168 results about "Uncertainty factor" patented technology

The invention belongs to the technical field of multi-energy micro-grid cooperative regulation and control. The invention discloses a multi-energy micro-grid coordinated regulation and control system based on cross-layer knowledge injection and federated distillation. The system is characterized by comprising a cross-layer knowledge injection network; a federal knowledge distillation module; and the uncertainty map regulation and control module is used for constructing an MEMG uncertainty association map, learning the influence weight of each uncertainty factor on a regulation and control decision through a map attention mechanism, and realizing a dynamic risk avoidance strategy. The invention discloses a multi-energy micro-grid cooperative regulation and control method based on cross-layer knowledge injection and federated distillation. The method is characterized by comprising the following steps: step 1, MEMG topology knowledge coding; 2, cross-layer knowledge injection training is carried out; step 3, federal knowledge distillation optimization; and step 4, performing uncertainty map regulation and control. According to the system and the method, the precision, the robustness and the data privacy protection capability of MEMG regulation and control are improved, and the system and the method are suitable for efficient collaborative optimization of the park-level multi-energy microgrid.

The invention provides a latticed column perpendicularity control method based on multi-source fusion and predictive compensation, and belongs to the technical field of perpendicularity control, a novel anti-interference sensor is combined with multi-point distributed installation and advanced data transmission and processing technologies, perpendicularity data of a latticed column can be accurately obtained in real time, and the perpendicularity control accuracy of the latticed column is improved. A reliable three-dimensional deviation cloud picture is generated; according to the intelligent hydraulic jack set control system based on real-time data feedback, the adjustment time is greatly shortened, the problem that the insertion deviation exceeds the limit due to adjustment lag is solved, the perpendicularity control precision of the latticed column is improved, and it is ensured that the insertion deviation of the latticed column is controlled within a small range; through comprehensive optimization of the construction process, including drilling pretreatment, equipment debugging and calibration, lowering process monitoring, concrete pouring process monitoring and the like, the stability and reliability of the whole latticed column perpendicularity control process are further improved, uncertain factors in the construction process are reduced, and the engineering quality and the construction efficiency are improved.

The invention discloses a network construction type novel energy storage multi-objective optimization coordinated scheduling method, which comprises the following steps: S1, energy storage classification and data acquisition: (1) according to application scene function requirements, dividing energy storage types according to rated continuous discharge duration; (2) data acquisition; s2, calling an energy storage scene and combining with weight parameter setting to construct a multi-objective optimization model: (1) establishing the multi-objective optimization model with the purposes of minimizing the system operation cost, maximizing the energy storage utilization rate and improving the power grid stability; (2) setting constraint conditions; s3, a dynamic optimization algorithm: (1) adopting an improved multi-target particle swarm algorithm or a genetic algorithm, introducing an adaptive weight mechanism, and combining real-time data to dynamically adjust the weight of a target function; (2) aiming at uncertainty factors, embedding a scene analysis method or a robust optimization strategy, and judging whether the comprehensive efficiency is optimal or not; and S4, scheduling execution and monitoring.

The invention provides an unmanned aerial vehicle adaptive path planning method and system fused with environment uncertainty, and the method comprises the steps: obtaining surrounding environment data based on an environment perception module, generating a local grid map and a dynamic obstacle list, inputting the local grid map and the dynamic obstacle list into an environment uncertainty evaluation module, obtaining an uncertainty factor, and carrying out the calculation of the uncertainty factor; and performing path planning through a global path planner and a local path planner based on the uncertainty factor, and adjusting the weight of a global path and the weight of a local path according to the environment uncertainty factor through a dynamic weight fusion device to obtain a final track. According to the method, the flexibility and the real-time performance of unmanned aerial vehicle path planning are effectively improved, the obstacle avoidance capability and the flight stability of the unmanned aerial vehicle in a complex environment are enhanced, and the flight efficiency is improved.

Disclosed in the present invention are a shared energy storage configuration optimization method and system for a wind farm cluster. The method comprises: constructing a shared energy storage double-layer programming model; on the basis of the output of a wind farm cluster, an electricity price in an electric energy market and a compensation price in a frequency-modulation auxiliary service market, obtaining multiple uncertainty sets, using an improved K-means clustering method to perform joint clustering on the multiple uncertainty sets so as to obtain a typical scenario, and on the basis of the annual occurrence frequency of the typical scenario, calculating a typical scenario probability; and using a distributionally robust optimization method to model the multiple uncertainty sets on a short-time scale so as to obtain an extreme uncertainty scenario, using the typical scenario probability and the extreme uncertainty scenario to update the shared energy storage double-layer programming model so as to obtain an updated shared energy storage double-layer programming model, and using a particle swarm algorithm to perform solving so as to obtain a configuration result. By means of the method, shared energy storage configuration optimization is performed by means of taking various uncertainty factors into consideration, thereby improving the accuracy of shared energy storage configuration optimization.

The invention relates to a power plant unit maintenance optimization method considering multiple uncertain factors of a power market, and belongs to the technical field of power system operation management. The method comprises the following steps: firstly, obtaining spot market electricity price and new energy output data in each time period of a historical year in a power plant; a random scene set considering spot market electricity price and new energy output is established based on historical data, time-varying features are described through data segmentation, state space division and a non-uniform Markov model, and the random scene set is generated by using a Monte Carlo method; and finally, taking maximization of the total expected income of the power plant as an objective function, constructing an optimization model in combination with preset constraint conditions of the unit, inputting the random scene set into the optimization model for solving, and outputting an optimized unit operation and maintenance strategy which comprises maintenance plans of wind power, photovoltaic and thermal power units and corresponding output plans. Multiple uncertain factors in the power market can be effectively handled, and the operation and maintenance benefits of a power plant are improved.

The invention discloses a mechanical arm path planning method, which belongs to the technical field of mechanical arm path planning, is used for planning a mechanical arm path, and comprises the following steps: acquiring parameter information and preset parameter information of a to-be-detected area, and positions of a starting point and a target point of a cooperative manipulator at the tail end of a mechanical arm; defining a state sampling space; performing oriented random sampling in the state sampling space according to the target deviation factor to obtain a random point; gravitational search is added in calculation of new nodes of the RRT algorithm; performing optimization processing on the found path by using a greedy algorithm, and performing smoothing processing on the found path by using a B spline curve; and initial moving target point location information of the mechanical arm tail end cooperative manipulator is obtained and analyzed, and the next moving target point location of the mechanical arm tail end cooperative manipulator is obtained. Compared with the prior art, the method has the advantages that a feasible path can be quickly found in a high-dimensional space, discretization is not needed, and a dynamic environment and uncertain factors can be processed.

The invention discloses a civil engineering construction task automatic scheduling method and system. The method comprises the steps that a heterogeneous graph model representing construction task elements and the incidence relation of the construction task elements is constructed; processing the heterogeneous graph model by using a graph neural network to mine a dependency relationship between processes and extract risk association features related to uncertainty to form a construction state graph; constructing a Bayesian network based on the construction state atlas; probabilistic reasoning is carried out through a Bayesian network, the influence of uncertain factors is quantified, and key information representing risk conduction is fed back to the graph neural network so as to dynamically correct recognition of the graph neural network on a process dependency relationship; the dependency constraint output by the graph neural network and the risk quantification result output by the Bayesian network are fused, and a construction task scheduling scheme is generated; and synchronously updating the heterogeneous graph model, the graph neural network and the Bayesian network according to field construction feedback data to realize closed-loop iterative optimization of a scheduling scheme.

The invention provides a comprehensive energy efficiency evaluation method and system for a smart park, and the method comprises the steps: constructing a nonlinear metabolism matrix reflecting the energy incidence relation between equipment, and solving the optimal metabolic flux distribution through mixed integer nonlinear programming; defining an interaction rule base with a metabolic network, dynamically triggering rules according to real-time sensor data, and adjusting real-time metabolic flux; calculating the quantized value of each current energy efficiency index according to the real-time metabolic flux, quantifying the uncertainty through a fuzzy entropy theory, dynamically distributing multi-target weights, and calculating a comprehensive energy efficiency score; and calculating the quantized value of each energy efficiency index in the optimal state according to the optimal metabolic flux distribution, comparing the fuzzy entropy, determining a to-be-optimized energy efficiency index, and identifying high risks and potential risks to formulate an optimization strategy. According to the method, various equipment and energy flow conditions in the park are considered, the energy efficiency level of the system is accurately reflected, dynamic adjustment and evaluation are carried out according to real-time data, uncertainty factors of the energy system are identified and solved, and efficient and stable operation of the system is guaranteed.

The invention relates to the technical field of hydropower station optimal dispatching, in particular to a hydropower station reservoir dispatching decision-making method, system and device and a medium, and the method comprises the steps: obtaining a multi-source data set of a target hydropower station reservoir, and carrying out the preprocessing; adopting a clustering algorithm to carry out meteorological and hydrological mode typing on the drainage basin where the target hydropower station reservoir is located, and extracting a typical meteorological and hydrological typing result; according to a typical meteorological and hydrological typing result, constructing a scheduling macro-variable for quantifying a collaborative imbalance state among meteorological, hydrological and scheduling subsystems; identifying a current scheduling mode based on the real-time storage flow, and extracting a multi-scale spatial-temporal feature corresponding to the current scheduling mode; and according to the current scheduling mode, the typical meteorological and hydrological typing result and the multi-scale spatial-temporal characteristics, dynamically selecting and fusing multiple types of pre-training models, and generating a reservoir scheduling strategy by using scheduling macro-variable rolling optimization. The objective of the invention is to improve the efficiency and robustness of hydropower station reservoir resource decision and the reliability of a reservoir scheduling strategy in an uncertain factor scene.

The present discourse provides a system and method for simultaneous energy scheduling and trading portfolio optimization for an energy hub (EH). In the present disclosure, an optimal strategy is generated for (i) energy scheduling and (ii) portfolio based trading in a simultaneous manner using a dynamic optimization model for the EH with multiple energy types, taking into account an uncertainty factor on each of a supply side and a demand side. The dynamic optimization model provides the EH with an optimal schedule for a plurality of energy assets, and provides an EH operator with an optimal trading portfolio for the multiple energy types in the EH. The optimal schedule for the plurality of energy assets and trading portfolio of the EH operator are calculated while maximizing the realized profit. Furthermore, the dynamic optimization model ensures the sustainability while minimizing the CO2 emissions and increased overall efficiency of the EH.

The invention relates to the technical field of data processing, and discloses an engineering construction whole process digital management method and system. The method comprises the following steps: collecting and standardizing engineering construction full-period multi-source data; constructing a stacked integrated prediction system by using the standardized data to obtain a three-dimensional index prediction value; allocating resources based on the predicted value in combination with the ergonomics risk index to obtain a configuration scheme; uncertainty factors are quantified for the configuration scheme, an optimal scheme set is calculated through a multi-target symbiotic algorithm, and a management strategy is formed. According to the method, high-precision prediction of the engineering three-dimensional indexes is realized through the stacking integration algorithm, ergonomics risk assessment and resource allocation optimization are combined, and the technical defect that human factor engineering is neglected in a traditional method is overcome.

The invention relates to a wind power plant station shutdown and energy storage cooperative operation optimization method and system under multiple uncertainty factors, computer equipment and a storage medium, and the method comprises the steps: carrying out the prediction based on the operation state data of a wind power plant station in a historical period through a pre-trained hybrid prediction model, and obtaining a power prediction value of a wind turbine generator in a future period; constructing a plurality of wind power output scene matrixes based on the power prediction value and the historical prediction residual error, and performing clustering to obtain a plurality of typical scene matrixes; constructing a coordination control optimization model which takes the target typical scene matrix with the maximum output and the probability of each of other typical scenes as input and takes the minimum shutdown cost and shutdown penalty cost of the wind turbine generator as a target function; and generating a coordinated operation optimization strategy through the coordinated control optimization model, wherein the strategy comprises a wind turbine generator shutdown sequence and an energy storage system operation mode. According to the method, various uncertain factors in the operation of the wind power plant can be effectively handled, and the economic benefit and the operation stability of the wind power plant are improved.

The invention provides a virtual power plant robust optimization scheduling method and system considering multiple uncertainties, and relates to the technical field of power market scheduling, and the method comprises the steps: building a virtual power plant scheduling model considering multiple uncertainties based on ARIMA-SVR, and the uncertain factors comprise distributed energy power generation, electricity price and load demands; extracting features of uncertainty factors, constructing an uncertainty set, and transforming the virtual power plant scheduling model into a robust optimization scheduling model; solving the robust optimization scheduling model by adopting a particle swarm optimization algorithm, and optimizing a scheduling strategy of each energy resource in the virtual power plant; according to the method, the robust optimization scheduling model considering multiple uncertainties is constructed, and an advanced solution algorithm is combined, so that optimization scheduling of the virtual power plant in a complex market environment is realized, and the scheduling efficiency, the economic benefit and the capability of coping with the uncertainties of the virtual power plant in the complex market environment are improved.

The invention belongs to the technical field of building group flood influence analysis, and provides a rural building group flood disaster loss assessment method and system for solving the problem that the accuracy of an assessment result is low due to the fact that an existing building flood loss function does not fully consider uncertainty factors in the loss assessment process. The method comprises the following steps: simulating a dynamic evolution process of a flood disaster by adopting a hydrodynamic model to obtain an inundation depth map and a maximum inundation depth map; comparing the submerging water depth map with the SAR image water body extraction data and the social media data to obtain a flood simulation result; combining the building classification information with the maximum submerging depth map to obtain the submerging depth of each building; calculating component information of the building; according to the component information and the submerging depth, building flood vulnerability analysis is carried out, and the total loss of all buildings is determined; and evaluating the effect of the current disaster prevention measure according to the total loss under different disaster prevention measures. The method can provide a scientific basis for building flood loss assessment and disaster prevention decision.

The invention discloses a scheduling method and system for a pumped storage-wind-light-fire multi-energy complementary power generation system, and relates to the technical field of power scheduling, and the method comprises the steps: constructing a power system multi-source uncertainty integrated frame covering the load, wind power, photovoltaic power and generator shutdown based on a plurality of uncertainty factors in a new energy power system; aiming at different uncertainties in the framework, generating corresponding scene representations by adopting different representation modes; on the basis of scene representation of multi-source uncertainty, the minimum expected power generation cost of the system is used as an objective function, thermal power operation constraint, pumped storage power station operation constraint, wind and light output operation constraint and system operation constraint are built, and a random unit combination model of the pumped storage-wind-light-thermal multi-energy new energy system considering uncertainty is built. And taking operation data of each generator set in the power system as input, solving the model by adopting a mixed integer linear programming method, determining an optimal scheduling operation mode of each generator set, and realizing optimal scheduling of the generator sets.

The invention discloses a kinetic parameter identification method, device and equipment of a collaborative robot considering uncertain factors, and relates to the technical field of robots, the method comprises the following steps: constructing an improved kinetic friction model considering uncertain factors and multi-factor coupling, and obtaining friction torques borne by joints according to the model; constructing a kinetic equation; solving the kinetic equation to obtain a recursive expression; the collaborative robot is driven according to the excitation track, an output parameter matrix is obtained, the joint angular velocity of the collaborative robot is calculated according to the output parameter matrix and the recursion expression, and high-order terms of the joint angular velocity are calculated step by step; when the error of the expansion values of two adjacent orders is smaller than an iteration convergence coefficient, outputting the angular velocity; and judging a motion stage to which the angular velocity belongs according to the velocity judgment coefficient, and dividing a parameter identification stage according to a judgment result to realize segmented identification of the kinetic parameters of the collaborative robot. According to the method, the identification precision and calculation efficiency of the kinetic model are improved.

The invention relates to the technical field of electricity price prediction, in particular to an electricity market long-period simulation-based medium and long-term price prediction method, which comprises the following steps of S1, market basic data acquisition: acquiring whole market basic data information; s2, unit commitment model construction and day-ahead simulation: constructing a unit commitment model considering power grid security constraints; s3, economic dispatching model construction: constructing an economic dispatching model considering power grid security constraints; s4, node marginal electricity price and load flow calculation: node marginal electricity price calculation and load flow calculation are carried out; s5, uncertainty factor evaluation: the uncertainty factors existing in the electricity market are evaluated; s6, analyzing technical performance and economic indexes: analyzing statistics of various technical performance and economic indexes of the system; according to the method, the stability and the reliability of an enterprise under an emergency situation are enhanced, so that the enterprise can better adapt to market changes, and the scientificity and the accuracy of decision making are improved.

This application provides a method for uncertainty analysis in mechanical product assembly performance simulation. This method considers the impact of various uncertainty factors at different stages of the simulation process on assembly performance, classifying these factors into two categories for quantitative analysis. The first category includes the measurement, modeling, and simulation analysis stages in the early stages of simulation; the uncertainty u or error of these factors can be directly calculated. The second category consists of assembly performance uncertainties caused by external loads, boundary conditions, and nonlinear processes primarily based on geometric error distribution. For this category, this method uses simulation analysis results from a physical digital twin model, combined with the construction of an optimal SVR surrogate model and Sobol global sensitivity analysis, to quantitatively analyze the sensitivity of assembly performance to process parameters. In summary, this method provides guidance for the product assembly process.

The invention discloses a response reliability evaluation method and system for a virtual power plant and a related product, and belongs to the technical field of virtual power plant evaluation optimization. Stochastic state simulation is carried out based on a Monte Carlo method, a stochastic state sequence is generated, uncertain factors such as wind and light output fluctuation and energy storage state randomness are quantified into the stochastic state sequence capable of being analyzed, and the limitation of deterministic hypothesis is broken through; through energy storage dynamic division and real-time updating of a division result, and in combination with a design of calculating a reliability index by a random state sequence and an energy storage execution model, a virtual power plant dynamic reconstruction demand caused by wind and light output fluctuation and user demand change can be responded; and through dynamic clustering and attenuation quantification, the operation adaptability of the system under the uncertain working condition is ensured, the finally calculated reliability index can comprehensively reflect the real response capability of the virtual power plant under the uncertain environment, and an accurate evaluation basis is provided for improving the adaptive capability of the virtual power plant under the uncertain environment.

The embodiment of the application provides a pre-drilling risk assessment method, and belongs to the technical field of oil and gas drilling engineering. The method comprises the following steps: obtaining well data of historical wells, extracting drilling field entities and establishing a correlation relationship to construct a drilling knowledge base; obtaining well site coordinates, regional structure zoning and pressure distribution parameters of a well to be drilled, determining the geological similarity with the historical wells and distributing the experience transfer weight; based on the accident cause and effect chain relationship in the drilling knowledge base, a knowledge enhanced model for risk cause and effect reasoning is constructed; the model is used for semantic analysis of the design text of the well to be drilled, risk entities are identified, and the accident cause and effect chain is inferred, and the model risk confidence is output; the model risk confidence is matched with the historical accident mode in the knowledge base to determine the associated historical well set; the experience transfer weight of each well in the set is aggregated to generate a historical experience confidence; and based on the confidence, the geological uncertainty factor and the rule confidence corresponding to the accident cause and effect chain, the pre-drilling risk score and the risk level of the well to be drilled are determined.

The application discloses a full-symmetrical polyhedral set-membership state estimation method based on coding and quantization mechanism, which comprises the following steps: a corresponding linear repetitive process system model is established; an augmented technology is applied to convert a time-varying linear multi-rate system model into a corresponding time-varying linear single-rate system model; the range of uncertainty factors such as disturbance and measurement noise in the system is expressed by a full-symmetrical polyhedron; a coding mechanism is introduced in the information transmission process of two types of sensors with different sampling rates, and the quantization effect is considered; the properties of the full-symmetrical polyhedron are used to design an estimator for the system, and the estimation value is obtained; and finally, the optimal full-symmetrical polyhedron in the F norm sense is obtained through the design of the parameters of the estimator. The application can process the information transmission security problem under a 2D system, introduces the coding mechanism to reduce the network communication burden, and solves the set-membership state estimation problem considering the coding mechanism under the linear repetitive process.

The invention discloses an optimal scheduling method for a virtual power plant, and relates to the technical field of power regulation, and the method comprises the steps: obtaining multi-modal data of a detection region, calculating a regional load fluctuation index and a resource mobility index, and generating a dynamic feature data set; performing real-time clustering division on the power grid nodes through a clustering algorithm based on the dynamic feature data set to generate a dynamic sub-region set; if the sub-region feature fluctuation exceeds a boundary stability threshold value, triggering boundary redivision; according to the dynamic sub-region set, comparing a building power consumption predicted value with the available load of the charging pile in real time to calculate a demand difference, and generating an elastic scheduling instruction through multi-objective optimization; uncertain factors are monitored in real time, a risk index is calculated, and the priority and the resource allocation strategy of the elastic scheduling instruction are dynamically adjusted based on the risk index; and generating a risk avoidance scheduling scheme through a robust optimization algorithm.

The invention discloses a method for analyzing the strength and rigidity related degradation failure reliability of an automobile crane boom. The method comprises the following steps: firstly, establishing a load-stress equation of the boom; constructing a single strength degradation model and a rigidity degradation model based on the equation; then quantizing uncertain factors influencing the reliability to obtain a failure probability function corresponding to single degradation; and finally, establishing a strength and rigidity related degradation failure model based on the functions, and analyzing the reliability. According to the method, an automobile crane boom system is used as a research object, uncertainty factors and strength and rigidity degradation laws in actual service are deeply explored, and the system reliability is evaluated through a reliability analysis method; aiming at the limitation that a traditional method only considers single degradation failure, a related failure analysis theory is introduced, and a related degradation failure model is established on the basis of a traditional single degradation model in combination with the working condition characteristics of the boom, so that a reliability analysis method considering strength and rigidity related degradation failure is formed.

The invention discloses a dynamic and static integrated digital twin modeling method for a multi-physical association open type assembly station, which comprises the following steps of: performing comprehensive and systematic digital description on geometric and physical attributes and assembly information of the assembly station through a multi-physical and multi-layer static model construction method, and constructing to obtain a static model. According to a dynamic and static integrated model construction method based on correlation analysis, a mapping relation between static parameters and dynamic behaviors is established, uncertainty factors in the assembly process are considered, and finally an integrated digital twinborn model supporting high-fidelity simulation and analysis is formed. According to the method, multi-physical and multi-level characteristics of the assembly station can be comprehensively considered, effective integration of dynamic and static models is realized, meanwhile, a deviation accumulation effect is considered, static information such as an assembly data set and an assembly process attribute set of a product can be comprehensively and accurately described, and a solid foundation is provided for subsequent dynamic simulation and analysis.

The application discloses a kind of based on ERP system's dynamic feeding back calculation processing method, equipment and medium, belong to enterprise informationization ERP technical field, for solving the existing feeding back calculation is prone to deviation, easily influenced by a variety of uncertain factors, it is difficult to accurate prediction and adjustment, increase the technical problem of production risk.Method includes: according to target production product, BOM table is matched and inquired and handled, and the required material information is obtained;According to the inventory result where the required material information is located, the available inventory usage of maximum production is judged, and the usage result is obtained;If the matching result is not check matching result, then according to the required material information, the product quantity of target production product is determined;If the matching result is check matching result, then the available inventory of each required material information in BOM table is circulated, and the product quantity of target production product is determined;Based on product quantity and BOM table, feeding production order information is generated.

The invention relates to a superspiral sliding mode active disturbance rejection control strategy based on a four-active bridge converter, and the method comprises the following steps: employing an extended state observer to estimate uncertain factors, unmodeled dynamics and coupling terms in a system, putting the disturbance estimation into superspiral sliding mode closed-loop control for elimination, and independent control of voltages of all ports is realized. After voltage and phase shift of each port of the four-active bridge converter are input into an extended state observer to estimate total disturbance of a system, given voltage is input into a differential tracker to eliminate influence of sudden change of the given voltage on a control effect, and obtained tracking voltage and observed voltage of the observer are input into a subtractor for subtraction; the method comprises the following steps of: inputting the obtained error quantity into a sliding mode controller, obtaining a phase shift ratio di through corresponding operation, giving phase shift ratios d2, d3 and d4 between primary and secondary side ports, generating corresponding PWM (Pulse-Width Modulation) signals, driving corresponding switching tubes to be switched on and switched off, and realizing independent voltage control and more efficient and reliable control operation of each port.

The invention discloses a safety assessment method for an offshore wind power flexible direct current grid-connected system based on reachability analysis. The method comprises the following steps: constructing a high-dimensional nonlinear differential equation model and a nonlinear hybrid system model of the offshore wind power flexible direct current grid-connected system based on a Kron simplification method; constructing an uncertain variable model with a random change range by utilizing a sino polyhedron, and adding the uncertain variable model into the nonlinear hybrid system model as an uncertain input item; based on a reachability analysis theory, designing an improved reachability set calculation algorithm based on an adaptive time step optimization method, and dynamically balancing the calculation precision and the calculation efficiency of the reachability set by adjusting the time step; and an improved reachable set calculation algorithm is applied to calculate reachable set results of the offshore wind power flexible direct current grid-connected system under the influence of different operation conditions and uncertain factors. According to the method, the operation state of the offshore wind power flexible direct current grid-connected system can be subjected to safety evaluation according to the reachable set result, and important guidance is provided for guaranteeing safe and stable operation of the system.

The application relates to a cloud platform-based water resource management method, device, equipment and medium. The method integrates multi-source water data through a cloud platform, constructs a hierarchical Bayesian model after data cleaning, uniformly quantizes model parameters, input and posterior distribution of structural errors, drives ensemble Kalman filtering dynamic assimilation of real-time observation data based on the same, generates a prediction distribution fusing multi-source uncertainty, further analyzes the contribution source of prediction variance in real time through a Sobol' index, identifies dominant uncertainty factors, constructs a coupled error model according to the same, feeds the coupled error model back to the assimilation cycle as a state variable for online correction, and finally forms an improved prediction model capable of dynamically tracking and quantifying the uncertainty coupling propagation mechanism, thereby significantly improving the accuracy and reliability of hydrological prediction, and providing risk quantification basis and optimization scheme for flood control scheduling, water resource allocation and urban drainage management decision-making.