492 results about "Perception" patented technology

The invention discloses a brain tumor multi-mode large model construction method, device and equipment and a storage medium, and is applied to the technical field of brain tumor imagines.The method comprises the steps that pixel-concept level alignment is conducted on a multi-mode MRI image and a pathological text; constructing a multi-modal feature fusion network for fusing image features and text features by adopting an attention mechanism of pathology perception and combining medical semantic information; training the multi-modal feature fusion network to generate an analysis report and a segmentation result; according to the technical scheme of multi-task cooperation, cross-modal pathological semantic accurate alignment, pathological knowledge graph injection and lightweight and continuous optimization parallelization, full-process coverage of brain tumor accurate segmentation, analysis report generation and prognosis prediction is achieved, the problems that a traditional model lacks pathological semantic support and is insufficient in clinical adaptability are solved, and the clinical adaptability of the traditional model is improved. And the deployment feasibility and the dynamic optimization capability are also considered.

The invention belongs to the technical field of artificial intelligence and health monitoring, and particularly relates to an emotion recognition and intervention system based on facial micro-expression and physiological signal fusion. The emotion recognition precision is improved through space-time alignment analysis of facial micro-expressions and physiological signals, adaptive feedback is achieved in combination with cognitive load correlation modeling and a wearable multi-channel regulation and control terminal, cross-period emotion evolution prediction and group situation awareness are supported, a'awareness-decision-intervention 'complete closed loop is constructed, and the emotion recognition efficiency is improved. And the accuracy and initiative of emotion management in a complex environment are enhanced.

Provided is a method for token-based representation and learning of robotic perception data based on a graph neural network, comprising: obtaining a plurality of types of perception data of a robot; performing token-based representation according to types of the plurality of types of perception data; constructing an initial feature graph based on the plurality of types of perception data after the token-based representation; learning a compact representation of the initial feature graph based on an autoencoder and reconstructing a graph structure; after the autoencoder completes learning of the graph structure, fixing the graph structure; and converting the plurality of types of perception data into node feature vectors, constructing a feature graph based on the graph structure, and performing numerical encoding on each of the node feature vectors by utilizing the graph neural network to obtain a representation of high-dimensional feature vectors of the plurality of types of perception data.

The invention belongs to the related technical field of brain image processing, and provides an Alzheimer's disease classification method and system based on topology perception and a group hypergraph in order to solve the problem of inaccurate classification of the Alzheimer's disease in the prior art. Constructing a dynamic function connection network sequence through a sliding window strategy; a local topology perception encoder and a global topology perception encoder are respectively used for extracting local topology features and global topology features of each time window, deep interaction and fusion are carried out, and comprehensive feature representation of a tested level is generated; according to the method, each subject is used as a hypergraph node, hyperedges are constructed on the basis of comprehensive feature representation of a subject level and by combining feature similarity calculated by diffusion tensor imaging features and clinical embedded features of the subject, then a group hypergraph is constructed, a classification result is obtained by using a hypergraph neural network, and the early classification diagnosis accuracy of the Alzheimer's disease is effectively improved.

The invention provides a port facility management and maintenance large model report review agent construction method and system, and the method comprises the steps: collecting a cross-modal original data set, constructing a multi-modal feature fusion perception layer, and generating facility damage feature alignment data; constructing a cognitive neural network four-level architecture, and generating an inference decision tree; constructing a root cause-path-result causal chain, and generating a fault attribution analysis report; constructing a prediction-intervention-verification active defense closed loop, and generating a Pareto optimal maintenance strategy set; and executing an intervention strategy and feeding back a verification result by using the digital twin verification platform and the block chain evidence storage system. According to the method, cross-modal data deep semantic alignment is realized through the multi-modal feature fusion perception layer, a data island is broken, and the damage feature extraction accuracy is improved; an interpretable causal chain is constructed based on related architecture and modules, the decision black box problem is solved, and a maintenance strategy has causal logic support; and real-time verification and credible tracing of a strategy effect are realized through an active defense closed loop.

The invention discloses a force sense feedback control method for an intelligent mechanical arm, which comprises the following steps of: 1, acquiring data through a multi-modal sensor and fusing the data to obtain a multi-dimensional perception vector; 3, calculating a force sense tracking error and a change rate and triggering an event-driven control decision mechanism; 4, designing a nonlinear compensation control rule and outputting a control torque instruction, wherein a control system comprises a multi-mode sensing module, a dynamic prediction module, an event-driven control module and a cooperative calculation module; according to the method, multi-mode sensing information is fused with the lifting force sense representation capacity, advanced adjustment is achieved in combination with a dynamic force sense prediction mechanism, event-driven control is used for reducing calculation redundancy, robustness to complex interference is enhanced through a nonlinear compensation strategy, and finally high-precision and low-delay force sense control of the mechanical arm in a dynamic interaction scene is achieved.

The invention relates to the technical field of medical image classification, in particular to a myopic macular traction lesion grading method and system. The method comprises the following steps: taking a convolutional neural network, a direction perception attention module and a classifier which are connected in sequence as an MTM classification model; a direction perception attention module extracts weight information of a space position through a direction perception space attention module, and a channel attention module is used for extracting weight information of a channel; an MTM classification model is used as a backbone network, direction perception attention modules and auxiliary branches which are connected in sequence are arranged after first M-1 feature coding stages of a convolutional neural network, and a self-distillation model is constructed; by combining a structural knowledge distillation strategy based on multi-stage feature fusion, a historical knowledge distillation strategy based on a linear growth mechanism and a category perception comparison learning strategy, multi-angle feature information interaction is fully utilized, multi-angle information collaborative optimization is realized, and the classification precision of the MTM classification model is effectively improved.

The invention discloses an intelligent perception analysis system for the real-time participation degree and cognitive state of a mathematics classroom. The intelligent perception analysis system comprises a multi-source data acquisition module, a fusion analysis engine module, a real-time feedback module and an offline optimization module. The method has the beneficial effects that a dynamic participation index is innovatively designed, and an attention attenuation resetting mechanism is introduced; the real-time feedback module generates a cognitive thermodynamic diagram of HSL color mapping to position group obstacle points, and triggers self-adaptive question pushing; and the off-line optimization module dynamically updates the edge weight of the knowledge graph through error co-occurrence analysis, and early warns a cognitive confusion relationship without textbook association. According to the method, the limitation of single-mode perception is broken through, the problem solving step quality visual diagnosis and the cross-cycle cognitive impairment prediction are realized, a teaching closed loop of'multi-source perception-hierarchical quantification-real-time intervention-knowledge evolution 'is formed, and the mathematical classroom cognitive state analysis precision and the teaching intervention timeliness are remarkably improved.

The invention provides a brain organism closed-loop rehabilitation system and a control method thereof, belongs to the field of brain-computer interfaces and rehabilitation medical treatment, and is used for solving the problems of insufficient brain state perception, poor adaptability and low robustness of a brain organism rehabilitation system in related technologies. The method comprises the following steps: synchronously acquiring multi-modal physiological signals by cooperating with physiological signal monitoring, task induction and regulation and control equipment modules, removing artifacts through preprocessing and redundancy check, extracting neuroplasticity characteristics, dynamically adjusting weight decoding brain activity intentions, regulating and controlling stimulation parameters in a closed loop, and training an optimization model in combination with a multi-center federation; accurate and highly-adaptive rehabilitation regulation and control are realized, and the rehabilitation effect and the model generalization are improved.

The invention discloses an end-to-end automatic driving method based on hierarchical uncertainty estimation and dynamic safety response, and belongs to the technical field of intelligent driving. The method comprises the steps of environment observation data acquisition and preprocessing, perception information enhancement, adaptive space-time attention fusion and trajectory prediction, hierarchical uncertainty estimation and dynamic safety response, experience pool storage and end-to-end deep learning optimization. By dynamically integrating the multi-modal features of the RGB image and the LiDAR point cloud, a space-time dependency relationship is captured, and high-precision trajectory prediction is realized; meanwhile, layering quantification cognition and random and time sequence uncertainty are carried out, and a five-level safety response strategy is triggered in combination with an environment self-adaptive threshold value; and carrying out reinforcement learning by adopting priority experience playback and a multi-task loss function. According to the method, the limitation of traditional fixed weight fusion is overcome, the prediction accuracy and the system safety are improved, the average displacement error (ADE) can be reduced in a complex traffic scene, the uncertainty calibration degree is improved, and the method is suitable for real-time decision making of an automatic driving vehicle.

The invention relates to the technical field of knowledge maps, in particular to a few-sample knowledge map completion method and system fusing relation perception information bottleneck. The method comprises the following steps: S1, preprocessing an input triple; s2, building a global aggregation module, and updating entity embedding; s3, establishing a relationship aggregation module, and updating relationship embedding; s4, establishing a relationship-based information bottleneck module, filtering noise irrelevant to tasks, and meanwhile, retaining relationship-specific information; s5, building an EM attention pooling module, adaptively aggregating multi-path semantic representation, and highlighting the correlation between the entity and the relationship; and S6, establishing a score calculation module, calculating a triple score and outputting the triple score. The invention provides a few-sample knowledge graph completion method and a few-sample knowledge graph completion system fusing relation perception information bottleneck, which are used for solving the problems of insufficient relation and entity representation coupling, high redundant information interference and difficulty in modeling due to high-order relation dependence in a knowledge graph completion task, and realizing efficient inference of potential relation facts.

The invention relates to an infantile autism classification method based on a double-branch functional topological graph neural network. The infantile autism classification method can realize the classification of the infantile autism by using functional magnetic resonance imaging data. According to the provided autism classification network, long-distance connection and short-distance connection are divided based on the shortest path between brain intervals, then an exponential decay mask is introduced through a functional topological graph Transform branch to adjust attention weight and accurately extract long-distance dependency features, a graph isomorphic network in the other branch is subjected to multiple neighborhood aggregation operations, short-distance dependency features are captured, and the short-distance dependency features are extracted. According to the method, multi-scale dependence of the brain network is extracted in parallel through a double-branch structure, information redundancy is reduced by means of a topology perception attention mechanism, and the adaptive ability of the model to the heterogeneous brain network is improved by using the adaptive fusion module, so that multi-scale dependence of the heterogeneous brain network is balanced in a self-adaptive manner. The classification accuracy is remarkably superior to that of an existing mainstream method, objective and efficient technical support is provided for autism diagnosis, and high interpretability is achieved.

The invention relates to the technical field of robot control, in particular to a cross-modal perceptual driving body robot compliance control system which comprises the steps that a sensor is adopted to synchronously collect environment information, multi-source data bias is eliminated through a space-time alignment algorithm, a radial basis function neural network is adopted to analyze multi-modal fusion features, and a multi-modal model is obtained; human operation intention probability distribution is extracted to decompose a task into a path planning layer and a motion control layer, a collision-free trajectory is generated through an RRT algorithm, a high-fidelity physical engine is adopted to construct a virtual interaction scene, and robot learning results are shared through federal learning. According to the method, the problems of inaccurate perception, incoordination between intention recognition and interaction control, difficulty in control strategy verification, slow new task adaptation and difficulty in multi-robot learning result sharing caused by multi-source data deviation and large multi-modal semantic difference of the body robot in a complex environment are solved.

The invention relates to the technical field of artificial intelligence, in particular to an interaction system and method with an emotion dynamic evolution memory function, a medium and a processor. The interactive system comprises a sensing layer, a processing layer and a decision-making layer, the perception layer comprises a semantic text coding module and a fusion processing module; the semantic text coding module outputs the acquired audio as semantic features and high-dimensional features, and performs linear space conversion processing; the fusion processing module and the data output by the semantic text encoding module are linearly spliced to form multi-modal feature vectors, and the multi-modal feature vectors are classified into the current emotion state of the intelligent agent and the emotion state of the user; the processing layer is used for correcting the data output by the sensing layer; and the decision-making layer is used for carrying out information decision-making and storage on the data output by the processing layer. The technical problem that in the prior art, the number of labels is limited, undefined interaction modes are difficult to process, and consequently the character of a robot is limited is solved.

The invention discloses a life omics knowledge graph construction method and system based on a large language model, and relates to the technical field of computer data processing. The method comprises the following steps: acquiring and preprocessing multivariate life omics data, wherein the multivariate life omics data at least comprises an unstructured biomedical text; performing information extraction on the text data based on a large language model to obtain entity mention and relation description; standardizing and normalizing the entity mention and the relation description on the basis of a large language model in combination with an external knowledge base to obtain a standard knowledge triple; and storing the triple into a graph data storage system, and constructing the knowledge graph. According to the method, the powerful natural language understanding ability of the large language model is utilized, efficient information extraction is achieved through structured prompt or field fine tuning, the model is innovatively utilized for entity standardization of relation perception, and the accuracy of knowledge fusion is remarkably improved.

The invention relates to the technical field of intelligent medical auxiliary diagnosis and neural function automatic evaluation, in particular to an NIHSS score automatic evaluation system for a stroke patient. Comprising a multi-mode induction and perception unit which is used as a front-end data entry and is used for collecting patient response in real time to generate a video stream containing depth and color information and a synchronous audio stream; the dynamic reference calibration unit is used for extracting kinematic characteristics to construct an individualized nerve reference template; the neural motion spectrum decomposition unit is used for generating a spectrum pathological feature vector for distinguishing myasthenia and ataxia; the opposite-side image rejection analysis unit is used for generating compensation and driving confidence for representing a real nerve driving intention; the cross-modal reflection analysis unit is used for generating a sensory pathway integrity index according to the nerve conduction velocity difference; and the collaborative scoring decision engine is used for mapping the multi-modal features into standardized NIHSS scores. According to the method, the interference of age and basic physique on scoring is effectively eliminated, and a high-precision comparison reference can be provided for subsequent abnormal judgment of the affected side.

The invention discloses a cerebral apoplexy focus detection method and system, and belongs to the technical field of medical image detection. Extracting a fusion feature map of the brain image; for each region type, obtaining a representative feature which has the highest similarity with the feature at each pixel point position in the fused feature map in the class prototype set and carries a corresponding region type label, and further determining the region type to which each pixel point position in the fused feature map belongs so as to obtain a corresponding pseudo-label map; the class prototype set comprises representative features of different region types and is obtained in the training process of the system, and feature distribution of different region types in the memory bank is calculated through a Gaussian mixture model; for each region type, sampling is carried out based on feature distribution of the region type, and a plurality of representative features are obtained; the prototype-like set in the cerebral apoplexy detection method has real global context perception ability, can clearly distinguish the focus and various complex background structures, and can accurately realize cerebral apoplexy detection.

The invention discloses a multi-modal sensing fusion dam body structure state intelligent diagnosis and early warning system and method and application. The system comprises a multi-modal sensing network composed of a space deformation monitoring subsystem, an internal response monitoring subsystem and an environment quantity monitoring subsystem; a data fusion and feature extraction module based on a space-time diagram attention network, which is used for deeply mining a complex space-time coupling relationship between multiple measurement points and multiple physical quantities; the health state diagnosis module based on multi-task learning can synchronously output dam body structure health indexes and abnormal types of key areas; and a future state evolution prediction and multi-stage early warning module based on a Transform model. According to the method, digital twinning of the dam body is constructed, a physical entity and an information model are closely combined, a full-chain closed loop from multi-dimensional perception to intelligent diagnosis to prospective early warning is achieved, and the accuracy, comprehensiveness and timeliness of dam body structure state evaluation are improved.

The invention discloses a spatial non-cooperative target form reconstruction-oriented perception planning control integration method, which comprises the following steps of: extracting local semantic features of a target component in a single-view observation image through a pre-trained semantic segmentation network, coding the local semantic features and RGB (Red, Green and Blue) information into an MLP (Markup Language Protocol) of NeRF, perceiving a target geometric form and component-level semantics, and obtaining a component-level semantic feature of the target component; the perception result is optimized along with fly-around observation, evaluation is carried out, and an uncertainty thermodynamic diagram is generated; based on the uncertainty thermodynamic diagram, a space observation value function is constructed, spacecraft dynamics and view field constraints are combined, and an initial fly-around trajectory is generated by adopting an information gain weighted three-dimensional A * algorithm and optimized in real time; and designing a trajectory tracking control law and an attitude stability control law based on a time synchronization stability theory, and controlling the spacecraft to execute the optimized fly-around trajectory. According to the invention, a perception-planning-control closed-loop execution system is realized, and a closed-loop collaborative process of perception-evaluation-planning-control-re-perception is formed.

The invention particularly relates to a multi-level self-cognition system based on a large model, and relates to the technical field of large models. A neural symbol world model module; a large language model cognition core module; and a hierarchical decision planning system module. According to the method, deep integration of perception, cognition and decision making is achieved through the hierarchical fusion architecture, and compared with the prior art, the method has remarkable advantages; the multi-modal perception encoder adopts layered encoding and a cross-modal attention mechanism, so that the semantic alignment problem of multi-source perception data is effectively solved, and the understanding ability of the system to a complex scene is greatly improved; according to the neural symbol world model, the neural network and symbol reasoning are combined, the limitation of a pure neural network method in physical modeling is overcome, meanwhile, the calculation complexity of a pure symbol system is avoided, and efficient and accurate environment characterization and prediction are achieved.

The invention discloses a driving track planning method and related equipment, and relates to the technical field of auxiliary driving, and the method comprises the steps: obtaining environment perception data and driving track data of a target vehicle; performing fusion reasoning on the environment perception data and the driving track data through a target large language model to generate a reasoning result text; matching traffic rules and driving experience related to the reasoning result text from a preset cognitive knowledge base through the target large language model; according to the traffic rule and the driving experience, generating a task planning instruction for the target vehicle through the target large language model; and determining a planned trajectory of the target vehicle based on the task planning instruction and the environmental perception data. According to the method, perception data and knowledge information are fused through a large language model, an intelligent decision closed loop from environment understanding to task planning to track generation is realized, and the accuracy, compliance and safety of automatic driving track planning can be improved.

The invention relates to the technical field of brain tumor image segmentation, in particular to a brain tumor segmentation method and system based on anatomical perception symmetric comparison and cross-modal migration. The method comprises the following steps: carrying out data preprocessing on acquired multi-modal MRI image data; constructing a brain tumor segmentation model based on anatomical perception symmetric comparison and cross-modal migration; performing model training based on a two-stage decoupling training strategy; and performing model reasoning by using the trained model, and outputting a brain tumor segmentation result. Through a self-supervised learning framework, pre-training is carried out by using unmarked MRI data, dependence on a large-scale marked data set is greatly reduced, the problems of time consumption and high cost of medical image marking are solved, and the applicability of a model in a limited data scene is improved.

The invention discloses a student psychological risk perception method based on multiple modes, and relates to the technical field of emotion calculation and intelligent education. The method comprises the following steps: firstly, extracting a facial expression feature vector and a voice intonation feature vector respectively by using a convolutional neural network and Fourier transform through a collected video stream and an audio stream; then adaptive denoising processing is carried out on environmental interference, timestamp alignment and dynamic time warping are carried out on the denoised multi-modal data, time sequence synchronization is ensured, and corrected multi-modal sequence data are formed; then, dynamic emotion track features are extracted from the sequence data, a preliminary emotion state label is generated by comparing the dynamic emotion track features with a baseline threshold value, and the threshold value is adaptively updated in combination with historical data so as to improve the judgment accuracy; and finally, aggregating the emotional state labels of a plurality of students to generate a visual group emotional thermodynamic diagram so as to realize macroscopic perception of group psychological risks. The accuracy, robustness and visualization degree of student psychological state analysis are effectively improved, and an efficient technical means is provided for campus psychological early warning.

The invention relates to a wharf safety intelligent monitoring method, device and equipment based on digital twinning and a medium. The method comprises the following steps: firstly, carrying out space-time alignment fusion on multi-source heterogeneous original data of a wharf site to generate a dynamic digital twinborn scene; extracting and matching entity relationships and events based on the dynamic digital twinborn scene and a pre-constructed wharf operation knowledge graph, and generating a dynamic knowledge situation sub-graph; performing graph neural network coding processing on the dynamic knowledge situation sub-graph to obtain coding features of nodes in the graph; risk and conflict analysis is carried out based on the coding features, and a current risk list and a potential conflict prediction list are generated; and generating security alarm information based on the list. By adopting the method, safety monitoring can be improved from a perception level based on simple rule matching to a cognition level fusing semantic understanding and context association reasoning, so that false alarm and missing alarm are remarkably reduced, and accurate early warning of potential risks in a complex working environment is realized.

The invention discloses a self-adaptive pipetting system and method based on multi-mode sensing fusion. The system comprises a PLC (Programmable Logic Controller), a fusion multi-mode sensing module, a high-throughput execution module, a self-adaptive protocol matching module and a man-machine interaction module. According to the invention, through multi-channel cooperation and continuous transmission, the experiment efficiency is greatly improved; multi-modal sensing data are fused, and real-time closed-loop control and dynamic compensation are performed by adopting algorithms such as fuzzy PID (Proportion Integration Differentiation) and the like, so that the pipetting volume precision reaches + / -1%, and the repeatability is improved by 20-30%; a protocol feature library and an intelligent matching algorithm are built in, and experimental parameters can be automatically analyzed, matched and optimized; equipment state and process parameters are monitored in real time, faults and pollution risks are automatically early warned, and the operation safety and reliability are remarkably improved in combination with automatic anti-pollution design; a PLC is deeply fused with a sensing module and a decision module, a sensing-decision-execution intelligent closed loop is constructed, and an accurate and reliable integrated solution is provided for automatic pipetting.

The invention belongs to the technical field of fetal brain age prediction, and relates to an earthworm cerebellar fetal brain age prediction method and system based on multi-modal feature fusion, an MST-Mamba segmentation network is adopted, and local-global aggregators are embedded in each level of an encoder, so that the cooperation of local detail capture and global semantic modeling is realized; meanwhile, a dynamic channel fusion device is deployed at the jump connection part of the encoder and the decoder, so that the problems of fuzzy boundary, missed division, wrong division and the like are avoided; through three parallel branches of a multi-granularity form-texture collaborative perception architecture, two types of explicit features of macroscopic geometry and topological form and implicit features of microscopic texture are synchronously extracted, and comprehensive characterization of the development features of the earthworm cerebellar part is realized; the explicit features are subjected to standardized calibration and then spliced and fused with the implicit features in the channel dimension, the problems that multi-modal feature fusion is insufficient and calibration lacks are solved, finally prediction is conducted through a multi-layer perceptron regression head, and the accuracy and stability of the brain age prediction result are guaranteed from the source.

The invention discloses a brain-computer interface interaction control device and method based on multi-mode brain signal fusion. The device comprises a perception acquisition layer, an edge processing layer, a fusion decoding layer, a control application layer and a closed-loop optimization cloud platform. The sensing acquisition layer synchronously acquires multi-modal brain signals; the edge processing layer adopts a condition alignment time sequence diffusion model to carry out signal enhancement and completion; the fusion decoding layer realizes multi-modal feature fusion and intention decoding through a lightweight graph neural network; the control application layer provides adaptive control mapping and multi-mode feedback; and the closed-loop optimization cloud platform realizes continuous performance optimization through federated learning and incremental learning. According to the method, the problems of single signal, insufficient precision and poor practicability of a traditional brain-computer interface are effectively solved, the decoding precision, the real-time performance and the individuation degree of the system are remarkably improved, and the method can be widely applied to the fields of intelligent home control, medical rehabilitation training, industrial control and the like.

The invention discloses a zero-sample multi-modal relation extraction method based on a multi-modal large model, which comprises the following steps of: constructing prototype information containing tag names, descriptions and aliases for seen and unseen relation categories, and encoding and aggregating the prototype information into prototype vectors; extracting feature representation of a training sample through a multi-modal large model, and performing fine adjustment on the model by updating low-rank adapter parameters based on the feature representation and a known category prototype vector; and for the input containing the unseen category, extracting the features of the input by using the fine-tuned model, and completing relation identification in combination with the prototype vector of the unseen category. According to the method, the structured prototype knowledge is injected into the low-rank fine tuning process, so that the model keeps semantic perception of the unseen relationship while absorbing the domain knowledge, the relationship extraction accuracy and generalization ability in a zero sample scene are remarkably improved, and the data annotation cost is effectively reduced.

The invention discloses a semantic and physical perception-based unmanned aerial vehicle navigation extension method during testing. The method comprises the following steps: S1, constructing multi-modal navigation input information of an unmanned aerial vehicle; s2, based on multi-modal navigation input information, judging whether a unique candidate navigation task target conforming to the description of the navigation task instruction exists in the current view image or not, and correspondingly constructing an initial candidate waypoint set; s3, generating corrected candidate waypoints and a corrected candidate waypoint set by using the visual language model; s4, performing multi-dimensional quantitative scoring on the corrected candidate waypoints in the corrected candidate waypoint set; and S5, selecting the corrected candidate waypoint with the highest multi-dimensional quantitative score as an optimal waypoint, and generating a track containing a continuous pose sequence based on the optimal waypoint to control the unmanned aerial vehicle. According to the method, the navigation planning and self-correction capability can be enhanced, and the navigation decision accuracy and reliability are effectively improved.

The invention provides a contract input method and device and a storage medium. The method comprises the steps of determining a target contract file; executing optical character recognition on the target contract file in multiple dimensions to obtain multiple contract elements; contract semantic information is added to the multiple contract elements according to a large language model; checking the multiple contract elements carrying the contract semantic information according to contract specifications; and if the multiple contract elements carrying the contract semantic information are successfully verified, mapping the multiple contract elements into a database according to the contract semantic information. According to the embodiment of the invention, through the perception-cognition integrated architecture design, the full-automatic input of the element from the target contract file to the structured contract is realized, the semantic analysis of the contract is provided by the large language model, the dependence on a rule engine is effectively reduced, the adaptability is strong, the precision is high, the maintenance cost is low, and the contract input efficiency is effectively improved.