970 results about "Torrential rain" patented technology

A dynamic evaluation method for an extreme rainstorm waterlogging disaster risk for disaster prevention and reduction. The method comprises: investigating and surveying urban system data and disaster prevention and reduction data, using GIS technology to divide disaster-bearing objects into refined risk units on the scale of urban buildings and road networks, and determining the spatial distribution of the disaster-bearing objects; on the basis of an extreme rainstorm waterlogging scene, simulating the disaster influence of a dynamic change process of a flood ponding depth on the disaster-bearing objects; developing refined dynamic evaluation on a waterlogging risk by combining the two methods of waterlogging process simulation and an indicator system; using a spatial complex network and a shortest path plan to calculate a traffic capacity and emergency service accessibility of a road network system; and on this basis, taking into comprehensive consideration the rational allocation of disaster prevention emergency drainage and emergency rescue services to a high-risk area, and proposing dynamic evaluation technology for a waterlogging risk that integrates a disaster evolution process and a disaster prevention response process, and ultimately realizing the dynamic evaluation of the waterlogging risk of each disaster-bearing unit during the waterlogging disaster evolution.

The invention discloses an intelligent monitoring system for a municipal drainage pipe network, and particularly relates to the technical field of drainage pipe network monitoring. The node operation mode identification module carries out real-time classification and confidence evaluation on the operation state of the pipe network, constructs a multi-attribute pipe network weighted graph based on pipe diameter difference, gradient and confluence density, and extracts multi-scale features through graph Fourier transform. A hybrid anomaly detection link is constructed in combination with an LSTM self-encoder, an isolated forest model and chemical oxygen demand and turbidity water quality verification, and the problems that traditional single-index monitoring is prone to false alarm and missing alarm and inaccurate in positioning are solved; sensor data compensation is realized through cooperation with digital twinning, a rapid detection mode is started during rainstorm early warning, key nodes are processed preferentially, and drainage scheduling is controlled in a closed-loop mode; and target nodes which are easy to accumulate grease are screened based on pipe network topology connectivity, accumulation risks are predicted by fusing multi-sensor data, and a preventive clearing instruction is triggered.

The invention discloses an urban inland inundation water recession process numerical simulation method and system based on physical-data fusion, and belongs to the technical field of urban flood control and intelligent water affairs. A hydraulic model and a data driving model are fused, a prediction result is dynamically weighted through gating attention, and real-time correction is performed by using Kalman filtering; the method comprises the steps of multi-source data fusion and enhancement, wherein data collection and processing are carried out, space-time alignment is achieved, and feature construction is carried out; mechanism and mathematical double-engine collaborative prediction: collaborative prediction is carried out based on a physical model engine and a data driving engine, and adaptive fusion is realized; online reasoning and dynamic correction, including Kalman filtering dynamic correction and extreme scene emergency processing; and performing model evaluation and iteration. According to the method, the problem of high-precision prediction of the recession time and the ponding range in a complex urban environment is solved, the generalization ability of the model in non-seen scenes such as pipeline blockage and rainstorm extrapolation is improved, and a reliable decision basis is provided for urban inland inundation emergency scheduling and drainage facility optimization.

The invention relates to the technical field of urban drainage and waterlogging prevention, in particular to an urban real-time drainage scheduling method and system based on deep reinforcement learning. The method comprises the steps of constructing a dual-target reward function and constraint conditions for minimizing the total amount of surface waterlogging and the overflow frequency of a combined system discharge port, performing rainfall runoff simulation by adopting a hydrodynamic mechanism model and combining basic data of a target area, and establishing a multi-agent model based on deep reinforcement learning. The method comprises the following steps: constructing interactive coupling of decision actions and simulated environment states, obtaining updated environment states and drainage scheduling reward values through the coupling, driving a multi-agent model to generate a strategy network, predicting and generating drainage waterlogging prevention engineering actions based on real-time rainstorm information, and dynamically updating the actions by utilizing the periodically generated strategy network. According to the method, it is ensured that multi-objective optimization decision is rapidly achieved within response time, the waterlogging relieving effect is achieved, urban rainfall flood toughness is enhanced, and the real-time requirement is met.

The invention discloses an extreme rainstorm cascade disaster emergency decision-making method and system fused with multi-source data, and the method comprises the steps: constructing a historical event knowledge graph and a current event dynamic evolution knowledge graph through integrating the multi-source data and using the integrated multi-source data; searching current and subsequent disaster risks and corresponding emergency decision-making schemes in the historical event knowledge graph according to static disaster characteristics of high similarity in attributes of the historical event knowledge graph and the current event dynamic evolution knowledge graph; and dynamically adjusting the emergency decision scheme and the historical event knowledge graph according to feedback information executed by the decision scheme. According to the method, the accuracy and the real-time performance of extreme rainstorm cascade disaster assessment and emergency decision making are improved, and meanwhile, the prediction capability and the adaptive capability are improved.

Disclosed in the present invention is an intelligent generation method for urban waterlogging point-surface combined continuous monitoring data based on multi-modal data. The method comprises the following steps: 1) collecting multi-modal data from a plurality of sources to form a multi-modal data set; 2) intelligently extracting rainstorm flood information on the basis of a deep learning algorithm; 3) matching the rainstorm flood information on a spatio-temporal scale; 4) generating spatio-temporally continuous rainstorm flood data by means of a generative adversarial network algorithm; and 5) using a comprehensive evaluation method for deduplication processing to generate an urban waterlogging point-surface combined continuous monitoring database based on multi-modal data. In the present invention, on the basis of conventional rainstorm flood monitoring data, multi-modal data collected from the plurality of sources is supplemented, rainstorm flood information contained in the multi-modal data is intelligently extracted on the basis of the deep learning algorithm, the urban waterlogging point-surface combined continuous monitoring database based on the multi-modal data is constructed, and the defects that conventional monitoring data is scarce and has fixed sources are overcome.

The invention relates to the technical field of remote monitoring, in particular to a remote monitoring method and system for intelligent water purification equipment. The system comprises a sensing execution unit, a data routing unit, an intelligent central unit and an interactive service unit. The intelligent central unit is used for comparing underground water quality data collected in real time with corresponding month threshold values over the years, water quality fluctuation caused by natural changes such as seasonal replacement can be accurately distinguished, the water quality data interpretation accuracy is improved, when the water quality data deviates from a normal range and influences of natural laws are eliminated, the system introduces real-time weather data, and the water quality data interpretation accuracy is improved. And the analysis module considers the effect of weather on water quality by means of a dynamic structure equation model and resets a threshold value. The intelligent self-adaptive mechanism can effectively distinguish water quality abnormity caused by non-natural laws, for example, normal fluctuation of water quality after rainstorm is recognized, accuracy and intelligence of water quality monitoring are enhanced through multiple times of accurate judgment, and abnormity can be quickly recognized and early warned.

The invention discloses an urban area runoff pollutant transportation process simulation forecasting method, which is characterized in that a building area runoff production calculation method under the influence of a wind field is provided, and the building area runoff production calculation method is coupled with an SWMM rainfall flood model; secondly, constructing a two-dimensional hydrodynamic model based on an FVCOM and a Lagrange particle model, coupling the two-dimensional hydrodynamic model with the SWMM rainfall flood model, and driving the two-dimensional hydrodynamic model by taking the overflow flow and the pollutant concentration simulated by the SWMM rainfall flood model as boundary conditions; simulating a transportation process of pollutants along with evolution of surface ponding by establishing a'concentration-quantity 'conversion relation between the pollutants and particles at grid units and nodes, and obtaining a time sequence pollutant concentration data set; and training and establishing a pollutant concentration prediction model based on the obtained data, and then predicting the pollutant concentration of each prediction point after specific time in an actual rainfall event. According to the method, the runoff pollutant concentration can be predicted, and the urban non-point source pollution risk under the rainstorm weather is reduced.

The invention discloses a non-point source pollution emergency bypass decision control method, particularly relates to the technical field of automation and process control, and aims to solve the problems that existing scheduling cannot align multi-source measurement and evaluate credibility in minutes under a unified time base, and is lack of risk measurement integrated with a tide level phase and a moisture regain path and same-layer gating. The bypass rhythm is easy to misjudge, and the oscillation is difficult to audit. A minute-level risk density is constructed through multi-source alignment and credibility grading under a unified time reference, injected tide level phases and moisture regaining paths, bypass rhythms are constrained by same-layer gating and tide windows, and beam search, boundary arbitration and fixed window closed-loop correction are matched, so that the risk density is improved. Therefore, under the superposition of rainstorm and tide, decision making and execution are stably limited in a non-backflow red line, bearing and equipment limit, misjudgment and oscillation are remarkably reduced, the problems of unavailability and difficult auditing caused by multi-source asynchronization and moisture regain coupling are solved, intelligent decision support is provided for non-point source pollution treatment, and it is ensured that the treatment technology is accurate and efficient.

The invention discloses a sponge city high-terrain rainwater management method and system, particularly relates to the technical field of rainwater prediction management, and is used for solving the problem of poor high-terrain rainwater prediction management scheduling. For a high terrain small watershed, data such as elevation, permeability coefficient, soil layer thickness and vegetation coverage are collected and preprocessed, a continuous physical field is generated, a facility pipe network is mapped, and then grid units are divided; a multivariate predictor is constructed based on grid cell center attributes, edge node increment fine adjustment is issued after cloud training, and a model is optimized through scene matching and federal learning; after rainstorm, automatically extracting observation and prediction residual increment to train a neural network and generating a confidence interval; and the edge nodes divide risks according to the confidence interval, the flood control water depth and the minimum infiltration threshold value and output gate opening and pump set power suggestions, so that the high-terrain small-watershed extreme rainstorm peak prediction precision and response time efficiency are remarkably improved, and refined risk perception scheduling and rainwater resource utilization are realized.

The invention discloses a graph convolution Transform-based flood type prediction method, which comprises the following steps of: extracting a rainstorm event and a flood event according to flow and rainfall data observed by a hydrometric station and a rainfall station in a research area, and respectively constructing a rainstorm graph and a flood graph according to the rainstorm event and the flood event; extracting full-map comprehensive features of the flood map by using a map convolutional neural network considering an attention mechanism, determining an optimal clustering number by using an elbow rule, and then performing flood classification by using a K-means clustering method; extracting dynamic features of the rainstorm graph by using a graph convolutional neural network considering an attention mechanism, and obtaining rainstorm spatial-temporal features through Transform; and dividing all rainstorm spatial-temporal characteristics into a training set and a verification set, and training a flood type prediction model and performing flood type prediction by taking a flood type as a supervision signal. The method comprehensively considers the spatial and temporal characteristics of the rainstorm flood, and improves the prediction accuracy.

The invention discloses a multi-parameter fusion pipe network overflow prediction system and method, relates to the technical field of pipe network overflow prediction, and aims at overflow risks caused by equipment faults, unstable communication and extreme weather in a combined drainage system. A system scheme integrating five steps of predictive maintenance, reliable real-time control, digital twinning check, layered emergency plan and cloud edge cooperation is provided; in the first step, equipment health is monitored and faults are predicted to ensure that key pump stations and gates are available; 2, generating a fault-tolerant scheduling instruction by adopting reliable optimization and multi-agent reinforcement learning; 3, pre-judging and correcting the deviation by utilizing digital twinning check before execution; 4, dealing with a single-point fault and communication paralysis by using a layered distributed emergency mechanism; 5, cross-regional parallel simulation and self-adaptive scheduling are realized through cloud edge collaboration and federated learning; overflow can be reduced, environmental pollution and waterlogging loss under the rainstorm condition can be reduced, and efficient collaborative management of the drainage system is achieved.

The invention discloses a rainstorm torrential flood physical constraint prediction method and system based on a graph neural differential equation, and belongs to the technical field of rainstorm torrential flood prediction. Carrying out space-time attention fusion based on a graph; carrying out modeling and dynamic deduction based on a graph neural differential equation of physical constraints; predicting and outputting a multi-task flood hydrograph; and carrying out joint loss function design and end-to-end training. The system comprises a multi-modal hydrological feature obtaining and coding module used for multi-source heterogeneous data feature extraction, a graph-based space-time attention fusion module used for deep fusion of multi-source heterogeneous features, and a physical constraint-based graph neural differential equation dynamic core module used for continuous dynamic process modeling. And the prediction output module is used for outputting the spatial distributed flood hydrograph. According to the method, the problems of low reliability, poor timeliness and poor extrapolation capability during rainstorm torrential flood prediction in the prior art are solved.

A cascade reservoir optimization dynamic control method based on different scheduling objectives of flood season staging comprises the following steps: step 1, performing flood season staging on a cascade reservoir based on rainstorm flood characteristics, and establishing a pre / main flood season'flood control-power generation 'and post flood season'flood control-water storage' optimization scheduling model; step 2, constructing a cascade reservoir dynamic control domain; step 3, based on the flood season staging result in the step 1 and the cascade reservoir dynamic control domain constructed in the step 2, constructing an optimization model taking the maximum cascade generating capacity and the minimum flood control risk as targets; setting constraint conditions of the optimization model; and solving the optimization model through a multi-target intelligent optimization algorithm to obtain a dynamic scheduling scheme. According to the method, optimal scheduling methods such as flood season staging and'aggregation-decomposition 'are adopted, flood control risks of drainage basin reservoir group scheduling are fully considered, and efficient utilization of cascade reservoir water resources is realized by utilizing a cascade reservoir flood control capacity complementation mechanism.

The invention discloses an island shoreline identification method and system based on artificial intelligence, and relates to the technical field of shoreline change monitoring. According to the invention, through fusion modeling of the optical image, the SAR image and the topographic data, the limitation of a single data source is broken through; a vegetation coverage area and a non-vegetation shoreline can be accurately distinguished through the multispectral characteristics of the optical image, and the problem of monitoring blind areas in scenes such as cloud and mist and nighttime is solved through the all-weather penetrating power of the SAR image; a random forest classifier can effectively distinguish bedrock steep cliff, sandy sand beach and the like in combination with superpixel unit feature vectors constructed by topographic data, and accurate partitioning of a target island shoreline is completed; moreover, a dynamic global correction mechanism further eliminates space-time error accumulation, a standard deviation mean value is calculated through cross-season historical data, accidental errors such as typhoon and rainstorm are filtered, a future global correction coefficient is predicted in combination with a time sequence model, and the reliability of long-term monitoring is remarkably improved.

The invention provides a vehicle control method and device for an intelligent driving scene, electronic equipment and a medium. The method comprises the steps of obtaining multi-mode information collected by multiple sensors installed on a vehicle, vehicle system fault diagnosis information and a current driving environment; on the basis of the multi-modal information, the confidence degree of each piece of modal information in the current driving environment is calculated; according to the confidence degree of each piece of modal information, performing feature fusion on the multi-modal information to obtain an environment state vector; and generating a vehicle control strategy based on the environment state vector, the system fault diagnosis information and the current driving environment. According to the method, by introducing a confidence coefficient evaluation mechanism, unreliable modes affected by environment interference or faults can be automatically inhibited, and the perception accuracy and stability under complex scenes (such as night rainstorm and strong backlight) are remarkably improved.

The invention provides an automatic driving test scene generation method based on real traffic data, and solves the problems of low accident data utilization rate, SIL / HIL test splitting and insufficient boundary coverage in the prior art. Comprising the following steps: acquiring multi-source heterogeneous traffic accident data; cleaning data by adopting a joint interpolation-anomaly detection mechanism; vehicle dynamic sudden change characteristics within 0.5 second before braking are extracted through LSTM and DTW algorithms; constructing a three-dimensional scene pipeline driven by a physical engine, and dynamically associating the pavement slippery coefficient with the rainfall intensity; analyzing the accident text into simulation parameters by using a semantic-physical parameter converter; performing SIL-HIL cooperative verification: performing extreme illumination perception test and narrow road planning verification in an SIL environment, and realizing 1ms step length fault injection test in an HIL environment; positioning failure parameters based on Bayesian optimization; a GAN is adopted to generate a long-tail scene, and a test boundary is expanded by coupling extreme conditions such as rainstorm / low visibility; and outputting a standard scene library containing the collision probability thermodynamic diagram. The safety verification efficiency under the extreme working condition is remarkably improved.

The invention relates to the technical field of rainstorm early warning, in particular to a deep learning-based rainstorm terminal early warning method and system, which introduces an image perception and semantic recognition mechanism to carry out consistency judgment on an overturning state of a rain gauge and image features such as a rain curtain and a water mark so as to realize effective elimination of unnatural trigger factors (such as manual water pouring and the like). And the reliability of rainfall sensing data is improved. A double-branch deep learning model capable of being deployed locally is adopted, multi-source data such as rainfall, temperature and humidity and topographic factors are fused, feature extraction and recurrence period prediction are performed based on a cross attention mechanism, and a threshold correction coefficient is dynamically generated in combination with historical events, so that self-adaptive adjustment of an early warning standard is realized, and misinformation and missing report risks are effectively reduced. The system supports low-power-consumption local operation, early warning decision is executed, and timeliness and accuracy of local early warning response are effectively improved.

The invention discloses a rainstorm disaster loss assessment method and system based on machine learning, and the method comprises the steps: taking data of a rainstorm disaster data source in a preset region in a specified time period as to-be-analyzed data; dividing the preset area according to the to-be-analyzed data based on the rainfall, taking the preset area greater than a rainfall threshold as a disaster area, otherwise, taking the preset area as an uncertain area, and calculating the destructive power of the uncertain area according to a hydrological mechanism and a drainage system. Adding the uncertain area greater than a destructive power threshold value into the disaster area; performing comprehensive risk analysis on the to-be-analyzed data of the disaster area to obtain a comprehensive risk index, calculating a damage degree index of the disaster area, and performing real-time dynamic evaluation on the disaster area according to the damage degree index and the comprehensive risk index to obtain a loss index; and constructing a rainstorm disaster loss evaluation model according to the loss index, and outputting an evaluation result.

A windshield wiper control method of a new energy automobile includes the following steps: when a windshield wiper controller is in a first wiping state, if the windshield wiper controller receives a high-speed gear signal sent by a windshield wiper control terminal or receives a rainstorm instruction sent by a central control display screen, the windshield wiper controller entering a rainstorm mode; when the windshield wiper controller is in a second wiping state, if the windshield wiper controller receives a rainstorm instruction sent by the central control display screen, the windshield wiper controller entering a rainstorm mode; wherein the rainstorm mode is used for indicating the windshield wiper controller to control the windshield wiper windshield wiper, the key area of the automobile windshield is wiped at the safety frequency.

The invention discloses a rainstorm torrential flood disaster-causing element prediction method and system based on modal decomposition and deep learning, and solves the problems that a traditional model is insufficient in non-linear time sequence feature capture, and a physical model depends on complex data and is weak in generalization ability. Comprising the steps of collecting flow, flow velocity and water level data of an upstream site as input, and taking downstream disaster point data as output; preprocessing the data; a frost ice optimization algorithm is adopted to optimize variational mode decomposition parameters; a Fourier transform high and low frequency feature enhanced attention network is constructed, low-frequency and high-frequency components are divided, trend features are extracted through a fluctuation enhancement module, dynamic changes are captured through a multi-path difference calculation unit, self-perception attention is introduced to achieve feature weighted fusion and long-term memory, and a downstream hydrological state prediction result is output. By optimizing a modal decomposition and deep learning cooperation mechanism, the precision, robustness and generalization ability of sudden mountain torrent prediction are significantly improved, and the method is suitable for disaster emergency management in complex scenes of small and medium watersheds.

The invention relates to the technical field of meteorological disaster monitoring and early warning, in particular to a rainstorm early warning method and system based on a wireless network, a terminal and a storage medium, and the method comprises the steps: collecting multi-source data through distributed sensor nodes; preprocessing the multi-source data to obtain real-time data; constructing a spatial-temporal feature extraction model; generating a dynamic threshold function, performing weighted fusion on the dynamic threshold function and a preset static threshold, and outputting a graded early warning threshold; judging whether the current accumulated water depth acceleration exceeds a gradient critical value of a graded early warning threshold value or not; and if so, outputting a rainstorm early warning level. The method has the advantages that the problem that a static threshold mechanism cannot dynamically adapt to rainstorm disaster risks in a complex environment is solved, and the accuracy and practicability of the rainstorm early warning system are improved.

The invention discloses a flood type landslide disaster monitoring and early warning method, and belongs to the technical field of geological disaster prediction. The method comprises the following steps: step 1, acquiring a flood type landslide disaster case in which a rainstorm event and a landslide event coincide in time and space, collecting landslide factor data, environment data, monitoring data and historical landslide data, and constructing a historical database; step 2, constructing and training a Bayesian network model based on a historical database; step 3, acquiring landslide monitoring data and preprocessing the landslide monitoring data; step 4, training an LSTM time sequence prediction model; 5, inputting the real-time monitoring data into the LSTM model, and predicting to obtain future landslide monitoring data; and step 6, inputting future monitoring data into the Bayesian network to obtain a slope instability probability based on a future trend. According to the invention, through organic fusion of the Bayesian network and the LSTM, dynamic prediction and real-time response of the landslide instability probability are realized, and timeliness, accuracy and robustness of early warning are significantly improved.

The invention relates to a monitoring method for prediction and early warning of hidden leakage of a concrete dam, and aims to solve the technical problem of lack of timely prediction and early warning of hidden leakage danger of the dam at present, and adopts the technical scheme that a long and short-term memory network model fused with an attention mechanism is constructed, and leakage data is modeled and predicted; an improved CUSUM change point detection algorithm is introduced, a sliding monitoring window and a residual threshold are set, an accumulated residual between a predicted value and a measured value is calculated, the accumulated residual is compared with the threshold after being processed by a control function, the leakage trend is recognized and early warned in advance, and a real-time meteorological sensitive factor is introduced, so that the leakage trend is accurately detected. The monitoring sensitivity is dynamically adjusted under extreme events such as rainstorm and earthquake, and the response capability to the sudden leakage risk is improved. The early warning accuracy and timeliness of the dam leakage accident can be effectively improved, time is won for dangerous case disposal, and the life and property safety of people is guaranteed.

The invention relates to a river flood flow monitoring method based on remote sensing image and hydrodynamic model fusion, and belongs to the technical field of flood monitoring. The method comprises the following steps: dividing flood levels in a river drainage basin by combining historical rainfall and flood conditions, drawing flood hydrographs of different flood levels, determining 24-hour design rainstorm of each level of flood, establishing a river one-dimensional hydrodynamic model by utilizing section survey data, and constructing a water level-water surface width relationship of a section to be measured; solving the model to obtain a water level-flow relation curve of riverway water rising and water recession under each level of flood, determining the recurrence level of the flood, calculating the current water surface width of the to-be-measured riverway section, obtaining the corresponding water level, and calculating the water level of the to-be-measured riverway section. And selecting a corresponding water level-flow relation curve according to the flood level of the river channel and the stage of the flood flow process, and substituting into the curve to obtain the flood flow of the section to be measured in the current time period. According to the method, through water level inversion of different levels and different stages, the accuracy of inversion of the flood peak flow by the satellite remote sensing image is improved.

The invention discloses an extreme rainstorm disaster urban waterlogging point early warning method and system based on hybrid intelligence. The method comprises the following steps: collecting waterlogging points of which the waterlogging risk needs to be assessed in a current city; performing risk prediction on each waterlogging point to obtain a machine score by adopting a machine learning model and based on the waterlogging-related multi-source heterogeneous data; according to the performance measurement of the machine learning model, generating the confidence coefficient of the machine score of each waterlogging point; performing risk assessment on the waterlogging point set based on expert experience to obtain an expert score of each waterlogging point; based on the evaluation consensus degree of the expert individuals and the expert groups, calculating the confidence coefficient of expert scoring of each waterlogging point; corresponding weights are dynamically optimized and generated according to confidence coefficients of scoring of machines and experts of the waterlogging points; and finally, according to the man-machine score and the respective weight, performing fusion calculation to obtain a waterlogging risk value of each waterlogging point, and further providing risk early warning. According to the method, the comprehensiveness and robustness of waterlogging point early warning under the extreme rainstorm are improved through dynamic weight optimization and man-machine cooperation.

The invention relates to the technical field of disaster early warning, and discloses a rainstorm early warning method based on multi-source forecast product dynamic fusion, which comprises the steps of collecting multi-mode rainfall forecast data, constructing a rainfall cumulative distribution function, performing dynamic updating based on Kalman filtering, and obtaining frequency correction rainfall forecast products of each mode; historical forecast and live monitoring data samples are obtained, comprehensive weight coefficients corresponding to all levels of rainfall are calculated, and a fusion initial rainfall distribution field is obtained; performing matching reconstruction on the corrected rainfall forecast field to generate a reconstructed rainfall distribution field; collecting latest real-time monitoring data, and carrying out space-time dynamic correction on the reconstructed rainfall distribution field to generate a real-time rainstorm potential field; and comparing the real-time rainstorm potential field with the multistage rainstorm early warning threshold, generating rainstorm early warning information, and issuing the rainstorm early warning information. According to the invention, the timeliness and accuracy of rainstorm early warning response are improved, the public personal safety is guaranteed, and the city emergency disposal capability is improved.

The invention relates to the technical field, and discloses a power distribution network multi-time scale fault scene deduction method comprising the following steps: obtaining meteorological and power grid operation data, establishing a time sequence fault tree model, and analyzing the trigger probability of multi-line disconnection and rainstorm short circuit faults in a target time period; extracting cross-level fault propagation features, analyzing a dynamic coupling relationship among multi-line disconnection, transformer substation flooding and cascading trip, forming a fault feature mode set, and identifying a single fault and a cascading fault in combination with time sequence analysis; historical fault data are analyzed, time sequence features and topological features of single and cascading faults are extracted, if a single fault propagation path is in a single level, a support vector machine is used for being combined with the features to judge fault types, and a classification result is output; and performing anomaly detection and confidence evaluation on a fault classification result, outputting a fault evolution path and risk evaluation, and analyzing the contribution degree of cascading trip to a large-area power failure risk in combination with historical blackout data to obtain power failure risk probability distribution.

The invention relates to the technical field of mine environment risks, and discloses a mine environment risk multi-modal analysis and early warning decision-making method, which comprises the following steps of: performing time and space reference alignment on multi-source observation data, constructing a unified spatio-temporal data set, and establishing a troposphere disturbance model to realize observation disturbance coupling. And constructing a physical evolution operator and a rainfall-driven external input operator based on a seepage mechanical relationship, and generating a state evolution mechanism. Semantic direction parameters are generated by mapping semantic query information to a target attention dimension, and a lagging propagation operator is constructed by setting a rainstorm ending moment as a starting point. And determining a non-regular spectrum transition index and a peak lag moment through the non-regularity measurement and the dynamic change rate of each lag propagation operator, and setting a reference threshold value based on the spectrum transition index of a historical non-risk time period. Finally, when the early warning intensity meets the condition, early warning is output, and a query result is generated in combination with the lagging propagation operator and the semantic direction parameters.

The invention relates to the technical field of data processing, in particular to a multi-series short-duration rainstorm data frequency analysis method. The method comprises the following steps: obtaining rainfall original data of multiple series of short duration, and carrying out missing data interpolation and sequence extension processing, duration consistency and rainfall intensity rule check to obtain a rainstorm data set; respectively performing reliability, representativeness and consistency review and sample validity screening on the rainstorm data set; a Pearson III type probability density function model is constructed, probability distribution fitting is carried out, and a frequency distribution curve is generated; and carrying out extra heavy rain value analysis and empirical frequency adaptive line analysis adjustment, generating a corrected frequency parameter, further calculating a design heavy rain value of each design return period, and carrying out duration consistency and spatial rationality check to obtain a final design result output data set. According to the invention, through multi-dimensional data complementation, checking and optimization fitting, the system improves the accuracy, reliability and engineering applicability of short-duration rainstorm frequency analysis.