve the problems of knowledge solidification, insufficient timeliness and illusion of a traditional LLM (Logistics Language Model). A multi-granularity knowledge base is dynamically constructed, and a rule and semantic partitioning technology is combined, so that a text is converted into a normalized vector, and a hybrid index is established; a two-channel retrieval triggering mechanism is adopted, keyword matching scores and BERT semantic probability analysis are fused, and retrieval requirements are intelligently judged; vectorization retrieval is realized through a BGE-M3 model, and candidate results are reordered in combination with a cross encoder to improve the precision. The system supports multi-language adaptive processing, dynamic switching of word segmentation strategies and cross-language retrieval, and introduces real-time knowledge updating and version control. According to the method, the answer timeliness and accuracy are remarkably improved, the context coherence of multiple rounds of dialogues is optimized, the method can be widely applied to the fields of intelligent customer service, professional questions and answers and the like, the LLM illusion risk is effectively reduced, and the knowledge traceability is enhanced.

A method is provided for estimating the number of distinct tokens in a text stream using a modified text-to-text variational autoencoder (T5VQVAE) model. The method includes receiving a continuous input of a text stream; dynamically maintaining a buffer that stores a probabilistic subset of tokens from the text stream; calculating a sampling probability for each token based on a condition related to the current state of the buffer; updating the buffer based on the sampling probability to include or exclude tokens; encoding the buffered tokens into a latent space using the T5VQVAE model; and estimating the number of distinct tokens in the text stream based on the tokens in the buffer and the corresponding sampling probabilities.

The invention discloses a Mama-based spectrum dynamic fusion and double-attention enhancement medical image segmentation method, which comprises the following steps of: firstly, constructing a Mama integrated spectrum domain and attention pyramid module, fusing spectrum dynamic characteristics and a self-attention pooling mechanism, and performing frequency domain information compensation and local characteristic enhancement to obtain a spectrum dynamic fusion image; the spatial correlation loss caused by image blocking processing is relieved; secondly, designing a layered enhanced U-shaped architecture, deploying an MISAP module in a shallow layer of an encoder to capture multi-scale global context features, introducing a bipolar routing attention mechanism in a deep layer, and dynamically allocating sparse attention weights to focus a key pathological region; according to the method, the segmentation precision of complex edge textures and tiny lesions in medical images can be remarkably improved, and the Dice coefficient in breast tumor, polyp and abdominal organ segmentation tasks is averagely improved by 6.5%.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses an intelligent anomaly recognition and intervention processing method, device, equipment and medium. The method comprises the following steps: carrying out feature fusion by using a gating fusion network and generating a preliminary abnormal score, determining a reconstruction error through an auto-encoder and triggering abnormal early warning, calculating a causal effect value of key features in combination with a causal graph model and anti-factual reasoning, and calibrating the abnormal score to generate a final abnormal score and an intervention instruction. And executing an intervention action and recording a result. According to the method, the multi-dimensional feature information and the causal reasoning mechanism are fused, the self-encoder reconstruction error is combined to carry out anomaly judgment, the intervention instruction is generated and executed, closed-loop control of anomaly detection, reasoning analysis and intervention execution is achieved, and the recognition accuracy of complex events and the system response capacity are improved.

The invention provides a network traffic anomaly detection model training method and device and a readable storage medium, and the method comprises the steps: extracting a traffic statistical feature vector according to original network traffic data, and generating an initial mixed data set; generating a confrontation disturbance sample output enhanced feature matrix based on the initial mixed data set; constructing a self-adaptive feature fusion rule based on the enhanced feature matrix, embedding asset association degree parameters into an attention calculation layer of a feature encoder, and outputting encoding features fusing threat intelligence; inputting the coding features fused with the threat intelligence into a pre-constructed initial detection model, generating false report and missing report correction labels based on the suspicious traffic fragments, and outputting an adversarial sample correction data set; and performing adversarial training on the initial detection model through the adversarial sample correction data set to obtain an incremental detection model for network traffic anomaly detection. According to the invention, the detection precision, the anti-interference capability and the real-time defense response capability of the detection model to novel attacks can be improved.

A system and method for multi-scale temporal attention processing in multimodal technology deep learning systems. This system processes time-series, textual, sentiment, and structured tabular data across three hierarchically-organized temporal streams—quarterly, weekly, and intraday levels—with bidirectional cross-temporal information flow. Scale-specific attention mechanisms are optimized for respective temporal granularities, while an adaptive controller dynamically weights each temporal level based on real-time market volatility indicators. A multi-scale fusion processor integrates attention-weighted representations to generate temporally unified representations preserving both short-term market dynamics and long-term trends. This approach enables superior forecasting and risk assessment by leveraging temporal correlations across multiple time scales while automatically adapting to changing market conditions. The system facilitates interpretable AI analysis through attention visualization and enables synthetic scenario generation for model testing.

The invention discloses an electrical equipment fault diagnosis and prediction analysis system, which relates to the field of intelligent operation and maintenance of a power system and comprises an acquisition and preprocessing module, an extraction fusion module, a fault diagnosis modeling module, a prediction evaluation module and an update feedback module. According to the invention, through fusion of structured sensing data and unstructured image data, multi-modal depth feature joint representation is realized, and the accuracy and robustness of fault identification are significantly improved; a fusion time sequence prediction model is introduced, and a health degree scoring system is combined, so that accurate prediction of key parameter trends and quantitative estimation of the residual life of equipment are realized; a transfer learning and incremental learning mechanism is adopted, when a new fault or small sample data appears, model parameters can be quickly updated, and efficient adaptation to a new scene is achieved; a data alignment mechanism with time-space synchronization and an auto-encoder anomaly detection algorithm are constructed, and the multi-source heterogeneous data processing capacity and the real-time fault early warning capacity are remarkably improved.

A method is provided for enhanced data routing in neural networks using Self-Organizing Maps (SOM) integrated with Autoencoder-GAN. The method comprises training an autoencoder to encode input data into a latent space representation; applying a Self-Organizing Map (SOM) to organize the latent space representation into a topological map; refining the latent space representation using a Generative Adversarial Network (GAN), wherein the generator generates enhanced latent space representations and the discriminator evaluates their quality; using the refined latent space representations to update the SOM topology dynamically; generating routing coefficients based on the updated SOM topology to guide data routing in a capsule network; and dynamically adjusting routing within the capsule network using the generated routing coefficients to enhance performance based on the refined latent representations.

A system and method for adaptive real-time multi-modal compression with dynamic resource allocation provides intelligent compression optimization based on continuously monitored device conditions. The system monitors battery level, CPU utilization, and memory availability while classifying incoming multi-modal data streams comprising image, audio, text, and sensor data to determine processing priorities. Multi-objective optimization balances compression efficiency, reconstruction quality, and energy consumption using evolutionary algorithms that generate optimal parameters for an adaptive variational autoencoder. The autoencoder features dynamically selectable processing complexity, adjustable latent space dimensionality, and modality-specific processing layers. The system automatically switches between operational modes including emergency mode triggered by resource constraints, which applies maximum compression settings and intelligent data triage. Continuous learning adapts compression parameters based on observed performance outcomes, improving future optimization decisions. The system enables homomorphic operations on compressed data and provides enhanced compression performance under varying resource constraints across diverse edge computing applications.

The invention relates to the field of electric power monitoring, and discloses an electric power monitoring system and method fusing image recognition and data analysis, and the method comprises the steps: carrying out the visual angle coverage modeling of a target equipment group through a multi-type visual collection unit disposed at a transformer substation and a power distribution terminal; performing cross-frame fine-grained texture differential analysis on the equipment state image sequence, and constructing an image event time window in combination with synchronous disturbance characteristics of multi-source monitoring parameters; based on the high-vigilance candidate frame set, fusing the image structure variability index and the operation data multi-dimensional deviation vector by using a feature encoder, and constructing a multi-modal state coupling feature tensor; map mapping is carried out on the potential fault evolution trend, and semantic association is established between structural nodes with abnormal attributes in the image and frequently fluctuating parameter indexes in the monitoring data; and combining a node interference path in the local fault association subgraph with fault precursor distribution induced in a historical accident sample. The method has the advantage that the operation safety is improved.

The invention relates to an underground equipment fault early warning and diagnosis method based on big data analysis. The method is suitable for equipment operation state monitoring and intelligent diagnosis in underground operation scenes such as mines. The method comprises the following steps: collecting multi-source data such as an equipment running state, environment parameters and operation behaviors and preprocessing the multi-source data; multiple signal features are extracted and fused to construct a unified feature vector; performing health modeling by using the residual self-encoder model to generate a health index; an early warning threshold value is dynamically set through clustering analysis and Bayesian reasoning, and anomaly recognition is achieved; after early warning is triggered, fault type identification is carried out by adopting the fusion discrimination model; performing causal reasoning and maintenance suggestion generation based on the equipment fault knowledge graph; and continuously optimizing the model in combination with operation and maintenance feedback information, and constructing a closed-loop diagnosis mechanism. The method has the characteristics of high recognition precision, high response speed, explainable result and sustainable optimization of the model.

The invention relates to a multi-modal AI data fusion processing method, device and equipment and a medium, and the method comprises the steps: firstly extracting visual, auditory and text modal features through a pre-training encoder, executing dimension alignment, and generating a standard data feature set with unified dimensions; a cross-modal semantic graph is constructed based on a cosine similarity algorithm, and the problem of semantic mismatch of heterogeneous data is solved; residual enhancement is carried out on the map nodes, and noise interference is eliminated; fusing the optimized features and the semantic topology in combination with a graph convolutional network to generate aggregation graph representation; the fusion features are mapped to a low-dimensional semantic space through a variational auto-encoder, and cross-modal correlation essence is captured; the key dimension contribution degree is quantified, a visual report is generated, and semantic association rules among modals are disclosed, so that the dimension isomerism limitation of a traditional fusion technology is broken through, quantifiable cross-modal semantic mapping is established, the whole process traceability from feature fusion to decision interpretation is realized, and the method is suitable for popularization and application. And the multi-modal decision black box problem in the fields of medical diagnosis, automatic driving and the like is effectively solved.

The invention discloses a power grid abnormal flow detection method based on multi-modal data fusion, and the method comprises the steps: collecting the multi-source heterogeneous data of a power grid through an edge calculation node, including the current waveform of an intelligent electric meter, the state variable of an SCADA system, network protocol metadata and an equipment log event; performing space-time alignment preprocessing on the original data, converting an unstructured log into a time sequence by adopting a sliding window mechanism, and eliminating sensor noise through an LSTM auto-encoder; constructing a multi-dimensional feature space which comprises a frequency domain feature, a spatial-temporal feature and a protocol feature, and obtaining a feature vector; the feature vectors are input into a hybrid detection model, the model is composed of an isolated forest algorithm, an improved CNN-LSTM classifier and an information entropy-based rule engine which are connected in parallel, and a dynamic weight fusion strategy is adopted to output an abnormal probability; and when the abnormal probability exceeds a dynamic threshold, triggering a multi-level response mechanism: sending a traffic shaping instruction to the edge device, generating a device fingerprint portrait on the cloud platform, and storing abnormal event features through a block chain.

The invention discloses an adaptive teaching real-time feedback method based on multi-modal fusion, and the method comprises the following steps: synchronously collecting text modal information, voice modal information and image modal information generated by students in a teaching process, forming multi-modal original data information, and extracting historical student interaction behavior data; preprocessing the multi-modal original data information, and respectively generating corresponding text, voice and image sequence features; a visual feature encoder and a sequence feature encoder are adopted to encode each modal sequence feature to obtain a high-dimensional feature; inputting the modal high-dimensional features into a cross-modal fusion network for deep fusion; parameters of the feedback model are optimized through a model-independent element learning feedback regulation and control algorithm, and a personalized feedback strategy is generated; generating comprehensive feature representation according to the fusion features, and outputting personalized teaching feedback; and the interaction information is updated based on the feedback behavior data to realize closed-loop optimization.

A method is provided for mapping the latent space of a Vector Quantized Variational AutoEncoder (VQ-VAE) to polynomial basis vectors. The method includes training a VQ-VAE model on a dataset to obtain a set of codebook vectors representing the latent space; defining a polynomial basis for the latent space, the polynomial basis containing terms up to a predetermined order; mapping each codebook vector to the polynomial basis by determining polynomial coefficients that represent each codebook vector in terms of the polynomial basis; and using the polynomial coefficients to reconstruct and manipulate latent space representations.

The invention discloses a box-type substation state monitoring and early warning method based on artificial intelligence, relates to the technical field of intelligent power grids, and aims to solve the problems of missing report, false report and response lag caused by the fact that an existing static threshold ignores multi-physical coupling and a depth model highly depends on scarce fault samples. According to the scheme, sliding window kernel density estimation is carried out on a multi-channel time sequence signal, a dynamic coupling matrix is constructed through recursion Copula decomposition, a three-level threshold surface is generated through time-varying quantile regression, abnormal samples and graph attention network extraction state representation are generated in combination with a conditional variation auto-encoder, lightweight recursion pruning is carried out, and the dynamic coupling matrix is obtained. An abnormal score is generated through a multilayer Bayesian network and particle filtering, a multi-step risk trend is discriminated through a Gaussian kernel derivative slope, and finally unscented Kalman filtering is used for smoothing and online threshold correction; according to the method, the detection sensitivity and the early warning recall rate of the box-type substation to the transient coupling fault are remarkably improved, the response speed is improved, and the false alarm frequency is effectively reduced.

A method for structuring the latent space of an autoencoder is provided. The method includes analyzing natural language descriptions related to input data; creating language-guided libraries that categorize and abstract data features based on the analyzed descriptions; mapping input data into the categorized and abstracted features within the latent space of the autoencoder; and training the autoencoder to minimize reconstruction loss while adhering to the structure imposed by the language-guided libraries.

The invention discloses an electric power system abnormal remote signaling detection method based on a graph auto-encoder model, and the method comprises the steps: collecting measurement data of an electric power system, carrying out the preprocessing, obtaining a graph data set with an abnormal label, and dividing the graph data set into a training set and a test set; inputting the graph data in the training set into the graph auto-encoder model for training; after training is completed, abnormal score distribution is counted based on normal edge samples in a training set, a threshold value is set to serve as a follow-up judgment basis, and threshold value selection takes the accuracy rate and the recall rate on a test set as an adjustment and optimization target; in a test stage, image data in a test set are input to carry out edge feature reconstruction and anomaly scoring, anomaly judgment is carried out on edges in combination with a set threshold value, and a preliminary abnormal edge detection result is output; and the output abnormal edge detection result is input into the graph restoration module, the restored edge structure and edge features are output, the damaged remote signaling state in the power grid is restored, and the integrity of the graph structure and the operation credibility of the power system are improved.

The invention provides a fragment data cross-modal label generation system and method based on deep transfer learning. The method comprises the following steps: extracting first high-dimensional feature vectors in different modes; mapping the first high-dimensional feature vectors of different modals into the same semantic space through a cross-modal comparison loss function to realize multi-modal alignment and fusion to obtain second high-dimensional feature vectors; labeling semantic tags corresponding to the second high-dimensional feature vectors based on the fragmented data components by adopting a small sample transfer learning algorithm; a multi-channel Hash encoder is adopted, a self-adaptive encoding strategy is called according to different modal data combinations, and the second high-dimensional feature vector is encoded into a multi-channel binary Hash code; in combination with an incremental graph neural network, the binary hash codes and the corresponding semantic tags are dynamically expanded into the historical knowledge graph; matched fine-grained tags are established for semantic differentiation features of different entity combinations in the target knowledge graph, and a cross-modal tag tree is obtained by combining three-matrix hierarchical construction.

The invention relates to the field of power equipment, and discloses a power equipment fault early warning system, which comprises a data acquisition module, a data fusion module, a state evaluation module, a trend prediction module, an early warning judgment module and an information interaction module. Key operation parameters are cooperatively acquired through multiple types of sensors, time series data are uniformly calibrated by adopting a timestamp mechanism, the problems of fragmentation of operation state information of power equipment and superposition of acquisition errors are effectively solved, and then feature fusion and dimension reduction compression are performed on high-dimensional heterogeneous data by introducing a principal component analysis and auto-encoder neural network, so that the operation state information of the power equipment is acquired. According to the method, redundant information is eliminated, meanwhile, key discrimination features are reserved, the sensing dimension of the system for the equipment operation state is more comprehensive, the representation capacity is higher, the Bayesian network and the support vector machine are adopted to jointly evaluate the equipment state health level, higher state recognition accuracy is achieved in a dynamic scene, and the method is suitable for popularization and application. And the model generalization ability is enhanced through historical samples, so that the equipment state can be judged more stably.

The invention discloses a robust polyp segmentation method based on improved SAM-Med2D, and the method comprises the following steps: S1, generating a record file for a given data set; s2, constructing an SAM-Med2D model, and keeping the aspect ratio of a non-square image; s3, extracting multi-scale features of the image through a double-branch encoder; s4, in a mask decoder, carrying out fusion processing on the multi-scale features; s5, generating a preliminary segmentation result based on the fusion feature and the prompt code; s6, constructing a lightweight reverse attention refinement module; s7, carrying out loss calculation and parameter optimization on the SAM-Med2D model; and S8, polyp segmentation of the current image is ended, the next image is entered, and the steps from S1 to S7 are repeatedly executed. According to the method, the ViT-CNN double-branch structure and the reverse attention mechanism are fused, so that high-precision and low-calculation-overhead robust segmentation of the polyp area in a complex endoscope scene is realized.

A system and method for temporal acceleration encoding in Lorentzian latent space enables real-time event forecasting within navigable spatiotemporal media. The system encodes media data into compact Lorentzian latent patches using variational autoencoders and organizes them within a multi-dimensional hyperspace spanning spatial, temporal, orientation, scale, and spectral coordinates. Temporal acceleration encoding computes velocity and acceleration vectors along geodesic trajectories, extracting event signatures through multi-scale aggregation over sliding windows. An acceleration-indexed memory stores dynamic descriptors with composite keys comprising hyperspace coordinates and motion characteristics. Event forecasting retrieves similar historical patterns and conditions a forecast head to produce event probabilities and time-to-event estimates with uncertainty calibration. The system streams forecast metadata to edge devices for real-time prediction and adaptive navigation, supporting applications in surveillance, autonomous systems, predictive media exploration, and anomaly detection where both temporal forecasting and multidimensional navigation capabilities are essential.

The invention relates to the technical field of multi-modal information processing, and provides a deep semantic collaborative fusion method for heterogeneous multi-modal data. The invention provides a dynamic adaptive fusion framework aiming at the problems that a modal interaction mechanism is rigid and semantic modeling is shallow in the prior art. The method comprises the following steps: carrying out feature coding and alignment on text, audio and video modal data to generate unified-dimension single-modal representation; dynamic interaction is realized through an enhanced multi-head gating fusion module, and double-path features are generated; and carrying out cross-modal depth modeling on the basis of a stacked Transform encoder, and outputting final fusion semantics. Wherein the multi-head attention path calculates cross-modal mapping by taking a text as a query vector and taking an audio / video as a key value vector; the gating path generates a dynamic weight through cosine similarity and a learnable temperature parameter; and the dual-path adaptive fusion adopts a balance factor alpha weighted combination. According to the method, the multi-modal data fusion precision and the system robustness are improved, and the method is suitable for government affair service, man-machine interaction and other scenes.

The invention relates to the technical field of electrical equipment fault diagnosis, in particular to an intelligent fault diagnosis method and system for electrical equipment, and the method comprises the steps: constructing a multi-dimensional tensor model, uniformly fusing the equipment state information, electrical distance weighted connection and phase dynamic coupling relation, and extracting an abnormal propagation mode through high-order singular value decomposition; designing a space-time-frequency coupling interference stripping mechanism, and combining structure guide disturbance deconstruction, multi-scale dictionary learning and sparse low-rank decomposition to accurately separate transmissible and non-transmissible interferences; reconstructing a fault trajectory based on a generative adversarial mechanism, coupling a graph structure dynamic encoder, a topology consistency discriminator and a time controllable generator, and restoring a real propagation path; and finally, tensor semantic compression, a three-view graph neural network and fault label back projection interpretation are integrated through a multi-source semantic fusion mechanism. According to the method, cross-space-time and cross-structure fault diagnosis and traceability are realized, and the accuracy and interpretability are improved.

The invention discloses a heterogeneous sensing early warning system based on decoupling perception and adversarial robust learning, and the system comprises a feature extraction module which processes heterogeneous sensor original data collected in real time through a multi-layer decoupling encoder, separates target related features and environment interference features, and suppresses noise pollution from the source; the multi-dimensional collaborative fusion module adopts a cross-domain adversarial robustness learning framework to carry out space-time sequence alignment and deep fusion on decoupling features to generate high-robustness joint representation, and a data missing problem is processed through a cross-modal generative feature completion mechanism; and the cognitive enhancement closed-loop decision module constructs a cognitive heuristic confidence evaluation model based on joint representation, realizes graded early warning by combining real-time quality scoring and behavior prediction, and dynamically optimizes system parameters through a feedback mechanism. According to the method, the problems of poor target detection robustness, high delay and low accuracy in a complex dynamic environment are solved, the detection precision is remarkably improved, the false alarm rate is reduced, and the all-weather adaptive capacity is enhanced.

The invention discloses a multi-modal semantic and physical law driven remote sensing image generation method, belongs to the technical field of computer vision and remote sensing image generation, and aims to solve the problems of insufficient cross-modal semantic alignment, low reliability of a generation result and insufficient physical mechanism fusion. The four-stage method comprises the following steps: firstly, rejecting low-quality samples from original data and unifying a spatial scale; then, extracting a multi-modal semantic vector by adopting a BLIP model and a CLIP model, and introducing a remote sensing physical rule to carry out vector optimization; then position coding and physical constraint conditions are embedded in the submerged space, and multi-source information joint modeling is achieved through a cross-modal encoder; and finally, by taking text description, physical priori knowledge and diffusion time steps as joint conditions, performing de-noising reasoning based on a Transform architecture, and completing back diffusion reconstruction by means of a trans-attention mechanism. According to the method, physical rationality and semantic consistency are improved, and a more reliable technical normal form is provided for remote sensing image generation in the fields of disaster monitoring, military simulation and the like.

A multimodal intelligent agent system for dynamic environmental monitoring and user-centered support, consisting of: a multimodal sensor module configured to continuously acquire environmental and behavioral data from multiple input modalities, including at least one visual sensor, at least one acoustic sensor, at least one environmental conditions sensor, and at least one proximity or motion detection sensor, each generating modality-specific data streams representing visual images, audio waveforms, physical environmental parameters, and motion signatures within a monitored environment; a data preprocessing and fusion subsystem that is operationally coupled with the multimodal sensor module and configured to normalize, temporally align, and transform the modality-specific data streams into high-dimensional feature embeddings using a variety of encoders, wherein the visual encoder uses convolutional or vision transformer architectures, the audio encoder uses a spectral-temporal feature extractor, and the sensor encoder transforms raw analog data into context vectors suitable for multimodal alignment; a multimodal processing unit consisting of a transformer-based large language model (LLM) trained on paired multimodal datasets and configured to perform semantic fusion, context abstraction, and inference across the aforementioned aligned multimodal feature embeddings to generate a contextual understanding of environmental and behavioral states; an adaptive agent controller coupled to the multimodal inference processing unit and configured to instantiate, manage, and terminate a variety of task-specific intelligent agents, each agent being a software unit configured to perform a specialized function selected from meeting summarization, behavioral analysis, misplaced object detection, or environmental anomaly identification, with the agents dynamically interacting with the inference engine to retrieve contextually relevant multimodal embeddings for task execution; a personalization and adaptive learning subsystem consisting of a user preference database and a neural memory structure configured to update and refine model parameters based on user-specific interaction history, thereby enabling personalized output generation, prioritization of recommendations, and long-term behavioral adaptation; and An output generation interface is operationally connected to the adaptive agent controller and configured to produce multimodal output in textual, visual, and auditory form. The interface is capable of displaying human-readable summaries, notifications, and visual reconstructions of identified entities or environmental states.

A deep learning system for time series prediction comprising a preprocessor that receives time series input sequences, truncates them by removing terminal values, and appends padding values to maintain the original sequence length. An encoder compresses these padded sequences into latent space representations, while a decoder reconstructs predicted sequences matching the original length, specifically trained to reconstruct values matching the removed terminal values in positions corresponding to the padding values. A training system optimizes the encoder and decoder by minimizing differences between original sequences and predicted sequences. The system can process multiple time horizons simultaneously while maintaining statistical properties and providing uncertainty quantification through confidence intervals. This approach enables accurate short-term forecasting while preserving both temporal patterns and statistical relationships in the predicted sequences.

The invention relates to the technical field of engineering control, in particular to a hoisting control method of a safe and portable hoisting cage, which comprises a hoisting pre-stage, data pre-acquisition and analysis before hoisting, a hoisting control stage and hoisting landing control. In the prior art, position closed-loop control mainly depends on a stroke encoder and a position sensor, dynamic swing deviation caused by wind disturbance is difficult to accurately pre-judge and counteract in real time, and the defects of control lag and insufficient response exist. According to the scheme, the motion trail and posture of the cage are predicted in advance based on simple pendulum dynamic model numerical solution and the Runge-Kutta method, high-precision deviation detection is carried out in combination with a virtual-real vision calibration technology and AR enhanced display, a cage motion state control model based on feedforward prediction is established, trail prediction errors are greatly compressed, and the accuracy of the motion state of the cage is improved. The forward-looking inhibition of the movement of the cage, especially wind-induced swinging, is realized, and the accuracy and the response speed of an active swinging inhibition measure are obviously improved.

The invention belongs to the technical field of network security, provides a network attack detection method based on dynamic graph coding, and solves the problems of poor dynamic adaptability of an attack path and missing of timing constraint in the prior art. The method comprises the following steps: constructing a dynamic threat map, extracting a triple of heterogeneous threat intelligence by using a RoBERTa model, and adding a timestamp and a confidence attribute; a dynamic graph encoder for time sequence perception is designed, semantic and evolution laws are fused through periodic time coding and a multi-head time sequence attention mechanism, and feature weights are adjusted in combination with a gating residual layer; an event-driven incremental updating strategy is adopted, and node similarity is calculated to achieve local subgraph updating; a time sequence rule base is established, three-dimensional parameter verification attack chain time sequence logic is defined, and abnormity is judged through conflict scores; and finally, integrating a graph updating module, a dynamic coding module and a constraint analysis module to realize multi-source threat feature matching and attack detection. According to the method, the adaptability of attack path evolution is improved through dynamic graph modeling and real-time increment updating.