226 results about "Power model" patented technology

The invention relates to the technical field of carbon footprint dynamics, in particular to a railway traffic carbon footprint dynamic tracking system and method based on a block chain and edge calculation. The data acquisition unit is used for acquiring train operation state, environment parameters and traction energy consumption data; the edge calculation unit fuses the multi-source data acquired by the data acquisition unit through adaptive Kalman filtering to obtain standardized data, converts a train operation state into carbon emission based on an instantaneous power model, and introduces a dynamic emission factor to calculate and obtain accumulated carbon emission; and the block chain network unit is used for compressing the accumulated carbon emission data and then generating a Merkle tree root hash upper chain, and storing the carbon emission data to a block chain. Incremental uplink and rapid verification of the data are realized, the on-chain data processing efficiency and the query traceability are improved, and the problems of high storage pressure and low verification efficiency of a traditional on-chain evidence storage mode in a high-frequency track data writing scene are solved.

A cooperative control optimization method suitable for a near-field fan wall micromodule, comprising the following steps: step 10: obtaining IT system scheduling data and air-conditioning system data of the near-field fan wall micromodule within a preset period of time, and separately establishing a micromodule server real-time power model, an air-conditioning system power model, and a rapid server temperature prediction model; and step 20: performing cooperative control on deployment of virtual machines and an air-conditioning system by using an optimization method of combining real-time joint optimization and timed decoupling optimization based on the micromodule server real-time power model, the air-conditioning system power model, and the rapid server temperature prediction model. The present invention provides a cooperative control optimization method suitable for a near-field fan wall micromodule, to resolve the technical problem that local hot spots and overcooling are prone to occur during energy-saving scheduling of a near-field fan wall micromodule.

The invention relates to the technical field of network security, and particularly discloses an edge device network threat detection method and system based on an electric power large model, and the method comprises the steps: capturing a network message sequence in real time, extracting a time sequence randomness feature and a semantic deviation feature from a time dimension and a protocol dimension, and carrying out the fusion to form a comprehensive threat feature vector; performing multi-dimensional feature analysis and time sequence modeling by adopting a lightweight electric power large model to realize millisecond-level threat assessment; establishing a multi-level response mechanism, dynamically triggering a differential protection strategy according to the threat level, and ensuring the reliability and consistency of response actions through digital signature and collaborative verification; according to the method, the complex network attack in the power edge equipment can be effectively identified, the threat detection accuracy and the system defense capability are improved, and the strict requirements of a power system on real-time performance and reliability are met.

The invention discloses a water supply pipe network leakage locating method based on a lightweight gradient elevator algorithm, and belongs to the technical field of intelligent water affair and pipe network monitoring. The method comprises the following steps: integrating hydraulic modeling basic information, and constructing an initial hydraulic model by utilizing hydraulic software; actual measurement values of key node pressure and main pipe flow under different working conditions are collected and compared with simulation values to be calibrated to reach the standard, and a high-precision hydraulic model is obtained; simulating each leakage working condition in different time periods on a selected potential leakage node / pipe section by using the calibration model, collecting pressure change data of a pressure monitoring node, and performing preprocessing and data division; the data set is used for training a LightGBM model, the LightGBM model learns the relevance of node pressure in spatial topology and the dynamic change characteristics of a time sequence at the same time, a spatial position differentiation weight mechanism is introduced, and accurate positioning of leakage points and quantitative estimation of leakage coefficients are achieved. According to the method, the pipe network leakage positioning precision and efficiency are remarkably improved, and support is provided for rapid and accurate leakage detection and leakage amount evaluation.

The invention relates to the technical field of power equipment fault detection, in particular to a power equipment fault diagnosis method and system based on a large power model, and the method comprises the steps: converting the multi-modal data of a power cable in operation into a feature vector, and inputting the feature vector into a pre-trained fault diagnosis model to obtain a preliminary diagnosis result and confidence; if the confidence coefficient is not lower than the threshold value, the preliminary diagnosis result is reserved; and if the confidence coefficient is lower than a threshold value, taking the feature vector as a current potential fault feature vector to perform secondary discrimination, namely obtaining a significance index by calculating the similarity and volatility of the current potential fault feature vector and a historical potential fault feature vector, analyzing a time change trend to obtain a cumulative trend index, and performing secondary discrimination on the cumulative trend index. And comprehensively determining a potential fault index through the significance index and the cumulative trend index, and determining a final diagnosis result according to the potential fault index. According to the scheme, the diagnosis accuracy of the fault diagnosis model on low-confidence potential faults and unknown faults is improved.

The invention relates to the technical field of model parameter calibration, and provides a hydrodynamic model calibration method and system based on an improved particle swarm optimization, and the method comprises the steps: S1, obtaining the corresponding relation between the section information of a hydrodynamic model and the roughness; s2, defining a roughness parameter range and an allowable error range of each section; s3, initializing a particle swarm; s4, performing parallel scheduling hydrodynamic model calculation according to the matrix number of the particle swarm to obtain a simulation result data set; s5, performing time sequence comparison on a simulation result and an actual measurement result, and calculating an error value; s6, screening a calibration optimal solution, finding out a particle with the minimum error, and recording a corresponding roughness parameter; s7, judging whether the optimal solution is within an error range or not, if yes, directly executing the step S8, and if not, repeatedly executing the steps S4 and S5 until the optimal solution meets the condition; and S8, outputting a calibration optimal solution, and displaying a calibration result, an actual measurement result process line and optimal parameters. According to the invention, the calibration efficiency can be effectively improved.

The invention relates to a coastal city flood multi-system coupling evaluation method and system, and the method comprises the steps: obtaining multi-source data, and carrying out the unified projection and time resolution normalization processing of the data; constructing an upstream river network one-dimensional hydrodynamic model, an urban drainage pipe network one-dimensional hydrodynamic model and an urban two-dimensional surface water dynamic model based on the multi-source data, and setting tide boundary conditions; coupling each model and the tide boundary; performing model calibration and error constraint on the coupled multi-dimensional hydrodynamic coupling model; constructing a composite scene, taking each group of scenes as an input boundary condition to drive the multi-dimensional hydrodynamic coupling model, and obtaining a flood evolution result; and performing risk assessment and sensitivity analysis based on a flood evolution result. According to the method, a unified river network-pipe network-earth surface-tide multi-scale coupling framework is constructed, so that the defect that a traditional single model or result superposition method is difficult to reflect multi-system interaction is effectively overcome.

The invention relates to a power transformation equipment operation and maintenance task intelligent arrangement method and system, equipment and a medium, and relates to the technical field of electric power. The method comprises the steps of obtaining real-time operation data and historical operation and maintenance data of power transformation equipment, constructing input information of a large electric power model by adopting a Prompt technology enhanced by a knowledge graph, and predicting an operation and maintenance task by utilizing the large electric power model; and generating an operation and maintenance task arrangement scheme, performing dynamic optimization of the operation and maintenance task arrangement scheme based on reinforcement learning, generating and issuing an operation and maintenance work ticket, and feeding back task execution state and equipment state information to the large electric power model in real time to optimize the operation and maintenance task arrangement scheme. Compared with the prior art, the intelligent arrangement of the operation and maintenance tasks of the power transformation equipment is realized, the operation and maintenance efficiency and the equipment reliability are improved, the human resource consumption is reduced, and the scientificity of operation and maintenance decision is enhanced.

The invention relates to the technical field of power distributed resource regulation and control, in particular to a peak regulation market electric quantity declaration method and system for distributed resources, electronic equipment and a medium. The method comprises the following steps: firstly, acquiring real-time operation data and peak regulation market policy data of load side distributed resources; constructing a double-constraint generation power model according to the real-time operation data and peak regulation market policy data; constructing a distributed resource aggregation model based on the double-constraint power generation power model; extracting an unsteady variable from the resource aggregation state and the electric quantity declaration reference value; according to the unsteady variables, an interval optimization logic solution is adopted to obtain an optimal electric quantity declaration strategy; and finally generating an electric quantity declaration scheme participating in the peak regulation market. Through a double-constraint generation power model and interval optimization logic, the problem of insufficient peak regulation market income optimization caused by neglecting adjustable load characteristics and wind and light abandoning punishment in the prior art is solved, and the adaptability and reliability of an electric quantity declaration strategy in a complex market environment are improved.

The invention discloses a flood storage and detention area flood diversion process simulation method and device based on a hydrodynamic model, and the method comprises the steps: obtaining the landform, river section, dike engineering parameters, and real-time hydrological and meteorological data of a flood storage and detention area, constructing a basic parameter library, and storing the basic parameter library in a data input module; constructing a hydrodynamic model calculation module by taking a hydrodynamic equation as a theoretical basis on the basis of the basic database, and combining a finite element volume method; receiving a numerical calculation result, analyzing real-time parameters through a dynamic simulation module so as to match actual hydrological parameters, and performing comparison under multiple scenes; and transmitting the hydrological parameters and the comparison data to a result output module, and outputting key parameters of the flood diversion process and a risk assessment result. The method has the advantages that high-frequency coupling of a hydrodynamic model, multi-source dynamic data, a cross-domain model and an engineering control system is achieved, and the problems of data lag, model isolation and control disjunction in traditional simulation are solved.

The invention relates to the technical field of micro-grid configuration, in particular to a wind and light storage micro-grid capacity optimization configuration method based on a PGWO algorithm, and the method comprises the following steps: building a wind and light storage micro-grid capacity optimization configuration model according to the output power model of each distributed power supply in a micro-grid; setting a micro-grid operation strategy; under the micro-grid operation strategy, solving the capacity optimization configuration model through a PGWO algorithm to obtain a micro-grid optimal configuration scheme; according to the method, the PGWO algorithm combining Bernoulli chaotic mapping initialization, individual historical memory and a border crossing processing strategy is provided, the method has higher global optimization capacity, capacity configuration optimization can be carried out on the premise of stable operation of the microgrid, and the operation cost is reduced.

The invention provides a distributed scheduling method for railway traction stations and peripheral micro-energy grids, and belongs to the technical field of energy supply of micro-energy grid groups containing the railway traction stations, and the method comprises the steps: building a micro-energy grid sharing mutual aid grid structure containing the railway traction stations, obtaining a power model of each railway traction station, and optimally configuring energy storage equipment, obtaining operation constraints of the energy storage equipment; constructing an energy sharing benefit function by taking each micro-energy network agent as a game main body and taking individual benefit maximization as a target, setting constraint conditions, and establishing an energy sharing non-cooperative game model; and solving the energy sharing non-cooperative game model by adopting an alternating direction multiplier algorithm to obtain a distributed energy transaction behavior result and a price result. According to the method, distributed solution is carried out on the micro-energy network energy sharing non-cooperative game model based on the alternating direction multiplier method, original centralized optimization is converted into coupling optimization among a plurality of micro-energy network agents through interactive variables, the efficiency of the whole iteration process is guaranteed, and the privacy of each energy network is protected.

The application provides a highway charging load prediction method and control system based on traffic flow power. Through a second-by-second specific power model of different electric vehicle types on the highway, factors such as road slope, vehicle load, electric vehicle braking recovery characteristics, instantaneous speed and acceleration can be considered to achieve more accurate energy consumption modeling. By establishing a specific power operation mode distribution library of different vehicle types, using a shock wave model and a cellular transmission model to solve, quantifying the congestion propagation process, dynamically correcting the specific power calculation parameters, only the road section parameters and average speed are needed to calculate the energy consumption under complex road conditions. The driver's range anxiety is quantified as a dynamic variable, and charging decision rules are established for different scenarios. The charging probability is dynamically corrected based on the queue length, weather, and driver type, and an electric vehicle charging time prediction model is established based on different service area charging scenarios.

The invention discloses a wind, light and green power coupled pumping unit well group operation collaborative optimization method. The method comprises the following steps: acquiring field data of a multi-energy micro-grid pumping unit well group; establishing a wind and light output uncertainty model, an energy storage battery model, a pumping unit power model and bidirectional AC / DC converter equipment; based on each model, establishing a multi-objective optimization scheduling model for the intermittent pumping operation of the multi-energy micro-grid pumping unit well group; an NSGA-II method is adopted to solve the established multi-objective optimization scheduling model, and a Pareto optimal scheduling scheme is obtained; and carrying out comprehensive evaluation and decision analysis based on an entropy weight TOPSIS method, and selecting an optimal scheme from a Pareto optimal scheduling scheme set obtained by NSGA-II. According to the multi-energy micro-grid rod-pumped well group off-peak interval pumping operation multi-objective optimization method based on NSGA-II and entropy weight TOPSIS, the power utilization requirement of the rod-pumped well group can be met, the working system of rod-pumped well group interval pumping operation is optimized, the economical efficiency and stability of the operation of a rod-pumped well interval pumping system under a wind-solar storage micro-grid are ensured, and the green power consumption capability of the system is enhanced.

The invention discloses a flood risk prediction method and system based on adaptive Bayesian model averaging, and the method comprises the steps: building a plurality of basic hydrodynamic models, and enabling the multiple of an upstream flow hydrograph and the multiple of roughness in the input data of different hydrodynamic models to be different; constructing the plurality of basic hydrodynamic models into a hydrodynamic model set, and performing iterative optimization on the hydrodynamic model set to obtain output data of the hydrodynamic model set; in the output data of the hydrodynamic model set, performing double correction on the output data of a single hydrodynamic model to form a hydrodynamic model after double correction; step 3, constructing a probability prediction framework, averaging the probability prediction framework by adopting a Bayesian model, and obtaining an optimal Bayesian model average structure; and applying the selected optimal Bayesian model average structure, and operating an EM algorithm to calculate the final downstream water level and flow predicted value of the flood. The accuracy of the prediction result can be improved.

The invention discloses a power system cascading failure chain modeling and blocking method, device and system considering source-load double-end uncertainty, and a medium. The method comprises the steps of constructing source-load uncertainty input distribution (wind power introduces Weibull distribution based on a wind speed segmented power model, photovoltaic introduces Beta distribution based on an irradiance output model, and a load side introduces a normal distribution fluctuation model); an MSFF method is adopted to construct a unified source-load stochastic power flow model, and a multi-scene power flow state set is obtained; introducing a power flow out-of-limit criterion and a hidden fault probability function, building a cascading fault propagation model fusing wind-solar load uncertainty, and dynamically simulating fault chain evolution; and formulating a staged collaborative blocking strategy (cutting off non-critical load emergency control, quickly responding a battery energy storage system and dynamically adjusting a critical line protection constant value) to realize real-time fault suppression. Simulation shows that the method is suitable for power system security defense in a high-proportion new energy penetration scene, and has good engineering adaptability and dynamic control performance.

The invention discloses a dynamic power management method for an all-electric ship hybrid energy storage system. The method comprises the following steps: S1, establishing a direct-current voltage dynamic model and a ship hybrid energy storage system model based on a ship micro-grid structure of a ship; s2, establishing a multi-working-condition propulsion power model based on the movement mechanism of the ship; s3, establishing a joint SOC-SOP estimation model based on the nonlinear relationship among the state of charge, the battery power characteristics and the ambient temperature; s4, on the basis of the direct-current voltage dynamic model, the ship hybrid energy storage system model and the multi-working-condition propulsion power model, establishing a dynamic power distribution model; and S5, estimating an available power range of the battery through a combined SOC-SOP estimation model, inputting the available power range into the dynamic power distribution model, obtaining a dynamic power distribution strategy, and realizing dynamic power distribution of the ship hybrid energy storage system. According to the method, the dynamic power management of the hybrid energy storage system of the all-electric ship under the uncertain navigation condition is optimized, and the stability and the economical efficiency of a ship power system are improved.

The invention relates to the technical field of information processing, in particular to a question number question and answer processing method based on an AI large model. By calculating the delay deviation risk highlighting representation value of each type of computing power model of the user question number questions and answers in each field, the delay deviation risk highlighting sorting sequence of each type of computing power model of the user question number questions and answers in each field is quickly determined, the efficiency of question number question and answer information processing is improved, and meanwhile, the user question number question and answer information processing efficiency is improved. The maximum delay deviation risk highlighting characterization value of different types of computing power models of users in the question number question-answering field can be accurately obtained by obtaining the user actual field of question number question-answering uploaded by the user side and the sample data information of the corresponding type of computing power model, and the accuracy of question number question-answering information processing is improved. And the information processing efficiency is improved on the premise of ensuring the reliability.

The invention discloses a polar region environment-oriented two-stage micro-siting optimization method for a wind generating set, and relates to the technical field of polar region renewable energy planning and wind power generation, the optimization method comprises the following steps: polar region environment parameter initialization and scene modeling; the first stage is grid primary selection based on mixed integer linear programming; the second stage is coordinate refinement based on a multi-population genetic algorithm; and scheme output and efficiency evaluation. According to the method, a sector reweighting mechanism of extremely cold correction parameters is realized through reweighting of biochemical parameters and scenes in an environment special for a polar region, the extremely cold correction parameters are directly embedded into a wind regime scene set and a power model, and the problem that a conventional land model can obviously underestimate or overestimate single-machine power and wake flow propagation distance under extremely low temperature and low roughness conditions, so that the reliability of the model is improved is solved. Therefore, the power prediction and wake flow loss evaluation are more practical under the polar region conditions such as the south pole, so that the engineering reliability of site selection decision and the accuracy of generating capacity prediction are improved.

The invention relates to the field of fish habitat evaluation, in particular to a spawning migration type fish full life history stage suitable habitat inversion method based on a hydrodynamic model, and the method mainly comprises the steps: (1) collecting spawning migration type fish data, hydrological data and topographic data; (2) determining life history stages of the fishes and the actual number of the fishes; (3) establishing one-dimensional hydrodynamic models of different life history stages; (3) grouping the water depth and the flow velocity of each life history stage, and calculating the number frequency of fishes in each interval; (4) constructing a habitat suitability curve of water depth and flow velocity; and (5) evaluating a suitable habitat. According to the method, the suitable habitat of the spawning migration type fish in the whole life history stage can be efficiently and accurately evaluated, and protection strategy formulation and measure implementation of aquatic organisms are facilitated.

The invention relates to the technical field of point cloud data processing, in particular to a power model parameter backfilling method and system based on point cloud. The method comprises the following steps: obtaining tower pole point cloud data, and carrying out tower pole model construction to obtain a tower pole model; performing residual focusing identification on the tower pole model to obtain residual focusing data; electromagnetic field back-pushing is carried out according to the residual focusing data, and pole parameter missing data are obtained; obtaining tower non-integrity data according to the tower parameter missing data; obtaining implicit missing data for the non-integrity data of the tower pole; obtaining pole missing data according to the implicit missing data; performing tower height reverse inference according to the tower rod missing data to obtain tower height inference data; and carrying out electromagnetic field fitting optimization on the tower height inference data to obtain tower height fitting data so as to carry out parameter backfilling operation. The method has the characteristics of information fusion and structural physical coupling optimization, and can effectively improve the automatic completion precision of key parameters (such as tower height) in the tower pole model.

The invention discloses a multi-source heterogeneous meteorological data adaptive fusion photovoltaic power prediction method, and belongs to the technical field of photovoltaic power generation prediction and meteorological data processing. The method comprises the following steps: acquiring regional meteorological data, site micro-meteorological data and wind cloud satellite secondary cloud picture data, and carrying out preprocessing and feature screening on the regional meteorological data, the site micro-meteorological data and the wind cloud satellite secondary cloud picture data; defining a coupling coefficient vector; calculating fusion temperature, wind speed and irradiance based on the coupling coefficient, and constructing an input feature vector; performing preliminary power prediction by using a time sequence prediction photovoltaic short-term power model and the input feature vector; optimizing a coupling coefficient by adopting a genetic algorithm; and performing final power prediction based on the global optimal coupling coefficient and the time sequence prediction photovoltaic short-term power model. According to the invention, through an adaptive weight optimization mechanism, the problem of insufficient adaptability of fixed weight fusion under different meteorological conditions is effectively overcome, and the engineering landing performance is high.

The present disclosure relates to the technical field of model construction, and provides a river water dynamics model construction method based on multi-source remote sensing, which comprises the following steps: acquiring a river remote sensing image to obtain a water edge line, acquiring water level data by means of an altimeter satellite, and giving an elevation value to a water edge line corresponding point according to the water edge line and the water level data; for a straight river section, a value is taken according to a specific rule, and for a curved river section, the value is determined according to linear fitting of multiple elevation measuring point values on a river section; based on the elevation values of the water edge lines of the straight and curved river sections, a river terrain digital raster model is further constructed by interpolation along the water edge line, the river water dynamics model is constructed by using the river terrain digital raster model, the simulation effect of the water dynamics model is determined by comparing the water level consistency or the water surface vector form consistency, and the calibration and verification of the river water dynamics model are realized. The present disclosure realizes accurate simulation of the river water dynamics process. The present disclosure can reduce the cost of model parameter acquisition.

The invention discloses an urban fine pipe network drainage effect simulation method based on a nonlinear reservoir method, and the method comprises the steps: obtaining the rainfall, terrain, underlying surface and trunk pipe network data of a research region, building a two-dimensional hydrodynamic model and a one-dimensional pipe network model based on the obtained data, dividing a control region for each rainwater node, and carrying out the calculation of the control region. Establishing a coupling relationship between a ground surface grid in the control area and a corresponding rainwater node; calculating the rainfall and infiltration process of the research area to judge whether the rainwater nodes overflow or not, if overflow occurs, performing overflow calculation, if overflow does not occur, deducting a certain amount of water from each earth surface grid, counting the water amount, adding the water amount into the corresponding rainwater nodes respectively, and correcting the water amount by adopting a nonlinear reservoir method; and performing data updating according to the calculation result until the set operation time is reached, and outputting the final calculation result of the research area. The problem that the drainage capacity of the pipe network is difficult to generalize in the absence of fine pipe network data in the prior art is solved.

The application discloses a kind of based on meta-reinforcement learning's family micro-grid energy optimization control method, under the condition of no building thermal power model and guaranteeing user comfort requirement, research includes the joint coordination problem of demand response and alternating current optimal power flow with renewable energy resource consumption, comprising: firstly, the minimum problem of family micro-grid operating cost is modeled as Markov decision process;Secondly, the Soft Actor Critic algorithm integrated with Normalizing Flow is constructed;Thirdly, the method of meta-learning is added in the training of the constructed algorithm.The application has the advantages that it can save more operating cost potential under the premise of guaranteeing the comfort requirement of user, and can quickly adapt to different family micro-grid topological structure and quickly adapt to new tasks.

The invention discloses a comprehensive energy supply system reliability detection method considering state reverse analysis. The method comprises the following steps: firstly, constructing an energy conversion model of a multi-energy coupling element in a comprehensive energy supply system; thirdly, constructing an output power model of the multi-energy coupling element based on the energy conversion model of the multi-energy coupling element and initial input energy of the comprehensive energy supply system; and finally, the reliability of the comprehensive energy supply system is rapidly detected by using the output power model of the multi-energy coupling element. The method provided by the invention focuses on the component combination of which the output power is smaller than the energy demand to realize the rapid evaluation of the reliability of the comprehensive energy supply system.

The invention relates to the technical field of sediment prediction, in particular to a method for predicting sediment deposition in a channel based on a hydrodynamic model. Comprising the following steps: correcting the critical start-up flow velocity of the sediment according to the biomass, metabolic rate and biological membrane thickness of a microbial community to obtain a corrected critical start-up flow velocity; on the basis of the corrected critical starting flow velocity, the actual suspended sediment concentration in the navigation channel is calculated in combination with the turbulence intensity, the propagation distance and the ship draught depth of ship wake flow; according to the suspended sediment concentration, the sediment deposition amount in the channel is calculated by combining the cementation strength, the water content and the organic matter decomposition rate of the sediment on the surface layer of the riverbed. According to the method, ecological and navigation related influence factors can be comprehensively considered, and the prediction accuracy in a complex scene is improved.

The present invention discloses a method for replacing manually observed thunderstorm days, comprising the following steps: S1: acquiring lightning data through a lightning location system; S2: performing quality control on the acquired lightning data; S3: establishing a linear model and a power model for the annual average cloud-to-ground lightning density and the number of manually observed thunderstorm days; S4: analyzing the consistency of the lightning location data and the number of manually observed thunderstorm days; S5: verifying the reliability of the lightning location data in replacing the number of manually observed thunderstorm days. The present invention establishes a linear model and a power model for the annual average cloud-to-ground lightning density and the number of manually observed thunderstorm days after quality control of the lightning location data, performs a consistency analysis of the lightning location data and the number of thunderstorm days, and then verifies the reliability of the lightning location data in replacing the number of manually observed thunderstorm days, thereby concluding that the lightning location data is feasible in replacing the number of manually observed thunderstorm days.

The invention provides a hydrodynamic model-based mountain river flood diversion tunnel line and scale optimization design method, which comprises the following steps of: constructing a one-dimensional hydrodynamic mathematical model of a research area drainage basin by adopting MIKE11 software, and constructing a one-dimensional hydrodynamic model of the research area drainage basin according to the one-dimensional hydrodynamic mathematical model of the drainage basin, boundary conditions and calibrated comprehensive roughness; and calculating designed flood water surface lines of different frequencies of the urban section. According to the actual situation of a city, aiming at urban flood causes and main problems existing in a current flood control system, engineering measure schemes such as flood control reservoir construction, beach retreating and river returning, river regulation, embankment heightening and flood diversion channel building are proposed, and a flood diversion tunnel route and planning optimization research is developed on the basis of a hydrodynamic model. Numerical simulation of flood disasters is completed by means of the modern information technology, a complete disaster prevention and control system is established for achieving the purposes of scientization, high efficiency and automation of disaster prevention and relief decision, the urban flood prevention capacity can be comprehensively improved, and the life and property safety of the masses is guaranteed.

The invention discloses a cascade reservoir backwater jacking influence quantification and tail water level prediction method based on hydrodynamic simulation and regression analysis. The method comprises the following steps: step 1, data collection and preprocessing; 2, constructing a one-dimensional hydrodynamic model, simulating water surface lines under different constant flow conditions by taking the upstream reservoir outlet flow as an upper boundary and the downstream reservoir dam front water level as a lower boundary, and calculating the upstream reservoir tail water level; 3, multiple groups of upstream reservoir outlet flow and downstream reservoir dam front water level combinations are set, the corresponding tail water level is calculated through a hydrodynamic model, and a data table is formed; 4, establishing a function relation model among the tail water level, the reservoir outlet flow and the dam front water level by using a multivariate nonlinear regression method; step 5, performing significance test and error analysis on the regression model; 6, a calculation page rule of the influence of the downstream reservoir backwater jacking on the upstream reservoir tail water level under different working conditions is developed; the method is beneficial for improving the refined scheduling level of the cascade reservoirs.