199 results about "Model graph" patented technology

The invention relates to the technical field of intelligent operation and maintenance, in particular to an execution method of a large model graph retrieval enhancement system oriented to software and hardware monitoring operation and maintenance, which comprises the following steps: S1, inputting fault information to the graph retrieval enhancement system; s2, a graph construction and updating module receives and processes the fault information, generates a triple, and writes the triple into an operation and maintenance / fault knowledge graph; s3, the fault information is input into a graph retrieval enhancement module for two-stage filtering retrieval, and sub-graph information is screened out; s4, the prompt word construction module converts the fault information and the screened sub-graph information into structured natural language prompt segments; and S5, a reasoning generation module performs natural language question and answer to generate a fault analysis result. Based on the above scheme, the execution method enhances the adaptive capacity of knowledge reasoning and fault positioning, gives play to the generalization reasoning capacity of a large language model while ensuring the accuracy, and enhances the practical value.

The invention discloses an MCP-based multi-agent collaboration method and device and a medium, and relates to the technical field of artificial intelligence, the method comprises the following steps: obtaining a model registry form through a standardized model registration interface, analyzing model metadata in the registry form, and extracting a core field of a current registration model; creating a model node in the model map, and initializing the model node based on the core field; receiving a task request, triggering a scheduling engine, determining a plurality of candidate model nodes in the model atlas according to task content, respectively calculating task execution scores of the candidate model nodes, and generating a model execution chain based on the task execution scores; and based on the model execution chain, scheduling a candidate model corresponding to the candidate model node, and executing the corresponding task content. Dynamic access of multiple types of models is supported through a standardized model registration interface, system expansibility and integration efficiency are improved, and matching precision of tasks and models and multi-model cooperation continuity are enhanced.

The invention discloses a large-scale heterogeneous computing power cluster training and pushing acceleration method, which comprises the following steps: S1, collecting operation characteristics of each node of a heterogeneous cluster, and constructing an equipment state parameter set; s2, loading a training and reasoning model, constructing an intermediate representation graph structure, and generating graph structure information; s3, inputting the graph structure information and the equipment state parameter set into a scheduling strategy model constructed based on a neural network ordinary differential equation, and generating an optimization decision result; s4, reconstructing a model graph according to an optimization decision result, and dividing the model graph into a training sub-graph and a reasoning sub-graph; s5, mapping the sub-graphs to heterogeneous nodes, performing resource matching and task distribution according to the equipment state parameter set, and generating a scheduling result; s6, collecting performance feedback information; and S7, inputting feedback information into the scheduling model to update parameters, and adjusting evolution function strategy parameters. According to the method, the scheduling acceleration of the training and pushing task of the deep model in the heterogeneous cluster is realized.

The invention discloses an oil product pipeline leakage monitoring method, device and equipment and a medium, and relates to the technical field of pipeline monitoring, the method comprises the steps that a graph model of a pipeline monitoring network is constructed, and each node pipeline in the graph model is provided with a measuring point; collecting pipeline data of each measuring point in real time, wherein the pipeline data comprises temperature, pressure and flow data; performing feature extraction on the pipeline data to obtain various feature data; determining a pipeline working condition for the various characteristic data corresponding to each measuring point, and adjusting a fusion weight for fusing the various characteristic data in real time by adopting a reinforcement learning algorithm based on the pipeline working condition; according to each fusion weight, performing feature fusion on the multiple feature data to obtain a multi-source fusion feature; inputting the multi-source fusion feature of each measuring point into a sensor fault recognition model, and outputting a sensor fault mark; and inputting the multi-source fusion feature of each node carrying the sensor fault mark into a leakage identification and positioning model, and outputting the leakage probability of each node. The prediction accuracy can be improved.

The embodiment of the invention discloses a three-dimensional object texture enhancement method based on coordinate mapping and super-resolution. A specific embodiment of the method comprises the steps of obtaining a scene image data sequence corresponding to a target scene; constructing a three-dimensional scene grid model; generating a scene texture expansion view and image data of each model; the following training steps are executed: obtaining a high-resolution texture expansion graph; generating image data of each rendering model; generating a pixel loss value and a perception loss value; generating an adversarial loss value; generating an image loss value; in response to determining that the image loss value does not meet the preset loss condition, adjusting the parameters of the initial neural network, and executing the training step again; in response to determining that the image loss value meets the loss condition, determining a generative network in the initial neural network as a generative model; and generating a target three-dimensional scene grid model. According to the embodiment, enough training data can be provided for training of the generative network, and the precision and accuracy of the generated three-dimensional grid model can be improved.

The invention discloses a knowledge distillation-based lightweight diffusion model drawing generation method and system, and relates to the technical field of drawing generation, and the method comprises the steps: collecting electric power engineering drawing data, preprocessing the drawing data, constructing a basic model, training the model, obtaining the trained basic model, and designing a lightweight model. And defining a knowledge distillation loss function, combining with the output of the basic model, training a lightweight model, outputting an intermediate feature map and a prediction result by using the trained lightweight model, evaluating the quality and diversity of an electric power engineering drawing sample generated by the lightweight model by using indexes, and adjusting the model and data and optimizing model parameters according to an evaluation result. According to the lightweight diffusion model provided by the invention, the parameter quantity and the calculation complexity of the model are remarkably reduced while relatively high quality of the generated image is maintained, the operation efficiency and the mobility of the model are improved, and the lightweight diffusion model can be operated on equipment with limited resources to meet the requirement of real-time application.

The invention discloses an unmanned aerial vehicle performance pattern generation method and device, computer equipment and a storage medium, and the method comprises the steps: collecting historical unmanned aerial vehicle performance patterns, and carrying out the text marking of the historical unmanned aerial vehicle performance patterns, so as to construct a training data set; training a text graph model and a corresponding control model by using the training data set so as to construct a pattern generation model; and generating a corresponding unmanned aerial vehicle performance pattern by using the pattern generation model based on a set pattern generation requirement. According to the method, historical unmanned aerial vehicle performance patterns are collected and text marking is carried out to construct a training data set, the data set is utilized to train a text graph model and a control model thereof, a pattern generation model is constructed, and finally patterns are generated through the model pattern generation model according to set pattern generation requirements. In this way, the process of manual direct design or repeated adjustment can be reduced, the generation period of the unmanned aerial vehicle performance pattern is remarkably shortened through automatic model generation, and the generation efficiency is improved.

The invention discloses a multi-level network threat dynamic identification method based on a graph neural network, and the method comprises the following steps: collecting multi-source heterogeneous network security data, and carrying out the preprocessing; constructing a multi-level network threat graph; inputting the multi-level network threat graph into an improved GraphSAGE network to carry out graph embedding modeling; identifying an attack propagation path, and extracting a risk sub-graph region serving as a candidate attack chain; performing threat level evaluation on the risk sub-graph region, and calculating an overall threat score of the risk sub-graph region; and comparing the overall threat score with a preset threshold value, if the overall threat score exceeds the threshold value, determining that the threat is a high-risk threat, and outputting an early warning result. The multi-level threat graph is modeled through the graph neural network, attack chain recognition and threat evaluation are achieved, and the method has the advantages of being high in expressive power, accurate in recognition and fast in response.

The invention relates to a modeling method, in particular to an unmanned aerial vehicle spectral image BRDF rapid acquisition and adaptive generalization modeling method. The method comprises the following steps: 1, determining a research area, obtaining multi-angle BRDF data of the research area, and obtaining illumination change data at the same time; 2, preprocessing the acquired multi-angle BRDF data in combination with illumination change data to obtain preprocessed multi-angle images, splicing the multi-angle images to generate a panorama of the research area, and acquiring a digital surface model graph at the same time; 3, carrying out pixel-by-pixel classification on the research area based on the panorama to obtain a ground feature category map; calculating the gradient and the slope direction of the research area pixel by pixel based on the digital surface model graph; 4, correcting a preset BRDF model by using the acquired illumination change data, gradient and slope direction; and 5, self-adaptive generalization modeling is carried out. According to the method, high-efficiency BRDF acquisition can be realized, and the influence of regional change, space environment and topographic factors on BRDF modeling precision can be eliminated.

The invention discloses a robot path planning method for warehouse logistics cluster operation, and belongs to the technical field of path planning. The method comprises the following steps: constructing a two-dimensional model diagram by using a grid method; the ant colony algorithm is improved; an improved ant colony algorithm is adopted to plan an operation path for each logistics robot; a local obstacle avoidance strategy is carried out through a dynamic window algorithm, and whether obstacles exist around is detected; and the collision type is judged, and a corresponding collision avoidance strategy is adopted until all the robots safely and correctly arrive at the preset target point. According to the method, the pheromone concentration is initialized by introducing the Sigmoid function, blind search in the early stage is avoided, the self-adaptive slope parameter is introduced, and the steep degree of the Sigmoid function is dynamically adjusted according to environmental characteristics; meanwhile, the ant colony algorithm and the dynamic window algorithm are fused, parameters are dynamically adjusted according to the number of iterations, the obstacle rejection weight is introduced, the algorithm is prevented from falling into local optimum, and global path planning and local dynamic obstacle avoidance are achieved.

The invention discloses a civil engineering construction task automatic scheduling method and system. The method comprises the steps that a heterogeneous graph model representing construction task elements and the incidence relation of the construction task elements is constructed; processing the heterogeneous graph model by using a graph neural network to mine a dependency relationship between processes and extract risk association features related to uncertainty to form a construction state graph; constructing a Bayesian network based on the construction state atlas; probabilistic reasoning is carried out through a Bayesian network, the influence of uncertain factors is quantified, and key information representing risk conduction is fed back to the graph neural network so as to dynamically correct recognition of the graph neural network on a process dependency relationship; the dependency constraint output by the graph neural network and the risk quantification result output by the Bayesian network are fused, and a construction task scheduling scheme is generated; and synchronously updating the heterogeneous graph model, the graph neural network and the Bayesian network according to field construction feedback data to realize closed-loop iterative optimization of a scheduling scheme.

Disclosed in the embodiments of the present application are a model rendering method and a related apparatus. The method comprises: for an initial three-dimensional model to be processed, simplifying the model structure of the initial three-dimensional model on the basis of a topological structure feature of the initial three-dimensional model, so as to obtain a simplified three-dimensional model, wherein the number of meshes comprised in the simplified three-dimensional model is less than the number of meshes comprised in the initial three-dimensional model, and the difference between the simplified three-dimensional model and the initial three-dimensional model meets a preset similarity requirement; by means of lighting simulation processing, determining a lighting intensity corresponding to a vertex in the simplified three-dimensional model when the simplified three-dimensional model is in a virtual environment; and on the basis of the simplified three-dimensional model and the lighting intensity corresponding to the vertex in the simplified three-dimensional model, rendering the simplified three-dimensional model, so as to obtain a model image for representing the simplified three-dimensional model. The method can automatically reconstruct and render a three-dimensional model, thereby improving the reconstruction efficiency of the three-dimensional model and reducing labor costs.

The invention provides a laser interference processing optimization method and system for improving the performance of a micro-nano structure, and relates to the technical field of laser micro-nano machining.The method comprises the steps that firstly, micro-nano structure sample processing parameters are extracted, a sensitive tracking model is constructed to analyze parameter sensitivity, and a lifting-retaining parameter set is obtained; according to the simulation modeling, processing and lifting a dynamic model diagram, analyzing the period consistency of the dynamic model diagram and a reference design diagram through foreground and background maximum flow cutting, and determining a structure period; if the local photoetching model diagram is of a periodic structure, projecting the actual local photoetching model diagram to a local photoetching model diagram of one type in a sine manner, analyzing the curve slope, and optimizing a periodic interference angle and a light beam wavelength; and if the structure is a non-periodic structure, interpolating a gradient processing chromaticity layout for the second-class local photoetching model diagram according to a chromaticity tradeoff criterion, and analyzing and optimizing a dynamic phase in real time. According to the method, high-precision optimization of the processing parameters of the micro-nano structure with consistency in different periods is realized, and the overall performance and stability of the micro-nano structure are effectively improved.

The invention belongs to the technical field of model-driven system engineering and intelligent system modeling, and particularly relates to a test requirement formalized design method based on primitive intelligent extension. The method comprises the following steps: obtaining a semantic map, and defining semantic nodes in the semantic map as type modules or intelligent modules; instantiating the type module to obtain a type model instance node, and instantiating the intelligent module to obtain an intelligent model instance node; organizing the type model instance nodes and the intelligent model instance nodes into a model graph; constructing an extended state primitive, constructing a plurality of interactive combination states based on the extended state primitive, defining an intelligent migration mechanism and constructing an extended state machine diagram; respectively testing the model diagram and the expansion state machine diagram, and if the model diagram and the expansion state machine diagram are tested to be qualified, exporting the model diagram and the expansion state machine diagram; the method can solve the problems of dynamic behavior description fragmentation, multi-agent interaction mechanism stiffness and insufficient model reusability of a traditional system modeling language in intelligent system modeling.

A meta-model topology comprises a plurality of functions and conforms to a global label schema. A new function not included in the plurality of functions is integrated into the meta-model topology. A particular label of interest that is associated with the new function is identified and the new function is configured such that an output from the new function conforms to an output form corresponding to the particular label of interest from the global label schema. The new function is then integrated into the meta-model topology and the meta-model topology that includes the new function is used to generate a model graph. The model graph is then deployed to a remote application that is configured to receive data prompts comprising input data processed by nodes of the model graph.

The invention discloses a human behavior prediction method based on a double-flow space-time diagram convolutional network, and belongs to the technical field of computer vision and deep learning, and the method comprises the following steps: S1, obtaining a human skeleton data set; s2, preprocessing skeleton point data; s3, processing the preprocessed skeleton point data through a double-flow space-time diagram convolutional network; s4, constructing a multi-scale joint point spatial dependency relationship; s5, processing the output features of the first stream through frequency weighting and a channel feature importance attention mechanism, dynamically adjusting the feature weights of high-frequency and low-frequency channels, and generating enhanced spatial-temporal features; and S6, fusing the output features of the double-flow network, generating a final behavior prediction result, and realizing human body behavior prediction. According to the method, through a multi-space category modeling graph convolution module and a frequency weighting and channel feature importance attention mechanism, a multi-space category modeling double-flow space-time graph convolution network is further provided, and the accuracy of behavior prediction is remarkably improved.

In one aspect, a method for modeling graphs on distributed storage, comprising: receiving graph data including vertices, edges, and properties; mapping each vertex to a vertex record in a distributed storage system; mapping each edge to an edge record in the distributed storage system; mapping each property to a property record in the distributed storage system; determining, for each vertex, whether a number of incident edges exceeds a maximum record size threshold; storing pointers to incident edge records directly within vertex records when the number of incident edges does not exceed the maximum record size threshold; utilizing an adjacency index to lookup incident edge records when the number of incident edges exceeds the maximum record size threshold; and executing graph traversal operations that navigate from vertex to vertex by accessing the appropriate edge records through either the stored pointers or the adjacency index.

The invention provides a secondary equipment debugging intelligent auxiliary method and system based on a secondary logic model, and relates to the technical field of debugging assistance, and the method comprises the steps: collecting multi-source structured data related to secondary equipment; constructing a secondary logic model diagram for debugging assistance by taking the equipment nodes as objects and the communication relationship and the logic control relationship as connecting edges based on the multi-source structured data; obtaining a debugging task; mapping the debugging task to a corresponding node in the secondary logic model diagram through a semantic and configuration fusion matching mechanism; based on the secondary logic model diagram and the debugging task mapping result, generating a structured debugging operation template in a mode of combining template retrieval, state injection and logic verification; based on the structured debugging operation template, comparing and checking the running state data of the current debugging object equipment; and on the basis of the comparison check result and the structured debugging operation template, executing online monitoring of the link state, and assisting in realizing intelligent analysis of the debugging state.

The embodiment of the invention discloses a self-adaptive merging method and system based on multiple single BIM models, and the method comprises the steps: carrying out the projection of the single BIM models from three dimensions to two dimensions, obtaining a two-dimensional model diagram on a two-dimensional plane, and extracting feature intersection points and feature edges from the two-dimensional model diagram; on the basis, combining vertexes and edges in the single BIM models and feature intersection points and feature edges corresponding to the two-dimensional model graph to form a model feature vector matrix, calculating model feature values, and obtaining a model feature value set of the multiple single BIM models; and carrying out clustering analysis on the model characteristic value set to obtain a fitted and combined target model, and carrying out combination to realize light weight of the model. And moreover, the single BIM models are automatically processed in the whole process, manual operation of designers is not needed, and the model merging efficiency is improved.

Provided are an explainable artificial intelligence (XAI) apparatus and a method for analyzing a model thereof. The XAI apparatus according to the present invention may use bytecode of a model to generate a model graph even for models including dynamic control flow, which are otherwise unable to generate a graph, in order to provide visualization of the decision-making process of an XAI algorithm.

The invention discloses a spherical panoramic image feature matching method and system, a terminal and a storage medium, and the method comprises the steps: obtaining a spherical panoramic image, and carrying out the preprocessing of the spherical panoramic image; screening the preprocessed spherical panoramic image based on a spatial constraint model, and constructing a scene graph based on the screened spherical panoramic image; segmenting the scene graph into a plurality of sub-scene graphs, and performing priority ranking on the sub-scene graphs based on a graph sequence scheduling algorithm; and performing geometric correction and feature extraction on all the screened spherical panoramic images, and executing a feature matching task of the spherical panoramic images in the sub-scene images according to the priority ranking of the sub-scene images. Through a spatial constraint model, a graph order scheduling algorithm, geometric correction and a cascade Hash matching algorithm, the problem that feature matching efficiency and precision are difficult to consider when a spherical image feature matching technology processes large-scale high-resolution spherical images is solved.

The invention provides an education large model tuning method and device based on dynamic optimization, and relates to the technical field of artificial intelligence. The method comprises the following steps: acquiring multi-modal data; the multi-modal data comprises physiological data, learning behavior data and emotion feedback data; inputting the multi-modal data into a heterogeneous model in the education large model, processing the multi-modal data by using the heterogeneous model to obtain multi-modal feature vectors, and fusing the multi-modal feature vectors to obtain a fused feature vector; the heterogeneous model comprises a GLM-4 model, a graph neural network and a convolutional neural network; and inputting the fusion feature vector into a reinforcement learning model in an education large model, determining a reward through a reward function, determining a Q value according to the reward, and determining a recommendation strategy based on the Q value. According to the method, the individuation capability and the real-time response efficiency of the education large model can be comprehensively improved.

The embodiment of the invention discloses a model rendering method and a related device, and the method comprises the steps: carrying out the simplification processing of a model structure of a to-be-processed target three-dimensional model based on the topological structure characteristics of the target three-dimensional model, and obtaining a simplified three-dimensional model; wherein the number of grids included in the simplified three-dimensional model is smaller than the number of grids included in the target three-dimensional model, and the difference between the simplified three-dimensional model and the target three-dimensional model meets a preset difference requirement; through illumination simulation processing, determining illumination intensity corresponding to a vertex in the simplified three-dimensional model when the simplified three-dimensional model is in the target virtual environment; and based on the simplified three-dimensional model and the illumination intensity corresponding to the vertexes in the simplified three-dimensional model, rendering the simplified three-dimensional model to obtain a model image for representing the simplified three-dimensional model. According to the method, the three-dimensional model can be automatically reconstructed and rendered, the reconstruction efficiency of the three-dimensional model is improved, and the labor cost is reduced.

The application relates to the technical field of image processing, and provides a model material replacement method and device for a design drawing. The method comprises the following steps: determining a target model from each model of a design drawing according to a received material selection instruction; obtaining a target material map corresponding to the material selection instruction from each material map of the target model; and merging the target material map, a light map, a diffuse reflection map and a reflection refraction map of the design drawing to obtain a model image of the target model in the design drawing; wherein the design drawing is an image obtained through rendering; the light map, the diffuse reflection map and the reflection refraction map are each layer obtained after the design drawing is rendered. The model material replacement method for the design drawing provided by the application can reduce the calculation amount of the model in the design drawing when the material is replaced, and reduce the resources consumed when the model material is replaced.

The invention discloses an LDPC (Low Density Parity Check) code check matrix construction method, which is used for processing a low earth orbit satellite ground foundation zone, and comprises the following steps of: A1, determining degree distribution of a basis matrix under a high code rate by adopting an extrinsic information transfer graph EXIT, and realizing construction of the basis matrix under the constraint of optimal degree distribution through extrinsic information transfer P-EXIT based on a basic model graph; a2, based on the code length and the code rate of FEC coding of the DVB-S2 protocol, determining the size and the expansion factor of a basis matrix corresponding to each code rate; a3, determining a finite field and a generator based on an expansion factor, and constructing a linearly independent shift value row vector and cycle coefficient table; a4, selecting row vectors from the cyclic coefficient table to construct a cyclic shift matrix, and performing short ring identification and elimination on the cyclic shift matrix; and A5, performing matrix hashing on the basis of the basis matrix and the cyclic shift matrix to obtain an LDPC check matrix.

This application relates to the field of simulation technology and discloses a method, apparatus, electronic device, and storage medium for simulation result analysis and evaluation. The method includes: in response to receiving any simulation result, determining whether the simulation result meets a performance target; when the simulation result meets the performance target, at least extracting the model diagram of the simulation model to which the simulation result belongs and at least one directly related scheme parameter, and obtaining at least one non-directly related scheme parameter related to the performance target and / or the simulation result, to determine the evaluation value and influence coefficient corresponding to each non-directly related scheme parameter; calculating a simulation evaluation score based at least on the evaluation values ​​and influence coefficients corresponding to all non-directly related scheme parameters, and then outputting an overall simulation evaluation; calling a simulation report template, and generating simulation optimization suggestions and an evaluation report based on the template, at least according to the overall simulation evaluation, the model diagram, and at least one directly related scheme parameter. This application facilitates the automated optimization and evaluation of simulation results.

The application provides a hierarchical automatic layout method and device for internal module graphs, comprising the following steps: constructing a preliminary shape of a model graph layout; scanning each node, if there is a nested relationship, taking the nested information as a subgraph of the current node; scanning each node of the nested information, if there is a nested relationship, taking the nested information as a subgraph of the current node, and constructing a preliminary shape of a nested graph layout; determining virtual connections between different node nested relationships; and correcting the preliminary shape of the model graph layout to obtain a final shape of the model layout. In the layout process, the ports and graph element nesting involved in the internal module graph are considered, and a hierarchical automatic layout method completely covering the internal module graph elements is realized.

The invention discloses a protein and small molecule binding structure prediction method, device, equipment and medium, and relates to the technical field of protein and small molecule binding prediction.The method comprises the steps that a first modeling graph structure and a second modeling graph structure after a protein block and a to-be-tested small molecule compound are modeled respectively are obtained; the first modeling graph structure and the second modeling graph structure are input into a TANKBind model, and a predicted protein small molecule compound is output through the TANKBind model; generating a first topological file and a second topological file corresponding to the protein structure file and the small molecule structure file of the protein small molecule compound; performing molecular dynamics simulation by using a preset molecular dynamics simulation tool according to a simulation file generated by the first topological file and the second topological file to obtain a molecular dynamics simulation track file; and analyzing the molecular dynamics simulation track file, the first topology file and the second topology file to obtain a simulation analysis result.

The application provides a large model inference optimization method, system, medium, terminal and program product for Ascend processors. The method comprises: comparing the automatically detected version with a built-in compatibility table; if the versions do not match, automatically loading a runtime patch or calling a backup library; if the versions match, processing a user request based on a preset hybrid scheduling mechanism; dynamically selecting a MindIE inference framework or a vLLM inference framework based on a runtime adaptation layer according to the user request parameters; converting large model weights into low-bit weights and compensating based on an error compensation matrix; identifying subgraphs and fusing operators for a model graph according to the computing characteristics of the Ascend processor, and rearranging intermediate tensors according to the 512B memory access alignment rule of the Ascend processor; performing pipelined overlap processing on the matrix multiplication calculation of the current token and the full aggregation operation based on an asynchronous execution flow; and performing inference and generating an inference result according to the user request. The application can improve the efficiency and reliability of large model inference.

The invention relates to an automobile automatic window falling method, system and device, and belongs to the technical field of automobile control. The water falling working condition is analyzed based on the real-time invasion speed of the water surface relative to an automobile door and the distance between the water surface and the lower edge of an automobile window, and an analysis result is obtained; the method comprises the following steps: acquiring environment characteristic data information around an automobile by using an outside-automobile look-around fisheye camera and a millimeter-wave radar, and performing abnormality judgment according to the environment characteristic data information around the automobile and a model graph loaded by the automobile to obtain a first abnormality judgment result or a second abnormality judgment result, and generating an emergency window descending scheme according to the first abnormity judgment result or the second abnormity judgment result. Through multi-sensor fusion and multi-condition judgment after the automobile falls into water, the window falling function can be provided when the automobile falls into water, whether obstacles exist around after the automobile falls into water to prevent opening of automobile windows or not is judged, passengers can be further prevented from being trapped in the automobile after the automobile falls into water, and great life safety hidden dangers brought to the passengers are avoided.