201 results about "Storm" patented technology

The invention discloses a typhoon wave height prediction method based on a deep learning MOE-Transform model, and the method comprises the following specific steps: collecting historical typhoon data of a research region, and constructing a virtual typhoon data set in combination with a central pressure difference formula; processing the historical and virtual typhoon data sets through a Holland typhoon empirical model and a storm growth relation to obtain a wind field, an air pressure field and a significant wave height field; correcting the field data by using an error model on the basis of the ERA5 data set to form a typhoon space-time fusion database containing meteorological data, the significant wave height field and typhoon data; and training and testing the MOE-Transform model to obtain a typhoon wave significant wave height prediction model for typhoon wave height prediction. According to the method, the multi-task adaptability and generalization ability are improved, and the precision and timeliness of typhoon wave height prediction are improved.

The invention relates to a ship operation energy consumption prediction method considering an actual storm condition. The method comprises the following steps: fusion and data processing of multi-source navigation environment data; the still water resistance and the wave increase resistance are calculated through a program, and the navigational speed-power relation of the ship under the actual wind wave condition is calculated; predicting the power-oil consumption relation of the ship according to the route segments: based on the navigational speed-power relation of the ship under the actual storm condition, establishing a prediction agent model of the power-oil consumption relation of the ship according to the historical oil consumption data of the ship; and ship operation energy consumption prediction: combining the annual route segment information of the ship, predicting the agent model based on the branch route segment ship power-oil consumption relation, thereby obtaining the oil consumption of each route segment of the ship in the whole year, and calculating the annual operation energy consumption of the ship. According to the method, the prediction precision is remarkably improved, the generalization ability and the real-time response speed of the model are enhanced, and powerful support is provided for energy conservation and emission reduction of ships.

The invention discloses a radar echo extrapolation method based on a space-time attention mechanism, and the method comprises the steps: firstly obtaining the radar echo data of a target region, carrying out the preprocessing of the obtained radar echo data, carrying out the sliding grouping of the preprocessed radar echo data, carrying out the data augmentation, and obtaining a radar echo sequence data set, dividing the data into a training set and a test set; a radar echo prediction network model based on a SimVP architecture is constructed, training and testing are carried out, and a multi-target loss function is adopted to supervise model training; and a future radar echo image is obtained through real-time prediction. Through the method, the dynamic evolution of complex weather phenomena such as storm can be more accurately captured, the precision and interpretability of short-time approaching weather forecast can be remarkably improved, more reliable technical support is provided for timely and accurate early warning of meteorological disasters, and the method has important practical application value.

The invention discloses a wave height short-term prediction system and method based on variational mode decomposition and wind and wave time correlation, and belongs to the technical field of ocean engineering.The method comprises the steps that firstly, original storm time series data are decomposed into IMF components with different center frequencies through variational mode decomposition; secondly, performing data cleaning on the decomposed IMF component based on a Pearson's correlation coefficient; then combining two wind speed IMF components with the strongest correlation with the wave height IMF component based on correlation analysis to form a wind speed wave height component combination, and determining an input time length through an autocorrelation coefficient ACF of wave height data and a cross correlation coefficient CCF of the wind speed IMF components and the wave height IMF components; and finally, according to the formed wind speed wave height component combination and the determined input time length, carrying out short-term prediction on the wave height based on a long short-term memory neural network. According to the system and the method provided by the invention, the influence of noise on prediction is reduced, and the prediction precision is improved while the prediction efficiency is improved.

The present invention discloses a method for analyzing the spatio-temporal variation of the earthquake geomagnetic field for short-term and impending earthquake monitoring, which is as follows. First, the geomagnetic stations within a given range of longitude and latitude are divided into multiple rectangular grids according to longitude and latitude. The multi-component data of each station within the range of longitude and latitude on the detection day and the previous 60 days are preprocessed into multiple segments of standard time series. Then, the daily absolute mean error of each component prediction sequence is taken, and the difference sequence is subjected to anomaly detection through a sliding interquartile range detection algorithm to obtain the daily anomaly value of each component. Then, according to the daily geomagnetic disturbance index, the anomalies caused by magnetic storms are removed. Finally, the daily anomaly index matrix of each station is generated according to the grid size, and the sum of the anomaly index matrices of all stations on the detection day and the previous 29 days is obtained to get the final regional daily anomaly index matrix, and the range of anomalous longitude and latitude is obtained. This method monitors the anomalous changes of the geomagnetic field within the region and provides a reference range for the geographical locations where earthquakes may occur.

The invention discloses a medium-low latitude ionosphere space-time prediction method and system during a strong magnetic storm, and the method comprises the steps: fusing ground-based GNSS and air-based COSMIC occultation ionosphere observation data, employing a multi-mode space-time sequence ionosphere model, integrating the advantages of a time convolution neural network and a Transform model through a cross attention mechanism, constructing a dual-space-time-flow TransTCN-XA prediction model, and carrying out the prediction of the medium-low latitude ionosphere through a time convolution neural network. And the spatial-temporal change of the medium-low latitude ionosphere during the strong magnetic storm period can be effectively captured. By adopting the technical scheme of the invention, higher resolution and more accurate ionosphere forecasting can be provided for the fields of space weather forecasting and navigation positioning, so that the accuracy and reliability of forecasting are remarkably improved.

The invention relates to a control method of a multi-parameter oblique single-axis photovoltaic tracking device and the oblique single-axis photovoltaic tracking device. The control method comprises the steps that S1, the current illumination intensity, the current environment temperature, the current environment wind speed and the current photovoltaic output power fluctuation rate are acquired; and S2, according to the current illumination intensity, the current environment temperature, the current environment wind speed and / or the current photovoltaic output power fluctuation ratio, a target control scheme of the inclined single-axis photovoltaic tracking device is determined, and the target control scheme comprises a conventional weather control scheme and an extreme weather control scheme. According to the invention, the inclination angle of the inclined single-axis photovoltaic tracking device is cooperatively regulated and controlled through multiple parameters, the inclination states of the photovoltaic panel in different weathers are dynamically switched, the adaptability to extreme environments such as high temperature, strong wind, rainstorm and snow is enhanced, the damage of the extreme weather to the photovoltaic panel is effectively resisted, and the service life of the photovoltaic panel is prolonged. Therefore, the problem that the photovoltaic panel is possibly damaged in extreme environments such as high temperature, strong wind, rainstorm and snow is avoided.

The invention discloses an ionosphere TEC forecasting method, system, medium and equipment fused with a geomagnetic field, and belongs to the field of ionosphere TEC forecasting, the method comprises the following steps: obtaining current ionosphere TEC grid data, solar activity index data and global geomagnetic field observation data, and constructing to obtain multi-channel fusion data; based on a preset ConvLSTM model, processing the multi-channel fusion data, and outputting an ionized layer TEC forecast result; wherein the ConvLSTM model is obtained by training an input sequence in historical multi-channel fusion data and a corresponding target sequence; the input sequence is composed of multi-channel fusion data of a plurality of consecutive time steps, and the target sequence is composed of ionized layer TEC grid data immediately after the input sequence. By implementing the method, the forecasting precision of the ionized layer TEC under complex space weather conditions such as magnetic storm can be improved.

The invention provides a hail identification and early warning method based on a storm space-time channel, and the method comprises the steps: constructing a complete system from the tracking of a storm to the early warning of a disaster-bearing body, storing the information of a storm carrier in all directions through the space-time channel, and precisely identifying hail through multi-feature fusion and an SVM algorithm. According to the method, radar data inversion and a semi-Lagrange extrapolation method are used for realizing hail extrapolation forecasting, the hail forecasting and disaster-bearing body data are deeply fused, accurate early warning is provided for different disaster-bearing bodies, and the loss caused by hail disasters is reduced to the greatest extent. According to the invention, the historical motion trail and the coverage range of the storm carrier can be stored in all directions, the states of the storm at different moments can be known by analyzing data in the space-time channel, and the evolution trend of the storm on the time axis can be clearly seen; through combination of multi-source data, the accuracy and reliability of hail early warning are remarkably improved, hail falling area forecast and disaster-bearing body data covering population, building and agricultural distribution are subjected to overlay analysis, and the risk is accurately evaluated.

The invention discloses a rainstorm flood risk early warning method and system based on machine learning and multi-source data fusion, and belongs to the technical field of flood prediction.The method comprises the steps that multi-source sample data is constructed based on flood disaster historical data; the multi-source sample data type comprises rainfall characteristic data, hydrological characteristic data, landform characteristic data, earth surface attribute characteristic data and social economic characteristic data; an XGBoost ensemble learning algorithm is adopted to construct a risk prediction model, and training and evaluation are carried out based on multi-source sample data; historical rainstorm flood event records are taken as labels in the training process; performing quantitative and application verification on the risk prediction model, and calibrating a risk level probability output by the risk prediction model based on a risk level distribution probability of historical disaster situation data; performing real-time estimation based on the optimized risk estimation model; and generating a spatial refined rainstorm flood risk grade early warning map in a future preset time period according to an estimation result in a rolling manner.

The invention provides a simulation system and method for a typhoon rainstorm induced combined flow-slip disaster, and the system comprises a side slope simulation box which is used for simulating a side slope covered by vegetation; the wind load control system is used for simulating a wind load applied to the slope simulation box and controlling a wind load time history according to a set typhoon level; the rainfall control system comprises a variable-frequency water pump and is used for determining the rainfall intensity according to the set typhoon level, determining the target power of the variable-frequency water pump according to the rainfall intensity, simulating rainfall and controlling the rainfall time history; and the spatial three-dimensional visual data acquisition system is used for acquiring multi-section visual three-dimensional data of the three-mechanism coupling catastrophe simulation process of the slope model in real time under the action of the wind load, rainfall and the plant model. Through the application, the three-mechanism coupling catastrophe process of the slope model under the action of wind load, rainfall and the plant model is accurately simulated, the three-mechanism coupling catastrophe mechanism in the soil body is disclosed, and dynamic three-dimensional analysis of engineering is realized.

The invention relates to the technical field of rainstorm and flood disaster assessment, and particularly provides a rainstorm and flood disaster assessment method and system based on multi-scale rainfall data, and the method comprises the steps: obtaining a digital elevation model of a target region, calculating a terrain curvature distribution field and an elevation mutation characteristic field, a terrain vertical gradient zone is obtained, and multiple layers of ground precipitation observation station point groups are arranged; generating a vertical weight factor according to observation station altitude distribution, and combining a radar beam direction and a terrain slope to generate a slope direction shielding coefficient; fusing ground-based radar, satellite remote sensing and ground station data, and generating a terrain optimization precipitation field through a vertical weight factor and a slope shielding coefficient; and inputting the precipitation field into the terrain coupling flood model, and deducing disaster space distribution in combination with a water flow path network and a soil infiltration correction mechanism. According to the method, the problem of observation distortion caused by terrain shielding and rainfall vertical differentiation in alpine and valley areas is solved, and the precision and reliability of rainstorm flood disaster assessment are remarkably improved.

The invention discloses an extreme rainfall prediction method based on deep learning and numerical calculation, and the method comprises the following steps: employing a random forest classification method to process a rainfall data sequence and multi-element physical quantity data, employing a long-short-term memory network to optimize the processed data, and obtaining an optimized multi-element association sequence; performing feature recognition on the optimized multi-element association sequence, and performing dynamic association modeling and iterative parameter updating on multi-element expression to obtain prediction model input of comprehensive expression; performing regression and deviation correction on the spatial-temporal distribution data of the extreme rainfall event based on prediction model input of comprehensive expression, and performing multi-element dynamic association integration on the residual error part of the multi-scale characteristics by adopting a feedback cycle mechanism to obtain a final rainstorm event judgment basis; and based on a final rainstorm event judgment basis, carrying out evolution process simulation on real-time environmental factor updating data, and determining an accurate output sequence of extreme rainfall prediction.

The invention relates to the field of multi-modal data fusion, and discloses a response progress collaborative management system based on multi-modal data fusion, which is used for solving the problems of inaccurate rainstorm sediment interference identification, response lag and insufficient multi-system collaboration caused by single-source data limitation in traditional water affair monitoring. Comprising a multi-source data acquisition module, a multi-modal feature learning and interference identification module, a space-time consistency verification module, an intelligent scheduling and cooperative command module and a repair work order acquisition module. A multi-modal feature vector is constructed, a cosine similarity algorithm is adopted to compare with historical disaster data, accurate recognition of storm sediment interference is achieved, multi-dimensional cross validation is conducted on abnormal data through a space-time consistency verification module, the misjudgment rate is remarkably reduced, a repair work order is generated in combination with intelligent scheduling, and accurate dredging treatment is achieved.

The invention provides an adjustable floating wind power foundation based on reinforcement learning and a modular construction method, and belongs to the technical field of offshore wind power. Comprising a fan, and the bottom of the fan is fixed to a tower drum; the bottom of the tower drum is fixedly connected with the floating base; the floating base comprises a first buoy, a second buoy and a third buoy which are arranged in a triangular shape; wherein the bottom of the tower drum is fixedly connected with the first buoy; the first buoy, the second buoy and the third buoy are fixedly connected through supporting steel pipes; the tower drum is fixedly connected with the second buoy and the third buoy through inclined supports; the buoy is further connected with an anchor chain, and the other end of the anchor chain is connected with the suction barrel and anchored to the seabed through the suction barrel. By adopting a coupling connection mode of the inclined support, the tower drum and the floating drum, the storm resistance of the whole structure is improved. A steel-concrete composite structure is adopted, the steel consumption is reduced, and the material cost is reduced. A modular construction method is adopted, the offshore construction time is greatly shortened, and the influence of weather and sea conditions on engineering is reduced.

The invention discloses a large hail identification method and system based on dual-polarization radar three-body scattering characteristics, and relates to the technical field of meteorological identification, and the method comprises the steps: obtaining dual-polarization radar observation data of a historical hail event, and extracting a characteristic threshold value of a hail region dual-polarization radar parameter; acquiring dual-polarization radar base data in real time, retrieving the dual-polarization radar base data according to the characteristic threshold value, and judging whether a hail area exists or not; if the hail region exists, TBSS dual-polarization radar feature recognition is carried out on the hail region of the dual-polarization radar base data, and whether TBSS dual-polarization radar features exist at the hail region is judged; and when TBSS dual-polarization radar characteristics exist, determining that large hail exists at the position. According to the invention, quantitative analysis is carried out on the large hail based on the observation data of the dual-polarization weather radar, and then the large hail is identified, so that the vacancy of dual-polarization radar products is solved, and a powerful means is provided for monitoring and early warning of severe convective storms.

The invention relates to the field of global satellite navigation system service quality evaluation, and particularly discloses a method for evaluating influence of geomagnetic storm on aviation GNSS service precision based on ADS-B data, which comprises the following steps: S1, acquiring two different content messages in ADS-B, namely, messages of state vectors SV and mode states MS of all ADS-B in a to-be-analyzed range, acquiring flight positions of all aircrafts in the concern area range and navigation precision categories NACp and vertical geometric precision GVA of the aircrafts at the positions through a fusion algorithm; s2, carrying out fusion processing on the two messages in the S1, and carrying out data alignment according to timestamps in the SV message and the MS message; and calculating the position information in the MS message according to the time information and the position information in the SV message through interpolation. According to the method for evaluating the influence of the geomagnetic storm on the precision of the aviation GNSS service based on the ADS-B data, the flight position and navigation precision parameters of the aircraft are adopted, and then the precision change of the GNSS service under different geomagnetic conditions is evaluated.

The invention discloses a comprehensive assessment method for weak links of a power system involving multi-dimensional indexes in extreme weather. The method comprises the following steps: firstly, establishing a multi-dimensional index system containing safety and optimized operation, wherein the system covers the line outage probability, the load loss probability, the electric energy insufficiency expectation, the line heavy load rate and the network loss rate; secondly, constructing a line fault model and a cascading fault model under extreme storm weather, describing fault probability change in combination with a Gaussian function and Weibull distribution, and simulating line segment failure by adopting Poisson distribution; a system state after a fault is calculated through a direct-current optimal power flow model, and a large amount of simulation data is obtained in combination with a non-sequential Monte Carlo method. Furthermore, a fuzzy analytic hierarchy process and a CRITIC method are adopted to calculate subjective and objective weights respectively, subjective and objective comprehensive evaluation is realized through weight fusion, and weak links of the system are identified. The method gives consideration to evaluation scientificity and calculation efficiency, and is suitable for real-time risk evaluation and weak link identification of a large-scale power system in extreme weather.

The invention discloses a coastal vegetation damage and recovery evaluation method after an extreme storm and related equipment, and relates to the technical field of data processing, and the method comprises the steps: constructing a synchronous grid stack; four states of storm, moisture, scattering and damage are mapped into a node set, and a pixel-level community lag causal graph is constructed according to each node; solving according to the pixel-level community lag causal graph to obtain a water film scattering expected value, and determining a net damage sequence by using the water film scattering expected value; dividing to obtain ecological partitions of different environments, and calculating a scattering value in each ecological partition so as to obtain a natural fluctuation envelope; and packaging the net damage sequence, the natural fluctuation envelope, the edge weight of pixel-level moisture and scattering in the pixel-level community lag causal diagram and the confidence factor into match input and inputting the match input into a parameter player, further outputting various parameters of vegetation damage and recovery, and then determining the peak damage rate, the half-recovery duration and the complete recovery duration of the vegetation. According to the invention, the evaluation accuracy of vegetation damage and recovery can be improved.

The invention relates to the technical field of thunder and lightning mode recognition, and discloses a thunder and lightning intelligent target early warning method and system for a local key target, and the method comprises the steps: collecting target feature description, estimating storm motion information based on lightning positioning data and radar echo images at continuous moments, and carrying out the recognition of a thunder and lightning mode. And generating a risk corridor in the upstream direction of each target area, and forming a target area around the target area in combination with the risk corridor. Unified spatio-temporal data is obtained, spatio-temporal feature extraction is carried out to obtain storm features, cross-modal correlation selection and weighting are carried out on the storm features with target feature description as a drive, and target features are constructed. And generating an early warning result in a predetermined time window according to the target feature. And when the thunder risk probability reaches an early warning threshold and the spatial risk distribution intersects with the buffer range of the target area, generating targeted early warning information. According to the invention, thunder and lightning targeted early warning is realized, the hit rate is improved, and false alarms are reduced.

To provide a plant cultivation device that is resistant to typhoons, violent storms, and other natural disasters, and that enables adjustment of spatial environmental elements such as light, temperature, humidity, and wind speed with minimal electricity.SOLUTION: A plant cultivation device 100 comprises: a concrete outer wall member formed in a cylindrical or substantially cylindrical shape, in which the part installed on the ground has a circular or elliptical cross section, and its internal height is 7 m to 13 m and its length is 25 m to 35 m; a cooling pad disposed at one end surface of the outer wall member; an exhaust fan disposed at the other end surface of the outer wall member; and lighting fixtures provided on the outer wall member.SELECTED DRAWING: Figure 1

A Surface-affixed, Storm-Resistant Containment Unit involves a shelter structure, with internally-mounted photovoltaic solar panels, and movable doors which may be ‘closed’ to protect the photovoltaic solar panels from storm-related damage, e.g. hail, extreme wind and airborne debris. When weather conditions improve, the movable doors may be ‘opened’, allowing sunlight to strike the undamaged photovoltaic solar panels, and renewable energy production to resume. These Storm-Resistant Containment Units may be installed, and electrically connected in multiples to comprise a storm-resistant, commercially-sized, renewable power generating station (or ‘Micro-Grid’) for use in geographic areas prone to severe storms and hurricanes. Such storm-resistant, commercially-sized, power generation station may be advantageously applied at existing ‘Brown-Field’ industrial sites that have existing, unused electrical-grid infrastructure.

A method of modifying weather by seeding storm clouds at various altitudes with a polymer. The storm clouds are seeded by dispersing a superabsorbent polymer into the cloud in sufficient quantities to cause a large absorption of water. The reaction of the water with the polymer creates a gel-like substance that precipitates to the surface, thus causing an internal constriction within the cloud to lessen storm velocities. The polymer is placed in drop bags and dispensed from an airplane at predetermined altitudes.

The utility model discloses a courtyard umbrella with umbrella cloth convenient to replace, which belongs to the technical field of courtyard umbrellas and comprises an umbrella stand, adjusting mechanisms are arranged on the upper portion and the lower portion of the umbrella stand respectively, and a supporting mechanism is arranged below the adjusting mechanisms. The adjusting mechanism is of a double-end-adjustable folding structure, the erected courtyard umbrella can be quickly and integrally placed on the ground, the front face of the supporting framework faces upwards, a user can conveniently trim or replace the umbrella cloth body, in addition, due to the adjustability of the adjusting mechanism, the whole courtyard umbrella can cope with more severe storm weather, and the service life of the courtyard umbrella is prolonged. The wind force borne by the umbrella cloth body is greatly reduced, and the umbrella cloth body is effectively protected; the supporting mechanism can be installed through bolts or embedded underground and can be installed and adjusted according to actual needs, and in addition, a balancing weight, a limiting inserting groove, an inserting ring and a limiting inserting rod in the supporting mechanism are used in cooperation with the adjusting mechanism to assist in folding or erecting the courtyard umbrella.

The utility model relates to the technical field of box-type transformer substations, in particular to a dustproof box-type transformer substation shell which comprises a shell body, ventilation grooves are formed in the two sides of the shell body, a plurality of rotating shafts are rotationally installed in the ventilation grooves, one end of each rotating shaft and one end of a louver board are fixedly installed, a vertical groove is formed in the shell body, and the louver board is fixedly installed in the vertical groove. And the other end of the ventilation groove penetrates through the vertical groove and is provided with a worm gear, a worm meshed with the worm gear is rotationally installed in the vertical groove, and a protection assembly for protecting the worm gear and the worm is arranged in the vertical groove. According to the utility model, the worm is rotated to enable the worm gear to drive the rotating shafts to rotate, so that the louver boards can be driven to rotate, in case of heavy rain or sand storm, the louver boards rotate downwards to relatively seal the ventilation slots, so that rainwater or sand dust entering the box transformer substation can be reduced, and the plurality of rotating shafts rotate simultaneously and the louver boards rotate synchronously through the meshing of the worm and the worm gear, so that the box transformer substation is prevented from being damaged. And one-by-one adjustment is not needed.

The present application relates to a kind of underpass peak-shifting regulation and storage anti-flooding system and regulation and storage method, belong to urban drainage field, especially to a kind of urban road overpass underpass anti-flooding system.By regulation and storage unit, pump station, control unit is formed, regulation and storage unit is set in underpass approach, does not occupy land additionally, several series regulation and storage pools are arranged in ladder shape, and the road surface rainwater of underpass is collected and discharged into regulation and storage pool temporary storage.In the whole rainfall process, control unit according to rainfall amount dynamically controls the flow of regulation and storage pool into pump station, so that rainwater in storm peak time period is stored by peak-shifting regulation, reduces the flow of regulation and storage pool into pump station in rainfall peak, effectively deal with extreme weather rainfall, prevent pump station from exceeding discharge capacity and not discharging to cause underpass waterlogging.

The invention discloses a processing method and system for a ship intelligent navigation scene, and relates to the technical field of ship intelligent navigation, and the method comprises the following steps: carrying out the continuous waveform analysis of an energy return path of a laser ranging link based on the sudden increase of the density of seawater suspended particles caused by the frontier of a storm, constructing a particle disturbance recognition zone, and carrying out the recognition of the particle disturbance; generating a disturbance identifier carrying an energy rebound mode characteristic according to an analysis result; and expanding the ranging pulse to a time domain by using the disturbance identifier, performing pulse form reconstruction on an energy abnormal section of the ranging pulse, so that the ranging pulse still keeps a stable main peak structure under the influence of energy rebound, and outputting a stable main peak waveform after pulse form reconstruction. According to the invention, through constructing a disturbance identification and form reconstruction mechanism, continuous restoration and smooth output of the distance measurement signals are realized, and the stability and anti-interference performance of distance sensing are improved; and through approaching artifact weakening and gradual change operation control, excessive operation is prevented, and stable course and avoidance safety of the ship in a storm environment are ensured.

The invention belongs to the technical field of flood forecasting, and relates to a flood forecasting method and system based on a rainstorm flood atlas method, and the method comprises the steps: 1), determining a drainage basin needing flood forecasting; 2) obtaining all rainfall sites in the drainage basin range and calculating the weight of each rainfall site; 3) calculating the area rainfall of the drainage basin in the time period based on the rainfall of each rainfall station in the time period and the corresponding weight thereof; 4) calculating static rain of the drainage basin in the time period based on the area rainfall and the average loss rate of the drainage basin in the time period; according to the flood forecasting method, the flood forecasting speed is high, the flood forecasting process and the flood forecasting result are closer to the real situation of the drainage basin, and accurate data support can be better provided for flood prevention decision making.

This application discloses a method and related equipment for assessing coastal vegetation damage and recovery after extreme storms, relating to the field of data processing technology. The method includes: constructing a synchronous grid stack; mapping four states—storm, moisture, scattering, and damage—to a set of nodes, and constructing a pixel-level community lag causality graph based on each node; solving for the expected value of water film scattering from the pixel-level community lag causality graph, and using the expected value of water film scattering to determine the net damage sequence; dividing the area into ecological zones of different environments, calculating scattering values ​​in each ecological zone to obtain the natural fluctuation envelope; encapsulating the net damage sequence, the natural fluctuation envelope, and the edge weights and confidence factors of pixel-level moisture and scattering in the pixel-level community lag causality graph as game inputs and inputting them to the parameter player, thereby outputting multiple parameters of vegetation damage and recovery, and determining the peak damage rate, half-recovery time, and full recovery time of the vegetation. This application can improve the accuracy of vegetation damage and recovery assessment.

This invention provides a peak-scale response analysis method based on nested sub-basin division. By constructing a nested sub-basin structure controlled by area thresholds, and based on a unified design storm conditions and hydrological model parameter system, the method calculates peak discharge under different scale conditions by only changing the sub-basin division scale. It also establishes a logarithmic decay function model between peak discharge and the number of sub-basin units, achieving a quantitative expression of peak-scale response. Furthermore, by comparing and analyzing the scale decay coefficients under different design frequencies, the method quantifies the sensitivity of peak discharge to changes in the division scale. This method forms a complete technical process from data acquisition, structure construction, control variable setting, model running to function modeling and sensitivity analysis. It features a clear logical closed loop, strong repeatability, and good generalization, and does not depend on specific hydrological model software or specific watershed conditions. Under the premise of meeting the unified storm input and nested division principles, it can be widely applied.