2629 results about "Feature mapping" patented technology

The invention belongs to the technical field of equipment management, and discloses an industrial equipment fault prediction and health management method based on multi-sensor fusion, and the method comprises the steps: obtaining multi-source sensing data of industrial equipment, carrying out the signal decoupling analysis, and obtaining a decoupling characteristic spectrum; performing frequency domain conversion and modulation analysis to form a multi-dimensional characteristic spectrum system; analyzing the modal correlation of the multi-dimensional feature pedigree to obtain a fault feature mapping network; a mixed time sequence prediction model is constructed, residual life prediction and degradation trend evaluation are carried out, and an equipment health trend graph is obtained; establishing a health state evaluation index system, and performing reliability evaluation to obtain an equipment health state report; and generating a maintenance decision suggestion, and realizing real-time anomaly detection and maintenance suggestion pushing through edge calculation. Through multi-sensor data fusion and advanced analysis technologies, early warning and accurate prediction of industrial equipment faults are realized, and the operation reliability and production efficiency of the industrial equipment are remarkably improved.

The invention provides a virtual DPU power plant simulation fault restoration method and system based on digital twinning, and relates to the technical field of digital twinning, and the method comprises the steps: carrying out the preprocessing of collected DPU power plant operation data, including noise reduction, time sequence alignment and abnormal point elimination, carrying out the data quality evaluation, training a fault feature mapping model based on the processed data, and carrying out the fault restoration of the DPU power plant. The model is used for recognizing abnormal clusters in real time, a fault evolution path is searched and determined in combination with a conditional random field and a Monte Carlo tree, an optimal path is determined through a particle filtering algorithm, fault root causes are determined in combination with causal analysis, spectral clustering and a Bayesian network, and a fault diagnosis report is generated.

The invention discloses an archive knowledge base construction and retrieval method and system based on multi-modal data fusion. The method comprises the steps that heterogeneous archive data are cleaned, image features are extracted through CNN, text features are extracted through Transform, audio is converted into text and then subjected to similarity, a unified feature vector is generated, and metadata is constructed according to archive code association; creating a graph database instance, defining nodes and relationship types, importing entities and relationships, and storing feature vectors and metadata; the features are mapped to a high-dimensional shared semantic space, positive and negative sample pairs are constructed to update embedded layer parameters, self-attention is used in modalities, a shared attention mechanism is used between modalities, weights are adjusted according to archive features, and unified knowledge representation is generated; segmenting the steering quantity of the multi-modal data, storing the steering quantity into a database, and adopting hierarchical indexing and optimizing as required; related document fragments are retrieved through RAG technology vectors, answers are generated with the help of a large language model, and session feedback is provided. The file retrieval efficiency and accuracy are improved.

The invention relates to the technical field of intelligent control, provides a flow regulating valve servo force control method and system based on a force sensor, and aims to solve the technical problems of response delay, weak overshoot suppression capability and poor long-term operation stability. The method comprises the following steps: acquiring a servo driving current data set and a valve displacement track data set of a target regulating valve; performing pressure feature mapping processing on the servo driving current data set to generate a pressure fluctuation feature set corresponding to the driving current waveform data; the pressure fluctuation characteristic set and the valve displacement track data set are input into a preset force control decision model for dynamic matching processing, and a servo control instruction set is generated; executing multi-stage dynamic adjustment operation on a servo driving unit of the target adjusting valve according to the servo control instruction set, and generating real-time pressure balance state data; and iteratively updating the dynamic matching processing parameters of the force control decision model based on the deviation value of the real-time pressure balance state data and the preset pressure reference value.

This invention relates to a gait emotion recognition method, system, storage medium, and computer device based on spatiotemporal graph convolution. The method includes the following steps: S1, data augmentation by reversing the temporal direction of gait; S2, obtaining deep emotion features and prior emotion features respectively through a spatiotemporal graph convolutional network and prior feature statistical methods; S3, performing nonlinear mapping on the prior emotion features using a feature mapping layer; S4, inputting the fused features of the deep emotion features and prior emotion features into an emotion classifier to obtain the emotion category. The feature mapping layer of this invention achieves more effective feature fusion by performing nonlinear mapping on prior features; it also introduces causal temporal convolution to replace general temporal convolution, effectively extracting fine-grained temporal features by enhancing temporal correlation and cross-period feature fusion. Furthermore, a walking direction recognition auxiliary task is designed to accelerate the training and convergence speed of the model, enhancing the ability to extract temporal-dependent features and the performance of emotion recognition.

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 light industry supply chain multi-modal data fusion analysis method based on deep learning, and the method comprises the following steps: carrying out the cleaning and standardization processing of text, image, audio and video data collected in a supply chain environment, and constructing a standardized multi-modal data set; then, a special feature extraction network is adopted to generate each modal feature vector, and a feature incidence matrix is constructed through cross-modal correlation analysis; feature weights are dynamically adjusted in combination with a domain knowledge rule base, multi-modal feature interaction is achieved through a cross-modal attention fusion network, and unified fusion features are generated through a self-attention mechanism; and finally, constructing a supply chain decision model, and mapping the fusion feature into a supply chain state evaluation result and an optimization parameter. According to the method, knowledge rule constraint and a deep attention mechanism are fused, supply chain situation awareness precision and decision reliability can be effectively improved, and technical support is provided for intelligent management of the light industry supply chain.

The invention provides a photovoltaic intelligent sensing fault diagnosis method and system for an energy internet of things, and relates to the field of photovoltaic technology, and the method comprises the steps: deploying a multi-dimensional sensor network based on a star topology structure; a deep learning model based on a graph attention network and a bidirectional gating loop unit is utilized to extract space-time correlation features, and matching learning is carried out on the space-time correlation features and historical fault samples to generate fault feature mapping; constructing a fault diagnosis classifier by adopting a comparative learning method and optimizing the fault diagnosis classifier through a knowledge distillation technology, and classifying the fault feature mapping to obtain a fault type probability distribution matrix and a fault early warning level; constructing a multi-dimensional fault evaluation model based on Bayesian reasoning and time sequence correlation analysis, and evaluating fault credibility; and finally, constructing a fault evolution prediction model based on deep reinforcement learning, predicting the fault of the photovoltaic module in combination with the fault credibility and historical data, and generating a health assessment report.

The invention relates to the technical field of industrial nondestructive testing and computer vision, and particularly discloses an intelligent detection method for surface defects of a container bottom plate based on deep learning. The method comprises the following steps: synchronously acquiring data through a laser radar and a line scanning camera, complementing a shielding area of a point cloud, and executing coordinate normalization to generate preprocessed data; constructing a lightweight feature alignment network to realize cross-modal feature mapping and pixel-level error correction; segmenting a defect area by adopting an improved PointNet + + network and reconstructing a three-dimensional model; extracting defect geometric parameters and combining with material attributes to perform stress simulation and life prediction; aggregating multi-port safety life data to construct a federated framework to update model parameters; and synthesizing physically real defect samples based on false detection cases, and injecting the defect samples into the network for training. According to the method, the bottleneck of missing detection of internal defects in traditional two-dimensional detection is overcome, the defect detection rate and quantification precision are remarkably improved, full-life-cycle safety evaluation of the container is supported, and the efficient requirement of automatic port inspection is met.

The invention relates to the technical field of equipment fault diagnosis, in particular to an extruder equipment fault recognition method and system based on artificial intelligence, and the method comprises the following steps: collecting key fault features of an extruder in real time based on a multi-mode sensor network, optimizing the signal quality through data preprocessing and feature decoupling, and obtaining a fault recognition result; and the generalization ability of the model is improved by using cross-device feature mapping and transfer learning, a hybrid neural network is combined, a physical constraint layer is embedded on the basis of a data driving layer, a feature incidence matrix conforming to the dynamic characteristics of the extruder is constructed, and a fault prediction model can be adjusted in real time through a dynamic weight distribution mechanism and dual-target loss optimization, so that the fault prediction efficiency is improved. The method adapts to the change of the operation state of the equipment, and realizes the real-time detection, graded early warning and precise operation and maintenance of faults in combination with an intelligent early warning mechanism and a multi-target optimization decision. According to the invention, the operation stability and maintenance efficiency of the extruder equipment are obviously improved, and the method is suitable for equipment health management in the field of intelligent manufacturing.

The invention relates to the technical field of agricultural meteorological prediction, in particular to an agricultural meteorological disaster time sequence prediction system and method based on multi-modal data fusion, and the method comprises the steps: collecting and preprocessing agricultural meteorological disaster related data, constructing a dynamic semantic association graph, carrying out the multi-layer feature abstraction processing, and generating a semantic enhancement feature vector; the dual-branch prediction network processes time sequence dependence and local mode features, multi-granularity attention processing identifies key feature information, multi-scale feature fusion extracts different time scale feature information, and cascade fusion is carried out; the multi-target optimization module carries out model training based on the comprehensive feature representation and optimizes a plurality of targets; the multi-time scale prediction output module generates short-term accurate prediction, medium-term trend prediction and long-term risk assessment results, and provides prediction confidence, error range and risk level information; a dynamic semantic association graph and a multi-layer feature mapping mechanism are constructed, and deep fusion of multi-modal data on the semantic level is achieved.

The invention discloses a lithium battery health state assessment method and system, and particularly relates to the technical field of battery health state assessment. The method comprises the following steps: performing time sequence alignment and structured preprocessing on multi-source operation data of a target lithium battery in a plurality of historical work cycles to construct a structured data set; constructing a spatial-temporal characteristic residual error map based on residual error mapping analysis, and extracting a spatial heterogeneity index; in combination with a spatial heterogeneity index, generating regional degradation feature mapping; through high-dimensional feature embedding and evolution path clustering, a heterogeneous aging mode is identified, and a classification result is generated; evaluating the health state grade of the target lithium battery according to the regional degradation characteristic mapping and heterogeneous aging mode classification result; whether the battery has a local potential thermal runaway risk or not is judged based on the evaluation result, and a corresponding risk early warning signal and a safety disposal suggestion are generated, so that the nonlinear influence of the lithium battery aging heterogeneity can be accurately identified, and the health state evaluation precision and the safety risk early warning capability are effectively improved.

The invention discloses a multi-modal entity and relation extraction method and system based on cross-modal alignment and fusion, and the method comprises the steps: carrying out the processing and coding of an input text and an image, and obtaining a plurality of types of image and text features; performing feature alignment on the fine-grained and coarse-grained text features and the pixel-level image representation by taking the semantic representation of the image as an anchor point, and mapping the image and the text features to the same semantic space; performing multi-granularity feature fusion through text-guided dynamic gating aggregation, visual prefix cross-modal fusion and cross-modal image-text matching, modeling association between noun phrases and image objects in a text while increasing feature complementarity, and obtaining multi-granularity multi-modal feature representation; fusing multi-granularity multi-modal features through entity guidance attention gating, and gathering visual information related to a text entity to obtain final multi-modal fusion representation; according to the multi-modal fusion representation, task prediction of multi-modal named entity recognition and multi-modal relation extraction is carried out.

The invention discloses a cross-modal image fusion hyperspectral point cloud waste plastic bottle intelligent sorting method, and relates to the technical field of neural network-based data processing, and the method comprises the steps: obtaining hyperspectral image data and point cloud data of a target object through a multi-modal collection system; preprocessing the collected hyperspectral image data and point cloud data, respectively extracting features and constructing a hyperspectral image and a point cloud image; constructing an adjacent matrix through nodes and edges by taking the constructed hyperspectral image and the constructed point cloud image as a reference, and performing normalization; carrying out single-mode feature extraction on the normalized adjacent matrix; carrying out cross-modal fusion on the extracted single-modal features; performing fine-grained modeling on a cross-modal fusion result through a multi-head attention mechanism to generate a final fusion feature; and mapping the final fusion feature to an output space of a regression task, and training network parameters to obtain a cross-modal fusion model to realize identification of a target object.

The invention provides a service prediction method and device based on a time sequence model, equipment and a storage medium, and the method comprises the steps: carrying out the preprocessing of historical time sequence data, and obtaining a training data set; performing fast Fourier transform on the training data set to obtain frequency domain data and frequency component intensity; extracting a plurality of main frequency components and corresponding period values from the frequency domain data, and determining a plurality of two-dimensional feature tensors according to the period values and the training data set; constructing L layers of time blocks including feature mapping, an attention mechanism and a feedforward network according to the two-dimensional feature tensor, and establishing residual connection between adjacent time blocks to obtain a time sequence model to be trained; performing feature extraction on each two-dimensional feature tensor by applying a multi-branch convolutional network to obtain a one-dimensional representation vector, and performing weighted fusion on the one-dimensional representation vector according to the frequency component intensity to obtain a fusion feature; and training the to-be-trained time sequence model through the fusion features, and outputting an analysis report of historical time sequence data.

The invention relates to a supporting structure stress state monitoring method based on artificial intelligence, and belongs to the technical field of artificial intelligence and data processing. The method comprises the following steps: acquiring and marking strain data of a supporting structure; after abnormal values are removed, normalizing the multi-sensor data to generate a normalized strain sequence; a state monitoring model is constructed, a deep time sequence neural network architecture is adopted, and the state monitoring model comprises an input layer, a self-adaptive wavelet attention feature mapping layer, a time domain gating convolution module, a global maximum pooling layer, a dynamic feature importance reweighting layer and a full-connection classification layer; inputting a normalized data training model; optimizing a loss function through a quantile interval adaptive learning rate and a momentum updating strategy; after real-time monitoring data is processed, inputting the data into the training model according to time window slices, outputting four types of probabilities, and taking the maximum value as a prediction state; and if a plurality of continuous windows are early-warning and dangerous, triggering the terminal to give an alarm. The accuracy of monitoring the stress state of the supporting structure can be improved.

The invention discloses a multi-modal medical image fusion diagnosis system based on artificial intelligence, and the system comprises the following steps: extracting shared features of CT and MRI images through a convolutional neural network, and mapping the shared features to the same feature space; a two-way step-by-step alignment strategy is adopted, a three-dimensional deformation field matrix is generated, and cross-modal image anatomical structure alignment is achieved; calculating modal feature weights and eliminating distribution differences through an attention mechanism and an adversarial domain adaptation layer; constructing a CT-MRI image block contrast learning task, and optimizing a shared feature encoder; a conditional generative adversarial network is used for generating a false image of a missing mode according to the semantic segmentation map, and data distribution is constrained through a Wasserstein distance; uniform feature extraction of multi-modal medical images is realized through a shared feature encoder, the cross-modal image alignment accuracy is improved in combination with a bidirectional deformation field prediction module, and the comprehensiveness and accuracy of fusion features are enhanced by using a multi-modal feature fusion module.

The invention provides a digital twin construction method for metal stamping forming and a computer system, and the method comprises the steps: obtaining a multi-source monitoring data set in a metal stamping forming process, carrying out the collaborative feature extraction processing, and generating a material structure feature set and a process dynamic feature set; a mixed feature space is constructed based on the material structure feature set and the process dynamic feature set, multi-dimensional feature matching processing is executed in the mixed feature space, a feature mapping relation network is generated, a dynamic twin model is constructed according to the feature mapping relation network, and real-time state deduction is conducted on the stamping forming process based on the dynamic twin model. And a forming quality prediction result and a process defect positioning result are generated, and a stamping process parameter adjustment scheme is generated and fed back to a stamping control system to trigger parameter optimization operation. According to the method, the early warning capability of process defects and the precision of process parameter adjustment under complex working conditions are improved, and meanwhile, the real-time performance and engineering feasibility of digital twin construction in the large-scale stamping forming process are guaranteed.

The invention relates to the technical field of unmanned aerial vehicles, and discloses an unmanned aerial vehicle battery endurance flight capability prediction system, which comprises a multi-dimensional data acquisition module, a feature mapping module, a prediction module, an optimization module and a feedback optimization module, and can be additionally provided with an early warning module. The multi-dimensional data acquisition module acquires battery data and cleans the battery data to generate standardized data; the feature mapping module maps the data to a feature space, and generates a feature sequence cluster containing a multi-dimensional association relationship by using a time sequence segmentation algorithm; the prediction module divides prediction intervals based on a support vector machine algorithm and extracts prediction indexes; the optimization module generates an endurance prediction strategy by predicting and optimizing the network model; and the feedback optimization module performs multi-source data fusion optimization and outputs a prediction instruction. The early warning module can associate the prediction instruction with the battery health degree, output a grading early warning signal and trigger a response mechanism.

The invention discloses a bridge structure health monitoring data anomaly detection method based on deep learning, particularly relates to the technical field of structure health monitoring, and is used for solving the problems of high environmental interference sensitivity and insufficient cross-modal data fusion capability caused by image enhancement and feature extraction process splitting in the existing method. A cross-domain feature mapping relation is generated through combined training of dynamic image enhancement and a deep learning model, and collaborative optimization of enhancement parameters and feature space is achieved; time-frequency resonance parameters of visual images and acoustic emission signals are fused based on cross-modal convolution, and damage feature space distribution is corrected in combination with an attention mechanism; analyzing and quantifying the structural difference of the cross-domain features by using topology persistence coherence, and iteratively optimizing the feature mapping network through an optimal transmission theory; and finally, a multi-level feature template matching and self-adaptive threshold judgment mechanism is adopted to output an abnormal detection result, so that the robustness and generalization ability of bridge structure health detection in a complex environment are remarkably improved.

The invention discloses a financial data generation method and system based on a rule configuration engine, and the method comprises the steps: converting a business demand into structured rule description through a semantic analysis engine according to the financial business demand, dynamically configuring a financial data processing rule through a context-aware rule template library, and generating financial data according to the financial data processing rule. Generating an executable rule configuration set; extracting related data from a plurality of heterogeneous financial data sources according to the rule configuration set, and mapping features of different data sources to a unified high-dimensional vector space by adopting a multi-source data fusion algorithm to generate a fused financial feature matrix; and inputting the fused financial feature matrix into a rule configuration engine, adopting a rule execution optimization model, dynamically adjusting a rule execution sequence and parameters, generating financial data, and performing noise addition processing on the financial data to ensure data privacy security. According to the embodiment of the invention, the financial data can be flexibly and efficiently generated, and the safety and efficiency of overall data processing are improved.

The invention provides a high-speed traffic flow high-precision prediction method based on multi-source disturbance characteristics, and relates to the field of data prediction, and the specific steps are as follows: firstly, a multivariable entropy driving interaction field module maps the multi-source disturbance characteristics into a unified energy field, calculates joint information entropy density and constructs a joint interaction field; processing the original feature sequence; secondly, the collaborative disturbance reconstruction module adopts a learnable mapping matrix and a multi-scale mechanism to extract dynamic differences of features under different time scales, and generates enhanced disturbance response features through a decoupling network after global disturbance collaborative response is fused; then, a spatial manifold mapping and partitioning module realizes spatial expression and partitioning modeling of a traffic flow tension evolution trend; and then, the prediction module constructs an asymmetric prediction structure in combination with the disturbance amplitude factor and the weighted disturbance characteristics, adopts a mean square error, introduces a disturbance constraint term to train the model, and outputs a final traffic flow prediction result through the trained high-speed traffic flow prediction model.

The invention discloses a multi-modal data fusion method and system based on a large-model intelligent agent, belongs to the technical field of artificial intelligence, big data processing and intelligent agents, and aims to solve the technical problem of how to effectively integrate data of different modalities such as texts, images and voices by using the large-model intelligent agent. According to the technical scheme, the method comprises the steps that data collection and preprocessing are conducted, specifically, various modal data of texts, images and voice are collected through a web crawler, an API interface, a camera and microphone equipment, the collected data are preprocessed, the preprocessed multi-modal data are obtained, and the data quality is ensured; feature extraction and mapping: extracting corresponding modal features from the preprocessed multi-modal data through CNN and Transform models, mapping the different modal features to the same space, and combining the aligned features to form comprehensive feature representation; carrying out multi-modal fusion processing; and performing intelligent decision and feedback.

The invention provides a production process intelligent monitoring method and system based on an intelligent mine, and the method comprises the steps: collecting a target monitoring data set in the real-time production process of mine equipment, covering equipment vibration time sequence signals, environment temperature and humidity distribution data and an equipment energy consumption fluctuation curve, and carrying out the dynamic standardization processing, the method comprises the following steps: obtaining a standardized monitoring data set matched with an equipment type and a production process stage, calling a pre-trained multi-dimensional feature extraction model to carry out joint feature mapping, generating an equipment operation state, environment association and data abnormal fluctuation features, and carrying out dynamic fusion analysis on the features based on a preset abnormal mode identification model, and finally, according to the risk level, an alarm instruction is triggered, an optimization strategy is fed back to a production control terminal to adjust equipment operation parameters, and intelligent monitoring and optimization of the intelligent mine production process are realized.

The invention provides a personalized recommendation system and method for an intelligent terminal, and belongs to the technical field of artificial intelligence and big data, and the system comprises a five-element multi-mode dynamic perception module, a space-time attention feature fusion unit, a hierarchical federal transfer learning framework, a context perception enhanced recommendation engine and an edge-cloud co-evolution mechanism. The five-element multi-mode dynamic sensing module is used for synchronously collecting physiological features, environmental parameters, behavior data, spatio-temporal information and a social relation graph; and the space-time attention feature fusion unit is used for fusing the multi-modal data by adopting an ST-Transform model. According to the system, on the premise of ensuring the privacy of the user, the recommendation accuracy and real-time performance in a complex scene are remarkably improved, and a new technical normal form is provided for the personalized service of the intelligent terminal; according to the personalized recommendation method, cross-device transfer learning enables the time consumption of new user feature mapping to be greatly shortened, environment-driven brightness adjustment effectively reduces the visual fatigue of the user, and incremental learning obviously reduces the data volume updating demand of the model.

The invention relates to the technical field of data processing, and provides a multi-heat-source networking heat supply optimization operation method and system.The method comprises the steps that environmental parameters, heat source data, market dynamic information, user behavior characteristics and a pipe network topological graph are collected by deploying IoT equipment; acquiring a thermal load time sequence predicted value in a future preset time period; the heat source data and the pipe network topological graph are processed, and a pipe network operation state matrix is obtained; performing feature dimension alignment processing on the pipe network operation state matrix to obtain a pipe network spatial topology feature mapping value; performing weighted fusion on the thermal load time sequence prediction value and the pipe network spatial topological feature mapping value to obtain a final thermal load prediction value; and optimal operation of heat supply is realized according to the final heat load predicted value. According to the method, a real-time response mechanism for environmental parameters, market dynamics and user behavior characteristics can be realized, collaborative optimization of multiple heat sources can be realized, the heat source collaborative efficiency is improved, and energy waste and operation cost are reduced.

The invention discloses a multi-modal fused intelligent question answering and knowledge retrieval method and system, and the method comprises the steps: building a multi-modal data index model oriented to a heterogeneous knowledge source, carrying out the feature mapping of text, image, table, chart, audio and video contents through a unified semantic embedding space, and generating a cross-modal index set; after a query request is received, performing semantic matching and structure matching on the cross-modal index set by using a multi-channel retriever to obtain candidate evidence fragments; and based on an evidence granularity decomposition strategy, performing minimum evidence unit division on text statements, table units, chart data points and multimedia frame contents in the candidate evidence fragments, and establishing a semantic consistency graph among the units. According to the method, high-credibility traceable generation of question and answer results is realized through multi-modal fusion and space-time consistency constraint, and the retrieval precision and interpretation transparency in a complex knowledge scene are remarkably improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a decision-making method and device guided by a multi-modal semantic map, equipment and a medium. Extracting a visual feature vector, a language feature vector and an action feature vector, splicing to generate a multi-modal initial feature, mapping the multi-modal initial feature to a shared semantic space, constructing a multi-modal semantic map, and inputting a map-guided attention mechanism to generate a cross-modal alignment feature; the cross-modal alignment features and task targets are input into a meta-learner to generate task adaptability features, the task adaptability features are input into a parallel reasoning network to execute subtasks in parallel, and a gating fusion network integrates output results to generate a global decision. According to the method, cross-modal semantic association and task adaptability are enhanced through the combination of shared semantic space mapping, map guiding attention and a meta learning device, and the accuracy and efficiency of multi-modal decision making are improved through the combination of parallel reasoning and gating fusion.

The embodiment of the invention relates to the technical field of tunnel environment monitoring, and particularly discloses a tunnel environment monitoring method and system based on multi-source data fusion. According to the embodiment of the invention, multi-source monitoring is carried out on the target tunnel, and data synchronization and preprocessing are carried out on the multi-source monitoring data; performing spatial-temporal feature extraction on the multi-source standard data, and performing unified feature mapping; performing multi-source fusion on the multi-modal feature sequence; performing environment classification identification on the fused feature data; and constructing an information visualization platform, and monitoring and displaying the abnormal event data and the plurality of environment identification data. Multi-source monitoring, data synchronization and pre-processing, spatial-temporal feature extraction, multi-source fusion and environment classification identification, construction of an information visualization platform for monitoring display, data fusion processing of multi-source monitoring and comprehensive perception of multi-source data can be performed, so that linkage coupling analysis can be performed on various data, and multi-source data can be acquired. The abnormity identification capability and the response capability are improved, and the false alarm rate is effectively reduced.

The invention provides a multi-modal fusion sensing robot dog inspection slope disaster risk assessment method and device and a storage medium, and relates to the field of slope disaster assessment, and the method comprises the steps: obtaining multi-modal sensing data; performing space-time alignment processing on the multi-modal sensing data and then converting the multi-modal sensing data into a voxel coordinate system; performing feature extraction on the multi-modal sensing data after time-space alignment, and mapping each extracted feature to a unified voxel unit to form a three-dimensional voxel structure containing each extracted feature; constructing a three-dimensional multi-modal data fusion model; identifying disaster types based on the three-dimensional multi-modal data fusion model, wherein the disaster types comprise the ground surface crack length, the underground cavity volume, the water seepage point number, the local collapse and bulging area, the vegetation degradation area and the slope gradient; and calculating a risk index based on the identified disaster type in combination with the association degree of the disaster type. By adopting the evaluation method provided by the invention, rapid and efficient evaluation of slope disasters can be realized, and the method has relatively good accuracy.