661 results about "Robust optimization" patented technology

The invention provides a demand side response-based distributed power supply optimal scheduling method and system, and relates to the technical field of power grids, and the method comprises the steps: building a distributed power supply prediction model based on a deep neural network, and predicting the photovoltaic power generation power, the energy storage charge state and the user load. Then, a robust optimization model is constructed, prediction errors and energy storage nonlinear characteristics are considered, an optimal time-phased load scheduling scheme and an energy storage charging and discharging strategy are generated, the scheme comprises an interruptible load execution time sequence, and the strategy comprises an energy storage charging and discharging power curve; and finally, establishing a hierarchical optimization execution system based on model predictive control, monitoring the state of the system in real time, and correcting a control instruction according to the deviation to realize optimal scheduling of the distributed power supply. The method can improve the consumption capability of the distributed power supply, reduce the power consumption cost, and enhance the stability of the power grid.

The invention relates to the technical field of smart power grids, and discloses a smart power grid power dispatching optimization method, which comprises the following steps of: firstly, acquiring power grid operation data, and processing data missing and noise problems by utilizing federal learning; and constructing a load prediction model through a dynamic time warping algorithm and a specific network. A multi-energy coupling scheduling model and a demand response game model are constructed, and a multi-time scale rolling optimization framework is established. And carrying out sensitivity analysis on scheduling parameters, designing a hierarchical collaborative optimization mechanism, and constructing a robust optimization model to cope with the power flow uncertainty. And integrating a scheduling instruction verification module, and deploying an online incremental learning mechanism. The method can effectively process data, accurately predict load, optimize multi-energy scheduling, guide demand response, deal with uncertainty, verify scheduling instructions and update the model in real time, improves the safety, reliability and economy of smart grid power scheduling, and realizes optimal configuration of power resources.

The invention discloses an optical storage direct-flexible system power scheduling method based on market model prediction response, which comprises the following steps: determining the composition of an optical storage direct-flexible system, and collecting operation state data and external environment data; constructing a hybrid prediction model, and predicting the market price and the uncertainty interval of the market price in the scheduling period; establishing a power scheduling optimization model, and solving to obtain an optimal power scheduling strategy; according to the optimal power scheduling strategy, a control instruction is issued to each control unit of the optical storage direct flexible system, and cooperative adjustment is carried out through a bidirectional coupling feedback mechanism of voltage and power; the market price and the system operation state are monitored in real time, and when the market price deviation exceeds a price deviation threshold value, scheduling strategy online correction is triggered. According to the robust optimization architecture, traditional power scheduling is converted into a confrontation game structure between the system and the market, and the resistance of the system to extreme price situations is improved while economic benefits are guaranteed.

The invention provides an intelligent power grid load dynamic monitoring method and system, and the method comprises the steps: collecting real-time power consumption data, and generating a real-time power consumption data sequence; constructing a time sequence model based on historical synchronous data, generating an expected power load data sequence, and comparing the real-time power consumption data sequence with the expected power load data sequence to obtain a load trend prediction result; analyzing by applying a self-adaptive dynamic clustering algorithm based on a graph neural network, generating a configuration scheme of a clustering center and a clustering number, determining an optimal power distribution scheme in combination with a bilevel programming model and a robust optimization algorithm, and evaluating the risk of the optimal power distribution scheme to generate a power distribution strategy; based on a power distribution strategy, a demand side management strategy based on the game theory is formulated to generate an intelligent load response strategy so as to realize dynamic load monitoring; according to the invention, the management level of the smart power grid is improved, and a solid foundation is provided for future energy management and optimal scheduling.

A distributionally robust optimization method and apparatus for an electric-hydrogen-thermal integrated energy system, relating to the field of power system optimization operation. The method comprises: establishing a multi-agent optimization scheduling model of an electric-hydrogen-thermal integrated energy system; using a data driving-based distributionally robust optimization model to describe the source-load bilateral uncertainty of a power grid system agent optimization scheduling model, establishing a three-layer two-stage power balance model of a power grid system, and using a column and constraint generation algorithm for solving; and using an alternating direction method of multipliers to alternately iterate the three-layer two-stage power balance model of the power grid system, a hydrogen network system agent optimization scheduling model, and a heat network system agent optimization scheduling model, to realize distributed parallel solving of the multi-agent optimization scheduling model of the electric-hydrogen-thermal integrated energy system. Compared with the prior art, each agent scheduling scheme is solved by means of interactive iteration in the present method, thereby protecting the information privacy of each agent while improving the operation economy and reliability of an interconnected power grid.

The invention relates to the technical field of energy storage scheduling, and discloses a distributed energy storage scheduling prediction method and system driven by intelligent environment monitoring data. The method comprises the following steps: acquiring environmental parameters and processing to obtain an environmental data set; decomposing the environment data in a multi-level manner to obtain feature vectors and uncertainty indexes; inputting a hybrid deep network to obtain demand prediction distribution; constructing a robust optimization model to obtain a scheduling instruction sequence; performing decomposition execution through a layered architecture to obtain a running track; and carrying out deep reinforcement learning iterative optimization to obtain an energy storage scheduling strategy. According to the energy storage scheduling prediction method, the communication delay influence is overcome, the self-optimization capability is achieved, and efficient cooperative operation of the distributed energy storage system based on environmental data driving is achieved.

A new-power-system power grid restoration method and apparatus based on second-order cone transformation. The method comprises: acquiring power data of a new power system (101); on the basis of the power data and taking the output uncertainty of a new energy unit into consideration, constructing a mixed-integer nonlinear two-stage robust optimization model based on a multi-state uncertainty set (102), wherein the model comprises an alternating-current power flow constraint; using a second-order cone relaxation technique to simplify the alternating-current power flow constraint, and converting the mixed-integer nonlinear two-stage robust optimization model into a mixed-integer second-order cone two-stage robust optimization model (103); decomposing the mixed-integer second-order cone two-stage robust optimization model into a main question and a sub-question, and solving the main question and the sub-question, so as to obtain an optimization result (104); and on the basis of the optimization result, generating a power grid restoration scheme for the new power system (105).

The invention discloses a high-performance low-cost multi-modal data fusion model and equipment for complete driving environment state perception, and the model comprises a feature extraction module which is used for extracting initial potential features of a camera image and a laser radar point cloud; a probability cross attention mechanism constructs a feature fusion model of uncertainty perception; a query initialization model capable of reducing uncertainty is adopted, and the initialization process of query is enhanced by utilizing two-dimensional target detection priori; and based on a robustness optimization training strategy of query denoising, stable optimization is realized under uncertainty. A nuScenes data set is selected for training, verifying and testing the comprehensive performance of the model, and the TensorRT realizes CUDA acceleration and is quantitatively deployed on a vehicle-mounted calculation experiment platform. According to the multi-modal data fusion system, a perception state uncertainty explicit measurement calculation theory based on data and a model is innovated, a perception model for coping with environment uncertainty is established, the completeness, reliability and reconfigurability requirements of a driving environment are met, and driving safety and decision accuracy are ensured.

A method and system for robust optimization of microgrid scheduling, relating to the technical field of microgrid scheduling. The method comprises: constructing a multi-interval uncertainty set by means of an uncertainty prediction parameter (S1); on the basis of the established multi-interval uncertainty set, constructing a robust scheduling model of a microgrid (S2); and using a column constraint generation algorithm to iteratively solve the constructed robust scheduling model, to obtain a net load curve and an operation plan for energy output (S3). In the present invention, on the basis of traditional single-interval robust optimization, a multi-interval uncertainty set is constructed on the basis of wind power prediction data, a multi-interval two-stage robust optimization model is established on the basis of the foregoing, and the conservative nature of single-interval robustness is reduced. A nested column and a constraint generation algorithm are used for solving. In the first stage, using minimum net load fluctuation as a goal, planning is carried out on the basis of the prediction data, and in the second stage, considering the uncertainty of wind power, wind power output in a worst-case scenario is searched for, and the policy of the first stage is adjusted, so that the stability of the microgrid is ensured.

The invention provides an electric heating gas energy storage collaborative charge state disturbance suppression optimization method, and relates to the technical field of information, and the method comprises the steps: carrying out the filtering processing of a second-level disturbance signal in the real-time operation data of a power grid through an adaptive Kalman filtering algorithm, so as to improve the signal-to-noise ratio; performing time-frequency domain feature extraction on the filtered disturbance signal by using a wavelet analysis method, and judging the type and severity of the disturbance signal in combination with a preset disturbance mode library; and according to the type and characteristics of the disturbance signal. According to the method, an electric energy storage collaborative optimization control model is constructed by adopting a robust optimization theory, the uncertainty of disturbance signals and electric energy storage characteristics is considered, an optimal power instruction is solved, in the control process, the state of an electric energy storage unit is dynamically evaluated, a strategy is adjusted, online optimization is carried out according to power grid index changes, the control effect is continuously improved, and the control efficiency is improved. Through cooperative optimization control of electric energy storage, second-level disturbance of the power grid is effectively suppressed, and safe and stable operation of the power grid is guaranteed.

The invention discloses a robust optimization method based on D optimum and considering intake and exhaust influences, a medium and a program product, and belongs to the technical field of aerodynamic optimization design of aircrafts. According to the method, a representative small sample set is constructed by introducing a D optimal criterion, optimization coverage of a design space is realized by maximizing an information matrix determinant, and an optimization objective function is formed by a weighted sum of a mean value and a standard deviation of aerodynamic performance so as to balance performance improvement and robustness optimization. An FFD method is adopted to carry out geometric parametric modeling on the aerodynamic configuration and the intake and exhaust system of the aircraft, an uncertainty quantitative agent model is established through a GPCE method, statistical characteristics and gradient information of a target function are analyzed and calculated, iterative updating is carried out through a gradient optimization algorithm, and it is ensured that design constraints and convergence criteria are met. According to the method, the uncertainty influence of geometric parameters and intake and exhaust conditions is fully considered, the aerodynamic performance of the aircraft is improved, and the adaptability and robustness under actual flight conditions are enhanced.

The invention relates to the technical field of power electronics, in particular to a filter equivalent circuit design simulation system suitable for an energy storage converter, which comprises a main circuit topology unit, a control strategy unit, a parameter optimization and design unit, a simulation engine unit and a performance evaluation unit, wherein the main circuit topology unit is used for building a filter equivalent circuit topology of the energy storage converter and configuring passive element parameters of an inductor, a capacitor and a resistor. According to the method, automatic high-robustness optimization of filter parameters is realized through genetic algorithm-particle swarm hybrid optimization, multi-objective collaborative optimization and digital twinning auxiliary optimization, and a'design-optimization-verification-iteration 'closed loop is formed by combining dynamic working condition evaluation, reliability modeling and artificial intelligence auxiliary analysis, so that the method has the advantages of high robustness and high robustness. The harmonic suppression capability, efficiency and long-term operation reliability of the filter are remarkably improved, the design period is shortened, and the development cost is reduced.

The invention relates to the field of power systems and automation thereof. The invention relates to a power distribution network energy storage optimization configuration method considering dynamic uncertainty and multi-target cooperation. The method is characterized by comprising the following steps: 1) constructing a two-stage robust optimization model: constructing the two-stage robust optimization model with a min-max-min structure; in the first stage, the energy storage construction position and capacity are determined with the lowest annual investment cost of energy storage as the target; in the second stage, the system scheduling cost is minimized in the worst new energy output scene; 2) convex relaxation processing of network constraint; 3) implementation of an iterative solution algorithm: based on a KKT principle and a column constraint generation algorithm, decomposing an original problem into a mixed integer linear main problem and a sub-problem; the main problem optimizes an energy storage configuration scheme, and the sub-problems solve a scheduling strategy in the worst wind and light output scene and feed back to the main problem through cut plane constraint; and carrying out iterative calculation until the solutions of the main problem and the sub-problem converge, and obtaining an optimal energy storage configuration scheme. According to the method, more accurate and efficient energy storage planning can be realized.

The invention discloses a digital twin-driven machine tool adaptive control method and system and a machine tool control system, and the machine tool control system comprises a data collection unit, a data transmission unit, a twin model unit, a data processing unit, a control decision unit, an adaptive algorithm unit, a control execution unit, a machine tool execution unit and a feedback monitoring unit. The output ends of the data acquisition unit, the twin model unit and the data processing unit are connected with the input end of the data transmission unit; the output end of the data transmission unit is connected with the input end of the control decision unit; according to the method, the control strategy is adjusted in real time through the self-adaptive control algorithm to cope with the change of system parameters and external disturbance, and the advantages of dynamic response capability, robustness enhancement, performance optimization, manual intervention reduction, nonlinear system processing, safety improvement, complex task implementation and the like are provided. The advantages enable the adaptive control algorithm to become an important control means in many modern engineering applications.

The embodiment of the invention discloses a Beidou precise dynamic positioning method considering a satellite geometric configuration, and relates to the technical field of satellite navigation positioning, and the method comprises the steps: obtaining a satellite geometric configuration composed of satellites corresponding to each observation value of a current epoch, and calculating the weight of each satellite in the satellite geometric configuration; rTK floating point solution calculation is carried out, the weight proportion in the filtering process is adjusted through an IGG-III robust optimization method considering the geometric configuration of the satellite, and an adjusted floating point solution is output; fixing the ambiguity, fixing the partial ambiguity which does not pass the ratio test based on a preset elimination sequence, and outputting an ambiguity fixed solution or an ambiguity floating point solution; and outputting a positioning result of the current epoch. According to the method, the satellite configuration and the error of each satellite are considered during floating point solution calculation, so that the precision of the floating point solution is effectively improved; by introducing an ambiguity subset optimization strategy considering the geometric configuration of the satellite, the ambiguity fixed rate and the precision of a fixed solution are improved.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a 3D representation of a scene. In particular, one of the methods includes obtaining a plurality of training images of a scene; initializing a three-dimensional (3D) representation of the scene, the 3D representation comprising a set of Gaussian distributions; and generating a final 3D representation of the scene, comprising, at each of a plurality of update steps, updating, using rendered images rendered using the set of Gaussians and corresponding training images, the set of Gaussian distributions. As part of the updating, gradient-based pruning, semantically-aware optimization, or both can be performed.

The invention provides an integrated energy system optimal scheduling method based on multiple time scales, which comprises the following steps of: constructing a radix uncertainty set, constructing a day-ahead robust optimization model based on the radix uncertainty set, inputting day-ahead one-hour-level power prediction data of an integrated energy system into the day-ahead robust optimization model, and performing optimal scheduling on the day-ahead robust optimization model. Obtaining a day-ahead scheduling scheme of the integrated energy system; generating an intra-day random scene set, constructing an intra-day stochastic optimization model based on the intra-day stochastic scene set, acquiring intra-day power prediction data of the integrated energy system by taking 15 minutes as a period, inputting the intra-day stochastic optimization model to obtain an intra-day optimization scheduling scheme, and repeating the steps until optimization scheduling of all periods in the day is completed; and generating a real-time random scene set, constructing a real-time random optimization model based on the real-time random scene set, and inputting the real-time 5-minute-level power prediction data of the integrated energy system into the real-time random optimization model to obtain a real-time scheduling scheme of the integrated energy system.

The invention discloses a machine learning-based weld defect intelligent identification system, relates to the technical field of weld defect intelligent identification, solves the technical problems of multi-modal data fusion precision and robustness optimization and defect shielding or overlapping feature deficiency, and provides a machine learning-based weld defect intelligent identification method based on PSNR dynamic parameter adjustment and gradient weight optimization. The limitation of existing fixed parameter denoising is solved, the edge feature retention rate of cracks, air holes and other defects is improved, the omission ratio is reduced, improved DeepLabv3 + segmentation semantic masks are introduced and mapped to point cloud voxels, geometric + semantic double-attribute enhanced point clouds are formed, the defect area positioning accuracy is improved, and through a cross-modal attention module, the defect area positioning accuracy is improved. Weights are dynamically distributed according to illumination intensity and workpiece materials, feature waste caused by fixed weights is avoided, depth mutation and a shielding area with semantic defects are positioned by utilizing depth information of enhanced point cloud, real overlapping and projection overlapping can be effectively distinguished by combining an improved Poisson fusion algorithm, and the overlapping defect recognition accuracy is improved.

The invention belongs to the field of intelligent manufacturing, and particularly relates to an intelligent scheduling method for collaborative manufacturing of tunnel boring machines, in the method, multi-source heterogeneous data including design data, process data, production data, material data and the like are acquired, and a rich data basis is provided for intelligent scheduling. The data fusion method based on the ontology is adopted, and the problems of semantic heterogeneity and structural heterogeneity of multi-source heterogeneous data are effectively solved. By constructing the tunnel boring machine manufacturing field ontology model, semantic association and sharing of data from different sources are realized, and the availability and understandability of the data are improved. An algorithm combining mixed integer programming and deep reinforcement learning is provided for intelligent scheduling of tunnel boring machine collaborative manufacturing. A robust optimization method is introduced to enhance the robustness of the scheduling scheme, and the influence of uncertain factors such as raw material supply and equipment maintenance in the manufacturing process of the tunnel boring machine is considered.

The invention discloses a virtual power plant capacity configuration and regulation operation optimization method, which comprises the following steps: constructing an aggregation model and physically consistent digital twinning, establishing a linearized power distribution network model containing voltage and power flow constraints, and depicting resource efficiency and time delay characteristics; forming a time-varying uncertainty set through quantile calibration and set drift constraint; establishing a capacity-operation joint double-layer optimization model, realizing capacity configuration under the constraint of the whole life cycle cost, and obtaining a rolling scheduling strategy through distributed robust optimization; before issuing, control shielding and formalized constraint are adopted to ensure the safety of the power grid; a multi-variety collaborative quotation is generated on the market side through opportunity constraint and risk measurement; hierarchical adaptive re-optimization is realized based on a trigger criterion, and a twin model is continuously calibrated by using a hardware-in-the-loop experiment; model updating is realized by adopting federated learning and differential privacy; executing degradation control under an abnormal condition, and performing smooth rollback after recovery; and finally, the capacity and operation parameters are evaluated and corrected through performance and service life linkage.

The invention relates to an intelligent production management system and method for high-concentration polyacrylamide fracturing fluid, and the method comprises the steps: obtaining multi-source heterogeneous data through designing a unique algorithm model and an innovative data processing flow: an innovative data collection and fusion strategy, and designing a comprehensive and high-precision data collection system; based on intelligent control algorithm innovation of a fuzzy neural network and MPC, the fuzzy neural network and model prediction control are combined to carry out production process control, a robust optimization method is introduced to enhance the robustness of a control strategy, and meanwhile, a rule-based emergency processing mechanism is established; intelligent mechanism innovation of quality monitoring and optimization is realized, and real-time monitoring and analysis of product quality are realized by establishing a quality prediction model based on combination of support vector machine regression (SVR) and a particle swarm optimization (PSO) algorithm.

The invention relates to the field of control systems, and discloses a vent valve control system with a pressure regulation function for a generator set, which is used for breaking through the limitation of static state, single target and passive response of a traditional vent valve control method. Comprising the following steps: acquiring pressure, flow velocity and valve opening data of a ventilation system in real time, generating a dimensionless parameter set for dynamic calibration, integrating parameter uncertainty disturbance terms to form a hybrid prediction model, solving an optimal valve opening instruction sequence by adopting a multi-target robust optimization algorithm, and designing a layered safety control mechanism. The system safety is guaranteed through dual-threshold monitoring and emergency intervention, redundant sensor data fusion and parameter reverse optimization are introduced, and fault tolerance and model self-adaption are achieved. According to the method, through collaborative innovation of dynamic modeling, robust optimization and intelligent fault tolerance, the control precision, the anti-interference capability, the safety and the reliability of the ventilation system of the generator set are remarkably improved, and the method is suitable for complex and changeable industrial operation scenes.

The invention relates to a computer security protection system based on artificial intelligence, which comprises a data acquisition layer, a data processing and feature engineering layer, an AI model layer, a real-time detection and response layer, a feedback and self-adaption layer and a management visualization layer. According to the method, multiple learning paradigms are fused, the problems of model outdated, attack bypassing and alarm fatigue can be solved, the robustness and the optimization accuracy can be improved, a decision-making basis is generated by adopting tools such as SHAP, the interpretability is improved, man-machine cooperation is achieved, key decisions are reserved for manual auditing, and the safety is improved.

The invention provides a new energy power grid prospective scheduling method and device, and relates to the technical field of electric power system intraday economic scheduling. The method comprises the following steps: firstly, constructing an opportunity constraint optimal power flow model of prospective scheduling, analyzing the influence of new energy uncertainty on opportunity constraint, establishing a constrained Markov decision process of prospective scheduling, and then utilizing a risk evaluator network fitting risk function probability distribution and an actuator network considering extreme scene performance to determine the opportunity constraint optimal power flow model of prospective scheduling. The processing capability of the intelligent agent on a prospective scheduling scene containing a new energy extreme climbing event is enhanced; and finally, the training of the intelligent agent is accelerated by utilizing an imitation learning technology in a power grid prospective scheduling off-line simulation environment. According to the method, the solving speed and the strategy robustness and safety of the double-layer robust optimization model of the look-ahead scheduling can be considered.

The invention relates to the technical field of power grid side large-scale wind and light and energy storage configuration, in particular to a wind and light storage capacity distribution robust optimization configuration method, device and equipment and a medium. Establishing a power grid side wind and light energy storage optimization configuration model considering inertia constraint; uncertain factors in the optimal configuration model are considered, and a probability distribution fuzzy set based on a mixed norm is constructed; establishing a two-stage distribution robust optimization configuration model based on data driving; converting the two-stage distribution robust optimization configuration model into a mixed integer linear programming model by using a linearization technology; and solving the mixed integer linear programming model by using a column and number generation algorithm to obtain an optimal configuration and operation scheme of each device. According to the method, the economical efficiency and load fluctuation can be balanced, and the problems of frequency instability and the like of a large-scale wind and light energy grid-connected power grid are avoided by considering inertia constraint.

The invention provides an uncertain scene-oriented micro-grid and shared energy storage collaborative robust optimization method. The method comprises the following steps of S1, constructing a micro-grid group and shared energy storage collaborative scheduling optimization operation framework; s2, constructing a wind and light uncertain scene set by adopting a scene generation technology; s3, constructing a load uncertainty scene set based on data-driven K-means clustering; s4, the activation probability of the load disturbance boundary is stably estimated based on the Wasserstein distance; s5, constructing a microgrid group and shared energy storage two-stage robust scheduling optimization model; step S6: carrying out dual transformation and Camp; solving the two-stage robust optimization model through a CG algorithm; step S7, designing an improved Shapley value method income allocation mechanism based on the network topology sensitive model; through load scene construction of data driving and clustering analysis, wind and light scene generation and clustering reduction technologies and in combination with collaborative optimization scheduling of the micro-grid group and shared energy storage, the renewable energy utilization efficiency can be improved, the intraday operation cost can be reduced, and collaborative development of energy storage and new energy can be assisted.

The invention relates to a power capacity evaluation method based on a dynamic confidence interval and double-layer optimization, which solves the problems of inappropriate single-side modeling, static evaluation, source-load coupling modeling data processing mode and the like in the prior art, and adopts the technical scheme that a prediction error of wind power output is obtained based on historical data and a prediction value of the wind power output; based on the prediction error, determining an influence parameter generating the error; creating a power supply side model based on the prediction error and the influence parameter; obtaining a demand response sensitivity coefficient and a time-varying adjustment parameter based on the historical load data, the demand response execution record and the external influence factor; creating a load side model based on the demand response sensitivity coefficient and the time-varying adjustment parameter; creating a source-load joint scene model based on the power supply side model and the load side model; optimizing the source-load joint scene model through a double-layer robust optimization model; an assessment risk probability index and a capacity vacancy expectation are determined based on the optimized source-load joint scenario. The method has the advantages that a source-load joint scene model is created, and the coverage range and precision of an evaluation result in time and space dimensions can be remarkably improved; meanwhile, by means of a dynamic confidence interval adjustment mechanism and a double-layer optimization strategy, effective balance between the electric power market economy and the operation safety is achieved.

The invention relates to the technical field of aero-engines, and discloses a dry friction damper multi-target robust optimization method considering multi-source uncertainty, and the method specifically comprises the following steps: 1, quantifying the uncertainty parameters of a dry friction damper; step 2, performing hyper-Latin cube (LHS) sampling on value ranges of geometric parameters and contact parameters caused by abrasion to obtain parameter combination sampling capable of expressing global characteristics in a global sample space; 3, calculating a corresponding nonlinear dynamic response by using a multi-dimensional harmonic balance method (MHBM) by using the nonlinear parameters obtained by sampling; by means of the mode of synchronously optimizing key design parameters of the friction-damping system, the optimal robustness design of the friction-damping system is allowed to be carried out under parameter uncertainty with the purpose of vibration reduction, the damping effect of the dry friction damper is improved, the Pareto optimal solution of the neural network is solved through the genetic algorithm, and the optimal stability of the friction-damping system is improved. Therefore, the multi-objective optimization is realized and the calculation efficiency is improved.

The invention discloses a hydrogen fuel cell hybrid electric vehicle energy management method based on robust optimization, and the method comprises the following steps: building a vehicle model: building a longitudinal dynamic model of a hydrogen fuel cell hybrid electric vehicle, determining a vehicle demand power model, and building an evaluation function of instantaneous hydrogen consumption, constructing an output power model of the hydrogen fuel cell and the capacitor; performing uncertainty analysis on the hydrogen fuel cell hybrid electric vehicle, determining uncertain parameters in the vehicle model, and constructing an uncertainty set of the uncertain parameters; the method comprises the following steps: constructing an sLSTM vehicle speed prediction model through vehicle historical speed data, predicting a future driving speed, analyzing the uncertainty of the prediction model, and outputting a prediction speed error in a current prediction time domain; and according to the optimization target, constructing a target function for minimizing the hydrogen consumption, solving the target function by applying a robust optimization algorithm, and outputting a first control result of the optimal control sequence.

The invention relates to the technical field, in particular to a multi-mode classroom teaching optimization system integrating space intelligent management and somatosensory annotation. According to the technical scheme, the system comprises a space intelligent management module which dynamically adjusts the environment through a genetic algorithm to enable the comfort level index to be optimal, a somatosensory annotation interaction module which is used for recognizing gesture tracks of teachers and students and automatically controlling teaching equipment according to the intention of a user, and a multi-modal data fusion module which is used for carrying out data fusion on the teaching equipment according to the intent of the user. The module improves the accuracy of data analysis and the robustness of system interaction, and the optimization scheduling and resource allocation module optimizes teaching resource allocation based on a Lagrange multiplier method and intelligently schedules resources. Through intelligent environment management, multi-modal data fusion, high-precision gesture recognition and an optimization algorithm, the intelligent level of classroom teaching is improved, and more advanced technical support is provided for future intelligent education.