449 results about "Optimal decision" patented technology

The present invention relates to an integrated dispatch decision-making system and method for a hub supporting multi-modal transportation coordination, and a device. The system comprises: a multi-modal hub passenger flow data fusion and analysis module, which is used for arranging and classifying collected multi-modal hub passenger flow data, performing preprocessing, performing conversion and mapping to obtain normalized passenger flow data, and finally extracting a data fusion feature, and using a normalized transport passenger flow model to perform multi-dimensional data fusion and analysis computation; a hub transportation coordination decision-making module, which is used for performing transport capacity and volume assessment on the basis of the fusion and analysis of the multi-modal passenger flow data, and using an optimal decision-making model to compute an optimal strategy result; and a hub coordination dispatch and command application module, which is used for providing an intelligent command assistance function to a rail transit dispatch and command personnel on the basis of information access of hub-line-network, and the normalized transport passenger flow model. Compared with the prior art, the present invention has the advantages of improving the connectivity and coordinated dispatch of multi-modal transportation in a complex hub scenario, etc.

A method and device for optimizing comprehensive production of an oil reservoir. The method comprises: on the basis of historical production data, performing regional level recognition, single-well level recognition, and production optimization model recognition, and updating oil reservoir dynamic recognition; on the basis of the historical production data, using the following steps to perform cyclic historical fitting on model parameters of a plurality of production optimization models: performing historical fitting on the model parameters, and performing well-to-well communication relationship calibration on the plurality of production optimization models; determining, on the basis of the updated oil reservoir dynamic recognition, a constraint condition corresponding to each production optimization model, solving an objective function corresponding to each production optimization model, obtaining an optimal decision variable corresponding to each production optimization model, and obtaining a development regulation scheme corresponding to each production optimization model; obtaining a comprehensive development regulation scheme of an oil reservoir; and updating the oil reservoir dynamic recognition on the basis of a regulation effect obtained by real-time monitoring of a development operation.

The invention discloses a multi-agent collaborative decision-making system and method for neural symbol fusion, and relates to the technical field of artificial intelligence, and the system comprises a neural symbol fusion engine which constructs a knowledge double-layer representation architecture, and achieves the organic fusion of symbol reasoning accuracy and neural learning adaptability; the intelligent agent coordination optimizer quantifies the intelligent agent difference through cognitive state mapping, constructs a consensus feasible region, carries out hybrid verification and constraint optimization, and selects an optimal decision scheme; and the adaptive interpretation system constructs a decision evidence chain and realizes continuous optimization of system parameters through feedback learning. The technical challenges of symbol reasoning and neural learning fusion, multi-agent cognitive difference coordination, decision reliability and interpretability and the like are effectively solved, and the method is suitable for complex decision scenes of medical treatment, finance, intelligent manufacturing and the like.

The invention discloses an oil reservoir comprehensive production optimization method and device, and the method comprises the steps: carrying out the region level recognition, single well level recognition and production optimization model recognition according to the production historical data, and updating the oil reservoir dynamic recognition; based on production historical data, performing cyclic historical fitting on model parameters of the various production optimization models by adopting the following steps: performing historical fitting on the model parameters, and performing inter-well communication relation calibration on the various production optimization models; determining a constraint condition corresponding to each production optimization model according to the updated reservoir dynamic cognition, solving a target function corresponding to each production optimization model, obtaining an optimal decision variable corresponding to each production optimization model, and obtaining a development regulation scheme corresponding to each production optimization model; an oil reservoir comprehensive development regulation and control scheme is obtained; and according to the regulation and control effect obtained through real-time monitoring of the development operation, oil reservoir dynamic understanding is updated. According to the invention, the efficiency and precision of oil reservoir production optimization can be improved.

The invention relates to the technical field of electric power asset management, and discloses an electric power equipment full life cycle management system based on a digital twinning technology, and the system comprises a causal knowledge graph construction unit, a strategy rule learning unit, a decision option value quantification unit, a combined decision optimization unit, and a maintenance decision voucher management unit. The method comprises the following steps: constructing a knowledge graph containing a causal relationship among equipment, environment and management activities; carrying out anti-fact deduction based on the atlas to dynamically learn strategy rules; quantizing the value of the potential maintenance decision by adopting a physical option model; performing combination optimization under resource constraints of budget, spare parts and the like to generate an optimal decision combination; and finally, generating a standardized maintenance decision voucher for the decision in the combination. According to the method, decision-making flexibility and environment uncertainty can be quantified into specific economic values, future-oriented and globally optimal resource allocation is realized, and a decision-making process and a decision-making result are solidified into traceable and manageable digital assets.

The invention relates to a rail transit multi-scene intelligent inspection device, method and equipment based on reinforcement learning, and the device comprises a sensing and state characterization module which is responsible for collecting multi-modal information from a rail transit inspection environment, and processing and fusing the multi-modal information into a structured environment state; the reinforcement learning decision-making module is used for selecting and outputting a current optimal decision-making action by utilizing the trained strategy network and value network based on the current structured environment state and the inspection task; the action execution and control module is used for converting the decision action into a bottom layer physical instruction sequence and controlling an intelligent agent to execute; the reinforcement learning training and optimization module is used for carrying out iterative optimization on the strategy network and the value network according to the inspection task and empirical data, and updating model parameters; and the task planning and scheduling module is used for defining inspection tasks and task allocation. Compared with the prior art, the method has the advantages of improving inspection efficiency and quality, realizing risk active early warning and the like.

The invention discloses a photovoltaic cluster flexible grid-connected regulation and control method and system, and the method comprises the steps: collecting the state data of a power distribution network node and a photovoltaic cluster, carrying out the data cleaning and timestamp alignment preprocessing, and obtaining a system state comprehensive data flow; constructing a multi-target optimization model containing power grid friendliness and photovoltaic power generation benefits based on the system state comprehensive data flow, and solving by utilizing a reinforcement learning algorithm to obtain a control strategy of a photovoltaic inverter and an energy storage unit; according to the control strategy, active power output and reactive power output of the photovoltaic inverter and charging and discharging power of the energy storage unit are controlled in a coordinated mode, and flexible grid connection of the photovoltaic cluster is achieved; and based on system response data, optimizing a control strategy by adopting an optimal decision tree algorithm, updating the reinforcement learning model, and establishing an optimized experience knowledge base. According to the invention, efficient flexible grid-connected regulation and control of the photovoltaic cluster are realized, and the problems of voltage out-of-limit, reverse power transmission and the like when large-scale photovoltaic access to the power distribution network are solved.

The invention relates to the technical field of mine disaster monitoring and prevention and control, in particular to a deep mine disaster big data analysis and prevention and control decision-making system. Comprising a data pollution quantification unit used for collecting a multi-source heterogeneous data stream and reference prediction data and carrying out noise pollution quantitative analysis to obtain a data pollution index; the toughness entropy evaluation unit is used for collecting physical risk factors, resolving the basic safety entropy, correcting the basic safety entropy according to the data pollution index to obtain the toughness entropy of the mine system, and judging the toughness entropy of the mine system to obtain a risk level; the anti-fragility decision-making unit is used for dynamically adjusting a risk aversion coefficient according to the risk level, and performing utility function solution on a preset decision to be selected to obtain an optimal decision; and the closed-loop scheduling control unit is used for matching a preset scheduling instruction set according to the optimal decision and the risk level, and generating and executing a hierarchical control instruction. The problem that a traditional risk assessment model fails due to serious pollution of deep mine monitoring data is solved.

The embodiment of the invention discloses a collaborative optimization method and device for the power quality of a high-permeability transformer area. The method comprises the following steps: acquiring three-phase voltage, three-phase current and environment temperature data; the voltage deviation, the three-phase unbalance degree, the total harmonic distortion rate and the reactive vacancy of the high-permeability transformer area are calculated based on the three-phase voltage and the three-phase current; taking voltage deviation as a primary optimization target, considering optimization of three-phase unbalance degree, total harmonic distortion rate and reactive vacancy, constructing a multi-target optimization function, and carrying out dynamic constraint on an optimization process based on environment temperature data; solving the multi-objective optimization function by adopting an optimization algorithm to obtain a group of optimal decision variables, and converting the decision variables into corresponding control instruction sets; and the control instruction set is issued to the on-load voltage regulating transformer, the reactive compensation equipment and the power electronic compensation module for execution. Therefore, the problems of voltage out-of-limit, harmonic pollution and imbalance are effectively solved through a multi-target cooperation and temperature self-adaption mechanism.

The invention relates to the technical field of agricultural facility management, and discloses an AI-based agricultural scene omnibearing perception system and an implementation method, and the system comprises an order analysis module which is used for receiving order data and generating decision variables based on the order data; the collaborative optimization module is used for obtaining an optimization scheme based on a multi-objective function; the resource arrangement module is used for calculating a resource configuration scheme based on multi-target scheduling optimization; the sensing correction module is used for updating model parameters of the prediction model; the settlement loop module is used for performing delivery settlement based on the actual execution data; according to the invention, through adoption of a collaborative architecture, full-chain intelligent decision-making from market order analysis to agricultural product delivery is realized, through full-chain optimization and accurate decision-making, the net income of agricultural production is improved, and the production cost is reduced; through multi-objective function construction and a robust optimization algorithm, an optimal decision scheme can be found under complex constraint conditions, and the overall benefit of agricultural production is significantly improved.

The invention provides a self-adaptive source-load-storage coordinated optimization control method and system, and the method comprises the steps: S1, collecting multi-element load data generated during the operation of a microgrid, and forming a time series data set; s2, key variable characteristics which have the largest influence on the micro-grid load operation state in the micro-grid load requirements are recognized; s3, determining and constructing a source-load-storage cooperative control model based on the key variable characteristics, and determining an optimal decision variable value meeting a real-time load demand; and S4, making a regulation and control strategy according to the optimal decision variable value, and realizing optimal operation of the micro-grid. According to the method, the load demand of the power system is predicted based on the trained recurrent neural network model, the most critical characteristic variable is identified, the time sequence data which can most represent the power grid load operation state during the microgrid operation period is extracted and analyzed, and the unnecessary calculation amount is reduced by focusing on the most critical characteristic variable, so that the power grid load operation state is optimized. Therefore, the whole analysis process is more efficient and accurate.

The invention relates to the technical field of energy storage battery management, and discloses a capacity recovery method based on the full life cycle of a lithium ion battery. According to the method, the uncertainty of power grid demand and environment temperature is modeled through a scene tree generation algorithm, a low-sensitivity temperature region is identified through sensitivity analysis to generate a robust temperature parameter candidate set, and a recovery time window is identified based on a battery aging characteristic prediction sequence. Constructing a dual-objective optimization problem of power grid auxiliary service income and battery full life cycle value, solving a conditional optimal decision scheme under each scene through a dynamic programming algorithm, selecting a decision scheme with a maximum worst condition target value by using a robust optimization criterion, extracting an execution instruction, outputting the execution instruction to a control system, and performing power grid auxiliary service income and battery full life cycle value optimization. And the decision scheme is adjusted in real time through a rolling optimization mechanism. According to the method, the robustness of balancing economic benefits and battery health in an uncertain environment by a capacity recovery decision is improved.

The invention discloses an intelligent manufacturing real-time decision-making method and system based on digital twinning, and relates to the technical field of intelligent manufacturing. Comprising the following steps: S1, collecting multi-source operation data in a physical production system in real time; s2, dynamically constructing and updating a digital twinborn model of the physical production system based on the multi-source operation data acquired in real time; and S3, based on the digital twin model, monitoring the operation state of the physical production system in real time. According to the method, the multi-source operation data of the physical production system are collected in real time, and the digital twin model is dynamically updated, so that no lag deviation between the model and the physical system is ensured; when a dynamic disturbance event is identified, event-driven real-time simulation and predictive analysis are started immediately, delay caused by traditional batch processing is avoided, and disturbance influence can be accurately evaluated in combination with historical data and expert experience; and then an optimal decision scheme is generated and screened in real time through a multi-objective optimization algorithm.

The invention discloses a vector evaluation gradient multi-objective optimization algorithm for data center cooling system decision making. The overall thought comprises the steps that multi-source sensor data related to a cooling system is obtained according to data center machine room operation logic; performing corresponding preprocessing on data acquired by the sensor to obtain an overall sample; designing a vector evaluation gradient multi-objective optimization algorithm oriented to a data center cooling system decision; the optimization algorithm comprises the definition of a multi-objective optimization problem, uniformly distributed initial populations, population division based on vector evaluation and gradient updating. The optimal decision of the air supply amount and the air supply temperature of the cooling equipment in the cooling system is mainly obtained through an optimization algorithm, and optimal decision parameters are sent to the cooling equipment so as to achieve optimal control over the cooling equipment of the data center. The algorithm provided by the invention ensures that the decision strategy of the cooling equipment can be reasonably and quickly given, so that the appropriate temperature of the machine room and the energy conservation of the cooling equipment are simultaneously realized to meet the requirement of efficient operation of the data center. The energy consumption of the data center can be reduced, and the method is of great significance in promoting green and sustainable development of the data center.

The invention relates to the technical field of intelligent decision, and discloses an intelligent decision support method based on reinforcement learning. The method comprises the following steps: acquiring a historical interaction data set containing a decision action sequence, an environment state sequence and a corresponding instant reward signal in a target decision scene; the data set is input into a state feature extraction network for spatial-temporal feature coding, and a state feature vector set with time sequence relevance is generated; constructing a decision action space mapping table based on the set, wherein candidate decision actions and expected accumulated rewards corresponding to the state feature vectors are recorded in the table; dynamically updating the mapping table by adopting a strategy gradient algorithm to generate an optimized strategy gradient parameter set; and constructing a real-time decision support engine according to the parameter set, wherein the engine can respond to the environment state change and output the optimal decision action. The method adapts to a dynamic decision-making scene, and assists in efficiently outputting decision-making results meeting requirements.

The invention discloses an injection molding production optimization method based on multi-agent cooperation, and the method comprises the following steps: decomposing a plurality of targets in an injection molding production process, and constructing a layered multi-agent structure; collecting data in the injection molding production process in real time, and constructing a global expert behavior track and a local expert behavior track; based on the global target and each local target, obtaining initial parameters of a global reward function and a local reward function; adopting an inverse reinforcement learning method to obtain an optimal global reward function and an optimal local reward function; obtaining an optimal decision strategy of each agent through a hierarchical collaboration mechanism by each hierarchical agent; when detecting that a conflict exists between the targets, dynamically adjusting the reward weight of the reward function by adopting a conflict coordination mechanism; and continuously collecting new expert behavior data, and updating the optimal decision strategy of each agent. According to the invention, a layered multi-agent inverse reinforcement learning method is adopted, and multi-target dynamic collaborative optimization of injection molding production is realized.

The invention provides a generative intelligent optimization method and device, and relates to the technical field of generative artificial intelligence optimization computation.The method comprises the steps that a mathematical model containing decision variables, an objective function and constraint conditions is established for a target optimization problem, and scene parameters are pre-calculated through a traditional optimization algorithm to obtain a theoretical optimal solution; constructing a training data set containing scene parameters and a theoretical optimal solution; training the data set through a conditional generation type artificial intelligence model, minimizing the difference between a generation decision and a theoretical optimal solution, and establishing a mapping relation from scene parameters to an optimal decision, so that the mapping relation implicitly learns a constraint condition satisfaction mode; current scene parameters are collected in real time and input into the trained model, a near-optimal decision scheme is generated through reverse denoising or hidden variable decoding, and the near-optimal decision scheme is applied to real-time scenes such as industrial control. According to the method, a high-quality solution close to theoretical optimum is realized, the online solution time consumption is remarkably reduced, and the real-time requirement is met.

The invention discloses a gas storage integrated operation model construction method and system based on digital and intelligent twinning, and relates to the field of gas storage integrated operation model construction, and the method comprises the steps: constructing a single well IPR and VLP curve; a mode of combining forward pressure regulation and reverse pressure regulation is adopted, a pressure value set of all nodes after pressure stabilization is output, and a preliminary pressure steady-state scheme is formed; an economic value evaluation index function is constructed, and the total daily operation cost of the system under the given gas production rate is evaluated; a comprehensive optimization model based on a neural network is constructed, and injection-production quantity reference values of all wells and target values of all nodes are output; and verifying the stability of the comprehensive optimization model based on the neural network according to the output injection-production quantity reference values of all wells and the target values of all the nodes. The method has the advantages that the physical law of gas storage injection and production, the system pressure balance and the economical efficiency target are deeply fused, and a fast and reliable closed loop from pressure steady-state optimization to an economical optimal decision is achieved.

The invention relates to the technical field of pilotless automobiles, in particular to a local obstacle avoidance decision-making method and system based on a dynamic risk map and a real-time game, and the method comprises the steps: obtaining the state information of an automobile and the sensing data of the surrounding environment through a multi-mode sensor; predicting a future trajectory based on the real-time state information of the dynamic obstacle; constructing a rasterized dynamic risk map which takes the vehicle as the center and is updated in real time, and distributing a comprehensive risk value for each grid; the interaction modeling of the self-vehicle and the dynamic obstacle is a non-cooperative game model, the dynamic risk map and the final prediction trajectory of the dynamic obstacle are used as input, the Nash equilibrium of the non-cooperative game model is solved in real time, and the optimal decision strategy of the self-vehicle is output; and converting the optimal decision strategy into a travelable trajectory conforming to vehicle dynamics constraints, and outputting the travelable trajectory to a vehicle control module to execute local obstacle avoidance. The method overcomes the defects of fixed parameters and poor adaptability of a traditional method, and gives consideration to the safety and high efficiency in different scenes.

The invention relates to the technical field of intelligent construction, and discloses a bent cap intelligent jacking construction progress intelligent prediction method, which comprises the following steps: collecting multi-source real-time data such as equipment, structure, personnel and environment in a construction process, and calculating a construction entropy for measuring system uncertainty; establishing a causal state evolution model, and predicting the evolution trend of the construction entropy in a future time period; and when it is predicted that a construction progress disturbance risk exists, performing anti-fact deduction based on the model or the dynamically trimmed sparse model thereof, and generating and recommending an optimal adaptive correction strategy. According to the method, the overall uncertainty of the construction system is quantified into the construction entropy, and the prospective deduction capability of the causal state evolution model is combined, so that the conversion from the risk passive response depending on experience to the data-driven beforehand active early warning and optimal decision recommendation is realized; and improvement of scientificity, foresight and timeliness of construction risk management is facilitated.

The invention relates to the field of data decision analysis, in particular to a unit internal control management decision method and system based on multi-modal data analysis. The method comprises the following steps: collecting heterogeneous original data streams of a plurality of heterogeneous data sources; performing space-time dimension entropy change differential analysis and multi-dimensional entropy change characteristic mining, performing multi-level semantic analysis on the heterogeneous original data stream, performing layer-by-layer abnormal mode semantic deconstruction, and constructing a precise semantic feature portrait of each abnormal mode; performing multi-target coordination request analysis and dynamic target priority evaluation on the cognitive entropy change reference characteristic spectrum to obtain a coordination request priority; and carrying out maximum decision capacity calculation on the cognitive entropy change reference feature map, and carrying out dynamic optimal decision boundary calculation based on the coordination request priority and the accurate semantic feature portrait so as to generate a dynamic tradeoff constraint range. Through multi-target collaborative decision analysis, the decision target conflict is smoothed, and the decision demand execution efficiency of the user is improved.

The invention discloses a dual-channel fusion cancer drug response prediction method, and belongs to the technical field of biological information. The method aims at solving the problems that an existing prediction method is only limited to intra-modal feature extraction, and a complex nonlinear cooperative relation between a drug and cancer cells is difficult to capture. Multi-omics data, drug molecular structure information and cancer cell line drug reaction data are integrated from databases such as CCLE, GDSC and PubChem, and unified input features are formed through standardization and feature construction. Introducing a hierarchical double-attention conversion network into the first channel to carry out characterization learning on the multi-modal features of the drug and the cell line, and constructing a high-order attention neighbor interaction graph convolutional network in the second channel to capture graph structure information of cancer drug response. And carrying out adaptive weighting on output results of the two channels by using a PPO-based fusion module so as to realize a dynamic optimal decision. And finally, generating a drug sensitivity prediction result of the cancer cell line through a classification predictor.

The invention discloses a multi-modal-based computing power scheduling method and device, a server and a storage medium, and relates to the technical field of computing power allocation, and the method comprises the steps: receiving a multi-modal computing task, analyzing a task description file, and obtaining a current available computing resource snapshot of each computing node; according to a preset static performance library, a decision variable set is constructed, a multi-objective optimization task is established, an improved NSGA-III algorithm is used for solving the multi-objective optimization task, an optimal decision variable set is formed, and a computing power scheduling decision is made according to a preset service strategy. An optimal decision variable set is generated through an improved algorithm to improve the accuracy of a computing power scheduling decision, and meanwhile, a static performance library and a current resource utilization rate of a computing node are introduced for performance correction; through multi-objective collaborative optimization, the service quality can be ensured, the energy consumption can be reduced, the utilization efficiency of computing resources is improved, and conflicting indexes such as the service quality QoS and the energy consumption can be effectively balanced.

The invention provides a product full life cycle management method based on digital twinning, a medium and a product full life cycle management system based on digital twinning, and belongs to the technical field of digital twinning. Based on a super-sparse pre-training model of a liquid neural network architecture, in combination with spectral clustering and an attention mechanism, precise prediction of product performance degradation is realized, and intelligent maintenance suggestion generation and dynamic optimization of operation parameters are realized through a Hilbert matrix state evaluation and decision adjustment mechanism. The balance between calculation efficiency and prediction precision is realized by using a multi-precision simulation analysis and incremental updating technology, a backtracking decision matrix is established to support the optimal decision of product decommissioning and recovery, and the technical problem that the real-time perception and dynamic prediction optimization management of the full-life-cycle multi-dimensional state of the product cannot be realized in the prior art is solved.

The invention relates to the field of air traffic management, in particular to a flight conflict resolution method considering uncertainty of man-machine interaction and air-ground communication, which comprises the following steps: acquiring conflict resolution condition information when detecting that an aircraft track has a potential conflict, establishing a controller workload constraint based on controller task occupation time length information, and determining whether the conflict resolution condition information is a conflict resolution condition; establishing a safety interval constraint based on the aircraft flight state, and establishing an aircraft performance constraint based on the aircraft performance parameters; establishing a release track search grid, determining a decision variable, determining a target function based on a robust index, and searching in a limited search area in the release track search grid based on the constraint condition and the target function to obtain an optimal decision variable; generating a conflict resolution track and a controller workload timeline according to the optimal decision variable and conflict resolution condition information; according to the method, the robustness and the safety of the planned release trajectory can be improved.

The invention belongs to the technical field of robot safety control, and discloses a safe motion planning method for a dual-redundancy mechanical arm. And a linear mapping relation between the joint space speed of the redundant mechanical arm and the speed of an end effector is established. And establishing an end effector speed constraint, a safety distance constraint and a joint amplitude limiting constraint, and fusing a differential kinematics model and the three constraints to establish a multi-target combined constraint model. The optimal decision variable meets the Carlo demand-Kuhn-Tuck condition, the original expression form of the Carlo demand-Kuhn-Tuck condition is defined, and a nonlinear complementary function is introduced to reconstruct the Carlo demand-Kuhn-Tuck condition. Based on an improved Carlo demand-Kuhn-Tuck condition, a vector error function is defined, and an adaptive return-to-zero neural network is designed; and performing iterative solution on the vector error function through an adaptive return-to-zero neural network to obtain an optimal decision variable of the multi-target joint constraint model, taking the optimal decision variable as a mechanical arm joint motion control instruction, and outputting the mechanical arm joint motion control instruction to complete mechanical arm motion planning.

The invention relates to a digital twin-driven distributed warehousing system and method, and belongs to the technical field of intelligent logistics. The system comprises a sensing execution layer, a network transmission layer, an edge computing layer, a cloud platform layer and an application service layer. The method comprises the following steps: acquiring storage physical entity data through a perception execution layer; the data is transmitted to the edge computing layer and the cloud platform layer through the network transmission layer; in the cloud platform layer, a high-fidelity virtual warehouse is constructed and synchronized through a digital twin engine; using a multi-agent reinforcement learning algorithm to perform centralized training in a digital twin environment to generate a distributed cooperative scheduling strategy; and issuing the strategy to an edge computing layer and a sensing execution layer, and driving an autonomous mobile robot cluster to execute a cooperative task. According to the invention, step-type improvement of the warehousing system in flexibility, intelligence and cooperative capability is realized, and the problems of rigid bottleneck, non-optimal decision and low cooperative efficiency of a traditional system are solved.

The invention discloses a virtual power plant multi-temporal-spatial-scale regulation and control system based on deep reinforcement learning, and relates to the field of intelligent power regulation and control, and the system comprises an acquisition construction module, a definition constraint module, a design optimization module, a strategy training module and a regulation and control execution module. According to the method, by introducing a multi-temporal-spatial feature coding structure combining the graph neural network and the time sequence convolutional network, the spatial coupling relationship and the time dynamic evolution features between distributed energy nodes can be captured at the same time, so that the global perception and prediction capability of a regulation and control strategy to a complex power system is improved. And a deep reinforcement learning algorithm is adopted to continuously train the strategy network, so that the regulation and control system can realize adaptive adjustment and dynamic optimal decision in uncertain environments such as electricity price fluctuation, load sudden change, meteorological disturbance and the like.

The invention discloses an urban multi-objective collaborative optimization method and system based on deep reinforcement learning, and the method comprises the steps: providing or constructing a building performance agent model, taking a group of urban form design variables as input, and taking a building performance evaluation index as output; constructing a multi-objective collaborative optimization problem into a Markov decision process, and defining the action, state and reward function of an intelligent agent; and training the intelligent agent by adopting a deep reinforcement learning algorithm, and generating an urban form design scheme by applying the optimal decision strategy after training convergence. According to the method, a static and high-dimensional optimization problem is converted into a dynamic decision learning process, and the multi-target and nonlinear complex optimization problem in urban form design is efficiently solved by utilizing the autonomous exploration and optimization capability of deep reinforcement learning; the optimal solution set considering energy efficiency, renewable energy utilization and human settlement environment quality can be quickly generated, and automatic and intelligent decision support is provided for planning and design of sustainable urban communities.

A computer-implemented method for optimizing a video encoder. The method includes the step of defining a plurality of CUs of the video encoder that correspond to a plurality of stages. The plurality of stages includes at least a first stage, and a second stage immediately after the first stage. The method further includes the steps of providing a decision space of encoding parameters of the video encoder, and defining, for the second stage, a subspace being a subset of the decision space. The subspace contains b1 optimal decision paths from the first stage to the second stage, wherein b1 is defined as a beam size for the first stage. The b1 optimal decision paths are the b1 decision paths that have lowest accumulated cost among all decision paths from the first stage to the second stage.