164 results about "Distance analysis" patented technology

The invention relates to the technical field of screen control, in particular to a computer screen control method and system. The method comprises the following steps: collecting a computer lens image to carry out scene modeling, generating a three-dimensional usage scene model, carrying out scene privacy evaluation and generating a privacy degree parameter, analyzing a peep-proof demand according to the privacy degree parameter, generating a screen peep-proof demand, carrying out portrait positioning when peep-proof is needed, generating a portrait positioning set, and carrying out image processing according to the portrait positioning set. And calculating a virtual contact distance based on the portrait positioning set, performing contact sight simulation to obtain a figure sight track, analyzing a screen visible range according to the sight track and the virtual contact distance, and dynamically adjusting the screen brightness to realize the peep-proof control method. According to the invention, a safer computer screen control method is realized.

The invention discloses an industrial robot intelligent obstacle avoidance control method and system based on visual identification, and relates to the technical field of robot obstacle avoidance control, and the method comprises the steps: carrying out the calibration of a multi-mode visual sensor, obtaining an initial depth map and point cloud information, and combining the visual identification and depth compensation technology; recognizing and positioning obstacles in the operation area, and constructing a dynamic environment map layer; and the current robot state is collected, kinematics calculation and collision distance analysis are carried out, whether obstacle avoidance operation needs to be executed or not is judged, if yes, an obstacle avoidance path is generated in combination with the dynamic environment map layer and the target point location, feasibility verification is carried out after the path is generated, and the path passing the verification serves as an execution track to be issued to the control module. According to the invention, the recognition precision and depth perception integrity of the industrial robot on obstacles in a complex environment are improved, high feasibility of path planning and high-reliability obstacle avoidance capability in a dynamic environment are realized, and the intelligent decision-making level and operation safety of the system are remarkably enhanced.

An automotive control system and method, includes a radar sensor, attached to a vehicle body panel, for receiving radar signals having a frequency, and processing circuitry configured with neural network encoders for encoding multiple frames of Angle-Doppler (AD), Range-Doppler (RD), and Range-Angle (RA) feature maps from the radar signals, an adaptive-directional attention block to sample rows and columns and apply self attention after each sampling instance, and a RD decoder and a RA decoder that generate RD and RA probability maps. Each map is a colorized feature map, with each pixel color representing a predicted class label for objects. An object detection component identifies the objects, and an object distance analysis component predicts a distance to the identified objects. An object velocity component predicts a velocity of the identified objects.

The invention relates to the technical field of building structure health monitoring, in particular to an unmanned aerial vehicle laser radar-based building point cloud deformation quantitative extraction method, which comprises the following steps of: based on building corner structure characteristics in point cloud data, carrying out quantitative evaluation on the overall inclination state of a building through point cloud segmentation, straight line fitting and angle calculation; based on an ideal plane hypothesis, performing plane fitting and point-to-plane distance analysis on the building facade point cloud in the point cloud data, identifying a local concave-convex deformation area of the building facade, and performing quantitative evaluation; and roof plane detection and wall surface normal vector analysis are carried out based on the point cloud data, and accurate quantification is carried out on parapet wall inclination based on region growth segmentation and PCA principal direction analysis. Through automatic processing and quantitative analysis of an algorithm, subjective errors and inconsistency possibly occurring in a traditional manual evaluation method are avoided, and objectivity and repeatability of an evaluation result are enhanced.

Systems and methods are provided for scaling a 3-D representation of a building structure by selectively pairing camera poses generated by augmented reality frameworks. The geometric information provided by augmented reality frameworks enables scale for non-augmented reality cameras, such as SLAM derived camera solutions, associated with the augmented reality cameras. To reduce the noise and error that augmented reality frameworks can impart into their camera solves, only reliable augmented reality cameras are used for scale calculations of associated non augmented reality cameras. Reliable augmented reality cameras are identified based on translation distance analyses and comparisons. The method includes obtaining world map data including a first track of real-world poses for a plurality of images. The plurality of images comprises non camera anchors.

The invention provides a geological disaster monitoring method based on BIM and LiDAR combination. The method specifically comprises the steps that coordinate conversion from a BIM model to LiDAR point cloud data is carried out; bIM + point cloud three-dimensional space analysis is carried out, and the spatial position relation between the surface collapse area and the tunnel is calculated; carrying out multi-time sequence LiDAR point cloud earth surface monitoring, and carrying out filtering classification on the ground point cloud of each time sequence to obtain real ground feature points; through point cloud space distance analysis, the 3D deviation of surface deformation is obtained and is used as a supplement of a foundation SAR monitoring result. Therefore, the spatial position relation between the ground collapse area and the tunnel can be accurately obtained. Belongs to the field of geological disaster monitoring and rescue.

The invention relates to the technical field of wireless communication, and discloses a wireless communication operation and maintenance data analysis method and system, and the method comprises the following steps: in all sub-regions of a wireless communication operation and maintenance region, carrying out the high-frequency similarity analysis of the business flow value data of each historical time period, and constructing the data analyzed in each time period to obtain a first set; performing distance analysis on the first set of each time period, and constructing a second set through a distance analysis result; and performing influence analysis on the sub-regions in the second set, and constructing a third set according to an influence analysis result. According to the method, the analysis sub-regions which influence each other and are key in the wireless communication network can be accurately identified, so that operation and maintenance personnel can focus on key regions, carry out key monitoring on the key analysis sub-regions, distribute network resources more reasonably, increase bandwidth in the key regions, optimize base station configuration and the like, and the resource utilization efficiency is improved.

The invention relates to the technical field of building engineering management, in particular to a building water supply and drainage system based on a building BIM (Building Information Modeling), which comprises a pipeline drawing generation module, an interface arrangement verification module, an arrangement space detection module, an operation data monitoring module and a fluid risk early warning module. According to the method, standardized drawing mapping of the pipeline trend, size and node layout is achieved through structured extraction of the BIM model, it is ensured that drawing output is complete and consistent, interface offset is automatically compared, abnormal arrangement is accurately recognized, the installation accuracy is improved, component conflicts can be pre-judged through space node clear distance analysis, field coordination is assisted, and the construction efficiency is improved. The method comprises the following steps: monitoring flow velocity pressure of a key drainage section, screening abnormal nodes, filing a hydraulic state, analyzing high-frequency risk points in a time sequence, marking continuous abnormity, providing operation early warning support, constructing drawing generation, arrangement verification, space coordination and a data feedback closed-loop process, and improving construction design accuracy and system operation stability.

The invention relates to the technical field of bioinformatics, in particular to a mining method and system for synthetic biology functional elements and a storage medium. The method comprises the following steps: acquiring functional demand data of a target metabolic pathway; performing evolutionary distance analysis and function prediction on homologous genes of a plurality of species according to the function demand data, and performing protein structural domain conservative classification on preset genome sequence data to obtain candidate element cluster data with function relevance; obtaining expression system data of the host cells; and according to the candidate element cluster data and the expression system data, scoring each functional element based on codon optimization degree, protein folding stability and metabolic load prediction to obtain hierarchical element suitability data. According to the method, factors such as sequence characteristics, expression efficiency and regulation and control network interaction are considered at the same time, multi-level accurate screening is achieved, and therefore the low efficiency and limitation of a traditional screening mode are overcome.

The invention provides a river turbidity partition prediction method and device and a server, and relates to the technical field of water quality monitoring, and the method comprises the steps: obtaining a training data set through a water quality monitoring buoy, carrying out the data analysis processing of the training data set through a preset distance analysis model, and determining the comprehensive distance between all measurement points in a monitoring water area; carrying out clustering analysis processing on the training data set by utilizing a density-based spatial clustering algorithm and based on the comprehensive distance so as to divide the monitored water area into a sub-area set, and by calculating the closeness of each sub-area, determining the sub-area with the closeness greater than a preset closeness threshold value as a key region of interest; and determining a target influence factor influencing the turbidity through a random forest algorithm, determining a target turbidity prediction model based on the target influence factor through a graph convolutional network, and predicting the river turbidity by using the target turbidity prediction model to obtain a river turbidity prediction result. According to the method, the river turbidity prediction accuracy can be remarkably improved.

The invention discloses a power transmission line hazard source early warning method based on image and point cloud fusion, and relates to the technical field of intelligent early warning, and the method comprises the steps: connecting monitoring equipment to obtain monitoring image information, and carrying out the positioning matching with a back-end point cloud library, and obtaining the abnormal deviation information; time positioning is carried out according to the deviation positioning coordinates, a time sequence observation window is constructed, and a time sequence monitoring data set is established; performing deviation value change analysis to obtain an approximation trend; carrying out distance analysis according to the deviation value through a distance measurement strategy, and positioning a dangerous source distance; and performing early warning risk assessment, and performing early warning feedback. The technical problems that in the prior art, monitoring equipment can only carry out dangerous source identification on collected images, repeated and invalid alarming exists, risk distance measurement cannot be carried out, and the requirement of power system development for power transmission safety is difficult to meet are solved, the dangerous source distance measurement function is achieved, and the safety of power transmission is improved. The distance between the dangerous source and the power transmission line is accurately judged, and the safety of the power transmission line is improved.

The invention relates to the technical field of image processing, in particular to a concrete member surface defect detection method and system based on image segmentation, and the method comprises the steps: obtaining a surface image of a concrete member, and dividing the surface image into a plurality of image blocks; and performing frequency domain transformation on any image block to obtain a power spectrum. According to the method, the feature space period of each image block is analyzed and calculated through frequency domain transformation, and adaptive weighted fusion is carried out on texture features at different distances by using Gaussian weight on the basis of the feature space period. According to the method, the feature extraction process can dynamically adapt to the physical scale of image local textures, namely, small distance analysis is automatically emphasized on fine textures and large distance analysis is automatically emphasized on rough defects, so that scale-perceived composite texture features are constructed; and the accuracy of identifying the concrete surface defects under the complex texture background is obviously improved.

The present invention relates to an intelligent wooden man robot and motion analysis system capable of measuring skill. The system analyzes the weights of the hitting points in each area of ​​the wooden man according to the distance from the key hitting point; uses a pressure sensor to obtain the force data of hitting the wooden man; monitors the hitting points and force of the wooden man, and calculates the hitting skill value in combination with the weights of the hitting points in each area of ​​the wooden man; uses a K-means cluster analysis method to obtain the skill level of different hitting skill values, and emits lights of different brightness at the hitting points according to different skill levels; extracts the image feature data of the hitting action video stream, compares it with the standard action image feature data, and evaluates the trainee's action in combination with the corresponding skill level; and emits lights of different colors according to the evaluation results of the hitting action. The present invention solves the problem that it is difficult to interact with lights according to the accuracy of different hitting parts of the wooden man in the prior art to accurately evaluate the trainee's skill and action.

The invention discloses a UUV trajectory data enhancement method, system and device based on time sequence fusion, and the method comprises the steps: obtaining the position data, time index data and navigation data of an underwater unmanned vehicle, and analyzing the position data to obtain three-dimensional distance data; performing normalization processing on the time index data and the three-dimensional distance data to obtain an initial time sequence data set, performing distance analysis on the initial time sequence data set to obtain initial distance data, and performing preprocessing to obtain a time sequence data set; establishing a time series data clustering model to analyze the time series data set to obtain a clustering time series data set; performing iterative updating through a particle swarm optimization algorithm and a fitness function to obtain an updated time sequence data set; and performing fitting correction on the updated time sequence data set to obtain a corrected time sequence data set. According to the method, the time index data is combined, updating is carried out through the particle swarm optimization algorithm, and then the accuracy of trajectory data simulation of the unmanned underwater vehicle is improved through fitting correction.

A method and a system for establishing a route of an unmanned aerial vehicle are provided. The method includes identifying an object from surface scanning data and shaping a space, which facilitates autonomous flight, as a layer, collecting surface image data for a flight path from the shaped layer, and analyzing a change in image resolution according to a distance from the object through the collected surface image data and extracting an altitude value on a flight route.

The invention relates to the field of electric data processing, in particular to a data management method based on a digital twin smart factory, and the method comprises the steps: obtaining the related data of the digital twin smart factory, carrying out the preprocessing, clustering, obtaining a clustering result, calculating the Gaussian density of each data point in each clustering cluster, and calculating the Gaussian density of each data point in each clustering cluster; obtaining the high-density optimization degree of each data point according to the Gaussian density of all data points in each cluster, and determining a high-density center point; counting the number of added real-time data points, classifying the data points into each cluster according to Euclidean distance, dynamically updating the average real-time distance of the data points in each cluster, analyzing and adjusting necessity according to the real-time distance, and judging whether to iterate a high-density center point or not so as to measure the uniformity of data distribution and complete digital twinning intelligent factory data management. According to the method, the clustering center is dynamically optimized, so that the timeliness, the accuracy and the early warning reliability of digital twin factory data classification are remarkably improved.

The invention belongs to the technical field of battery data processing, and particularly relates to an electric vehicle battery fault analysis method, system and device and a medium, and the method comprises the steps: firstly collecting multi-dimensional time sequence data of a battery pack, and generating a target matrix through a preset processing strategy; thirdly, extracting feature vectors in the battery data by using a convolutional neural network, storing the feature vectors in a vector database, and adding fault / normal labels; and then, based on a vector clustering method and an Euclidean distance, the similarity between incremental data and historical data is analyzed, and the fault state of the battery pack is judged. And finally, performing fault analysis according to a classification result. A time sequence database and an early warning mechanism are further introduced, the change of battery characteristics can be monitored in real time, and fault early warning can be triggered in time. Through deep learning and data analysis technologies, the intelligent level of battery fault diagnosis can be effectively improved, and the safety and reliability of the battery pack are improved.

The invention relates to a seed-metering device performance detection method and device based on high-frame line scanning, and is suitable for performance detection of a seed-metering device. According to the detection method, the linear array camera is used for carrying out real-time image information acquisition on the falling state of the seed flow in the seed metering process, and then an image is transmitted to the image processing system for information processing and analysis; the information processing and analysis comprises the steps of accurately obtaining a time sequence space distance of a seed-metering seed flow through image preprocessing and training based on an improved deep learning network model, analyzing and calculating to obtain key performance indexes of the seed-metering device, such as a qualified index, a miss-seeding index and a reseeding index, and realizing seed-metering performance detection of the seed-metering device; the device applied by the invention comprises a rack, a seed metering device, an image acquisition device, an image processing device, a seed receiving disc and an integrated aluminum frame. Scientific basis is provided for research and development of accuracy and high efficiency of the seed-metering device which is a core component of agricultural machinery of a seeding machine.

The invention relates to the technical field of food detection, and discloses a quality detection system and a detection method for baked food production, a region relationship is constructed by collecting packaging bag edges and food edges, and a region to be detected caused by packaging bag deformation or content deviation is screened out in combination with a dynamic point distribution algorithm and redundancy distance analysis. Detection point selection is optimized by calculating the length of a vertical line section of a target area, and precise puncture and gas sampling are achieved in combination with a self-adaptive extrusion device and a residual oxygen detection technology. The system comprises an image acquisition module, an edge processing module, a distribution point calculation module, a region analysis module, a detection decision module and an execution unit, and full-process automation of quality judgment is realized. According to the method, the detection precision and efficiency are remarkably improved, the problem that detection points are selected blindly in traditional manual sampling inspection is solved, and the method is suitable for online quality control of a large-scale baked food production line.

The invention discloses an AI-based unmanned aerial vehicle routing inspection path planning method and system, and relates to the technical field of unmanned aerial vehicle intelligent path planning, and the method comprises the steps: achieving the reasonable division and task distribution of an unmanned aerial vehicle routing inspection region through the combination of a K-means clustering method and a greedy algorithm; according to the method, the balance of sub-region division and the rationality of unmanned aerial vehicle load are improved while the routing inspection coverage rate is guaranteed, an initial routing inspection path conforming to energy consumption and path constraints is generated through combination of an A * algorithm and three-dimensional search space modeling, conflict detection is performed on the initial path through combination of a space-time distance analysis method and an indicator function method, and the routing inspection efficiency is improved. The security and robustness of the unmanned aerial vehicle group in a dynamic scene are enhanced, the collaborative path optimization based on the time-limited reward is realized by combining the AI model serialization action with the multi-agent DQN model, and the multi-unmanned aerial vehicle collaboration efficiency and task completion degree are effectively improved.

The invention discloses a data real-time monitoring and alarming system for an intelligent networked automobile. The data real-time monitoring and alarming system comprises a main controller MCU; the pressure sensor is used for detecting whether a person is in the vehicle; the door and window control system is used for judging whether the automobile is in a closed state; the countdown control device is used for setting detection time; the laser detection module is used for detecting oxygen concentration; the alarm module is used for receiving an alarm signal and controlling an alarm circuit; the main controller MCU is connected with the door and window control system, the pressure sensor, the countdown control device, the laser detection module and the alarm module, the laser detection module adopts a TDLAS technology, and the concentration of oxygen is determined through the Lambert-Beer law; the alarm module adopts one of light alarm, sound alarm and sound-light alarm. According to the invention, the safe distance analysis module intelligently analyzes and confirms the safe distance to be maintained according to the real-time vehicle speed and the dry-wet condition of the road surface, and the condition of insufficient safe distance is monitored and pre-warned in real time by combining the measured data of the distance measuring device, so that the driving safety is improved.

The invention relates to the technical field of construction management, in particular to a highway construction scheduling optimization method and system based on big data, and the method comprises the following steps: screening abnormal points and calculating a driving distance based on the coordinate data of a transport vehicle, analyzing the matching condition of a track and route planning, and recognizing progress offset and time period rearrangement signals; and dynamically adjusting material putting and equipment operation nodes to obtain a field operation response beat amount. According to the invention, based on big data real-time processing and sequence analysis, time sequence screening and abnormal point elimination are carried out on vehicle coordinate data, remaining road section features are extracted, progress offset is continuously tracked, rhythm offset features are dynamically generated by comparing arrival time and node demands, a periodic trend monitoring and abnormal state identification mechanism is established, and the abnormal state identification accuracy is improved. And dynamically triggering resource allocation time sequence rearrangement, carrying out integrated linkage on data such as a job node state, a material release rhythm and an equipment job window, and realizing resource release rhythm optimization and task parameter self-adaptive adjustment.

The invention discloses a coal mine underground unmanned aerial vehicle autonomous inspection method, and relates to the technical field of image analysis, and the method comprises the steps: S1, importing a to-be-inspected coal mine internal trend map into an intelligent inspection terminal of an unmanned aerial vehicle; and S2, the intelligent inspection terminal performs real-time image acquisition on the interior of the coal mine well according to the to-be-inspected coal mine well interior trend graph. The method comprises the following steps: firstly, carrying out feature analysis on an internal color image and an internal depth image of a coal mine well, and determining obstacle position information, obstacle actual shape information and obstacle actual size information; the method comprises the following steps: acquiring obstacle position information, obstacle actual shape information and obstacle actual size information, performing risk degree analysis on the obstacle position information, the obstacle actual shape information and the obstacle actual size information to determine the obstacle risk level, and finally performing distance analysis on the obstacle and the surrounding well wall of the coal mine well to determine that the unmanned aerial vehicle can cross the obstacle to perform subsequent inspection, thereby shortening the inspection period of the coal mine well, and meanwhile, improving the inspection efficiency. And the potential safety hazard of the inspection personnel is reduced to a certain extent.

The invention discloses an intelligent low-altitude traffic unmanned aerial vehicle situation awareness method and system. The method comprises the steps of image acquisition, perception enhancement border crossing sample generation, boundary distance analysis feature extraction unit design, dynamic cross-specification air situation fusion, airspace anti-change loss calibration, unmanned aerial vehicle situation awareness model design and low-altitude traffic unmanned aerial vehicle situation awareness. The invention belongs to the field of image processing, and particularly relates to an intelligent low-altitude traffic unmanned aerial vehicle situation awareness method and system.According to the scheme, texture constraint loss is constructed, and situation misjudgment caused by detail loss is reduced; based on boundary distance analysis operation, separating low-altitude traffic noise and target features; based on height attitude self-adaptive pooling size design, feature mismatching is avoided; introducing a motion speed compensation factor to correct frame offset caused by high-speed flight of the low-altitude unmanned aerial vehicle; a dynamic noise suppression coefficient is introduced to suppress harmful gradient interference; and the low-altitude traffic unmanned aerial vehicle situation awareness effect is further improved.

The invention discloses a carbon emission calculation method and device supporting urban pollution reduction and carbon reduction collaborative development, and the method comprises the steps: firstly, carrying out the quantitative observation and analysis of the correlation between each impact factor of carbon emission and the carbon emission through a gray correlation analysis method improved based on a distance analysis method, and screening main impact factors; secondly, constructing an LSTM carbon emission prediction model based on the analyzed and determined main factors, optimizing hyper-parameters of the LSTM carbon emission prediction model through a GCS algorithm, forming a GCS-LSTM carbon emission prediction model, and realizing annual prediction of urban carbon emission based on the GCS-LSTM carbon emission prediction model; and finally, on the basis of the obtained carbon emission predicted value, constructing a composite system collaboration degree model to measure and calculate the future pollution reduction and carbon reduction collaboration degree. The device comprises a processor and a memory.

The invention relates to the field of ad hoc network scheduling, and discloses an ad hoc network scheduling method and device, and a storage medium. The method comprises the steps that a scheduling system receives an execution task and analyzes an initial address corresponding to the execution task, and the initial address is a networking address of the execution task in an ad hoc network; according to a preset distance analysis algorithm, calculating distances between the N self-organizing subsystems and the initial address to obtain a task distance set; extracting the minimum task distance in the task distance set; analyzing the system state of the self-organizing subsystem corresponding to the minimum task distance; when the system state is busy, re-extracting the minimum task distance in the task distance set; and when the system state is idle, sending the execution task to the self-organizing subsystem corresponding to the minimum task distance. In the embodiment of the invention, the task execution distance of the execution task in the plurality of ad hoc subsystems is calculated through the scheduling system, so that efficient scheduling in the ad hoc network is realized, and the networking scheduling transmission speed of ad hoc network communication is improved.

The invention discloses an intelligent cleanliness detection device and method, and belongs to the technical field of air cleanliness detection.The intelligent cleanliness detection device comprises a detector, a moving part, a data acquisition unit, a state prediction unit used for generating an air state prediction value, a state judgment unit and a control unit, the moving direction analysis unit is used for acquiring the wind speed of the space where the detector is located at present and generating the moving distance; the moving control unit is used for controlling the moving assembly to drive the detector to move according to the optimal moving direction and the moving distance; the pollutant diffusion trend can be predicted according to the real-time environmental parameters, the detection path can be automatically adjusted, the pollutant diffusion trend can be dynamically tracked, the detection path can be automatically adjusted, and the detection accuracy and efficiency are improved.

The invention discloses an identification intercommunication analysis method and system for heterogeneous science and technology resources, and relates to the technical field of identification analysis, and the method comprises the steps: collecting heterogeneous science and technology resource data, carrying out the hierarchical definition of triple nodes, analyzing the attribute association degree, and carrying out the weak association elimination through a SimHash screening technology, and analyzing the attribute similarity among the resources based on the attribute vector distance of the triple node data, and performing matching analysis of function vectors according to the support information of the resources to the functions to establish the function association of the resources. According to the method, a multi-dimensional supplementary association mechanism is provided for function association values based on task function support conditions among resources through definition and matching of function vectors, attribute weights of attribute similarity are adjusted based on dynamic loads, the real-time states of the resources have a dynamic adjustment effect on the association weights, and the dynamic load adjustment effect of the resources is improved. And the adaptation flexibility of the system is improved.

Disclosed in the present application are a result generation optimization method, apparatus, device and system for a large language model. The method comprises: concurrently sending a preset prompt word to a plurality of preset large language models, so as to obtain a plurality of generation results; and performing comprehensive optimization processing on the plurality of generation results on the basis of a preset comparison selection algorithm or a preset merging algorithm, so as to obtain an optimization result, wherein the preset comparison selection algorithm is constructed on the basis of similar-distance analysis, and the preset merging algorithm is realized on the basis of merging the generation results. A plurality of preset large language models are used to simultaneously perform result generation on the basis of the same preset prompt word, and these generation results are then comprehensively optimized to obtain an optimized result; and during the process, optimization can be performed by means of selecting a preset comparison selection algorithm or a preset merging algorithm on the basis of the actual situations, so that it can be ensured that the optimized result is more accurate and reliable. In this way, the present application can solve the technical problem in the prior art of it not being possible to meet actual application requirements due to a result generated on the basis of a single large language model lacking accuracy and reliability.

The invention discloses a marine environment monitoring and early warning method and system based on big data, and relates to the technical field of marine environment monitoring, and the method comprises the steps: collecting environmental data in real time through a sensor in a monitoring station, and transmitting the environmental data to a database; acquiring the position information of each monitoring station by using a positioning technology, and calculating the distance between the randomly selected reference monitoring station and the corresponding comparison monitoring station; analyzing the change trend of the environment data, and determining the propagation speed of the abnormal environment data based on an analysis result; calculating the arrival time of the abnormal environment data propagated from the reference monitoring station to the comparison monitoring station by using a linear propagation algorithm in combination with the distance and the propagation speed; and setting alarm starting time according to the arrival time, and issuing alarm information. According to the invention, the real-time monitoring and data collection of the marine environment are realized through the deployment of the monitoring stations and the integrated sensor technology, and a comprehensive and accurate information basis is provided for subsequent data analysis and early warning.