20178results about How to "Easy to understand" patented technology

The invention discloses a medical diagnosis intelligent decision-making system based on multi-modal data fusion, and the system comprises the steps: obtaining a medical image, biological features and electronic medical record data of a patient through a patient information collection unit, and extracting feature vectors from different modal data through a feature processing unit; the core of the system is a modal fusion engine, and the module combines a deep neural network and a Bayesian inference engine to realize deep learning and probability-dependent modeling of an association relationship between features; carrying out weight optimization on different modal features by adopting an attention mechanism through a self-adaptive feature optimizer, and generating optimized feature representation; the intelligent diagnosis analysis unit calculates the diagnosis probability based on the optimized features, performs auxiliary diagnosis in combination with a case knowledge base when the confidence coefficient is insufficient, and outputs a diagnosis report containing diagnosis categories, basis and suggestions; according to the method, multi-source heterogeneous data are fused, deep learning and probabilistic reasoning are combined, intelligent medical auxiliary diagnosis is achieved, and diagnosis accuracy and interpretability are improved.

The invention relates to the technical field of autonomous robot decision-making systems, in particular to an autonomous robot decision-making system and method based on multi-modal sensing fusion, and the system comprises a multi-modal data collection module which is used for obtaining multi-modal sensing data of an environment; the data preprocessing module is connected with the multi-modal data acquisition module and is used for preprocessing the multi-modal sensing data; the feature extraction module is connected with the data preprocessing module and used for extracting features from the preprocessed multi-modal sensing data; the dynamic space-time diagram construction module is connected with the feature extraction module and is used for constructing a dynamic space-time diagram based on the features; the multi-modal data fusion module is connected with the dynamic space-time diagram construction module and is used for fusing the multi-modal data in the dynamic space-time diagram; through adaptive time alignment and a cross-modal space attention mechanism, the system can effectively process space-time inconsistency of data of different sensors, and centimeter-level positioning precision is realized.

The invention discloses a fault detection method and device for secondary equipment of a transformer substation. The method comprises the following steps: acquiring real-time operation parameters of the secondary equipment of the transformer substation, inputting the real-time operation parameters into a pre-trained graph neural network model to obtain equipment operation characteristics, and determining an equipment state vector according to a plurality of equipment operation characteristics in a continuous time window and a message passing mechanism; determining an equipment health degree score according to the equipment state vector and a knowledge graph, and triggering the intelligent inspection robot to execute a deep detection task under the condition that the health degree is lower than a preset health degree threshold value so as to collect target test data; determining a target fault type according to the target test data, the knowledge graph and the inference rule base, obtaining a fault processing scheme corresponding to a historical fault case, and generating corresponding maintenance guide information according to the fault processing scheme, thereby achieving the real-time detection, precise fault diagnosis and automatic maintenance of the secondary equipment of the transformer substation. And the operation safety and the maintenance efficiency of the power system are improved.

The invention provides a power plant intelligent early warning method and system based on big data, and relates to the technical field of power systems, and the method comprises the steps: generating a high-dimensional feature map, generating a multi-modal feature tensor through adaptive chaotic mapping, constructing a feature relation network, generating a time sequence multi-dimensional feature vector, and reconstructing a multi-dimensional state vector based on topological entropy. In combination with a topological data analysis method, carrying out high-dimensional topological space mapping on the multi-dimensional state vector, identifying an operation mode of the power plant equipment, calculating a fuzzy region and a mode abrupt change point of the operation state of the equipment, and obtaining a dynamic evaluation result of the health state of the equipment; a multi-level recurrent neural network is combined, a prediction result is obtained through multi-scale time sequence prediction, uncertainty corresponding to the prediction result is quantitatively solved based on a quantum probability graph model, quantum probability distribution of an early warning signal is generated, a real-time operation strategy of equipment is adjusted, and an intelligent early warning signal of the power plant equipment is generated.

The invention discloses a mirror image type industry linkage engine system and method based on a knowledge graph, and relates to the technical field of industry linkage engines, and the system comprises a data collection module which collects multi-industry data in multiple modes, and the cleaning accuracy is 90% or above; the knowledge graph construction module is combined from bottom to top and from top to bottom, and is used for distributed storage and block chain evidence storage; the industry mirror image model generation module is used for generating a model through machine learning, and the association accuracy is 95% or above; the linkage analysis and decision module is used for finding a linkage strategy through an intelligent algorithm; and the user interaction module has a graphical interface, is fast in response, can feed back optimization, and supports multi-language and VR / AR display. The system is accurate and efficient in data processing, integrates multi-source data to construct the knowledge graph, is deep and comprehensive in industry analysis, simulates industry relations to assist decision making, is intelligent and reliable in decision making support, provides strategies and evaluation, is convenient and friendly in user experience, improves interactivity through multiple technologies, promotes accurate decision making of enterprises, and promotes collaborative development and innovation of the industry.

The invention provides a structured data modeling analysis method based on Transform, and relates to the technical field of data analysis, and the method comprises the steps: carrying out the piecewise linear transformation and adaptive quantum coding of numeric features and category features through a double-flow feature encoder, and carrying out the fusion through a gating mechanism to obtain fusion features; inputting the enhanced training sample into a feature extraction network comprising a hierarchical attention module and a self-calibration module, and extracting global feature representation through a feature affinity graph and a causal attention mechanism; and constructing an information bottleneck feature filter to select a key feature subset, performing prediction and reconstruction by adopting a double-branch network, and analyzing the feature contribution degree by using a structured gradient method. According to the method, heterogeneous features can be effectively processed, the model performance is improved, and meanwhile an interpretable feature importance evaluation mechanism is provided.

The invention relates to a traffic flow prediction optimization method based on an intelligent optimization algorithm, which is applied to the technical field of traffic flow prediction. The system comprises a multi-modal data acquisition and fusion module, a hybrid deep learning prediction module, an intelligent parameter optimization module, an integrated learning module, a multi-scale error correction module, a prediction result analysis and visualization module, a distributed learning and privacy protection module and the like. Wherein the hybrid deep learning prediction module adopts an attention mechanism to fuse models such as CNN, Transform and LSTM, and introduces an adaptive error correction mechanism, so that the prediction precision is remarkably improved; the intelligent parameter optimization module accelerates the model search efficiency by using technologies such as quantum computing and swarm intelligence optimization; the integrated learning module and the adversarial training strategy enhance the robustness and generalization of the model; and the multi-scale error correction module adopts a time sequence decomposition and self-adaptive online learning method to improve the accuracy of long-term and short-term prediction.

The invention provides an interactive question and answer task processing method based on multi-agent cooperation and a related device, and relates to the technical field of artificial intelligence such as generative large language models and agents. The method comprises the steps of determining a to-be-solved target task according to natural language input of a user; the target task is decomposed into a plurality of sub-target tasks at least including interactive question and answer tasks, each sub-target task is correspondingly issued to each sub-target agent at least including an interactive question and answer agent, and different sub-agents are used for processing different types of sub-tasks; the interactive question-answering agent is controlled to present an interactive graphical user interface corresponding to the interactive question-answering task to the user, an answer result corresponding to the received feedback answer is presented, and the interactive graphical user interface comprises a question and an interactive answer area; an associated sub-target agent in the multiple sub-target agents is controlled to generate a corresponding associated processing result based on an answer result output by the interactive question and answer agent, and the associated sub-target agent is a sub-target agent which depends on the answer result and serves as part of task input information; and determining a target processing result corresponding to the target task based on the association processing result. Through application of the scheme, a better comprehensive task processing effect can be brought under the condition of lower comprehensive cost, especially for an interactive question and answer task in which a user needs to subjectively answer a proposed question and the effect is checked according to an answer result; the interactive questioning and answering agent provides the interactive graphical interface containing the question and the interactive answering area for the user, so that the task requirement of the user can be met more quickly in an interactive answering mode, and the task solving effect is improved.

The invention relates to the technical field of big data storage, in particular to a storage control method and device based on big data. The method comprises the following steps: acquiring an original data stream and application demand information; performing data dynamic correlation analysis on the original data flow according to the application demand information to obtain a dynamic update data correlation graph; performing feature vector generation on the dynamic update data association graph to obtain a data feature vector; performing intelligent quality evaluation on the data feature vector to obtain a data quality label; performing timeliness evaluation according to the data feature vector, and performing access popularity evaluation to obtain a timeliness score and an access popularity score; and acquiring system resource load condition information. The storage resource utilization rate, the data access performance and the overall efficiency of the system are improved by solving the limitation of a traditional method in the aspects of static data management and resource allocation.

The invention discloses an information interaction method for an intelligent robot with a body, and the method comprises the steps: S1, receiving the information which is transmitted by a user and comprises a task instruction, S2, carrying out the analysis of the information based on a context understanding module, so as to recognize the specific meaning and environment conditions of the task instruction, s3, operation corresponding to the specific meaning is executed, and execution parameters are adjusted in a specific environment to ensure that the task instruction is accurately implemented; according to the information interaction method of the intelligent robot with the body, environment perception and adaptive capacity are enhanced, various perception information such as vision, hearing and touch can be integrated, comprehensive perception of the environment is achieved, instruction understanding and execution accuracy is improved, the intelligent robot with the body can analyze complex user instructions more accurately, and user experience is improved. Through real-time multi-modal data fusion and decision optimization, the robot can realize real-time integration of different data sources in a highly dynamic scene, and the task execution efficiency and the user experience are improved.

The invention discloses a containerized resource dynamic allocation method for a power distribution network system and electronic equipment. The method comprises the following steps: judging that resource use data is greater than a preset resource threshold value by monitoring the resource use condition of each physical node in a power distribution network system; if yes, calculating a load balancing index of each physical node, and generating a container migration task list based on the load balancing index, the service priority and the service quality requirement information; packaging container data corresponding to the to-be-migrated container in the migration task list, and determining container state snapshot information and network connection information of the to-be-migrated container; establishing a secure channel between the source node and the target node based on the network connection information, and transmitting container data and container state snapshot information of the source node to the target node; reconstructing a container operation environment on the target node, and generating a target container; and suspending the to-be-migrated container on the source node, and activating the target container on the target node. And efficient migration of the container is realized.

The invention discloses a question and answer method and system based on tool calling, electronic equipment and a storage medium. The method comprises the steps that a user question and answer interface obtains question information of a user and sends the question information to a service layer; the tool retrieval module screens out IDs of candidate tools from existing tools according to the question information to obtain a candidate tool list; the tool calling module calls a large language model based on the candidate tool list and the question information; the big language model screens out an ID of a target tool associated with the question information from the candidate tool list, and generates a tool calling instruction based on the ID of the target tool; the tool calling module calls a target tool according to the tool calling instruction and obtains output parameters of the target tool; and the tool calling module calls a large language model to generate reply content based on the output parameters and the question information. The candidate tools are screened out through the tool retrieval model, then the large language model is called based on the ID of the candidate tools and the question information to determine the target tool, and the accuracy and comprehensiveness of reply content can be improved.

The invention discloses a display panel, a driving method of the display panel and a display device. The display panel comprises a display area and a non-display area. The display panel further comprises a plurality of sub-pixels located in the display area. Each sub-pixel comprises a pixel circuit and a light-emitting device; the pixel circuit comprises a driving module, an initialization module, a data writing module and a storage module, the initialization module is used for writing initialization voltage into the control end of the driving module and / or the anode of the light-emitting device; at least one multiplexing signal line; the input end of the initialization module of the pixel circuit of each sub-pixel and the cathode of the light-emitting device are connected with a multiplexing signal line; the multiplexing signal line is used for providing initialization voltage for the initialization module in the initialization stage and providing cathode voltage for the cathode of the light-emitting device in the light-emitting stage. And the wiring number of the display panel is reduced.

Along with promotion of industry 4.0 and rising of intelligent manufacturing, an industrial decision support system plays a more and more important role in improving enterprise operation efficiency and market competitiveness. The invention provides an industrial decision support system based on big data and artificial intelligence technology, and aims to provide accurate decision support for enterprises through deep analysis and real-time processing of massive industrial data. According to the system, by integrating an advanced data acquisition technology, monitoring and data capturing of the whole process of industrial production, including but not limited to production equipment states, process parameters, quality control, supply chain information and the like, are realized. By using artificial intelligence algorithms such as machine learning and deep learning, the system can identify a mode and predict a trend from complex data and construct a corresponding decision model. In addition, a built-in operation optimization engine of the system can provide an optimal solution for production planning, inventory management and resource configuration based on a data analysis result. Through an intuitive user interaction interface, a decision maker can easily obtain key business insights and intelligent decision suggestions so as to make a quick response in a continuously changing market environment. The industrial decision support system not only can improve the production efficiency and the product quality, but also can help an enterprise to reduce the operation cost, avoid potential risks, and promote the enterprise to transform to intelligence and digitization.

The invention provides an intelligent garden environment monitoring data analysis method and system. Sensors are deployed to collect temperature, humidity and other data in real time to form a data set. Analyzing an environment parameter change mode of each region by adopting a time sequence clustering algorithm, and determining characteristics and trends; and carrying out dimension reduction on the data by using a multi-dimensional scale transformation algorithm to a three-dimensional space for visualization, and generating an environment state map. And evaluating the plant fitness in combination with a plant physiological ecology knowledge base, and generating a scoring matrix to guide the preliminary layout scheme. And dynamically adjusting the layout by applying reinforcement learning, selecting an optimal scheme, and predicting a future environmental evolution path based on the optimal scheme and historical data. An automatic control system framework is constructed, a self-adaptive control algorithm is designed, intelligent regulation and control of plant layout are realized, garden management efficiency is improved, and ecological protection and sustainable development are promoted. Therefore, full-chain intelligent management from environment monitoring to plant layout optimization is realized, and the intelligent level of garden environment monitoring and management is remarkably improved.

The invention provides a multi-agent cooperation-based explicit result acquisition class task processing method and a related device, and relates to the technical field of artificial intelligence such as generative large language models and agents. The method comprises the steps of determining a to-be-solved target task according to natural language input of a user; the target task is decomposed into a plurality of sub-target tasks at least including clear result acquisition class tasks, each sub-target task is correspondingly issued to each sub-target agent at least including clear result acquisition agents, and different sub-agents are used for processing different types of sub-tasks; controlling each sub-target agent to output a sub-processing result corresponding to the subordinate sub-target task in combination with the personalized preference of the user, wherein the sub-processing result comprises a clear result which is output by the clear result acquisition agent and corresponds to the subordinate clear result acquisition class task; and summarizing the sub-processing results to obtain a target processing result corresponding to the target task. By applying the scheme, a better comprehensive task processing effect can be brought under the condition of lower comprehensive cost.

The invention belongs to the field of artificial intelligence, and particularly relates to a bill information extraction method and system and a storage medium, and the method comprises the following steps: obtaining an electronic file containing a bill, and preprocessing the electronic file into an image file facilitating information extraction; extracting bill information in the image file by using a multi-modal large model and an OCR model, and outputting text information in a structured format; and analyzing, correcting and comparing text information respectively output by the multi-modal large model and the OCR model through the large language model, and performing fine tuning or cue word engineering guidance on the large language model for different tasks, so that an output result conforms to expectation. Compared with the prior art, the problems that in the prior art, OCR recognition accuracy is not high, and extracted information is incomplete are solved, the bill information processing speed is increased, the precision of the automatic processing process is greatly improved, and in financial, auditing and other related business scenes, the working efficiency and the data reliability are remarkably improved.

The invention discloses a fault root cause analysis method and device for a distributed software system and a storage medium, and the method comprises the steps: obtaining fault data of the distributed software system, and extracting key information from the fault data; processing the key information according to a triple model to obtain a knowledge graph corresponding to the key information; when a fault analysis task is received, performing abnormal chain reasoning based on the knowledge graph, and generating a fault reason description text of the distributed software system based on an abnormal chain reasoning result; and inputting the fault reason description text into the large model to obtain a fault analysis result which corresponds to the fault analysis task and is deduced by the large model. According to the scheme, the technical problems that a traditional fault detection and root cause analysis method is low in analysis efficiency and inaccurate in root cause positioning are solved, and rapid and accurate fault attribution is achieved through the knowledge graph and the large language model.

The invention relates to the technical field of knowledge maps, and discloses a knowledge map searching method and system based on local semantics and global communitization, and the method comprises the steps: extracting entities and relationships thereof from a knowledge map to construct an undirected graph; the undirected graph is divided into multiple levels of community information, and a community report is obtained by generating a community information table; selecting an instance triple containing data source information from the knowledge graph, constructing an instance triple information source table, extracting IDs, names and attributes of entities, and generating an entity attribute table to obtain an entity attribute vector table; key entities in a to-be-queried question are extracted and subjected to vector representation, the similarity between the key entities and an entity attribute vector table is calculated, first N related entities are selected to generate contexts (including an entity context, a relation context, a data source context and a community context), and finally a search result of the to-be-queried question is obtained. According to the method, the information related to the to-be-queried question can be accurately retrieved from the knowledge graph.

The invention belongs to the technical field of separation membrane materials, and provides a covalent organic framework membrane as well as a preparation method and application thereof. The preparation method comprises the following steps: preparing an organic phase solution containing a polyaldehyde monomer and a water phase solution containing a polyamine monomer; dropwise adding the organic phase solution to the aqueous phase solution for reaction, and dialyzing the solution after the reaction is finished to obtain a purified aqueous dispersion of the covalent organic framework nanosheet; and dispersing the aqueous dispersion, carrying out suction filtration to a hydrophilic support, and carrying out heat treatment and curing. The invention also discloses the covalent organic framework membrane prepared according to the method and application thereof. The covalent organic framework membrane is used for removing divalent ions in a saline solution. The covalent organic framework membrane disclosed by the invention has an ordered pore structure with a specific size and high ion selectivity; due to the strong covalent bond connection characteristic, the covalent organic framework film is endowed with acid and alkali resistance and high stability; the preparation method disclosed by the invention is simple, mild in condition and wide in application range.

The invention provides a question and answer method based on a large model, and relates to the technical field of artificial intelligence, in particular to the technical fields of natural language processing technologies, deep learning, large language models, human-computer interaction and the like. The method comprises the steps of obtaining question information, reference information corresponding to the question information and prompt information; by calling a large model, processing the question information and the reference information according to the prompt information and a multi-task processing mode of answer generation and answer evaluation, and obtaining target answer information of the question information; and outputting the target answer information. The invention further provides a training method and device based on the large model, equipment, a medium and a product.

The invention relates to the technical field of quality traceability management, in particular to an organic fertilizer quality safety traceability management method based on multi-source data. The method comprises the following steps: collecting organic fertilizer source data in real time from links of organic fertilizer production, circulation, storage and use, performing standardization processing, and performing production batch synchronous set processing and traceability association connection file establishment to generate an organic fertilizer multi-source data traceability association connection file; and carrying out production batch tracing extraction processing on the organic fertilizer multi-source data traceability association connection file, carrying out organic fertilizer quality index evaluation analysis and quality index error limitation analysis, carrying out organic fertilizer quality safety abnormity traceability processing and abnormity decision support processing, and generating an organic fertilizer quality safety abnormity problem decision support report. Therefore, the corresponding organic fertilizer quality safety abnormity traceability problem management work is executed. The scientificity and accuracy of organic fertilizer quality safety management can be improved.

The invention provides a cue word template generation method and device, electronic equipment and a storage medium, and relates to the technical field of computers, in particular to the technical field of artificial intelligence such as natural language processing, large language models and deep learning. According to the specific implementation scheme, a first data set and an initial cue word template corresponding to a target task are obtained, and the first data set comprises sample data, text description corresponding to the sample data and reference answers; determining a first answer corresponding to the sample data according to the initial cue word template, the sample data and the corresponding text description; determining a first evaluation result corresponding to the first answer according to the reference answer and the first answer; and based on the first evaluation result and the text description, optimizing the initial cue word template to obtain a target cue word template.

The invention relates to the technical field of image processing, and discloses an adaptive underwater image enhancement algorithm, and the algorithm specifically comprises the following steps: S10, constructing a network architecture: designing a conditional diffusion model as a main frame of image enhancement, the model comprises a forward noise adding process and a reverse denoising process, and in the denoising process, the forward noise adding process and the reverse denoising process are carried out; a denoising network composed of a Transform block and an improved residual state space block RSSB is introduced for encoding and refining image features, the RSSB is combined with a discrete state space equation of Mamba, a long dependency relationship in an image is modeled in a recursive form, channel redundancy is avoided, a visual state space module VSSM is introduced, important pixel information in the image is captured in a self-adaptive mode, and the image quality is improved. And the modeling capability of spatial details is enhanced. According to the adaptive underwater image enhancement algorithm, the influence of underwater environment complexity on image quality is overcome, and the image contrast, the color rendition degree and the detail retention capability are improved.

The invention relates to the technical field of medical care, in particular to an intelligent query method for a nursing knowledge base, which comprises the following steps of: performing multi-form standardized processing on a nursing problem input by a user, deeply analyzing a query request by utilizing a semantic understanding model, and obtaining an intelligent query result based on an inference algorithm; the method comprises the following steps: automatically selecting related literatures, clinical data and nursing experience data from a nursing knowledge base, generating a preliminary nursing scheme candidate set in combination with a nursing professional knowledge graph, and performing personalized screening and optimization on the scheme by using an intelligent recommendation model in combination with the current illness state, treatment progress and historical nursing data of a patient; the intelligent recommendation model is self-optimized through user feedback, and the nursing scheme recommendation precision is continuously improved. The nursing knowledge base automatically fuses newest medical research results and clinical nursing practice data. The problems that in the prior art, nursing scheme recommendation accuracy is low, updating is not timely, and the individuation degree is insufficient are effectively solved.

The invention relates to the technical field of AI computing, in particular to a knowledge reinforcement learning method and system based on multiple agents. Modifying parameters of the large language model by using a supervised fine-tuning training method for the pre-trained large language model; constructing a knowledge base of a large language model, and vectorizing, storing and retrieving texts in various file formats; a multi-agent workflow optimization information processing and decision-making process is developed, a real-time problem is decomposed, a logic chain for solving the problem is constructed, and internet texts are obtained, filtered and summarized. Compared with the prior art, the method has the advantages that the knowledge quantity of the pre-trained large language model in the specific field is increased, the knowledge base is high in flexibility, the obtained text is high in real-time performance, certain interpretability is achieved and the like, and then the ability of the large language model to solve problems in the specific field is improved.

The invention discloses an Internet of Things equipment access protocol component development method, which is used for quickly developing an Internet of Things equipment access protocol analysis and processing component at an Internet of Things platform side, and comprises the following steps: S1, constructing a protocol analysis and processing general framework according to the protocol analysis and processing component of an Internet of Things platform; s2, abstracting a specific logic processing part of a protocol in the general framework into a method / function interface; s3, constructing a protocol expert AI knowledge base for data learning; s4, constructing an AI large model and a multi-agent processing flow, selecting a specified large model, configuring related parameters, extracting required parameter extraction / coding logic, and generating an interface implementation code; s5, integrating each generated interface implementation code with the universal framework; and S6, testing, adjusting and optimizing the integrated access protocol analysis and processing component. According to the method, the unified development template can be provided, so that the development technical threshold is reduced, and the development efficiency is greatly improved.

The invention relates to the technical field of geological disaster evaluation, in particular to a mine geological disaster risk comprehensive evaluation method considering spatial distribution characteristics. The method comprises the following steps: acquiring mine multi-source spatial data; performing mine geological disaster risk distribution analysis on the mine multi-source spatial data to generate a mine geological risk distribution map; performing spatial region potential disaster probability analysis on the mine geological risk distribution map to generate mine geological distribution disaster probability data; and performing high-complexity spatial region screening on the mine geological risk distribution map through the mine geological distribution disaster probability data to obtain high-complexity spatial region data. According to the method, multi-source data are integrated, spatial distribution characteristics are emphasized, active and inactive areas are distinguished, ecological and water body changes are analyzed, and the accuracy and comprehensiveness of mine geological disaster risk comprehensive evaluation are improved.

The invention relates to a system and a method for ground simulation of an in-orbit operation environment of a laser communication terminal. The system comprises a plurality of subsystems, and each subsystem comprises a control unit which performs data interaction with a time service unit, a two-dimensional turntable unit, a corresponding laser communication terminal and a test unit; the time service unit is used for realizing the same reference time service of the affiliated subsystem and other subsystems; the two-dimensional turntable unit is configured to simulate the attitude of a satellite platform where the laser communication terminal is located based on the satellite attitude control signal, and the laser communication terminal drives the rotating mechanism to adjust the pose of the laser communication terminal based on the pointing control signal of the control unit; the test unit is used for testing the laser communication terminal; and the light path combination unit is configured to form or disconnect a space laser link with the light path combination units of other subsystems. According to the invention, the in-orbit operation environment of the laser communication terminal can be simulated, and comprehensive and dynamic function testing can be carried out on the laser communication terminal.

The invention provides a real-time ship navigation route planning method based on augmented reality, and the method comprises the steps: collecting various environmental data around a ship in real time, and carrying out the preprocessing of the various environmental data; processing video data through a convolutional neural network, extracting environment features, extracting dynamic features through a long-short-term memory network, extracting spatial features through the convolutional neural network, extracting meteorological features by using a two-layer multi-layer perceptron, and fusing to obtain fused features; a generative adversarial network is utilized to generate various sea conditions and meteorological scenes, risk assessment is carried out, a risk map is generated, an avoidance strategy is generated, and a route is adjusted to avoid a high-risk area; in combination with the real-time environment perception data and the historical navigation data, dynamically adjusting the navigation route by adopting a reinforcement learning algorithm to obtain an optimized navigation route; the display is performed through real-time synchronization of AR display and a navigation system in combination with augmented reality technology superposition. According to the scheme, the safety and navigation efficiency of the ship are improved.