266 results about "Uncertainty factor" patented technology

The invention discloses a system combat ability dynamic game comprehensive evaluation method based on deep learning, and relates to the technical field of combat ability evaluation.According to the method, key features of time and space in a battlefield environment are dynamically extracted by combining a multi-sensor fusion technology with a time sequence convolutional network and a graph neural network, a situation model is updated in real time, and the dynamic game of the system combat ability is comprehensively evaluated. The optimization process of unmanned cluster marshalling and task allocation is accelerated by utilizing a near-end strategy optimization PPO and transfer learning technology, and adaptive adjustment of a marshalling scheme on complex task requirements is realized by defining a multi-task collaborative efficiency index; in the aspect of strategy optimization, a multi-agent game model driven by deep reinforcement learning is constructed, friend or foe resource allocation, dynamic environment change and uncertainty factors are integrated, an optimal strategy is effectively generated, and low-efficiency schemes are eliminated through combat scene simulation; and meanwhile, through a real-time monitoring and dynamic planning mechanism, emergencies such as target change and emergency task insertion are dealt with, and the task execution continuity and cooperative efficiency are improved.

The invention provides a harbor district energy double-layer coordination optimization method oriented to multiple interest subjects. The method comprises the steps of performing fusion processing and data cleaning on real-time operation data, historical operation data and environmental parameter data of a harbor district energy management system; performing multi-scale decomposition and feature importance evaluation by adopting a time sequence decomposition algorithm; probability modeling and multi-scale prediction are carried out on the basis of feature recognition uncertainty factors; constructing an upper-layer government regulation and control model and a lower-layer harbor district-ship east game model, and calculating a balance coefficient; and performing monthly strategy optimization, weekly scheduling optimization and hour-level real-time control based on the balance coefficient. By constructing a complete technical chain of data processing, feature extraction, prediction modeling, game optimization and real-time control, dynamic balance of benefits of the government, the harbor district and the ship eastern is realized, and the operation efficiency and the control precision of the harbor district energy system are remarkably improved.

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 belongs to the technical field of automatic driving, and particularly relates to an automatic driving decision-making method and system with dynamic risk perception and attention focusing and a vehicle, and the method comprises the steps: predicting the track of a surrounding vehicle in real time through a multi-feature Gaussian weighted particle filtering algorithm, and improving the prediction precision through combining a vehicle kinematic model and resampling optimization; constructing a comprehensive evaluation model fusing transverse and longitudinal risks, and dynamically quantifying the collision risk of the vehicle and surrounding vehicles; and inputting the risk value as a key state feature into a double-depth Q network based on attention mechanism enhancement, focusing key information through a feature attention distribution mechanism, and generating an optimal driving decision in combination with a multi-target reward function. Compared with the prior art, the method solves the problems of insufficient quantification of uncertainty factors, incomplete risk assessment and low decision-making efficiency of automatic driving in a complex dynamic environment, and significantly improves the risk perception capability and decision-making safety of the automatic driving vehicle.

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 discloses an urban multi-water-source joint scheduling method based on a discrete scene set and a robustness decision, and the method comprises the steps: researching the characteristics of multiple uncertainty factors of urban multi-source water supply, and generating a multiple uncertainty scheduling scene set through a Latin hypercube sampling method; constructing a water supply city regulation and storage reservoir and external water source joint scheduling model and a water receiving unit internal optimization scheduling model; and constructing a robustness decision-making-based urban annual multi-water-source combined scheduling model. A multi-uncertainty scene set and a robustness measurement method based on an expected benefit index are utilized, the minimization of the annual water level fluctuation risk and the urban water shortage risk of a regulation and storage reservoir is taken as a scheduling target, an urban multi-water-source combined scheduling model based on a robustness decision is constructed, the rationality and the applicability of the robustness scheduling model are verified, and the urban multi-water-source combined scheduling model is established. And analyzing a robust decision scheduling result under an uncertain condition.

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 discloses a Tube MPC-based hybrid electric vehicle layered energy management method. The method comprises the following steps of performing longitudinal dynamic modeling on a hybrid electric vehicle, determining a vehicle demand power formula, constructing an evaluation function of equivalent fuel consumption, and constructing an output power model of a vehicle engine and a battery; carrying out uncertainty analysis on the energy management system, determining uncertain parameters in the vehicle model, quantifying the uncertainty of the uncertain parameters based on probability distribution, and constructing an uncertainty set of the uncertain parameters; on the basis of a dynamic programming algorithm, under the global working condition, the minimum amount of equivalent fuel consumption serves as a target function, an optimal control sequence of the engine is output, and an SOC reference trajectory is generated; and performing tracking solution on the control variables and the state variables in the uncertainty set by applying a Tube model predictive control (Tube MPC) method to obtain an optimal control sequence under uncertain factors, and outputting a first control variable.

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 an elastic evaluation method for a comprehensive energy distribution network in the field of power systems and energy management, and the method comprises the following steps: S10, constructing a dynamic coupling model and a comprehensive evaluation index system of the comprehensive energy distribution network, the dynamic coupling model of the comprehensive energy distribution network comprises energy flow equations and equipment operation constraints of a power network, a natural gas network and a thermal network, and the comprehensive evaluation index system comprises an energy coupling index, a recovery capability index and an uncertainty index. According to the invention, through multi-energy coupling and uncertainty comprehensive evaluation, the coupling relationship of multiple energies and various uncertainty factors are comprehensively incorporated into an elastic evaluation system of the comprehensive energy distribution network, a set of complete comprehensive evaluation index system is established, and the limitation that a traditional evaluation method only pays attention to single energy or ignores uncertainty is broken through; and the actual elasticity condition of the system can be reflected more truly.

The invention relates to the technical field of power system real-time scheduling, and discloses a power system real-time scheduling method, system and device and a medium, and the method comprises the steps: predicting the wind and light load uncertainty of a power system under different time scales; constructing a flexible resource model adapted to different response times; constructing a net load set based on uncertainty; determining a power system flexibility constraint; and establishing and solving a scheduling model containing the constraints so as to realize real-time scheduling. According to the method and the device, the accuracy and the reliability of real-time scheduling are improved by considering uncertain factors and flexible resource response in the power system. According to the method, a flexible resource model and a net load uncertainty set adaptive to different time scales are constructed so as to adapt to dynamic change of a system and support stable operation. The method can also reduce the operation cost and improve the resource utilization efficiency, and has an important influence on the sustainable development of a power system.

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 discloses a numerical simulation method for a shear test of rock mass structural surfaces with different roughness, and belongs to the technical field of metal mine mining methods. According to the technical scheme, five typical structural plane contour lines are selected as models, a joint roughness coefficient JRC is checked by solving a root-mean-square Z2 of discretized mesoscopic node coordinates on a structural plane curve, and a check result is taken as a selected joint roughness coefficient JRC value to carry out a triaxial compression numerical simulation experiment; constructing a numerical model of a three-axis compression numerical simulation experiment of rocks penetrating through the structural plane with different roughness, and selecting structural plane model features and parameters; analyzing the influence of the roughness and the friction angle on the mechanical property of the structural surface; and carrying out structural surface shear strength comparison verification of different strength criteria and equivalent internal friction angle parameter verification analysis of structural surfaces with different roughness on a simulation result. According to the method, the interference of test equipment and uncertain factors is reduced, many factors which are not considered in tests are considered, and the numerical simulation result is more scientific and reasonable.

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.

Method and device for ship collision avoidance decision-making in restricted waters based on uncertainty modeling. The method includes: combining a ship motion model with a random variable representing the uncertainty of ship maneuverability to construct a motion model considering the uncertainty of ship maneuverability; constructing a ship observation model considering observation uncertainty using a Gaussian distribution; simulating the steering and speed changes of the ship using a Gaussian distribution to construct an uncertainty model for the ship's compliance with rules; capturing the motion uncertainty of other ships during navigation through a deep Gaussian process model and constructing a prediction model for the trajectories of other ships considering motion uncertainty; constructing an optimization model for ship collision avoidance behavior decision-making based on the above uncertainty models and solving the optimal collision avoidance behavior decision for the ship during navigation. The present invention comprehensively considers various uncertainty factors encountered by ships during navigation in complex and dynamic restricted waters, and establishes an optimization model for ship collision avoidance behavior decision-making with strong adaptability to effectively make ship collision avoidance decisions.

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 the technical field of intangible asset valuation, and discloses an intangible asset quantification algorithm incubation achievement value evaluation method comprising the following steps: S1, collecting and preprocessing multi-source data related to intangible asset quantification algorithm achievements to form a multi-dimensional feature set; s2, constructing a dynamic adaptive evaluation framework, updating value evaluation parameters based on a real-time data stream and a reinforcement learning technology, and generating value prediction dynamically adjusted along with time; s3, calculating a fusion value through a multi-dimensional value fusion model; s4, performing uncertainty quantification on the fusion value, generating value distribution, and calibrating a final value according to an uncertainty factor; and S5, outputting the final value. According to the method, the dynamic self-adaptive evaluation framework is constructed, the value evaluation parameters are updated by using the real-time data flow and reinforcement learning technology, and the value prediction adjusted along with time can be generated according to market price fluctuation, technical index updating or legal protection state change.

The invention relates to the technical field of oil exploration and development, in particular to a method for quantitatively determining the completion probability of water-drive oilfield development and production allocation. The method comprises the steps of selecting a target water-drive oil field and determining a target research area, analyzing uncertainty factors influencing the yield scale of the target water-drive oil field and extracting key indexes, and counting historical development data of batch wells in the current year in the target research area, quantitative characterization is carried out on the key indexes, expected yield values of the target water-driven oil field under different probability conditions are calculated, the risk degree range of the production allocation yield of the target water-driven oil field is obtained, and a risk assessment model of the production allocation yield of the target water-driven oil field is established according to the range; and reasonably predicting the yield of the next target water-driven oil field to complete the task according to risk analysis and evaluation of the model on the production allocation yield of the target water-driven oil field. According to the method, the feasibility target of the target water drive oil field is formulated through scientific guidance, the yield risk is avoided in time, and planning deployment is adjusted in time.

The invention is suitable for the technical field of wireless power transmission, and provides an electromagnetic shielding structure robust optimization method for a wireless power transmission system, which comprises the following steps: constructing a WPT system; an electromagnetic shielding structure is designed, energy transmission efficiency is improved, and an electromagnetic field in a non-working area is weakened; the method comprises the following steps: establishing a K-Trans proxy model, integrating uncertainty factors and shielding structure design parameters of a WPT system, and carrying out modeling on data by utilizing an improved Transform model and combining a Kolmogorov-Arnold representation theorem; and carrying out multi-target robust optimization based on a K-Trans proxy model by applying an MOEDO algorithm, generating a Pareto optimal solution set, and balancing the energy transmission efficiency and the electromagnetic shielding performance. The electromagnetic shielding structure is provided, the K-Trans proxy model and the MOEDO algorithm are used for optimization design, the problem of WPT system coupling mechanism offset is solved, and a new thought is provided for electromagnetic shielding and safety protection.

The invention discloses a synchronous phasor measurement-based power distribution network line parameter estimation method and device, a medium and a terminal, and the method comprises the steps: introducing a model comprising a system error and a random error when a single-branch measurement model is built, and comprehensively considering uncertain factors; therefore, the accuracy and practicability of the multi-branch line measurement model established by the single-branch measurement model are improved, and in addition, the weighted least square method is selected for iteration in iteration, so that the estimation accuracy is further improved. The method comprises the following steps: establishing a single-branch measurement model according to a single-branch pi model and a measurement error; the single-branch pi model is used for representing a single-branch line in the power distribution network line; establishing a multi-branch line measurement model on the basis of the single-branch measurement model; and based on the multi-branch line measurement model, estimating the line impedance of the power distribution network by adopting an iterative weighted least square method.

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 discloses a method for optimizing a wind-solar uncertain building integrated energy system based on graph reinforcement learning. The method comprises the following steps: initializing a feature matrix and an adjacent matrix of system topological graph nodes in an intelligent scheduling environment of the building integrated energy system; the scene analysis method is combined to elaborate the wind and light output uncertainty, and a building integrated energy system master-slave game bilateral optimization model based on the wind and light uncertainty is established; constructing a Markov decision process by considering node topological structure features; and an energy scheduling and pricing strategy is decided according to the topology state information by adopting a depth deterministic strategy gradient algorithm of a multi-head graph attention network of TopkPooling. Aiming at random dynamic events or uncertain factors occurring in the optimization process of the building integrated energy system, the optimal scheduling scheme and pricing scheme can be generated in time according to the change of the environment state.

The invention discloses a multi-modal unified representation-based video language time sequence positioning method and system, and the method comprises the steps: carrying out the preprocessing of an original video, extracting the features of a language query and the features of a preprocessed video through a backbone network, carrying out the unified representation modeling of the extracted features through a Gaussian mixture model, and carrying out the positioning of the video language time sequence. Inputting the features based on Gaussian mixture unified representation into a multi-layer perceptron model for mapping, and performing residual connection on the mapped features and the extracted features to obtain final features; capturing video features related to the language query using a cross attention mechanism; respectively inputting a time retrieval detection head and a highlight detection head to obtain a time retrieval result and a highlight detection result; introducing a learnable uncertainty factor to obtain a time retrieval loss function and a highlight detection loss function; and a moment retrieval loss function is used for the moment retrieval result to obtain a video moment retrieval result, and a highlight detection loss function is used for the highlight detection result to obtain a video highlight detection key frame.

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.