64 results about "Extreme events" patented technology

The invention relates to the technical field of meteorological monitoring and climate prediction, in particular to a Qinghai-Tibet Plateau composite extreme climate event attribution evaluation method. The method comprises the steps that ground observation, remote sensing and reanalysis data are integrated through a multi-source data dynamic space-time weight fusion technology, and abnormal value correction and non-uniform interpolation are achieved; identifying a composite event by adopting a multivariable combined extreme index and a space-time coupling graph model and generating a structured label, wherein the structured label comprises strength, range, duration and evolution path; constructing a multi-scale causal network to analyze the contribution of the driving factor, and implementing physical constraint disturbance based on causal weight; recovering high-resolution response by using a Bayesian agent model and combining topographic constraint random downsampling, and deducing spatio-temporal evolution through an event propagation network; and a kernel polynomial hybrid uncertainty propagation framework is adopted to generate a probabilistic scene set, and multi-level risk early warning and dynamic knowledge base optimization are realized. According to the invention, the attribution precision and early warning efficiency of plateau composite extreme events are comprehensively improved.

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 discloses a scene generation method and system for new energy extreme meteorological disturbance modeling, and relates to the technical field of power systems, and the method comprises the steps: recognizing a strong disturbance event under meteorological drive according to a constructed new energy effective output sample set, and constructing a generative adversarial neural network model embedded in a wind power photovoltaic physical boundary condition, acquiring a virtual disturbance section; and based on the virtual disturbance section, constructing a new energy annual extreme disturbance scene set by integrating a plurality of typical days embedded with disturbance, and applying the new energy annual extreme disturbance scene set to new energy extreme meteorological disturbance modeling and sample enhancement. The method has strong representation capability, good training stability and scene expansion capability, and can provide more representative and diversified input scenes for dispatching optimization and supporting power supply planning of the power system, so that the robustness and risk perception capability of the system for coping with extreme events are improved.

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.

The application relates to a key power transmission section optimization identification method under an extreme event N-K broken line fault scene, which comprises the following steps: S1, converting a power grid into a non-directed graph, searching and deleting a hanging node in the non-directed graph, and finding a cut point of the simplified non-directed graph through a Tarjan algorithm; S2, performing power flow calculation on a large power grid to obtain power flow of each line in the power grid, and establishing a network flow graph of each partition of the large power grid according to a power flow calculation result and a maximum transmission power rating of the line; S3, iteratively searching by excluding each edge in an initial cut set in turn, identifying and deleting redundant cut edges and invalid cut edges, until the search of all minimum cut sets in the network flow graph is completed, and a power transmission section is obtained; and S4, obtaining a key power transmission section with an overload tripping risk when N-K lines are broken. The application can quickly and effectively identify the key power transmission section of the large power grid under an extreme event such as a natural disaster, and ensures safe and reliable operation of the large power grid.

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 invention provides an integrated energy multi-agent collaborative optimization scheduling method and system under an extreme event, and belongs to the technical field of integrated energy system scheduling. The method comprises the following steps: extracting spatial-temporal characteristics of multiple agents of the comprehensive energy system, performing knowledge distillation lightweight, setting the spatial-temporal characteristics to be in a global system state, constructing an agent lightweight general model in combination with Nash equilibrium, and solving the model; obtaining a global system state and an optimal action strategy of each agent in the global system state; combining the optimal action strategy with a built power failure probability model of each extreme event, a built extreme event driven electricity-gas-heat-storage multi-energy disturbance propagation model solving model and a generated adjustment demand matrix; adopting a deep reinforcement learning method to construct a multi-agent collaborative strategy model; and inputting the real-time monitoring data of the comprehensive energy multi-agent into the multi-agent collaborative strategy model to obtain a comprehensive energy multi-agent collaborative optimization scheduling strategy under the extreme event. According to the invention, the regulation and control precision and safety of the integrated energy system under extreme events are improved.

The embodiment of the invention discloses a method and a system for identifying an asymmetric meteorological flood and drought sharp turn event. The method comprises the following steps: processing first rainfall data of a target area in a preset time on a time scale to obtain second rainfall data of a ten-day scale time sequence; according to the second rainfall data, generating a first standardized rainfall index of the target area in a single-ten-day-scale time sequence and a second standardized rainfall index of the target area in a multi-ten-day-scale time sequence, then, according to the first standardized rainfall index and the second standardized rainfall index, respectively determining a flood event and a drought event of the target area on the time sequence within the preset time, and finally, according to the flood event and the drought event of the target area on the time sequence within the preset time, determining the drought event. Therefore, the method can accurately recognize the flood and drought sudden turning event of the target area in the preset time, and has important theoretical significance and application value for risk assessment of extreme events and formulation of disaster prevention and reduction measures.

The invention discloses a drainage basin extreme hydrological event test and hydrodynamic condition change determination method, and belongs to the technical field of hydraulic engineering, and the method specifically comprises the steps: S1, calculating a runoff annual distribution difference coefficient and a characteristic flow process; s2, extreme flood and extreme dry water events are screened, namely, according to the extreme flood condition, the first 1% quantile of the annual maximum flow Qmax in the historical sequence is selected; an extreme low water condition: selecting a minimum value of a runoff distribution difference coefficient lambda to represent that the water quantity difference between the flood season and the non-flood season is extremely small; s3, a typical hydrological event is determined by analyzing the relation between the characteristic flow and the annual average flow; s4, analyzing a water depth-flow relationship and dividing extreme flood types; s5, deducing a water depth-flow relation physical formula; and S6, analyzing and testing the hydrodynamic condition difference of the extreme flood. By adopting the method, the problems that a traditional method is insufficient in sensitivity to short-duration extreme hydrological events and lacks quantitative analysis on a hydrodynamic mechanism under the extreme events can be solved.

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.

To provide an estimation and control method for frequency errors of extreme events.SOLUTION: The present invention proposes a frequency shift functional and a frequency shift functional estimation formula based on probability estimation, and constructs a method for reversely solving a confidence frequency shift value epsilon according to the frequency shift functional to improve a frequency distribution function. The present invention is suitable for estimating and controlling errors of occurrence frequency prediction of extreme events in various frequency statistical models by using hydrological observation data under probability statistical significance, so that the prediction accuracy and reliability of hydrological extreme events such as storm surge, rainstorm flood, tsunami, and the like are improved, risks caused by extreme natural disasters are reduced, and loss of life and property that may be caused is reduced.SELECTED DRAWING: Figure 1

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.

The invention discloses a visualization method and system for an extreme event scene of a distribution network and a storage medium, and belongs to the field of power systems. The method comprises the following steps: extracting spatio-temporal features and event intensity features as extreme event features; extracting distribution network disaster damage characteristics according to the distribution network disaster damage data; obtaining a disaster damage prediction result according to the contribution degree of the extreme event feature to the distribution network disaster damage feature, the extreme event feature, the distribution network disaster damage feature, the meteorological data and a preset feature nonlinear mapping model; obtaining a distribution network risk level according to the disaster damage prediction result, the extreme event features and the distribution network disaster damage features; and constructing a spatial attenuation effect annular graph according to the spatial-temporal features and the event intensity features, constructing a feature importance waterfall graph according to the disaster damage prediction result and the contribution degree, constructing a spatial-temporal correlation thermodynamic graph according to the distribution network risk level and the distribution network disaster damage features, and obtaining a visual image of the distribution network extreme event. By implementing the invention, the problem that complicated information of the distribution network in an extreme event scene is difficult to present in the prior art is solved.

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 invention discloses a probabilistic grading fault scene generation method for an electrical integrated energy system, and relates to the technical field of integrated energy system optimization, and the method comprises the steps: firstly, considering the earthquake intensity uncertainty, discretizing a target earthquake magnitude range into a plurality of equidistant intervals, and calculating the occurrence probabilities of earthquakes of different earthquake magnitudes; secondly, modeling disaster-causing properties of earthquakes with different earthquake magnitudes by adopting a vulnerability curve, and calculating expected values of damaged elements of a power distribution system and a natural gas system; then, randomly generating a fault scene under each classification magnitude by using a Monte Carlo method, carrying out analogue simulation on the cascade fault of the electrical integrated energy system under the random fault scene, and recording the total loss load of the system; and finally, calculating an average load loss amount under each classification magnitude, and selecting a typical fault scene to form a classification fault scene set. According to the method, the tail risk of the small-probability extreme event is quantified, and a scene basis is provided for formulating a toughness improvement strategy of a comprehensive energy system.

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.

This invention discloses a method for predicting heterogeneous extreme events based on tail data distribution, belonging to the field of disaster prediction. The method comprises: collecting historical data on the occurrence of scenario-dependent events as a scenario-dependent event dataset, generating a scenario-independent event dataset based on experimental simulation; constructing a distribution function set that fits the data distribution pattern, splitting the tail data into several segmented datasets, and using the distribution function model to fit the optimal distribution function model for each segmented dataset as a priori data equation; splitting the scenario-independent event dataset into several sub-datasets of continuous data, using the optimal distribution function model as the distribution equation for each sub-dataset; and using the data reconstruction equation to predict the upcoming scenario-dependent event data. This invention not only provides a scientific basis for disaster prevention and control, but also provides data support for relevant decision-making, helping to reduce the damage caused by extreme disasters to society.

This application belongs to the field of ecological risk prediction technology, and involves a method for predicting extreme weather events and ecological risks using a generative adversarial network. The meteorological generation branch uses a 3D residual network and a Transformer to synchronously extract the spatiotemporal characteristics of the meteorological field and predict future meteorological conditions. The ecological generation branch dynamically generates an ecological risk map based on meteorological forecast results and terrain data through a dilated convolution U‑Net, avoiding the resolution and dimensional mismatch caused by cross-model interpolation. The discriminator introduces a physical conservation verification module and an ecological association module to jointly optimize the physical rationality loss and ecological logic consistency loss of the generator in adversarial training. In addition, the dynamic feedback mechanism corrects the generation error through real-time observation data, and uses spectral normalization to constrain model parameters to further improve the accuracy of the coupled prediction of extreme events and ecological risks. Therefore, the present invention realizes seamless spatiotemporal coupling of meteorological and ecological fields, and the generated results have both physical conservation and ecological correlation.

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 relates to the technical field of energy storage systems, in particular to an energy storage emergency method for coping with extreme events. The method comprises the following steps: firstly, acquiring meteorological data of an energy storage equipment installation environment, main water flow water level change information, energy storage system operation data and real-time meteorological data, then carrying out local prediction and cloud prediction, and carrying out weighted fusion on a local prediction result and a cloud prediction result to obtain a final prediction result. In order to solve the problem of communication interruption and failure, a dynamic weight distribution mechanism is established, and the weight of each prediction model at the current prediction moment is calculated in real time according to the performance of each prediction model in the historical prediction process. A multi-disaster scene coping control strategy is established, and according to an early warning signal of an extreme event, rapid switching of operation modes of the energy storage system among three conditions of pre-disaster, in-disaster and after-disaster is realized. The method solves the problem that a traditional energy storage system control technology is insufficient in control strategy for dealing with the extreme events, and ensures that the energy storage system reliably and stably operates during the period of dealing with the extreme events.

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.