568 results about "Expert system" patented technology

The invention discloses a coal mine ventilation system intelligent regulation and control method based on environmental perception, and relates to the technical field of coal mine ventilation system intelligent regulation and control, and the method comprises the following steps: collecting environmental data of different areas of a mine, including gas concentration, temperature and humidity, air velocity and oxygen content, and monitoring the operation state of ventilation equipment in real time; data of all the sensors are synchronously processed through the data fusion technology, and a unified environment sensing model is generated. According to the invention, through cooperative work of the expert system and deep learning, real-time monitoring and emergency plan execution under extreme working conditions are realized, and the response speed and decision accuracy are improved. A reinforcement learning algorithm is adopted to optimize ventilation line switching, the adaptive capacity and redundancy guarantee of the system are enhanced, and it is ensured that airflow is not interrupted at the critical moment. Through abnormality comparison of the knowledge base and the rule base, false alarm and misjudgment are reduced, system efficiency is improved, operation burden is relieved, and safety and stable operation of mine personnel are guaranteed to the maximum extent.

A system is disclosed that uses expert systems to monitor and evaluate quality in a large language model. The system can include a prompt library that associates prompts with areas of expertise. The system selects prompts from the library and evaluates first responses generated by a large language model for the set of prompts against second responses generated by a modified version of the large language model to the set of prompts. The evaluation uses expert systems associated with the areas of expertise for the set of prompts. If the system determines that the evaluation indicates a degradation criterion is met, the system may take remedial action. The system provides an effective way to evaluate and prevent the use of modified language models that do not meet the required standards.

The invention relates to the technical field of agricultural intelligent detection, in particular to a fruit tree disease and pest detection method based on machine vision, which comprises the following steps: acquiring visible light, infrared light and ultraviolet light images of a fruit tree at multiple angles through a multispectral camera to obtain complete fruit tree surface information, and preprocessing the acquired images to obtain a fruit tree disease and pest detection result; comprising the steps of denoising, illumination correction and image alignment, extracting feature vectors, including the color, shape and size of scabs, pest distribution and other features, of pest and disease damage from preprocessed images through a convolutional neural network, fusing the pest and disease damage features with environmental data by using a multi-modal data fusion algorithm, and enhancing the accuracy of pest and disease damage classification. Classifying and identifying diseases and pests based on a support vector machine classification algorithm; according to the dynamically adjusted threshold value and suggestions provided by the expert system, a disease and pest diagnosis report is generated, and prevention and control measure suggestions are provided; according to the invention, efficient and accurate detection and management of fruit tree diseases and insect pests can be realized, and the intelligent level of agricultural management is improved.

The invention provides a shield intelligent auxiliary type selection system and method based on a large language model. The model selection system comprises a data input module, a rule knowledge base module, a large model reasoning module and a result generation module. The integrated decision-making system integrating a rule knowledge base, a deep learning model and expert system logic is constructed for the practical problems of complicated geological conditions, multiple rule constraints, high expert dependency and the like in shield construction, and the system combines a structured model selection rule and historical case data, and has the advantages of intelligence, standardization, self-learning, high efficiency and the like. The problems of low efficiency, high subjectivity, insufficient intelligent degree and the like of the existing shield tunneling machine model selection depending on artificial experience and partial standardized guide are solved, and the transformation of shield construction management from artificial experience to intelligent decision can be promoted.

The invention discloses a power distribution equipment state monitoring system and monitoring method based on the Internet of Things technology, and relates to the technical field of power distribution equipment monitoring, the system comprises the following components: a sound acquisition module, a data transmission module, a data processing center and an early warning display module; by combining the Internet of Things technology, the expert system and the feature engineering technology, high-precision acquisition and analysis of operation sound signals of the power distribution equipment are realized, and by constructing the fault voiceprint feature library, the system can quickly recognize and match equipment fault features, so that early warning is given out at the initial stage of the fault, and the fault early warning efficiency is improved. In addition, due to the application of the cross-equipment associated fault prediction model and the fault propagation prediction model, the system can predict the propagation conditions of cascading faults and system-level faults, more comprehensive fault information is provided for operation and maintenance personnel, prevention measures can be taken in advance, and the fault monitoring accuracy and timeliness are improved. The influence of faults on a power distribution system is reduced.

The invention discloses a city updating intelligent expert system architecture and method based on a large language model, and relates to the technical field of city planning and construction. The knowledge base management module supports efficient retrieval and application of professional domain knowledge; the expert agent group comprises a user-defined core expert and a system dynamic supplement expert; the expert team collaborative decision-making module is used for executing multiple rounds of hierarchical collaborative decision-making processes; the meta-agent module is responsible for project feature analysis and expert team dynamic configuration; the auxiliary agent module comprises a host agent which is responsible for guiding the decision making process; the recorder agent generates a decision summary report; the verification agent is responsible for verifying and evaluating the professional ability of the expert agent; and the scoring agent evaluates the decision quality. According to the method, the problems of dispersed professional knowledge, low cross-domain expert cooperation efficiency, high decision-making cost and the like in the existing city updating decision-making process are solved, and the decision-making efficiency and quality of city updating early-stage planning are comprehensively improved.

The invention provides a robot cable manufacturing process optimization method and system based on deep reinforcement learning, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: collecting cable manufacturing data, constructing a digital twin model, and achieving the process flow simulation through a graph network; building a deep reinforcement learning environment by taking the manufacturing data and the simulation data as state input; analyzing a causal dependency relationship of process parameter adjustment; constructing an industrial knowledge graph expert system to generate an optimization strategy; and a process optimization closed loop is formed. According to the invention, self-adaptive optimization of the cable manufacturing process is realized, and the manufacturing efficiency and quality are improved.

The invention discloses a motor vehicle maintenance management service system, and relates to the technical field of motor vehicle maintenance management, and the system comprises a client information management module which builds data for storing client multi-information, supports multi-format import and export, intelligently recognizes and updates, and guarantees the information to be timely and accurate; the vehicle fault diagnosis module is provided with various interfaces and expert systems, analyzes and diagnoses faults based on big data, and gives reasons, suggestions and cost estimation; the maintenance work order management module supports work order full-process operation, automatically distributes personnel and updates the progress in real time for clients to inquire; the maintenance accessory management module is provided with an inventory database and can intelligently match accessories and provide position query navigation; and the maintenance personnel management module is used for storing personnel information and evaluating performance. According to the invention, management efficiency and accuracy can be improved, accessory management and cost are optimized, personnel quality and service quality are improved, customer experience and satisfaction are enhanced, powerful support can be provided for enterprise decision making, and intelligentization of the motor vehicle maintenance industry is promoted.

The invention relates to a coal mine fully-mechanized excavation face intelligent ventilation dust control and removal system based on an optimal coordination mechanism, and belongs to the technical field of coal mining. According to the system, through integrating a plurality of functional modules such as data processing, area control, optimal parameter calculation, execution control, safety guarantee and expert system evaluation, intelligent management of ventilation, dust control and dust removal is realized. The system adopts a method of combining fuzzy control and PID (Proportion Integration Differentiation) control, and automatically calculates and adjusts operation parameters of the air control device and the dust remover according to environmental parameters such as dust and gas concentration which are monitored in real time, so as to realize an optimal ventilation and dust control effect. Through training and prediction of a machine learning model, the system can autonomously learn and optimize technical parameters of a dust control and removal process, the adaptive capacity and the operation efficiency of the system are improved, the dust and gas concentration of a working face is effectively reduced, and health of workers and safe production are guaranteed. The invention aims to improve the environmental quality of the coal mine fully-mechanized excavation face and promote the intelligent development of the coal mine ventilation dust control and removal technology.

The invention provides a bearing fault identification method based on a dynamic generative adversarial network and expert feedback, and relates to the field of bearing fault diagnosis, and the method comprises the steps: generating a high-fidelity fault vibration signal through employing a condition generator and a triple discriminator generative adversarial network; verifying and generating sample quality through a 1D residual verification network and adding the sample quality into a training set; segmenting the vibration signals passing the test by using layered adaptive sampling, and keeping high-frequency impact characteristics in the vibration signals; a dynamic sparse attention mechanism is adopted to reduce unnecessary attention calculation and improve calculation efficiency, and different types of faults are accurately recognized in combination with a hybrid expert system classifier; and detecting the confidence of the diagnosis result, and triggering a feedback mechanism to regenerate a sample to complete autonomous iterative optimization when the confidence is low. According to the method, a generative adversarial network, a fault diagnosis model and a feedback mechanism are fused, accurate diagnosis of bearing faults is achieved through multi-level data enhancement and screening feedback, the diagnosis precision is continuously improved in continuous iteration, and the method is suitable for solving the problem that a traditional method is poor in performance under data scarcity and noise interference. The innovative closed-loop evolutionary logic of generation-diagnosis-feedback is provided, and the robustness and accuracy of fault recognition are remarkably improved.

An intelligent fault-in-rail analysis method based on multimodal fusion learning. Ultrasonic analysis data of the rail and high-definition rail surface images taken by line scan cameras are exploited in combination to detect and display internal and surface faults of the rail through different renditions using multimodal data. Rail DEtection TRansformer (R-DETR) technology or a You Only Look Once (YOLO) inspection machine-learning algorithm are employed in a deep convolutional neural network to analyze B-scan and rail surface image data to recognize rail line faults. After obtaining expressive data through data enhancement by computer processing, faults are recognized and pinpointed using the deep convolutional neural network learning algorithm. Analysis results are corrected with expert systems. The disclosed automatic ultrasonic fault-in-rail inspection solves the problem of slow detection and difficulties in tracking faults in everyday routine maintenance while also protecting the safety of rail operation and maintenance personnel.

The invention discloses a multi-objective optimization dam crack detection enhancement method and system based on an expert system, and relates to an intelligent monitoring and detection technology, and the method comprises the steps: inputting original image data, carrying out the preprocessing, constructing and training a hybrid expert model MoE, and carrying out the primary coarseness detection through a gateway module of a gating unit; link adaptation of the gateway module and the hybrid expert model MoE is carried out, parameters of the gateway module and the hybrid expert model MoE are trained and adjusted through an optimization algorithm according to an adaptation result, and the hybrid expert model MoE is activated to carry out secondary fine-grained detection; and fusing output results of fine-grained detection of the hybrid expert model MoE, and generating and providing a final crack detection report. According to the method, the efficiency and accuracy of multi-target crack detection are improved, model parameters are finely adjusted by adopting multi-target optimization and Bayesian optimization, the performance of the model is improved, the adaptability and robustness of the model in different detection scenes are improved, and the advancement of the method in the technical field of crack detection is embodied.

The invention discloses a multi-mode electroencephalogram analysis model construction method and an online processing method and system. According to the method, multi-modal physiological signals such as electroencephalogram, electrocardio, skin electrical activity and eye movement are collected, and an integrated multi-modal signal collection device is used for preprocessing and feature extraction. A modal expert system based on an adaptive Transform architecture is adopted to replace a standard feed-forward network so as to enhance the feature processing capability. By means of the method, the features can be complemented under the condition of mode or data missing, the robustness, real-time performance and accuracy of the brain-computer interface technology in multi-mode data processing are improved, and the method is particularly suitable for the fields of emotion recognition, cognitive load monitoring, neural rehabilitation and the like.

The invention provides a virtual expert creation method and device, equipment and a medium, and relates to the technical field of artificial intelligence, and the method specifically comprises the steps: receiving an expert knowledge base uploaded by an artificial expert, and carrying out the preprocessing of the expert knowledge base; performing text splitting and extraction on the preprocessed expert knowledge base on the basis of a large language model, and constructing an initial knowledge graph on the basis of extracted knowledge points and semantic relationships; receiving a modification operation instruction of the artificial expert on the initial knowledge graph through a man-machine interaction interface of a large language model; and in response to the operation instruction, dynamically adjusting and optimizing the initial knowledge graph, and generating a virtual expert with a professional domain knowledge graph. The method is used for replacing a traditional rule-based virtual expert system, and is more intelligent and efficient.

The invention discloses a fault detection method and device for an overhead line of a wind power plant and a storage medium, and relates to the technical field of power systems, and the method comprises the steps: S1, intelligent data collection; s2, fault signal feature extraction and compression representation; s3, carrying out simulation modeling on a fault propagation path; s4, fault mode identification based on artificial intelligence; s5, a self-adaptive algorithm of fault positioning is carried out; s6, real-time detection of fault arc characteristics; s7, constructing a space-time dynamic fault early warning model; s8, constructing a multi-dimensional fault self-healing control strategy; and S9, fault cause analysis based on the knowledge graph. High-frequency signal features are quickly captured in the early stage of fault occurrence, and accurate positioning, prediction and real-time response to the fault are realized through multi-dimensional analysis and control strategies; meanwhile, the fuzzy logic and the expert system are combined, so that the system can dynamically adjust strategies, optimize a line operation mode, adjust load distribution and start a standby power supply when facing various fault types.

The present application relates to the technical field of artificial intelligence, and in particular to a multi-modal human physiological data classification model and a training method therefor, a multi-modal human physiological data classification method, and a device, aiming to solve the classification problem in cases of missing multi-modal physiological data. The classification model of the present application comprises a multi‑headed self‑attention module, a normalization module, a fusion expert system and a decision-making module. The fusion expert system comprises four expert subsystems: an electroencephalogram expert subsystem, an electrocardiogram expert subsystem, an electrodermal activity expert subsystem and a multi-modal synchronization fusion expert subsystem. The multi-headed self-attention module is configured to perform feature extraction on multi-modal synchronization data; the normalization module is configured to generate normalized feature data on the basis of the extracted features; the four expert subsystems are configured to perform classification tasks on the basis of the normalized feature data corresponding to electroencephalogram data, electrocardiogram data, electrodermal activity data and multi-modal synchronization data, respectively; and the decision-making module is configured to calculate a final classification result on the basis of the four classification results. The present application can improve the accuracy of classification results in cases of missing multi-modal data.

The invention discloses a virtual teaching training method and system based on a large language model and a medium, and the method comprises the following steps: determining preset feature parameters of virtual students according to teaching levels and subject contents; constructing a large language model, generating virtual students based on the large language model according to preset feature parameters of the virtual students, and inputting multi-modal teaching information; the virtual students perform model training and learning on the multi-modal teaching information to generate learning state information and feedback information; the virtual teaching expert proposes a suggestion for dynamically adjusting and optimizing the teaching strategy based on the evaluation result; dynamic learning and interactive feedback are carried out by simulating virtual students with different priori knowledge levels and cognitive competence, and meanwhile, real-time evaluation and personalized improvement suggestions are carried out on the teaching process by utilizing the constructed virtual expert system, so that a user is helped to continuously optimize a teaching strategy, and the teaching quality of a specific subject is improved.

The invention discloses an unknown pollutant non-target identification method and system based on mass spectrum and a generative model, and is applied to the technical field of environmental monitoring and analytical chemistry. The method comprises the following steps: collecting mass spectrum data of a water body sample to be detected; preprocessing the original mass spectrum data, and outputting standardized features; inputting the standardized features into a pre-trained generative model to generate a plurality of candidate molecular formulas; using chemical and physical constraints to eliminate candidate molecular formulas which do not conform to rules; executing a rule-driven algorithm to obtain candidate pollutant molecular structures; carrying out comprehensive scoring and sorting on candidate pollutant molecular structures through chemical prior and environmental prior; and semi-quantitative or relatively quantitative concentration determination is carried out. According to the method, a rule-driven expert system and a data-driven generation model are combined, unknown pollutants which do not exist in a standard library can be effectively recognized and analyzed, and full-process automatic processing from original mass spectrum data to pollutant structures and concentrations is achieved.

The invention discloses a high-voltage power distribution station room dynamic environment and equipment state monitoring system and method. The system comprises a multi-dimensional data perception acquisition integration module, a heterogeneous data feature extraction and fusion transmission module, a pre-analysis module based on composite weight distribution, an association mapping module combined with historical data, a core diagnosis module based on an expert system rule base and a multi-modal visual output module. Multi-source data such as temperature and humidity, gas concentration, vibration, voltage and current are collected, after feature extraction and fusion, a data potential relation is mined by using composite weight distribution and historical data association, then equipment state evaluation and fault diagnosis are performed based on an expert system rule base, and finally, visual display and early warning output are performed in a multi-mode form. The system and the method can comprehensively monitor the environment and the equipment state of the power distribution station house, accurately diagnose faults, and effectively improve the operation safety and the operation and maintenance efficiency of the high-voltage power distribution station house.

The invention discloses a construction method of an expert system based on an RAG technology. The construction method comprises the following steps: step 1, constructing a knowledge base; step 2, constructing a retrieval system; and step 3, generating system construction. In practical application, the retrieval system can take out corresponding knowledge fragments from the knowledge base according to problems of users; the generation system selects a correct answer most conforming to logic according to the knowledge fragment obtained through retrieval, namely a text block, and describes the correct answer by using a smooth language; the generation system includes a large language model. The application of the invention can simplify the maintenance of the knowledge base and provide more friendly and more logical answer presentation, thereby generating a better new generation expert system.

The invention discloses an urban financial service full-link risk prevention and control management system, and belongs to the technical field of risk prevention and control. Comprising a multi-source heterogeneous data acquisition and treatment module, a dynamic risk portrait construction module, a real-time risk monitoring and early warning module, a risk disposal and decision support module, a cross-scene risk linkage analysis module, a compliance and privacy protection module and a system adaptive optimization module. Automation and manual cooperation of risk disposal are realized; a low-risk event automatically executes a preset action through a formula, so that the disposal efficiency is improved; medium and high risk events are pushed to an expert system, a disposal scheme is generated in combination with historical cases, and a block chain records an operation log to ensure transparency and traceability; the module balances machine decision and manual auditing boundaries, and the misjudgment risk is reduced.

The invention relates to the field of broadband alarm, in particular to a broadband user-based optical modem power failure and fiber break alarm real-time monitoring method, which comprises the following steps of: S1, monitoring and recording the online state, the light attenuation level, the flow information, the packet loss rate, the network delay and the log of an optical modem under OLT (Optical Line Terminal) equipment in real time; s2, automatically identifying an abnormal mode and a potential fault according to a model established by a machine learning algorithm and a deep learning algorithm; s3, automatically diagnosing problem reasons according to the collected data, and optimizing the knowledge base in combination with an expert system; s4, performing deep mining and analysis on historical fault data, and predicting a potential fault risk based on the historical data and a real-time monitoring result; and S5, continuously updating and optimizing a fault detection and diagnosis model through a self-learning algorithm model, and uniformly monitoring and managing all data acquisition, analysis and alarm modules. The fault detection accuracy and timeliness can be improved, and potential fault prediction and preventive maintenance are realized.

The invention discloses a wrist joint spasm assessment method and a wrist joint spasm assessment device based on multi-source data fusion. The rotating speed data, the rotating angular speed data and the angle change data of the wrist joint, the pressure change data of the whole palm, the acceleration change data of the palm, the torque change data of the wrist joint and the electromyographic change data of related muscles in the movement process are measured, and scoring is carried out in combination with an expert system. The evaluation of wrist spasm is realized by combining a multiple linear regression algorithm with a differential evolution algorithm; and based on the multi-source data fusion, establishing an evaluation model by using a Bi-LSTM network so as to realize spasm evaluation. The wrist serves as a spasm detection part, and compared with traditional spasm detection equipment, a more convenient data acquisition mode is achieved while the evaluation precision is guaranteed.

The invention relates to an optimization method of a hybrid expert system, computer equipment, a readable storage medium and a program product. A plurality of experts contained in the MOE are deployed in a plurality of artificial intelligence chips in groups, and the method comprises the following steps: carrying out routing calculation on an original input tensor to obtain a routing calculation result; determining input element grouping information corresponding to each expert based on expert index information and expert weight information in the routing calculation result; taking the expert dimension as a parallel dimension, executing rearrangement operation for the original input tensor in parallel based on the input element grouping information, taking a rearrangement result as input information of an expert, executing matrix multiplication and accumulation operation, and taking the expert dimension as the parallel dimension, and based on the input element grouping information, executing anti-rearrangement operation of the matrix multiplication and accumulation operation result in parallel to obtain a final operation result. By adopting the method, the MOE reasoning performance can be improved.

The invention relates to the technical field of electromagnetic interference signal processing, and provides a grading method and system for partial discharge live detection electromagnetic interference signals of a traction substation. The method and the device are used for solving the problems of difficulty in accurately classifying various types of electromagnetic interference signals, difficulty in adapting to changes under different working conditions, poor adaptive adjustment capability and insufficient adaptability in the prior art. The method comprises the following steps: constructing a dynamic graph model to process an electromagnetic interference signal data stream, and generating fusion data; carrying out real-time anomaly detection on the data, and carrying out adaptive context awareness classification in combination with reinforcement learning and a deep belief network; extracting space-time spectrum characteristics of the electromagnetic interference signals, constructing an interactive expert system, generating target parameter configuration, and obtaining a preliminary grading result; and adjusting each algorithm parameter to generate a final grading result. According to the technical scheme provided by the invention, an efficient and intelligent electromagnetic interference signal processing method is provided, the accuracy and the reliability of a grading result are ensured, and the efficiency and the effect of electromagnetic environment management are improved.

The invention relates to the technical field of intelligent position information recommendation, in particular to an intelligent position recommendation method based on artificial intelligence, which comprises the following steps: collecting basic information and behavior data of a user, and constructing a user portrait; acquiring position information, extracting key information to establish a position information base, and introducing an expert system to supplement the position information; understanding implicit significance of position description through a natural language processing method and a semantic analysis method; performing feature extraction on the user portrait and the position information, and constructing a feature data set; training a recommendation model; user feedback is actively inquired through an active learning algorithm; and generating a position recommendation list for the user through the trained recommendation model. The method optimizes description of position information, is more convenient for users to understand, and solves the problem that accurate user portraits cannot be constructed due to lack of data for users and new positions in specific fields through an active learning algorithm and introduction of an expert system.

The invention discloses an unsupervised industrial defect detection method, system and device based on a multi-mode expert system. The unsupervised industrial defect detection method comprises the following steps: acquiring a label-free industrial defect image data set and a text cue word; inputting into a multi-modal expert system to obtain a corresponding prediction result; screening out a preliminary labeling result; filtering the preliminary labeling result based on a prior constraint to obtain a fine label; inputting the industrial defect image data set and the fine label into a self-supervised model for training, and outputting a pseudo label after training is completed; performing double filtering on the pseudo labels by adopting a dynamic threshold value and a prior constraint; inputting the industrial defect image data set and the pseudo label after the current training double filtering into the self-supervised model after the last training for re-training, outputting the pseudo label, and returning to execute the double filtering until the trained self-supervised model is obtained; and predicting an industrial defect image to be detected by adopting the trained self-supervised model to obtain a corresponding industrial defect detection result. And the detection accuracy and efficiency are improved.

The invention discloses an incomplete multi-mode crisis event detection method based on a memory pool and a mode perception expert system, and relates to the field of machine learning, and the method specifically comprises the steps: building a prompt strongly related to an event instance based on the memory pool, and complementing a missing mode through a large model; a CNN encoder, a ResNet encoder and a CLIP encoder are utilized to construct a double-flow encoder, and text and visual feature extraction is achieved; capturing fine-grained event information and clues; the distribution difference between a complemented sample and a complete sample is identified based on a modal perception expert system, and the quality of fusion representation is improved; and inputting the mixed features into the classification head to complete a crisis event detection task. According to the invention, through the prompt construction module based on the memory pool, a large model is introduced to realize careful completion of a missing mode, and the distribution difference between a completion sample and a complete sample is accurately identified based on a mode perception expert system, so that a multi-mode data missing scene can be effectively coped with, and the accuracy and timeliness of crisis event detection are improved.

The invention provides an Elasticsearch cluster fault intelligent detection system, which relates to the technical field of big data storage and retrieval, and comprises a data acquisition module, an Elasticsearch cluster fault analysis module, an expert system module and a model optimization and self-learning module, the current log data and the monitoring index data of the Elasticsearch cluster are analyzed through a fault detection model of the Elasticsearch cluster fault analysis module, the prediction fault type of the Elasticsearch cluster is determined, and automatic detection and prediction are achieved; a deep learning algorithm and an expert rule base are fused, so that complex and dynamic fault scenes can be efficiently processed, the false report rate and the missing report rate are reduced, and the detection accuracy is improved; and through the model optimization and self-learning module, self-learning and dynamic optimization of the fault detection model are realized, and the operation and maintenance complexity and cost are reduced.

The invention relates to the technical field of artificial intelligence auxiliary medical treatment, and provides an artificial intelligence-based marine first aid auxiliary diagnosis method and system. The method comprises the following steps: receiving a real-time multimedia data stream from an offshore first-aid site; carrying out conjoint analysis on the image and audio information, positioning key elements of the involved injured individuals and the surrounding environment, and obtaining an emergency scene description after enhanced analysis; extracting professional suggestions suitable for the current first-aid situation in combination with correction parameters of influences of specific variables of the marine environment on injuries and patients; activating a remote expert system, interacting with field first-aid personnel through two-way video communication, and generating personalized guidance information; and combining the personalized guidance information with the calibrated visual field of the first-aid personnel, and displaying decision support information provided by artificial intelligence in the visual field of the first-aid personnel. According to the technical scheme, the first-aid response speed and accuracy are improved, and it is ensured that the wounded patient obtains the most appropriate medical treatment in the first time.