54 results about "Adaptive reasoning" patented technology

The invention relates to a visual transformer substation intelligent inspection system and method based on a multi-mode large language model, and belongs to the technical field of power system intelligence. The system obtains image, temperature, vibration and noise data of substation equipment in real time through a multi-modal data acquisition module, and performs preprocessing and fusion. A high-precision three-dimensional semantic model is constructed by using a three-dimensional dynamic modeling module, and the device attributes are automatically labeled by fusing LLM semantic understanding capability. A multi-modal large language model (LLM) engine is combined with cross-modal feature extraction, a dynamic knowledge base and a self-adaptive reasoning unit to realize accurate diagnosis of equipment faults. An augmented reality (AR) interaction module displays the real-time state of equipment through AR glasses and supports natural language interaction. The self-interpretation decision support module generates interpretable fault reports and maintenance suggestions, and the communication and feedback module is responsible for data uploading and remote alarm. According to the method, multi-dimensional perception, dynamic knowledge reasoning and self-adaptive decision support of the equipment state are realized, the intelligent level of substation inspection is remarkably improved, the inspection efficiency is improved by more than 40%, the omission ratio is reduced to less than 1%, and rapid diagnosis of more than 95% of novel faults is supported.

The invention provides a complex scene-oriented end-to-end semantic extraction system, belongs to the technical field of artificial intelligence and natural language processing, and realizes cross-modal information association through a multi-source heterogeneous data fusion module to construct a dynamic semantic network model. A hierarchical attention mechanism is adopted to carry out context-aware coding on unstructured input, and unsupervised pre-training and a weak supervised fine tuning strategy are combined to optimize a feature representation space. And designing an adaptive inference engine, automatically switching semantic analysis paths based on scene complexity, and generating a structured output result. According to the method, the dependency on specific knowledge in the field is reduced, the semantic understanding generalization ability in a complex scene is remarkably improved, high-precision analysis performance can still be kept in a low-resource environment, meanwhile, calculation resource consumption is reduced, and the method is suitable for practical application scenes with multi-language mixing, serious noise interference and high real-time performance requirements.

The invention relates to the technical field of intelligent building safety and artificial intelligence, and discloses an intelligent building dangerous case identification and grading early warning system based on multi-source data fusion, and the system comprises a data collection and management module which is used for obtaining and outputting a standardized multi-dimensional time sequence data flow and a building logic relation; the dynamic state modeling module is used for establishing a normal state model library for different space-time scenes; the event processing and analysis core module is used for calculating a risk entropy value representing an abnormal degree and a risk entropy gradient vector indicating an abnormal source; the adaptive reasoning and detecting module is used for performing source reasoning and risk diffusion prediction according to the risk entropy gradient vector; and the early warning and decision output module is used for judging the risk evolution situation and outputting a graded early warning signal. According to the method, the source of the dangerous case can be accurately positioned, the diffusion can be predicted, intelligent conversion from passive alarm to active detection is realized, and the accuracy and timeliness of early warning and the scientificity of decision making are remarkably improved.

The invention discloses a real-time dynamic reasoning optimization method and system for an AI gateway, belongs to the technical field of artificial intelligence and edge computing, and aims to solve the technical problems of how to optimize the real-time dynamic reasoning capability of the AI gateway, improve the real-time reasoning efficiency of the AI gateway under complex tasks and improve the real-time reasoning efficiency of the AI gateway under complex tasks. According to the technical scheme, the method comprises the following steps: model lightweight preprocessing: constructing an AI model by adopting a lightweight network architecture, and reducing the model calculation complexity and the number of parameters by using a deep separable convolution and channel shuffling technology; carrying out model compression processing of pruning, quantification and knowledge distillation on the constructed model; real-time dynamic reasoning and decision making: in the operation process of the AI gateway, monitoring the complexity of input data, the real-time requirement of a current task and the use condition of computing power resources of the AI gateway in real time, and performing self-adaptive reasoning strategy decision making according to a monitoring result; reasoning engine and compiling optimization; data preprocessing and edge intelligence are realized; and edge-cloud collaborative reasoning.

The invention provides a nuclear power plant anomaly detection method and system based on auto-encoder edge calculation, and belongs to the technical field of nuclear power plant anomaly detection. Comprising the following steps: establishing multi-dimensional time sequence data by using key operation parameters of nuclear power plant equipment, and preprocessing the multi-dimensional time sequence data into a time window sample; calculating a reconstruction error according to the time window sample by using an auto-encoder and generating an abnormal score; performing anomaly judgment according to the reconstruction error and the anomaly score by adopting a threshold self-adaptive reasoning algorithm, and feeding back an anomaly judgment result to the central server; and updating the model parameters of the auto-encoder in combination with incremental learning, and issuing the model parameters to the edge nodes through a streaming updating mechanism to realize updating of the auto-encoder. According to the invention, continuous monitoring and adaptive optimization of the operation state of the nuclear power plant equipment can be realized.

The invention relates to the technical field of artificial intelligence and natural language processing, and discloses a large language model reasoning enhancement method based on multilayer reasoning chain verification, which comprises the following steps: constructing a multilayer reasoning architecture based on a pre-trained large language model, and establishing a reasoning state space and a reasoning step transfer function; receiving an input question, generating a parallel inference chain according to the multilayer inference architecture, and determining an inference step sequence and a confidence score of the parallel inference chain; performing consistency score calculation and logic consistency check according to the parallel reasoning chain, and determining a final answer according to the consistency score and the confidence score; and calculating complexity according to the input problem so as to perform adaptive reasoning depth adjustment, determining a reasoning process according to the reasoning depth, and outputting a complete reasoning result. According to the method, a multi-layer parallel reasoning architecture is constructed, and a reasoning chain cross validation mechanism and a self-adaptive reasoning depth adjustment strategy are combined, so that precise processing of a complex reasoning task is realized.

The invention provides a document structuring task processing method and system and a readable storage medium. The method comprises the steps of firstly obtaining a structured task request and corresponding unstructured document data, then dynamically determining an adaptive reasoning mode in combination with a task demand, a current time period and a load state of a preset processing model group, and associating the two modes with an exclusive target reasoning model in the model group respectively. In a high-load period or when a task needs to be quickly responded, a result-oriented reasoning mode is started, a structured document is directly generated by a correlation model, the process is simplified, and resource consumption is reduced; and when a load period or a task needs a clear basis, switching to an interpretable reasoning mode, synchronously outputting a structured document and a complete reasoning process by the association model, and clearly displaying a processing logic. According to the dynamic adaptation mechanism, the real-time performance of a high-demand scene is guaranteed, the interpretability defect of a model is overcome, and the real-time performance and decision transparency of text structured processing are doubly improved.

Embodiments provide an apparatus of a wireless communication network, the wireless communication network using one or more Artificial Intelligence / Machine Learning, AI / ML, models for one or more use cases, wherein the apparatus is to determine an inference time for one or more of the AI / ML models to be used in one or more network entities of the wireless communication network.

The invention discloses a dynamic tactical knowledge graph construction and target attackable value analysis system and method based on an environment embedded graph neural network, and belongs to the technical field of military intelligent decision making and firepower command control. The system constructs a tactical knowledge graph fused with firepower application expert experience for tactical rule recessive and environmental constraint dynamic problems in target threat assessment. Based on an entity-relation-environment attribute triple structure, an ontology model covering a typical combat scene is established, and dynamic quantitative analysis of target attackable value is supported. An environment embedded graph neural network is innovatively designed, a battlefield environment is used as an internal structure variable of a graph, and deep coupling and self-adaptive reasoning of knowledge representation and a real-time situation are achieved. The system can comprehensively consider information such as enemy conditions, my conditions, typical scenes and the like, realizes accurate evaluation of multi-target threats and values, assists a commander in accurately capturing important high-threat high-value targets, and quickly forms a wave-order striking target list.

The application relates to the technical field of laser system control, and discloses a multi-stage PID parameter regulation and control method for a laser frequency locking system based on deep learning, which comprises the following steps: acquiring an error signal time sequence of a PDH laser frequency locking system; outputting a frequency locking state score and fast-slow double-loop PID parameters through a deep learning model; judging whether the frequency locking state score and the fast-slow double-loop PID parameters both satisfy corresponding preset conditions; if the frequency locking state score and the fast-slow double-loop PID parameters do not both satisfy the corresponding preset conditions, iteratively optimizing the deep learning model until the frequency locking state score and the fast-slow double-loop PID parameters both satisfy the corresponding preset conditions; and outputting the fast-slow double-loop PID parameters of the optimized deep learning model. The application can realize online self-adaptive reasoning and closed-loop optimization of fast-slow double-loop multi-stage PID control parameters, and improve the intelligent level and the ability to adapt to complex working conditions of the PDH laser frequency locking system.

The present application belongs to the technical field of rotating machinery state monitoring and industrial deployment, and particularly relates to a reducer input shaft end-to-end rotating speed adaptive inference method based on variable-length sequence. In view of the fact that actual monitoring data is a continuous sequence of any length, and a deep learning model usually relies on fixed-length input, and traditional sliding window inference is prone to introducing phase discontinuity and splicing artifacts at the boundary, the present application establishes a deployment paradigm of "fixed-length training and variable-length inference". The continuous variable-length sequence is divided by a sliding window with an overlap rate, and a backtracking interception strategy is designed for the case of insufficient end sequence. After batch inference using a pre-trained model, the local prediction results are integrated and smoothed by using an overlap addition mechanism and a weighted average algorithm. The present application effectively eliminates the Gibbs effect at the window boundary, ensures the phase integrity of the terminal, and realizes seamless and smooth rotating speed curve reconstruction of continuous monitoring data of any length.

The invention discloses an electric power intelligent number asking method fusing adaptive reasoning and SQL (Structured Query Language) rectification, which relates to the technical field of electric power system intelligent number asking and comprises the following steps: A, converting a natural language query request proposed by a user into an SQL query expression conforming to an electric power business scene; b, introducing an SQL correction scheme based on abstract AST, performing structured analysis on the SQL query expression, identifying logic errors existing in SQL grammar and semantics, and performing correction and optimization; and C, the big language model carries out structured summarization on the original data obtained by query, and feeds back the data to a user in a natural language form. According to the method, the defects in the prior art can be overcome, and the data query efficiency and decision support capability of the power regulation and control business are improved.

The invention provides a self-adaptive reasoning visual question answering system and method based on humanity and geography common sense enhancement, and relates to the technical field of artificial intelligence, and the system comprises an obtaining module which is used for obtaining an input image and an input question; the image content and context analysis module is used for carrying out image content and context analysis on the input image and the input question to obtain context representation; the humanistic geography common sense retrieval and multi-hop reasoning module is used for performing problem decomposition on the context representation to generate a plurality of sub-problems, performing humanistic geography common sense retrieval and multi-hop reasoning on each sub-problem, and outputting a reasoning chain containing sub-problem-clue pairs; and the answer generation module is used for decoding the input question and the reasoning chain to obtain an answer of the input question. According to the method, the problems of low reasoning efficiency and insufficient accuracy of the existing humanity geography visual question-answering method are effectively solved.

The application discloses a kind of multi-modal psychological intention understanding methods based on zero sample and intention perception alignment, it is related to mental health monitoring and intelligent dialogue system technical field, including the following steps: step one, acquisition contains text, audio and video time synchronization dialogue data, constitute multi-modal data;Step two, construct multi-target fusion model, obtain multi-target joint optimization function by the multi-modal data of time sequence alignment combined with late fusion strategy;Step three, by adaptive reasoning strategy, multi-modal data is distributed according to dynamic calculation, and the predicted intention classification result is obtained.The application has the characteristics that it can be started and run without relying on large-scale artificial labeling data, fundamentally solves the bottleneck of high data labeling cost and difficult acquisition in the field of psychological dialogue, realizes accurate fusion of cross-modal information through time sequence alignment and significantly improves the accuracy of analysis.

This invention discloses a multimodal psychological intent understanding method based on zero-shot and intent-aware alignment, relating to the technical field of mental health monitoring and intelligent dialogue systems. The method includes the following steps: Step 1: Collecting time-synchronized dialogue data containing text, audio, and video to form multimodal data; Step 2: Constructing a multi-objective fusion model by combining time-aligned multimodal data with a late-stage fusion strategy to obtain a multi-objective joint optimization function; Step 3: Using an adaptive inference strategy, dynamically allocating the multimodal data to obtain predicted intent classification results. This invention features the advantages of being able to start and run without relying on large-scale manually labeled data, fundamentally solving the bottlenecks of high data labeling costs and difficulty in obtaining data in the field of psychological dialogue, achieving accurate fusion of cross-modal information through time alignment, and significantly improving the accuracy of analysis.

The application discloses a traditional Chinese medicine large model prescription generation method based on double-layer experience memory and adaptive reasoning. The method carries out structured processing on historical case data, forms standardized case-level memory units, and constructs diagnosis and treatment principle memory arranged according to two-level grouping, which together constitute double-layer experience memory. With patient four-examination abstract as retrieval basis, double-channel retrieval is executed and comprehensive retrieval score is calculated, and then diagnosis and treatment thinking card is obtained through syndrome type secondary positioning; the meta-cognition controller dynamically selects four kinds of prescription reasoning modes according to the meta-decision state vector, assembles and enhances the context to generate the initial prescription, relies on the comprehensive reward to complete multi-dimensional evaluation and multi-redundancy safety check, and finally updates the double-layer experience memory and adaptive reasoning strategy in a closed loop. The application improves the prescription traceability and syndrome accuracy, guarantees the safety of drug use, realizes the living inheritance of traditional Chinese medicine diagnosis and treatment experience and the continuous improvement of prescription reasoning ability.

The application relates to the technical field of pattern recognition, in particular to an end-side time sequence prediction method and device based on a light-weight large language model and electronic equipment, the method comprising: acquiring time sequence input data to be predicted and performing preprocessing; performing structured knowledge distillation and channel-level structure pruning on a pre-trained large language model; performing reinforcement learning fine-tuning on a light-weight backbone network by using an online reinforcement learning fine-tuning framework with low memory occupation on an end-side device; and performing closed-loop collaborative optimization on compact time sequence feature representation and an adaptive inference strategy to obtain a time sequence prediction result. Through the collaborative design of a time sequence perception structure distillation module and an end-side adaptive reinforcement fine-tuning module, the time sequence inference capability of the large language model is migrated to a light-weight backbone network with a parameter quantity of 1B-3B, and continuous adaptive updating on the end-side device is realized, so that the resource constraints of end-side deployment are met while the prediction accuracy is maintained.

Embodiments provide an apparatus of a wireless communication network that uses one or more artificial intelligence / machine learning (AI / ML) models for one or more use cases, where the apparatus determines an inference time of one or more of the AI / ML models to be used in one or more network entities of the wireless communication network.

The invention relates to the technical field of oral medical image processing, in particular to a lightweight oral image AI diagnosis system, which comprises front-end acquisition equipment and a server, the front-end acquisition equipment is used for acquiring an oral cavity image to be diagnosed, and the server comprises a data storage module, a model training module, a self-adaptive reasoning module and a result output module; the data storage module is used for storing historical oral images and corresponding diagnosis results; the model training module is used for constructing a teacher model and student model architecture, and enabling a lightweight student model to learn the feature extraction capability of a high-precision teacher model through a distillation loss function; the self-adaptive reasoning module is used for dynamically adjusting a reasoning strategy according to the difficulty level of the image; and the result output module is used for generating a clinical diagnosis report including tooth position identification and focus types and positions. According to the system, the balance of light weight and high precision is realized, the reasoning efficiency is improved, the clinical application threshold is reduced, and the model diagnosis precision can be continuously optimized through historical data.

The invention relates to the technical field of artificial intelligence, and provides an intelligent agent decision-making method, an intelligent agent decision-making device, a computer readable storage medium and electronic device.The intelligent agent decision-making method comprises the steps that a user query is received, and a retrieval document set related to the user query is obtained; performing joint embedding on the user query and retrieval document set to generate context vector representation; inputting the context vector representation into a hierarchical recursive memory network, and performing hierarchical recursive state evolution in a plurality of calculation segments through the hierarchical recursive memory network to generate a final internal hidden state; and decoding the final internal hidden state to generate decision output of the intelligent agent. According to the invention, a deep and adaptive reasoning process can be realized through the hierarchical recursive memory network, and the decision accuracy and reasoning efficiency of an intelligent agent under a complex task are remarkably improved.

The application discloses a high-speed rare thin flow field prediction method and system based on a mask self-encoder, and belongs to the technical field of cross hyper-sonic aerodynamics and artificial intelligence. The application mainly comprises the following steps: constructing a cross-flow multi-scale flow field database; constructing and pre-training a multi-scale mask self-encoder model, forcing the model to learn to reconstruct the full-field flow field from sparse information through a random mask strategy; introducing a physical information constraint to fine-tune the model, wherein the physical information constraint comprises a Chapman-Enskog distribution function constraint, a moment equation conservation residual and a wall Maxwell slip boundary condition; introducing a Knudsen number self-adaptive reasoning mechanism to automatically adjust the feature weight through a gate network; and using the trained model to predict the flow field. The application realizes cross-flow unified rapid prediction covering a wide Knudsen number range, guarantees the prediction accuracy, significantly improves the calculation efficiency, and has good physical consistency and generalization ability.

The invention discloses an incomplete multi-mode learning method based on graph routing and prompt distillation, which comprises the following steps of: firstly, constructing a mode sensing routing and element self-adaptive prompt distillation framework oriented to incomplete multi-mode learning, capturing a dependency relationship between condition modes through a mode sensing graph to obtain fusion representation, and then activating a special path by sparse routing to obtain a fusion representation; according to the method, a low-rank adapter and a meta-adaptive prompt are generated, flexible fusion aiming at various missing modes is realized, finally, uncertainty perception distillation is adopted to carry out final cross-modal reasoning and robust learning, and incomplete multi-modal learning is realized. According to the method, a mode perception element prompt framework is provided, uncertainty perception prompt distillation is designed, the problem that a multi-mode learning model faces mode deficiency uncertainty and the problem that the multi-mode learning model faces mode deficiency performance suboptimum are solved, instance self-adaptive reasoning is achieved, and the method has the advantages of being simple in structure, convenient to operate and high in practicability. Reliable cross-modal knowledge migration and coherent representation learning can be realized in various missing scenes.

The invention belongs to the technical field of three-dimensional modeling, and particularly provides a severe environment three-dimensional modeling method and system based on multi-modal fusion and generative repair. Comprising the steps of performing feature fusion on an original image and an original point cloud, and completing initial semantic segmentation to obtain an initial semantic segmentation map; adopting a double-branch mechanism to obtain a confidence map corresponding to the initial semantic segmentation map; obtaining an object segmentation mask based on the original point cloud; in combination with the initial semantic segmentation map and the corresponding confidence map, optimizing the object segmentation mask, and generating a guide map based on the optimized segmentation mask; restoring the original image based on the generated guide map to obtain a restored image; carrying out feature fusion on the original image and the restored image, and adopting an adaptive reasoning mechanism to obtain a semantic output graph; and constructing a three-dimensional environment model of the environment to be modeled in combination with the semantic output graph and the restored image. According to the method, the completeness, geometric accuracy and semantic rationality of the three-dimensional model of the complex industrial environment are improved.

The invention discloses a multi-protocol compatible power distribution cabinet communication method, and the method comprises the steps: carrying out the unified sampling, feature multi-layer analysis and clustering of the actual protocol communication data flow of equipment, achieving the multi-dimensional detection of the protocol micro variation, judging the difference attribution in combination with historical behaviors and similar features, and automatically generating a self-adaptive patch rule for unknown variation. And dynamic priority loading and adaptive reasoning are realized. Meanwhile, the rule base is continuously evolved through a transactional log and a manual optimization feedback mechanism, so that the method has the advantages of protocol adaptation intelligence, variation detection refinement, rule evolution automation and the like, and the compatibility and adaptability of a multi-manufacturer power distribution protocol system are remarkably enhanced.

The invention discloses a micro-service log analysis method based on dual-similarity retrieval and adaptive reasoning, relates to the technical field of computers, and aims to solve the analysis problem caused by semantic diversity and unstable structure of micro-service logs. The method comprises the following steps of: preprocessing and grouping an original log, and matching and multiplexing an existing template through a template cache; when the big language model is not hit, a difference log sample is selected on the basis of the editing distance, a reference example is selected through semantic-structure double-similarity retrieval, a structured cue word containing a task description module, a reasoning mode selection module, a thinking chain reasoning module and other modules is constructed, the big language model is guided to conduct adaptive reasoning extraction on a template, and a cache is updated. Experiments show that the method is superior to an existing mainstream method in indexes such as grouping accuracy and analysis accuracy, can adapt to micro-service complex scenes, and improves the accuracy and stability of log analysis.

The invention provides a multi-modal large model adaptive reasoning and pruning method based on evidence consistency, which is used for image-text questions and answers, reducing FLOPs and video memories and finely controlling calculation budget under the condition of not changing backbone parameters. The method comprises the following steps: firstly, extracting image and problem features by using a multi-modal basic model, and generating a structure sensing Top-k mask through QCEG-SA; evidence perturbation is carried out under the three views, evidence gain, consistency and unified uncertainty characteristics are obtained, and lightweight alignment is carried out with UAEL-mm; and in combination with short chain self-consistency (SCSC) and lightweight verification (CAV), off-line evaluation is performed on different pruning rates and reasoning actions, and an AARC adaptive resource scheduler is trained. In the online reasoning process, the AARC automatically selects the pruning rate, the SCSC chain number and the CAV configuration for each sample, structural perception pruning and multi-modal reasoning are executed, and efficient deployment of resource limited scenes such as a single-card GPU is achieved while the precision is basically not reduced or even slightly increased.

This invention provides a memory-dynamic adaptive DNN inference method and system for embedded RTOS, comprising: in an offline phase, parsing the computation graph structure of the model, identifying specific operator sequences and fusing them into new operators; pre-calculating the parameters required for inference of each operator; correcting all tensors in the model and removing redundant tensors; allocating memory for each tensor according to the execution order of each operator, and calculating the offset of each tensor relative to the starting position of the memory block; generating model code after each operator selects a kernel; in an online phase, based on the generated model code and a line-partial loading mechanism, allocating intermediate memory for intermediate tensors generated during the computation of each operator, and using a lightweight algorithm to calculate the offset of each intermediate tensor relative to the starting position of the intermediate memory block based on the available memory size, thereby realizing model inference. This invention can automatically adjust the memory usage of deep neural network inference tasks under extremely limited and dynamically changing memory resource conditions.

The invention discloses a logit-based zero-knowledge oil gas Internet of Things secure communication method, and relates to the technical field of Internet of Things security, the communication data volume is significantly reduced by introducing a consensus logit-based compression mechanism, the zero-knowledge protection of a server is realized by combining homomorphic encryption, and the privacy security is further enhanced; meanwhile, a double-layer distillation strategy is adopted at a client side, the performance of a personalized model is improved, a self-adaptive reasoning switch is added to reduce calculation consumption, the method is particularly suitable for an oil and gas Internet of Things edge environment with sensitive communication cost and remarkable data isomerism, communication consumption is greatly reduced, meanwhile, zero-knowledge safety of a server is achieved, and the method is suitable for large-scale popularization and application. Compared with the existing federated learning method, the method provided by the invention not only can realize lower communication consumption, but also can realize higher model performance in the actual environment of the Internet of Things in comparison with the existing federated learning method.

The application relates to the technical field of robot intelligent control, and discloses a mechanical arm coffee latte trajectory self-adaptive reasoning method based on a diffusion transformer model, which acquires multi-modal teaching data through a handheld acquisition device, converts the multi-modal teaching data into Lie algebra relative trajectories to train a conditional diffusion transformer. In an online execution stage, a hybrid dynamic model composed of a nominal model and a real-time updated residual neural network is constructed and used for capturing fluid nonlinear characteristics. A dynamic energy function is constructed by using the hybrid model, an energy gradient guide is introduced in a diffusion reverse sampling process, and a smooth trajectory conforming to current physical constraints is generated. Finally, combined with dynamic liquid surface compensation, an optimal moment command is solved through model predictive control to drive the mechanical arm. The application effectively solves the fluid control problem in an unstructured environment and realizes high-precision mechanical arm latte with physical self-adaptive capability.

The invention provides a visual language navigation method and device, an intelligent system with a body and electronic equipment. The visual language navigation method comprises the following steps: acquiring a historical action entropy sequence of a to-be-navigated agent, a current environment visual sequence observed by the to-be-navigated agent and a language instruction; a pre-trained visual language navigation model is called, a self-adaptive reasoning module is embedded in the visual language navigation model, and the self-adaptive reasoning module is used for determining a navigation reasoning strategy based on the historical action entropy sequence, the current environment visual sequence and the language instruction; and inputting the historical action entropy sequence, the current environment visual sequence and the language instruction into a visual language navigation model, obtaining a navigation reasoning strategy at the current moment based on an adaptive reasoning module, and obtaining the navigation action of the to-be-navigated agent at the current moment output by the visual language navigation model based on the navigation reasoning strategy. The navigation precision and robustness are improved under the condition of limited calculation and communication resources.