45 results about "Extreme events" patented technology

The invention discloses an electrical integrated energy system toughness planning method considering an extreme event tail risk, and relates to the technical field of integrated energy system optimization, and the method comprises the steps: firstly, considering the massive load shedding risk caused by a small-probability extreme event, and constructing a pre-disaster defense-post-disaster emergency response electrical integrated energy system toughness planning frame; secondly, comprehensively considering toughness planning cost, emergency scheduling cost and tail risk loss in a small-probability extreme scene, and establishing an objective function of a planning model; then, for a disaster stage, establishing disaster prevention resource deployment related constraints of the toughness planning model; then, considering operation constraints of the post-disaster power distribution system and the natural gas system, and establishing post-disaster emergency response related constraints; and finally, performing linearization processing on the established planning model, and calling a commercial solver for solving.

The invention relates to a flood and drought disaster risk assessment and deduction system and method based on slope multi-level regulation and storage, and belongs to the technical field of flood and drought disaster risk prevention and control and watershed hydrological regulation and control. The method comprises the following steps: acquiring multi-source data of a target drainage basin, performing water source refined analysis through coupling of water transfer coefficients (K1 and K2) based on a hierarchical synergistic effect of'upper closure-middle retention-lower dissipation 'of a slope, and calculating a controllable, effective and renewable water source amount; in combination with the regulation and storage capacity difference of each level, carrying out flood and drought disaster chain risk collaborative assessment, and outputting hierarchical risks and 0-10 level set risk indexes; a physical mechanism and data driving coupled scene deduction model is driven, and historical extreme event redisk and future climate scene prediction are completed; and after rendering by a visual engine, through hierarchical slope regulation and storage threshold comparison, outputting a regulation and storage measure layout optimization scheme. The system adopts a micro-service architecture and comprises a data management module, a multi-level regulation and storage collaborative analysis module, a water source analysis module, a risk evaluation module, a scene deduction module, a decision support module and a threshold comparison module. According to the method, high-resolution accurate positioning, low-error simulation and efficiency improvement of flood and drought disaster risks are realized, a decision closed loop from risk identification to optimization regulation is formed, and direct and quantitative technical support can be provided for flood and drought prevention and control engineering practice of drainage basins.

The application provides a power distribution network elasticity planning method, device and equipment considering extreme events, and relates to the technical field of power distribution network planning. The method comprises the following steps: fitting an influence expectation of extreme events in a region where a target power distribution network is located based on historical data of the extreme events; wherein the extreme events include meteorological extreme events, geological extreme events and human-caused extreme events; taking the influence expectation and the topological structure of the target power distribution network as inputs, taking reinforcement decision and elasticity resource allocation as outputs, constructing a target function based on node reinforcement decision cost, elasticity resource allocation cost, disaster loss cost and operation cost, constructing a constraint condition based on upper and lower limits of an expectation probability, a power distribution network resilience limit and extreme event measures, and obtaining an elasticity planning model of the target power distribution network; taking minimization of the target function as an optimization target, solving the elasticity planning model, and obtaining an elasticity planning result of the target power distribution network. The application can solve the problem of inaccurate extreme event evaluation in traditional planning.

The invention discloses an industrial park micro-grid toughness planning method and system based on opportunity constraint, and relates to the technical field of power grid planning, and the method comprises the steps: building a combined scene pool containing scene weight according to the extreme strength probability of risk factors in a historical scene based on the coupling and association failure of multiple risk factors; constructing a pre-disaster protection planning layer according to opportunity constraints and protection dimensions; constructing a post-disaster recovery planning layer by using a staged recovery mechanism and an emergency operation mode; constructing an optimization objective function by minimizing the protection cost and the recovery cost; constructing a dual-stage planning model by using the pre-disaster protection planning layer, the post-disaster recovery planning layer and the optimization objective function; and matching the current triggering scene and the occurrence probability according to the real-time industrial park micro-grid operation data and the combined scene pool, inputting the current triggering scene and the occurrence probability into the double-stage planning model, and generating a tough protection planning strategy. The method has the beneficial effects that the protection planning strategy gives consideration to the toughness requirement and disaster recovery requirement of the micro-grid under the extreme event.

This invention discloses an extreme wind field discrimination method based on non-equilibrium Wasserstein distance, belonging to the field of meteorological wind energy prediction technology. The method includes the following steps: obtaining a set of wind field samples in the study area during the target period to construct a reference field; calculating the non-equilibrium Wasserstein distance between each wind field sample and the reference field as an extremity index; fitting the statistical distribution of the extremity index; calculating an extreme discrimination threshold based on the fitted GEV distribution model; and comparing the extreme index with the extreme discrimination threshold to determine whether the wind field sample is an extreme event or a non-extreme event. This invention, employing the aforementioned extreme wind field discrimination method based on non-equilibrium Wasserstein distance, can provide a high-precision foundation for identifying typical extreme wind fields and constructing scenarios for research such as climate change scenario analysis, extreme scenario risk assessment, renewable energy uncertainty quantification, and energy system resilience optimization.

The application provides a method and system for classifying spatial heterogeneity extreme precipitation events, and the method comprises the following steps: obtaining original precipitation data in a preset time scale and spatial range, calculating a standardized precipitation anomaly index representing the abnormal degree of precipitation based on the original precipitation data; for the standardized precipitation anomaly index spatial field corresponding to each time point, calculating a statistical index for representing the overall spatial heterogeneity degree of the spatial field; based on the time series of the statistical index, screening out spatial heterogeneity extreme events according to a preset high quantile threshold; taking the spatial distribution of the standardized precipitation anomaly index corresponding to the spatial heterogeneity extreme events as an input sample, and performing unsupervised training based on a network model with a preset topological structure to cluster and generate typical spatial distribution modes of the spatial heterogeneity extreme events. Through the application, the extreme events with significant spatial differences can be identified, and the classification results are objective and stable, which provides a scientific basis for refined risk assessment.

The invention discloses a water-wind-solar complementary optimization scheduling method and system considering new energy extreme output events. The method comprises the following steps: constructing a new energy extreme output event library; a cascade hydropower dispatching risk assessment model oriented to the new energy extreme output event is constructed, the economy and reliability of cascade hydropower dispatching under different extreme events and water level states are quantitatively assessed, and weak links of system dispatching operation under the new energy extreme output event are identified; the method comprises the following steps: establishing a cascade hydropower multi-target optimization scheduling model giving consideration to the generating capacity and the scheduling risk, solving a non-inferior scheduling scheme set, selecting a compromise scheduling scheme giving consideration to economy and robustness by adopting a weight method, and adjusting a scheduling plan in a rolling manner according to real-time hydrological forecast and extreme event early warning. And the adjustment capability of cascade hydropower on the new energy extreme output event is improved. According to the method, the reliability of the water-wind-light complementary system under the new energy extreme event is improved, and technical support can be provided for formulating a system scheduling scheme for defending continuous multi-day extreme output of new energy.

The invention relates to the technical field of hydro meteorology, in particular to a method and a system for predicting and tracing extreme hydrological events in a reservoir area. The method comprises the following steps of S1, collecting meteorological and hydrological data, a global climate mode data set, geographic space data and stable isotope data of a reservoir area, and unifying a multi-source data scale through a time sequence fusion algorithm; according to the method, isotope data is adopted as an independent evidence constraint model, and compared with a traditional single model method, the method can more comprehensively capture a complex mechanism of occurrence of an extreme event, so that a simulation result is closer to an actual situation, and countermeasures can be taken in the face of an extreme climate event; the climate prediction and the isotope traceability technology are coupled for the first time, how causes of the extreme events change under different future climate scenes can be pre-judged in advance, and the research method and the model framework are not only suitable for specific areas, but also can be widely applied to research of the extreme events of large reservoirs and drainage basins.

This invention discloses a method for generating extreme power load samples, belonging to the technical field of extreme power load sample generation. The method includes preprocessing the original load sequence; separating the base load sequence and industrial disturbance components using an industrial load disturbance identification model; slicing the base load daily to form a daily base load sequence set; decomposing the daily base load sequence set to obtain multi-scale detail sequences; constructing multi-scale time-frequency feature vectors; performing density peak clustering based on the multi-scale time-frequency feature vectors to obtain extreme load pattern samples; performing fine-grained clustering of the extreme load pattern samples using a dynamic density clustering algorithm, followed by Markov random field optimization to obtain a spatiotemporal template for extreme events; designing a diffusion generation model based on the spatiotemporal template; training the model with the extreme load pattern samples; generating load sequences and superimposing them with the industrial disturbance components to obtain extreme load samples. This invention solves the technical problem that existing technologies cannot generate a large number of reasonable extreme load samples.

The application discloses an extreme event-oriented main network key element identification method and system, and mainly relates to the technical field of extreme event prediction. The method comprises the following steps: based on the importance of load and the remedial measures of the power system to cope with extreme events, taking the maximum value of the system loss caused by the power cut of the extreme event as a first objective function; using a direct current flow model to model power system operation and establish element constraints; based on the first objective function, power system operation modeling and element constraints, establishing upper and lower two-layer robust models and converting the two-layer robust models into a main problem and a sub-problem; solving the main problem and the sub-problem and outputting the key element combination in the power system; and determining the importance degree of each element in the element combination through the repair sequence of the element. The application has the beneficial effects that it solves the problem that the Monte Carlo simulation method cannot generate the corresponding scene due to the small event occurrence probability, and identifies the key element combination in the power transmission network.

The invention relates to the technical field of wind power extreme event identification, in particular to an offshore wind power cluster output steep rise and steep drop extreme event identification method and system, and the method comprises the steps: segmenting a wind power original signal long sequence of an offshore wind power cluster into non-overlapping windows, carrying out the preprocessing of each window through employing a residual estimation method, and carrying out the recognition of the output steep rise and steep drop extreme event of the offshore wind power cluster; calculating regularization parameters, applying the regularization parameters to corresponding windows, respectively performing L1 trend filtering, and finally combining results; reducing an edge effect occurring in a merging result through cosine weighted fusion, and outputting a smoothed sequence; performing dynamic window wind power abrupt rising and abrupt falling event detection on the sequence, firstly setting a minimum window, setting an initial window length, and judging whether a time interval accords with a continuous rising or continuous falling constraint; if yes, the window length is enlarged until the maximum window is broken through or trend continuity is lost, and if not, whether a sudden rising and sudden falling event occurs or not is judged through a set power change amplitude threshold value and a set ramp rate threshold value.

The application discloses a kind of rail transit resilience measurement methods under extreme weather event, the resilience measurement methods include the following steps: S1, construct resilience curve under extreme weather event;S2, the spatiotemporal similarity of each subway station is calculated;S3, site classification is carried out;S4, by comparative analysis, obtain the resilience performance of different functional type sites under extreme weather event;S5, obtain the regression analysis result of the resilience performance of each site under different typhoon intensity.The resilience measurement method of the application is used in urban traffic planning and emergency management, the resilience of rail transit under extreme weather event can be measured using the method, and the resilience performance of rail transit sites of different functions is accurately evaluated, thereby providing decision support for rail transit emergency management when extreme event occurs, helping to improve traffic disaster prevention and disaster resistance, improve urban public transport risk resistance, and protect passenger safety.

The embodiment of the invention provides a composite extreme event diagnosis method, device and equipment and a storage medium, and the method comprises the steps: determining the blocking high pressure of a first place and a second place, carrying out the strength diagnosis, and obtaining the formation condition of a dual-blocking system; determining a third place, determining a cold vortex center, calculating the vertical vortex advection intensity of the third place, and obtaining the formation condition of a cold vortex system corresponding to the cold vortex center; determining a double-blocking high-pressure center according to the first place and the second place, calculating the air pressure gradient force of the double-blocking center and the cold vortex center, and judging the mutual reinforcement degree of the two systems according to the air pressure gradient force; and determining a fourth place, constructing a decision matrix based on the four places, the formation conditions of the two systems and the mutual reinforcement degree, and diagnosing the extreme event by using the decision matrix. Quantitative evaluation of the concurrent probability of multiple disasters such as strong wind, violent snow and strong cooling is realized, the missing report rate of composite extreme events is reduced, and the early warning capability is improved.

The embodiment of the application discloses a kind of asymmetric meteorological waterlogging drought acute change event identification method and system.The method is by in time scale to the first precipitation data of target area in preset time is handled to obtain the second precipitation data of decadal time series, and based on asymmetric mode, according to the second precipitation data, generate the first standardized precipitation index of target area in single decadal time series and the second standardized precipitation index in multiple decadal time series, then according to the first standardized precipitation index and the second standardized precipitation index, determine the flood and drought event of target area in preset time in time series, finally, according to the flood and drought event of target area in preset time in time series, the waterlogging drought acute change event of target area in preset time can be accurately identified, it has important theoretical significance and application value to the risk assessment of extreme event, the formulation of disaster prevention and mitigation measures.

The invention discloses a method, a system, equipment, a medium and a product for controlling extreme events under typhoon, and the method is characterized in that the method comprises the steps: obtaining typhoon meteorological data, and the structure parameters and real-time operation data of a power distribution network; mapping nodes and lines in the power distribution network into a space-time diagram structure, and inputting the typhoon meteorological data and the real-time operation data into the space-time diagram structure to represent a dynamic coupling relationship between typhoon evolution and the state of the power distribution network so as to obtain coupled space-time characteristics; inputting the coupled spatial-temporal characteristics into a deep reinforcement learning control model to generate a discrete action control instruction based on the state vector, generating a corresponding continuous action control parameter according to the discrete action control instruction, and determining a typhoon control strategy of the power distribution network in combination with the discrete action control instruction and the continuous action control parameter; and carrying out collaborative optimization control on power distribution network equipment of the power distribution network under the typhoon disaster based on the typhoon control strategy. According to the invention, the accuracy of power grid cascade overload control under typhoon disasters can be improved.

The invention discloses a summer soil moisture-temperature coupling enhancement attribution and estimation method, and relates to the field of carbon neutralization and climate change, and the method comprises the steps: constructing a strength index for measuring soil moisture-temperature (SM-T) coupling; based on the reanalysis data set and a sixth coupling mode comparison item (CMIP6) mode, analyzing global SM-T coupling space-time change characteristics in summer in a historical period; quantificationally separating relative contributions of key driving factors such as soil moisture, total cloud amount and leaf area index by adopting regularization multiple linear regression; based on correlation analysis and a forced signal detection method, human climate change detection and attribution are carried out on historical changes of the SM-T coupling index; and the future evolution trend of SM-T coupling under different emission scenes is evaluated. According to the method, a summer SM-T coupling enhancement mechanism is quantitatively clarified from the global scale, so that a scientific basis is provided for relieving extreme event risks caused by climate changes.

The application discloses a kind of robust spline decomposition and extreme value extraction method of solar wind speed time series, comprising, after obtaining solar wind speed time series, according to predetermined spline node number n, utilize quantile position method in generated time index selects n node positions, constructs cubic B spline base function, forms spline base function;Then, realize robust fitting using Huber loss function, by iterative optimization dynamically reduce the weight of abnormal point, extract stable trend item, and utilize the absolute median deviation of residual to set extreme value discrimination threshold, accurately identify abnormal fluctuation;The final output decomposition result includes smooth trend item, extreme fluctuation item and weight vector;The above decomposition result can effectively distinguish long-term trend and extreme event in solar wind speed time series, improve solar background analysis and the identification ability of space extreme weather, applicable to space weather forecast and disaster risk assessment.

The invention provides a spatial heterogeneity extreme rainfall event typing method and system, and the method comprises the steps: obtaining original rainfall data in a preset time scale and a space range, and calculating a standardized rainfall anomaly index representing the rainfall anomaly degree based on the original rainfall data; for the standardized rainfall anomaly index space field corresponding to each time point, calculating a statistical index for representing the overall spatial heterogeneity degree of the space field; based on the time sequence of the statistical indexes, screening out spatial heterogeneity extreme events according to a preset high quantile threshold value; and taking the spatial distribution of the standardized rainfall anomaly index corresponding to the spatial heterogeneity extreme event as an input sample, performing unsupervised training through a network model based on a preset topological structure, and clustering to generate a typical spatial distribution mode of the spatial heterogeneity extreme event. Through the method, the extreme events with significant spatial differences can be identified, the typing result is objective and stable, and a scientific basis is provided for refined risk assessment.

The invention discloses a new energy extreme characteristic analysis method and system based on statistical analysis and an extreme value theory, and the method comprises the steps: collecting and sorting data, collecting new energy ideal output data, and carrying out the preprocessing of the data; three types of new energy extreme events are obtained from the three aspects of electric power, electric quantity and climbing and based on the preprocessed new energy ideal output data; analyzing statistical characteristics of the three types of new energy extreme events, namely analyzing occurrence frequencies, interannual characteristics, seasonal characteristics and spatial characteristics of the three types of new energy extreme events; and further evaluating the three types of new energy extreme events by applying an extreme value theory, and obtaining the recurrence levels of the three types of new energy extreme events in different recurrence periods and corresponding confidence intervals by adopting a maximum value sequence method. According to the invention, the influence of extreme events on the power system under high-proportion new energy can be further studied.

The application discloses an extreme environment cooperative simulation system and a control method, and relates to the technical field of intelligent control.The system comprises an environment simulation module, a cooperative control module, an extreme environment mutation module and a structure damage evaluation module.The environment simulation module comprises a wind field simulation unit, a wave flow simulation unit, a salt mist simulation unit and a temperature and humidity simulation unit.The environment mutation coefficient is obtained through the extreme environment mutation module, the tower load, the wind wave angle, the salt mist temperature and humidity and other factors are introduced through the cooperative control module, the environment coupling coefficient is dynamically calculated and graded, the difference parameter linkage rule is corresponded, and the deviation between the simulation and the actual environment is reduced.The extreme environment mutation module is based on the coupling coefficient, adopts the nonlinear logarithmic constraint to quantize the mutation amplitude in combination with the extreme index deviation, matches the marine extreme event characteristics, covers various extreme scenes, and effectively improves the extreme environment simulation quality and the data accuracy.

The utility model discloses a height-adjustable wave wall structure capable of installing handrails, which comprises a plurality of wave wall units, the plurality of wave wall units are sequentially inserted and fixedly connected along the height direction of a wave wall, a wave wall installation groove is arranged on a levee top used for installing the wave wall, and the bottommost wave wall unit is inserted in the wave wall installation groove; and a handrail is detachably connected in the topmost wave-resistant unit. By designing a plurality of wave-resistant units, the height of the wave-resistant wall is adjustable, the height can be adjusted in real time according to changes of tides, storm surges or flood levels, and excessive redundancy of a wall body with a fixed height at a low water level or insufficient protection at a high water level is avoided. Temporary heightening can be achieved in extreme events such as typhoon and rainstorm, and the protection capacity is enhanced; the height is reduced at ordinary times, and the influence on landscape or ecology is reduced.

The application discloses a power intelligent scheduling system and method for extreme climate events, and belongs to the field of extreme climate data analysis, and comprises the following steps: a risk characterization module is used for accessing and preprocessing extreme climate multi-source data, and each partition risk characterization facing extreme event supply guarantee constraints is formed based on the multi-source data; an upper limit checking module is used for correcting each partition net load prediction according to each partition risk characterization to obtain a conservative upper limit of each partition net load, and performing safety constraint checking based on the conservative upper limit of each partition net load to obtain a power grid key constraint state index; and a decision feedback module is used for generating a structured scheduling decision set based on each partition risk characterization, the conservative upper limit of the net load and the power grid key constraint state index. The technical problems that the existing technology causes the scheduling strategy to have response lag when the extreme event comes, the supply-demand gap of a high upper limit of a key period is not prepared, and the cross-region support plan is not sufficient in realizability are solved.

The invention discloses a raft anti-floating reliability analysis method considering underground water time variation and extreme events, and belongs to the technical field of geotechnical engineering and underground structure safety evaluation. The technical problem to be solved is that when the anti-floating reliability of the raft foundation is analyzed, the underground water level time-varying characteristic, the extreme event influence, the anti-floating measure random degradation characteristic and the like cannot be comprehensively considered or the combination thereof. According to the technical scheme, the method comprises the following steps that S1, limit state models are established, wherein the limit state models comprise an anchor rod working condition limit state model and an anchor rod-free working condition limit state model; s2, matlab software programming is adopted, and an underground water head simulation time-varying graph, an anchor rod working condition anti-floating accumulative failure probability time-varying graph, an anchor rod working condition parameter sensitivity ranking graph, a non-anchor rod working condition anti-floating accumulative failure probability time-varying graph and a non-anchor rod working condition parameter sensitivity ranking graph are obtained; and S3, analyzing a result obtained in the step S2.

The invention discloses an extreme event sensitive marine environment forecasting method and a related device. The extreme event sensitive marine environment forecasting method comprises the following steps: acquiring state data of a marine environment; preprocessing the state data of the marine environment to obtain a preprocessing result; inputting a preprocessing result into a probability statistical model to obtain a prior probability of each key environment scene in a physical mode; inputting the pre-processed result into a machine learning model to obtain an output result of the machine learning model; and fusing the output result of the machine learning model with the prior probability of each key environment scene in the physical mode to obtain a final marine environment forecasting result, and the method and the related device can accurately forecast the marine environment.

The invention discloses a weather forecast model comprehensive scoring method and system for an extreme rainfall event, and belongs to the technical field of weather forecast evaluation, and the method comprises the steps: calculating a multi-level smooth threat score, and carrying out the asymmetric processing of an overestimation forecast under an extreme event, so as to reduce the punishment; according to the actual rainfall intensity, performing segmented weighting and asymmetric penalty on the forecast error, and calculating a business enhancement weighted average absolute error; activating and calculating an incremental reward including trend and error control when the live precipitation meets extreme event conditions; calculating suppression score items such as a false alarm rate and a logarithm deviation score; and performing normalization and weighted fusion on each score item to generate a comprehensive score. According to the method, through asymmetric reward and punishment, dynamic reward and multi-dimensional index fusion, the rationality and service guidance quality of extreme event forecast evaluation are remarkably improved, the model can be effectively guided to achieve the disaster prevention target of Ningghou not leakage, meanwhile, system deviation and false alarm are restrained, and the evaluation result is more scientific and comprehensive.

This invention relates to the fields of short-term meteorological forecasting and deep learning spatiotemporal sequence prediction, specifically to an extreme precipitation prediction method based on high-resolution statistical extreme value recovery. This method includes variational dimensionality reduction mapping, spectral-spatial feature projection and pre-enhancement, dual-stream autoregressive evolution deduction, time-aware anchor point correction, frequency-aware back-projection decoding, and high-resolution reconstruction. It addresses the problems in weather forecasting, such as spectral shift leading to severe blurring and degradation of predicted images, low-pass filtering effects of convolution operators causing systematic underestimation of extreme events, and time-series drift compromising the physical fidelity of high-resolution predictions.

The invention discloses a comprehensive risk assessment method for extreme hydrological events. The method comprises the following steps: determining the type and occurrence frequency of an extreme hydrological event based on ten-day runoff data of a hydropower station, and performing trend inspection on the occurrence frequency of the extreme hydrological event to obtain a trend index and a trend significance index of the extreme hydrological event; determining a comprehensive evaluation index matrix of the hydropower station, and calculating a comprehensive weight combining the entropy weight and the trend weight based on the trend significance index of the extreme hydrological event; and based on the comprehensive weight combining the entropy weight and the trend weight and the comprehensive evaluation index matrix of the hydropower station, calculating the comprehensive risk score of the hydropower station in the extreme hydrological event by using an improved ideal solution similarity sorting method. According to the method, the bottlenecks of incomplete extreme event identification, static risk assessment and low small sample discrimination of a traditional method are solved, and a more accurate and dynamic risk assessment tool is provided for the cascade hydroelectric system to cope with extreme hydrological events.

The application provides a kind of extreme event under integrated energy multi-agent collaborative optimization scheduling method and system, belongs to integrated energy system scheduling technical field.The method includes: extracting the space-time characteristics of integrated energy system multi-agent and setting as global system state after being lightened by knowledge distillation, combining Nash equilibrium to construct agent lightweight general model and solve, obtain global system state and the optimal action strategy of each agent under global system state, and the blackout probability model of each extreme event constructed and the disturbance propagation model of electric-gas-heat-storage multi-energy driven by extreme event are solved model and the adjustment demand matrix generated are combined, and a multi-agent collaborative strategy model is constructed by using deep reinforcement learning method;The real-time monitoring data of integrated energy multi-agent is input into the multi-agent collaborative strategy model, and the integrated energy multi-agent collaborative optimization scheduling strategy under extreme event is obtained.The application improves the regulation and control accuracy and safety of integrated energy system under extreme event.

This invention discloses a method for detecting extreme hydrological events in a watershed and determining changes in hydrodynamic conditions, belonging to the field of water conservancy engineering technology. The specific steps include: S1, calculating the annual runoff distribution difference coefficient and characteristic flow process; S2, screening extreme flood and extreme drought events, i.e., extreme flood condition: selecting the annual maximum flow Q from the historical sequence. max The first 1% quantile value; extreme drought conditions: selecting the minimum value of the runoff distribution difference coefficient λ to characterize the minimal difference in water volume between the flood season and the non-flood season; S3, determining typical hydrological events by analyzing the relationship between characteristic flow and annual average flow; S4, analyzing the water depth-flow relationship and classifying extreme flood types; S5, deriving the physical formula of the water depth-flow relationship; S6, analyzing and verifying the differences in hydrodynamic conditions for extreme floods. This invention can solve the problems of insufficient sensitivity of traditional methods to short-duration extreme hydrological events and the lack of quantitative analysis of hydrodynamic mechanisms under extreme events.

The invention discloses a story line probability coupling attribution method for a heat wave flood space composite event, and relates to the field of climate change. The method comprises the following steps: firstly, quantifying a spatially compounded heat wave flood extreme event by utilizing a compound index; based on a story line attribution framework, similar circulation is constructed by extracting circulation characteristics and according to a multi-dimensional Euclidean distance, so that contribution of dynamic factors of atmospheric circulation and thermodynamic factors of man-made force to events is disassembled and evaluated; then, under a probability attribution framework, based on the Euclidean distance, the occurrence probability and the recurrence period of similar events in historical and future specific periods are evaluated, and a time change sequence of the future occurrence probability is analyzed; according to the method, quantification, driving factor contribution analysis and future risk estimation of the spatial composite extreme events are realized, and a scientific basis is provided for mechanism understanding, forecasting and early warning and disaster prevention and reduction of the events.