74 results about "Storm tide" patented technology

The invention relates to the technical field of storm surge disaster monitoring and intelligent forecasting, and provides a regional collaborative storm surge water increase forecasting method of a coupling graph attention and gated circulation network, which comprises the following steps of: obtaining storm surge water increase residual field data based on a mixed wind field driving two-dimensional hydrodynamic model formed by typhoon parameterization and reanalysis data fusion, registering the water-increasing residual field data and the observation time sequence in time and space, and taking the registered data as physical prior input; constructing a dynamic edge weight graph structure based on a space-time causal correlation analysis method, and adaptively updating the dependency relationship among multiple observation stations; physical priori and key features are used as node input, a graph attention mechanism of GATv2 and a time sequence feature extraction capability of GRU are fused, and time-space coupling modeling is carried out on storm surge water increase; and carrying out multi-station joint prediction through regional cooperation, carrying out model evaluation and result optimization, and outputting a storm surge water increase prediction sequence with 15-minute time steps and 15-4-hour advance.

The invention discloses an estuary coast embankment risk early warning evaluation system and method under composite disaster-inducing factors, and relates to the technical field of hydraulic engineering safety monitoring and risk evaluation. Comprising a multi-source data acquisition module, a data preprocessing module, a dynamic index system construction module, an adaptive weight determination module, a risk assessment module, a refined early warning grade division module and a visual decision support module. Real-time sensing, quantitative evaluation and graded early warning of the dike safety state are achieved, the method can be directly applied to coastal city disaster prevention and reduction, dike project full-life-cycle operation and maintenance, major storm surge disaster emergency response and other scenes, the technical bottlenecks of single disaster-inducing factor, static weight and insufficient early warning precision in a traditional evaluation method are effectively broken through, and the method has a wide application prospect. And a precise technical support is provided for the safety protection of the estuary coast embankment.

The invention discloses a coastal region storm surge disaster prediction method based on multiple modes, which combines multi-source meteorological environment data, three-dimensional geographic space data and storm surge historical occurrence data, and comprises the following steps: on one hand, performing long-term prediction and trend fitting on future storm surge conditions by using a multi-mode network; fitting to obtain a linear component and a nonlinear component related to storm surge prediction, combining the linear component and the nonlinear component, and converting the combined component into a storm surge prediction probability P1 through an activation function; on the other hand, for the three-dimensional geographic space data, respectively obtaining a predicted value of supervised learning of a labeled scene and a predicted value of hierarchical supervised learning of an unlabeled scene of the three-dimensional geographic space data; averaging the predicted values of the two, and converting the averaged predicted values into a storm surge prediction probability P2 through an activation function; and finally, weighting the storm surge prediction probability P1 and the storm surge prediction probability P2 to obtain a final storm surge prediction value.

The invention provides an evacuation path optimization method based on coupling numerical simulation and hierarchical reinforcement learning, and relates to the field of storm surge, and the method comprises the steps: constructing a coupling numerical simulation framework of high-precision storm surge; performing storm surge numerical simulation through a coupling numerical simulation framework to generate a submerged depth data set; performing high-resolution submerged depth interpolation through the submerged depth data set in combination with an inverse distance weighted interpolation algorithm to generate a high-resolution submerged depth interpolation data set; performing risk grading on the high-resolution submerged depth interpolation data set to generate a risk zoning map; generating an evacuation road network through a path planning neural network model based on a graph neural network and the risk zoning map; an evacuation strategy of an evacuation road network is optimized by adopting a hierarchical deep reinforcement learning framework, a combined optimal dynamic evacuation strategy is generated, and the generalization performance and the calculation efficiency of evacuation path planning are remarkably improved.

The invention relates to a combined support and anchor cable combined underwater immersed tube tunnel and a construction method thereof, the combined support and anchor cable combined underwater immersed tube tunnel comprises a tunnel body which is formed by tube joints which are connected end to end in the length direction; the lower part of the pipe joint supporting mechanism extends into the seabed, the upper part of the pipe joint supporting mechanism extends out of the seabed, and the pipe joint is supported on the upper part of the pipe joint supporting mechanism; the device is characterized by further comprising a pipe joint anchoring mechanism used for controlling vibration and / or displacement of the tunnel body, and the pipe joint anchoring mechanism is arranged between the outer wall of the pipe joint and the seabed; the piles are distributed on the outward sides of the pipe joints forming the tunnel body at intervals, the lower ends of the piles penetrate into the seabed, the upper ends of the piles upwards stretch out of the seabed and are fixed to the pipe joints, and the resultant force of the tunnel body in the X direction, the Y direction and the Z direction is close to zero. The device has the advantages that vibration and / or displacement generated by the coupling effect of factors such as ocean waves, ocean currents, ship waves, storm surge and dynamic loads are effectively controlled so as to cope with the complex ocean environment; and the technical difficulty, the engineering cost and the comprehensive cost are reduced.

The invention discloses a grading early warning method and system for a storm surge submerging area, and relates to the technical field of data processing. Comprising the following steps: S1, multi-source data preprocessing: a standardized data set is obtained through multi-source heterogeneous data acquisition, space-time reference unification and quality grading processing, and a quality grading result is used for weight distribution of subsequent feature extraction; s2, dynamic feature extraction; s3, performing initial submerging prediction; s4, deviation self-adaptive correction is carried out; s5, grading threshold value adaptation is carried out; and S6, issuing and feeding back graded early warning. According to the grading early warning method, effective integration of multi-source data is realized through sub-steps of multi-source heterogeneous data acquisition, space-time reference unification and quality grading processing. The uniform space-time reference ensures the consistency of the data, the three-level quality grading standard defines the use priority of the data, the utilization rate and reliability of the data are improved, and a solid foundation is laid for subsequent feature extraction and model prediction.

The invention discloses a storm surge artificial intelligence forecasting method based on physical equation constraints, and relates to the technical field of artificial intelligence storm surge forecasting. Comprising the following steps: step 1, collecting multi-source data; step 2, assimilating and preprocessing the data; step 3, realizing a machine learning calculation method of physical loss; step 4, constructing a physical and data co-constrained artificial intelligence model; 5, performing hyper-parameter adjustment by using a random search algorithm, and further optimizing the model; and step 6, carrying out interpretability analysis on the forecasting process and result. According to the invention, three control equations for storm surge calculation are introduced into the machine learning model, so that the dependence on the training data volume is greatly reduced compared with a traditional data-driven intelligent forecasting model; and analyzing the change trend of the physical variables in the forecasting process, carrying out visual display and uncertainty analysis, and solving the problem of uninterpretability of an artificial intelligence forecasting model.

The invention discloses a design method of an ultra-large-span tide gate with a self-stabilizing structure. The tide gate comprises gate foundations arranged on the two sides of a riverway estuary, and each gate foundation is provided with an arc-shaped guide groove and a spherical supporting hinge; the spherical trunnions are connected with one ends of the water retaining gates, the other ends of the water retaining gates make contact with each other to be in a closed state when storm surge comes, and the closing point of the water retaining gates is located on the open sea side of the connecting line of the two spherical trunnions. An arc-shaped stability maintaining piece matched with the arc-shaped guide groove is arranged in the arc-shaped guide groove, one end of the arc-shaped stability maintaining piece is fixedly connected with the water retaining gate, and the other end of the arc-shaped stability maintaining piece is connected with the hoist. Navigation and the hydrological river regime of the original river channel are not affected in the processes of structural design, manufacturing, processing, transportation, installation, overhaul and maintenance and the like, and the influence on the original river channel can be reduced to the maximum extent.

The invention discloses a storm surge numerical simulation method and system based on a parameterized typhoon model, and the method comprises the steps: obtaining typhoon data of a to-be-researched region, carrying out the assimilation simulation calculation, and constructing an assimilation typhoon model; obtaining topographic data and water depth data of a to-be-studied area, performing grid division and data interpolation processing, and constructing a two-dimensional storm surge model; setting boundary driving conditions, and performing parameter calibration verification on the assimilation typhoon model and the two-dimensional storm surge model to obtain a verified assimilation typhoon model and a verified two-dimensional storm surge model; and performing storm surge numerical simulation based on the verified assimilation typhoon model and the verified two-dimensional storm surge model to obtain a storm surge numerical simulation result. According to the method, the comprehensive influence of different factors on the storm surge can be analyzed more comprehensively, and the precision of storm surge simulation is improved. The storm surge numerical simulation method and system based on the parameterized typhoon model can be widely applied to the technical field of ocean disaster prevention and reduction.

The invention discloses a multi-algorithm fusion cyclone similarity identification method and a storm surge forecasting method based on weight optimization, relates to the field of cyclone similarity identification, and establishes a similarity calculation function by constructing a multi-dimensional time sequence containing a plurality of key factors and combining a weighted dynamic time warping algorithm. In this way, multi-dimensional comprehensive characterization of the cyclonic space-time evolution process is achieved, and the identification deviation caused by the fact that a traditional method only depends on a single feature is overcome. On this basis, a genetic algorithm, a particle swarm optimization algorithm and a differential evolution algorithm are respectively adopted to carry out iterative optimization on the weight vectors, minimization of a deviation coefficient of a candidate similar cyclone set is taken as a target, and a global optimal weight vector corresponding to each algorithm is searched; and based on a preset fusion strategy, determining a target optimal weight vector from the plurality of globally optimal solutions. According to the closed-loop recognition process, multi-dimensional feature modeling and scientific weight optimization are effectively fused, and objectivity and accuracy of cyclone similarity recognition are remarkably improved.

The invention discloses an ocean water pollutant monitoring device, belongs to the technical field of seawater detection, and aims to solve the problems that a maintainer needs to regularly and repeatedly calibrate and maintain detection probes on a buoy, the number of the detection probes on a traditional buoy is limited, only one sample can be detected each time, and the real-time sampling rate of data is low. A liquid storage tank is embedded in the bottom face of a buoy body, a mounting shell is fixedly arranged on the bottom face of the buoy body, a power assembly is fixedly arranged in the buoy body, a stirring assembly is fixedly connected to an output shaft of the power assembly, and a connecting assembly is arranged at the bottom of the stirring assembly; a sliding assembly, a compression assembly and a plugging assembly are separately arranged in the mounting shell, the high-frequency detection can be performed under the conditions of marine pollution accidents, red tide / algae bloom outbreak and typhoon / storm surge by changing the shape of the shifting assembly, and the automatic calibration can be realized after the detection probe is used every time.

The application discloses a nearshore disaster-bearing body disaster-causing path tracing method based on digital-analog experiments, relates to the technical field of ocean dynamic disaster simulation, determines the ocean dynamic disaster characteristics of a nearshore research area, integrates multi-source historical disaster data, real-time monitoring data and disaster-bearing body background data, and constructs a time-space correlation database; based on a three-dimensional ocean and a third-generation sea wave model, a nearshore storm surge-sea wave coupling numerical model is established and verified. Through the construction of the storm surge-sea wave coupling numerical model, the present application comprehensively considers the nonlinear interaction of multiple disasters, introduces an energy conservation equation to quantify the generation, transmission and dissipation process of disaster energy, solves the limitations of traditional single-disaster linear models, and combines the real-time feedback of the three-dimensional dynamic model of the disaster-bearing body to form a closed energy-damage correlation mechanism, significantly improves the tracing accuracy of the disaster chain propagation path, and ensures that the identification of key nodes and dominant paths is more consistent with physical reality.

The application discloses a storm surge intelligent early warning method and system based on multi-model coupling and a medium, and relates to the technical field of disaster early warning.The method comprises the following steps: establishing a basic data set; acquiring real-time meteorological data and marine data, performing fuzzy path fitting, and establishing a fitting result; performing regional grid segmentation by using the basic data set and the fitting result, and establishing a grid segmentation result; inputting the real-time data and the basic data into a simulation unit, performing simulation operation, and establishing node simulation data; acquiring a node preset safety response measure, performing joint early warning identification according to the response measure and the simulation data, and reporting an early warning signal, so as to solve the technical problems that the existing storm surge early warning method has strong subjectivity in experience prediction, inherent errors of a single model lead to insufficient simulation accuracy, and early warning timeliness and refinement degree are insufficient, and achieve the technical effect that the accuracy, timeliness and refinement degree of storm surge early warning are improved through fuzzy path fitting and multi-model coupling simulation operation.

This invention discloses a method for delineating key storm surge disaster prevention areas based on the cumulative frequency method. It avoids the subjective errors often encountered in empirical judgment methods and overcomes the problem of weak research foundations in the standardization of key storm surge disaster prevention areas, which prevents the finding of suitable empirical models for standard determination. The proposed method for determination and zoning not only provides a scientific, reasonable, and effective technical approach for delineating key marine disaster prevention areas in coastal cities of my country, but also provides a valuable standard determination method for other domestic zoning work based on indicator systems. It effectively supplements the commonly used subjective determination methods in my country, improves the scientific rigor of the standard determination results, and can be well applied to my country's current spatial planning field, enhancing the scientific validity and rationality of planning outcomes. This method has good market prospects.

The present application relates to the technical field of marine disaster risk assessment, and particularly relates to a storm surge risk assessment method for a seawater photovoltaic field area, comprising the following steps: S1: identifying a critical region in the field area where a bearing stable state is converted into a bearing attenuation state, and generating a corresponding support state transition unit; S2: determining the transmission direction and the expansion boundary of the support state transition unit between array regions, and generating a corresponding imbalance transmission unit; S3: dividing the overall risk level based on the transmission range and the expansion degree, and generating a corresponding storm surge risk assessment result. According to the present application, by combining support state transition identification, imbalance transmission path analysis and risk space partitioning, the storm surge risk of the seawater photovoltaic field area is changed from static determination to dynamic evolution analysis, thereby improving the accuracy and spatial directivity of risk assessment.

The present application relates to a kind of simple calculation method of shallow foundation and clay seabed interface failure time under extreme storm surge action.The technical scheme is: measuring the roughness R of shallow foundation a , making structure;Collecting seabed clay at shallow foundation, carrying out dynamic simple shear test, test structure and the initial undrained shear strength S of clay interface u ;Select and set 0.4S u , 0.6S u , 0.8S u And 1S u As the typical amplitude of extreme storm surge load;Measured structure and the corresponding period number N of clay interface failure;Fitting extreme storm surge load amplitude and period number N logarithmic curve;According to wave force calculation formula, load amplitude is calculated;The amplitude is substituted into logarithmic curve and the corresponding period number N of interface failure and failure time are obtained.The present application can calculate the failure time of shallow foundation and seabed interface under extreme storm surge, provide the time length of shallow foundation against storm surge, determine the stability of shallow foundation, provide feedback for whether the shallow foundation of engineering site needs to be reinforced to resist extreme storm surge.

This invention provides a method and apparatus for recommending emergency response plans for marine storm surges. The method includes: constructing a "scenario-response" disaster management system and a "scenario-response" visualized knowledge graph that integrates event chains and emergency plans. Through the constructed marine storm surge "scenario-response" disaster management system, "scenario-response" knowledge graph, and knowledge reasoning model, it can not only complete "scenario-response" matching of historical disaster events to achieve pre-disaster rehearsals and post-disaster analysis, but also perform "response" knowledge base text matching based on real-time generated disaster "scenario" text to generate emergency response plans and visualize the graph recommendation results, thereby achieving in-disaster response. This improves the entire process of disaster prevention and mitigation chain for marine storm surge disasters, enhances the generalization ability of knowledge reasoning methods, and provides important support for further improving the intelligent handling capabilities of marine storm surge disasters.

This invention discloses a method for determining the bottom deck elevation of an offshore platform, mainly comprising the following steps: collecting and processing relevant data such as wave and tide levels in the engineering area; selecting appropriate samples; considering the joint probability distribution of storm surge and wave-related marine hydrological environmental elements; generating an environmental envelope; and then obtaining the joint return period design parameters of relevant marine hydrological environmental elements from the environmental envelope, which are then used to calculate and determine the bottom deck elevation of the offshore platform. This invention reasonably considers the characteristic laws of important marine hydrological environmental elements in setting the bottom deck elevation of an offshore platform, and uses the idea of ​​statistical modeling of the joint distribution of marine hydrological environmental elements to construct environmental envelopes under different return periods. It can conveniently select the joint return period design parameters of marine hydrological environmental elements and obtain appropriate combinations of environmental parameter return values. The scheme is simple and easy to implement, with clear and reasonable engineering significance, and can effectively guide the design of actual engineering structures.

This invention relates to the field of storm surge disaster monitoring and intelligent forecasting technology, and provides a regional collaborative storm surge rise forecasting method using coupled graph attention and gated cyclic networks. The method includes: using a hybrid wind field-driven two-dimensional hydrodynamic model formed by fusing typhoon parameterization and reanalysis data to obtain storm surge rise residual field data; registering the rise residual field data with the observation time series in time and space, and using it as a physical prior input; constructing a dynamic edge-weighted graph structure based on a spatiotemporal causal correlation analysis method to adaptively update the dependencies between multiple observation stations; using the physical prior and key features as node inputs, fusing the graph attention mechanism of GATv2 and the temporal feature extraction capability of GRU to perform spatiotemporal coupled modeling of storm surge rise; conducting multi-site joint prediction through regional collaboration, performing model evaluation and result optimization, and outputting storm surge rise forecast sequences with a 15-minute time step and a lead time of 15 minutes to 4 hours.

The invention relates to the technical field of disaster early warning, in particular to a marine disaster risk prevention and control early warning system. Comprising a data fusion module used for collecting data from multiple channels in real time and carrying out standardized preprocessing and fusion; the risk prediction module is used for predicting the storm surge risk by utilizing a space-time diagram neural network component, a physical information neural network component and a layered deep neural network component which are included in the risk prediction module based on the fused data; the risk assessment module receives the generated storm surge risk prediction data, assesses the disaster risk severity degree in combination with the vulnerability information of the disaster-bearing body, and determines the corresponding risk level; and the early warning issuing module is used for generating corresponding early warning information based on the determined risk level and issuing the early warning information through multiple channels. According to the system, a high-fidelity prediction result is converted into operable risk assessment, storm surge early warning is automatically generated and issued, and the limitations of storm surge prediction precision, physical authenticity and long-term stability in the prior art are overcome.

The invention provides an R-KGCN emergency disposal method for the silt danger of a floating wing suspension door in the storm surge period of a super-huge tide gate. According to the method, a system framework covering knowledge modeling, reasoning definition and reasoning optimization is constructed on the basis of a relationship-enhanced knowledge graph convolutional network, and structured expression and intelligent reasoning of sediment disaster emergency disposal knowledge are realized. The method comprises the following main technical links: (1) constructing a knowledge graph mode layer and a data layer oriented to a sediment disaster scene, and establishing a'feature-event-disposal 'ternary model and a hierarchical relationship; (2) proposing a structured reasoning problem definition framework of an emergency processing task, and supporting modeling requirements of multi-source, multi-scale and multi-relation semantics; (3) designing an R-KGCN inference algorithm integrated with a semantic relationship enhancement module, and improving the accuracy and interpretability of inference; and (4) forming a knowledge reasoning and intelligent recommendation method oriented to sediment dangerous cases, and providing decision support and emergency response guarantee for operation scheduling of the oversize tide gate.

The application discloses a storm surge artificial intelligence prediction method based on physical equation constraints and relates to the technical field of artificial intelligence storm surge prediction, and comprises the following steps: step 1, collecting multi-source data; step 2, data assimilation and preprocessing; step 3, realizing a machine learning calculation method of physical loss; step 4, constructing an artificial intelligence model jointly constrained by physics and data; step 5, using a random search algorithm to adjust hyperparameters, and further optimizing the model; and step 6, performing explainability analysis on a prediction process and results. The three control equations used for storm surge calculation are introduced into the machine learning model, the dependence on the training data volume is greatly reduced compared with a traditional data-driven intelligent prediction model, the trend of the change of the physical variables in the prediction process is analyzed, visual display and uncertainty analysis are performed, and the unexplainability problem of the artificial intelligence prediction model is solved.

This invention provides a method and apparatus for assigning rescue vessels based on a combinatorial optimization algorithm in storm surge scenarios. The method includes: acquiring and updating information on stranded personnel and vessel status in real time; ensuring that vessels temporarily withdraw from task allocation once they reach their maximum capacity, and rejoin after completing the current rescue; dynamically determining the rescue weight of each stranded person by combining their personal attributes and location relationships; and using an improved Hungarian algorithm to dynamically generate an optimal vessel allocation scheme based on the real-time rescue weight of the stranded personnel, while fully considering vessel capacity limitations. Each generated allocation scheme is calculated in real time based on the latest rescue weights and vessel availability, ensuring the effectiveness and timeliness of the scheme. As stranded personnel are gradually rescued, the above process is repeated until all stranded personnel are rescued. This invention ensures the effectiveness, comprehensive coverage, and timeliness of the rescue operation.

The invention relates to the field of disaster risk assessment, in particular to a storm surge risk assessment system based on deep learning, which comprises an initial assessment module used for determining disaster exposure range parameters based on disaster-causing assessment indexes so as to determine a disaster analysis area; the area analysis module is used for determining the area toughness index of each disaster analysis area, and determining diffusion risk analysis or centralized trend analysis according to the reference area toughness index and the area toughness difference index; the diffusion analysis module is used for determining a diffusion interference area according to the difference conflict index or the difference amplitude index, and determining whether to optimize range setting for each disaster analysis area according to diffusion interference distribution parameters; and the centralized analysis module is used for determining whether to optimize the range setting for each disaster analysis area according to the centralized risk coefficient. According to the method, the effectiveness of the determination result of the influence range of the storm surge is ensured, and the risk assessment quality of the storm surge is further improved.

The application discloses a storm surge inundation area grading early warning method and system, and relates to the technical field of data processing. The method comprises the following steps: S1, multi-source data preprocessing: through multi-source heterogeneous data collection, space-time reference unification and quality grading processing, a standardized data set is obtained, wherein the quality grading result is used for weight distribution of subsequent feature extraction; S2, dynamic feature extraction; S3, initial inundation prediction; S4, deviation self-adaptive correction; S5, grading threshold adaptation; S6, grading early warning release and feedback. Through the sub-steps of multi-source heterogeneous data collection, space-time reference unification and quality grading processing, the application realizes effective integration of multi-source data. The unified space-time reference ensures the consistency of data, the three-level quality grading standard clearly defines the use priority of data, improves the utilization rate and reliability of data, and lays a solid foundation for subsequent feature extraction and model prediction.

The invention provides a mechanism digital twinning construction method and system for marine disaster safety analysis, and the method comprises the steps: constructing a two-dimensional and three-dimensional space-time digital twinning base, and forming a two-dimensional and three-dimensional space-time data set; determining a target area and influence factors causing the storm surge of the target area; based on an ADCIRC and SWAN coupling model, constructing a storm surge disaster prediction mechanism model matched with the target area features so as to predict water level and flow velocity data of each grid point; constructing a virtual meteorological model by combining the terrain and meteorological data of the target area; key airflow change points are extracted based on the virtual meteorological model, an adjustment coefficient corresponding to the current position area is calculated according to airflow change characteristics of the key airflow change points, and water level and flow velocity data of each grid point at the corresponding position are optimized and regulated based on the adjustment coefficient corresponding to each key airflow change point; the prediction mechanism model is embedded into a two-dimensional and three-dimensional space-time digital twinning base, and mechanism digital twinning of marine disaster safety analysis is achieved.

The invention provides a non-static-pressure hydrodynamic model cross-platform transplantation method and system, and relates to the technical field of program transplantation, and the method comprises the steps: obtaining a dependence library needed by a non-static-pressure hydrodynamic model, and installing and configuring the dependence library to a target platform processor; modifying a compiling link file of the non-static hydrodynamic model to enable the compiling link file to pass through a compiling environment of a target platform processor; adding a target platform compiling option and a corresponding compiling rule in the compiling link file; modifying a link position of the dependency library, and executing a compiling command after related configuration is completed; after the compiling is successful, according to a hotspot analysis tool, determining a hotspot function during the operation of the non-static hydrodynamic model, and rewriting the hotspot function into a many-kernel function; and compiling an incremental compiling script, and linking the many-core program to the non-static-pressure hydrodynamic model. According to the method, cross-platform transplantation of the non-static-pressure hydrodynamic model is achieved, the program running speed is increased through many-core optimization, and rapid simulation and prediction of tide, storm surge and tsunami are facilitated.

The utility model provides an anti-storm-surge integrated monitoring marker structure, which belongs to the technical field of anti-storm-surge monitoring markers and comprises an electro-galvanized steel pipe, a foundation structure, a heat dissipation case, a communication component and a rotatable support, the electro-galvanized steel pipe is fixed at the top of the foundation structure, the heat dissipation case is fixed at the bottom of the foundation structure, and the rotatable support is fixed at the bottom of the foundation structure. The foundation structure is fixed in an existing foundation; the rotatable bracket is fixed on the electro-galvanized steel pipe through a bidirectional hoop and is used for keeping stable under the action of external force; the heat dissipation case is fixed on the electro-galvanized steel pipe, the outer layer of the heat dissipation case adopts a shutter structure and is used for preventing water, ventilating and balancing internal and external air pressure, and the interior of the heat dissipation case adopts a separated cabin isolation design; the utility model provides an anti-storm surge integrated monitoring marker structure which can solve the problems that an existing monitoring marker is prone to loss and short in service cycle, and parts are fixedly arranged, replaced and maintained difficultly.

The invention discloses a storm surge inundation range identification method and system based on a multi-source remote sensing image, and particularly relates to the field of image analysis, and the method comprises the steps: obtaining a pre-disaster and post-disaster image and digital elevation model data in a target region, carrying out the feature extraction and multi-source image fusion, generating a broad-spectrum suspected inundation region image, and carrying out the recognition of a storm surge inundation range; according to the pre-disaster optical image, a complex underlying surface area is identified from the broad-spectrum suspected submerged area image, and a corresponding binary mask image is obtained; for each complex underlying surface area, texture feature analysis and pixel-level classification are respectively carried out, and an actual water body area is identified; and according to the actual water body area of each complex underlying surface area, generating a preliminary submerging range map, and performing terrain connectivity analysis on the preliminary submerging range map to generate a final storm surge submerging range map. According to the invention, based on the pre-disaster and post-disaster images of the storm surge, texture interferences of different complex underlying surfaces in the images can be filtered in a targeted manner, so that the accuracy and reliability of storm surge range identification can be improved.