2049 results about "State space" patented technology

The invention relates to the technical field of numerical control machine tools, and discloses a five-axis high-precision numerical control machine tool control method, device and equipment and a storage medium, and the method comprises the steps: collecting temperature data and displacement data of a five-axis high-precision numerical control machine tool, and carrying out state space modeling to obtain a machine tool motion state model; stress data and thermal error influence factors of the feed shaft are calculated; feeding shaft stress data and the thermal error influence factors are input into the compensation model, real-time compensation parameters are calculated, compensation control is conducted on the machine tool feeding shaft, and a compensation control result is obtained; according to the compensation control result, an initial machining track optimization model is constructed, and optimized machining parameters are generated; performing segmented machining control based on the optimized machining parameters to obtain real-time machining data; the initial machining track optimization model is optimized based on the real-time machining data, a target machining track optimization model is obtained, and the dynamic response performance and the control precision of the five-axis high-precision numerical control machine tool are improved through the method.

The present invention relates to the field of resource scheduling, and in particular to a resource scheduling method and apparatus based on computing cluster twin modeling. The method mainly comprises: constructing a time sequence matrix on the basis of available resource information of a computing cluster, and constructing a digital twin model for resource state on the basis of the time sequence matrix; extracting a time sequence from the digital twin model for resource state to construct a state space, solving a probability transfer matrix of the state space, and constructing a digital twin model for resource probability transfer on the basis of the probability transfer matrix; for the resource requirements, constructing a feasible solution set for resource allocation on the basis of the digital twin model for resource probability transfer, obtaining the feasible solution set for resource allocation on the basis of the feasible solution set, and forming a scheduling policy library on the basis of the feasible solution set; and receiving resource requests in parallel, extracting a corresponding feasible solution from the scheduling policy library, and allocating resources to each resource request by taking the feasible solution as an allocation policy. The present invention can realize unified quantitative description of real-time states of various resources, and can also realize dynamic intelligent optimal scheduling of the resources.

The invention discloses a drilling key parameter automatic regulation and control method based on an improved DDPG algorithm. The method comprises the following steps: collecting and preprocessing drilling data, and constructing a complete data set for training; a drilling speed (ROP) time sequence prediction model is constructed and trained on the basis of a self-attention mechanism (self-attention); building a simulation environment by using the trained ROP prediction model, and defining a state space, an action space and a multi-target reward function of a reinforcement learning DDPG algorithm model; a deep deterministic strategy gradient algorithm (DDPG) is adopted to train a reinforcement learning model, through an Actor-Critic network structure, an intelligent agent learns an optimal bit pressure (WOB) and rotating speed (RPM) regulation strategy in a simulation environment, and dynamic optimization of the drilling speed (ROP) is achieved. According to the method, through automatic and intelligent parameter regulation and control, the efficiency and stability of the drilling process are improved, the drill bit wear rate and the operation cost are reduced, and the method has good adaptability and multi-target optimization capacity and is suitable for drilling operation under the complex stratum condition.

The invention provides a servo hydraulic system optimization control method and system for a press machine test platform, and relates to the technical field of press machines, and the method comprises the steps: building a state space expression and building a mathematical model of a servo hydraulic system based on dynamic characteristics by deducing mathematical expressions of all components of the system; building a predictive control algorithm based on the mathematical model, solving a quadratic programming problem by adopting a rolling optimization strategy through building a state space predictive model and determining a multi-objective optimization function to obtain an optimal control sequence, and taking a first element of the optimal control sequence as control input at the current moment; feedforward compensation and pressure feedback compensation are introduced, the feedforward compensation constructs a compensation model based on an inverse system of a press transfer function and pre-corrects a control signal, and the pressure feedback compensation designs a digital filter based on a pressure error, fuses a feedforward compensation signal with a pressure feedback compensation signal and superposes the fused signal with the control signal; and obtaining a final control signal.

The invention discloses a computing power scheduling method and system based on dynamic load prediction and resource priority ranking. The computing power scheduling method comprises the following steps: collecting historical load data, task submission data and resource state data of each node in a computing power cluster; on the basis of the preprocessed multi-dimensional load feature data set, constructing an improved hybrid prediction model, optimizing model parameters through training, and predicting the load change trend of each computing power node in a future preset time period by using the trained model to obtain a node load prediction result; extracting a service level protocol parameter, a resource demand type and historical execution efficiency data of a to-be-scheduled task, and establishing a multi-dimensional resource priority evaluation index system; according to the computing power scheduling method, the problems of low resource utilization rate and high task response delay caused by low load prediction precision and mismatching of resource allocation and task priority in a traditional computing power scheduling method are solved, and the overall operation efficiency and service quality of a computing power cluster are improved.

The invention discloses an integrated energy system economic dispatching model method based on deep reinforcement learning, and the method comprises the steps: constructing a park equipment environment model, and carrying out the mathematical description of a park integrated energy system low-carbon economic dispatching problem through taking economy and carbon emission as targets; secondly, the scheduling problem is expressed as a Markov decision process framework, and a state space, an action space and a reward mechanism of the system are reasonably designed; then, a near-end strategy optimization algorithm is adopted to carry out low-carbon economic dispatching decision making, and an action noise dynamic updating strategy is provided to gradually improve the exploration capability and learning capability of a network model; according to the method, the action space, the state space, the reward mechanism, the neural network structure and the like of the intelligent agent are designed in detail, the minimization of the system operation cost is considered on the basis, and a deep reinforcement learning method is adopted for scheduling simulation. The method provided by the invention can perform optimal scheduling according to the load demand of the system, and has good scheduling performance and self-learning ability.

The invention discloses an intelligent digital operation and maintenance management system and method for a charging pile, and relates to the technical field of new energy infrastructure operation and maintenance, and the system comprises an information obtaining module which determines a delay information circulation channel, obtains real-time discussion content, processes a discussion text through employing a word segmentation technology, and carries out the operation and maintenance of the charging pile. If the confidence coefficient of the semantic feature vector is higher than a preset threshold value, the semantic feature vector is judged to be a specific question type through state space definition, classified question labels are obtained, the historical matching module obtains features associated with a historical question library, historical question records are matched, and the matching precision is evaluated through reward function design. Determining a historical solution most similar to the current problem; according to the intelligent digital operation and maintenance management system and method for the charging pile, the accuracy of problem classification and solution matching is continuously improved, and intelligentization and high efficiency of operation and maintenance management of the charging pile are achieved.

The invention relates to a task scheduling optimization method and device based on reinforcement learning, equipment and a medium. The method comprises the steps that firstly, system resource state data are collected in real time, dynamic environment characteristics are determined through preprocessing and time sequence analysis, task characteristic data are analyzed at the same time, and a task priority sequence and a resource demand vector are generated through a priority ranking algorithm and a resource evaluation model; and then a state space and an action space are constructed by adopting a reinforcement learning algorithm, an optimal task allocation scheme is generated through strategy iteration and reward function optimization, and if the scheme meets a resource balance threshold, scheduling is executed, and performance indexes are collected. And finally, fusing real-time indexes with historical data, and updating parameters of the reinforcement learning model through experience playback and gradient descent to form a closed-loop optimized improved scheduling strategy. By adopting the method, the accurate mapping of the resource state and the task requirement can be realized, and the problem of insufficient adaptability of the traditional static scheduling to a complex scene is solved.

The invention discloses an intelligent heat supply regulation and control system based on digital twinning and deep reinforcement learning. The system comprises a physical layer, a control layer and a control layer, wherein the physical layer is a physical heat supply system composed of heat source equipment, a transmission and distribution pipe network and a user terminal; according to the digital twinborn layer, a virtual heat supply system mapped with the physical layer in real time is constructed, the virtual heat supply system comprises a multi-physics field coupling model based on the thermodynamics and fluid mechanics principle, operation data of the physical layer are collected through a distributed sensor network, and the state vector of the virtual system is dynamically updated; and the intelligent decision-making layer is integrated with a DRL intelligent agent, the state space of the DRL intelligent agent is defined as a virtual system state vector output by the digital twin layer, the action space of the DRL intelligent agent is a regulation and control instruction combination of heat source power and pump valve opening, and a reward function fuses an energy consumption penalty term, a room temperature comfort reward term and a pipe network stability constraint term. According to the method, global optimization, high-precision continuous regulation and control and collaborative balance are realized through deep collaboration of digital twinning and deep reinforcement learning.

The invention provides a generator excitation regulation and control optimization system based on model prediction, and relates to the technical field of data processing, and the system comprises a data acquisition module which is used for collecting the operation parameters of a generator; the state estimation module is used for calculating a state variable of the generator excitation system and constructing a state space model of the generator excitation system; the model prediction control module is used for calculating excitation current prediction values at multiple moments in the future by adopting a rolling horizon optimization method, and performing dynamic correction based on a prediction error correction mechanism; the optimization calculation module is used for constructing a target optimization function which comprises a generator voltage stability target item and an excitation current adjustment smoothness target item, and calculating an optimal excitation current adjustment amount by adopting a constraint optimization method; the excitation adjusting module is used for controlling a generator excitation system so as to adjust excitation current; according to the invention, the autonomy and accuracy of the generator excitation regulation and control optimization system are improved.

The invention belongs to the technical field of resource scheduling, and particularly discloses a resource scheduling method and system based on reinforcement learning, and the method comprises the steps: describing the dependency and conflict relation between tasks through constructing a task causal relation graph which can be dynamically updated; constructing a state space and an action space based on a current task state and a causal relationship, training an intelligent agent by adopting a reinforcement learning model in combination with a multi-target reward function, and generating a scheduling and resource allocation strategy; linear programming and heuristic joint resource allocation are carried out under strategy guidance, and task priorities and causal relationships are dynamically adjusted in combination with task states; by collecting execution data and analyzing strategy deviation, a model structure and parameters are further adjusted, and a complete closed-loop optimization process is formed. According to the method, task priority dynamic adjustment, resource allocation strategy self-adaptive updating and scheduling process closed-loop optimization are realized, and the method is suitable for complex project management scenes under multi-task and multi-resource constraints.

The invention discloses an intelligent optimization control system and method for a battery material recovery and preparation process, and relates to the technical field of intelligent control. Training sample data and a training quality prediction model in the battery material recovery and preparation process in a test environment are collected, and then a state space, an action space and a reward function of a deep reinforcement learning model are constructed; fusing the prediction result of the quality prediction model and the monitoring data of the sensor in a test environment through a feature fusion strategy, generating a fusion process representation vector, inputting the fusion process representation vector into a deep reinforcement learning model, training the deep reinforcement learning model, and obtaining a fusion process representation vector; based on the real-time sensor data, the quality prediction model and the deep reinforcement learning model, obtaining an action output by the deep reinforcement learning model, and performing parameter regulation and control on the to-be-monitored recovery preparation process by taking the action as an optimization strategy; the production energy consumption is effectively reduced, and the resource utilization efficiency is improved.

The invention relates to the technical field of power distribution network evaluation, and discloses a power distribution network bearing capacity evaluation system based on dynamic correction. The system comprises a dynamic data acquisition module, a multi-dimensional state space construction module, a security domain analysis module, a partition coupling degree calculation module and a bearing capacity evaluation engine module. The dynamic data acquisition module acquires a power injection quantity sequence, a voltage deviation ratio sequence and uncontrollable parameter fluctuation data of each partition node of the power distribution network; a multi-dimensional state space construction module performs dimension raising mapping on the sequence to generate a linearized power flow state space model containing a power-voltage Jacobian matrix; the security domain analysis module corrects the boundary of the model according to uncontrollable parameter fluctuation and generates a dynamic security operation constraint set; the partition coupling degree calculation module quantifies an electrical independence index by means of a spectrum radius; and the bearing capacity evaluation engine constructs a chance constraint optimization model, outputs the photovoltaic maximum accessible capacity of each partition and a safety guarantee supply control strategy set, and improves the evaluation accuracy and practicability.

The invention discloses a target detection method based on Mama feature fusion, and relates to the technical field of image target detection. According to the method, the innovative implementation of the VSSA module is utilized, a selective scanning mechanism of the state space model is applied to 2D visual data processing, the long-distance dependency relationship in the image is effectively captured through state space modeling in four directions, the limitation of a traditional state space model in the two-dimensional visual data processing process is solved through the multi-direction processing strategy, and the processing precision of the 2D visual data is improved. The model can comprehensively perceive spatial dependency relationships in different directions in an image, the VSSA adopts learnable state space parameters to dynamically model a feature sequence, the ability of the network to understand a complex space structure is enhanced, the method is particularly suitable for processing scenes needing long-distance context information, and in addition, the method is combined with MTMHSA, so that the complexity of the network is reduced. And the fusion capability of different levels of features in target detection is further enhanced. Through the innovation, the model can better understand the target in the image, and the positioning and classification precision of the target is improved.

The invention discloses a distributed energy coordination control method based on multi-agent reinforcement learning, and the method comprises the following steps: S1, constructing a distributed energy coordination control architecture, taking each distributed energy as an agent, and defining a state space and an action space of the agent; s2, economics, environmental protection and reliability indexes in the distributed energy coordination control process are defined, and comprehensive indexes and constraint conditions are given; and S3, constructing a reinforcement learning network, carrying out training, integrating local information and a global target of each agent through a consistency coordination algorithm, ensuring that decisions of all agents can be coordinated and consistent, and obtaining a consistency coordination control strategy of the distributed energy. According to the invention, a deep reinforcement learning algorithm is utilized to realize adaptive coordination control of distributed energy, and the overall operation efficiency and reliability of the system are improved.

The invention discloses an AI model adaptive tuning method based on deep reinforcement learning, and belongs to the technical field of industrial process control, the method comprises the following steps: firstly, constructing a deep reinforcement learning environment comprising a state space, an action space and a reward function, the state space comprising a process variable measurement value, a controller parameter and a performance index; the action space is a parameter adjustment quantity vector, and the reward function comprehensively considers the stability, rapidity and accuracy of the system; then, based on a deep Q learning network of a dual-network structure, training data are stored and sampled by adopting an experience playback mechanism, and network parameters are iteratively optimized through a deep Q learning algorithm; and finally, realizing online adaptive adjustment of controller parameters by using the optimized deep Q network. The method has the advantages of being high in adaptability, good in optimization performance, high in practicability and the like, intelligent optimization control over an industrial process control system can be achieved, and the overall performance of the system is improved.

The invention discloses a reinforcement learning-based virtual power plant response optimization scheduling system and method, and relates to the technical field of virtual power plant intelligent scheduling. The system comprises an environment modeling module, an intelligent agent module, a multi-agent coordination module and a self-adaptive optimization module which are respectively used for constructing a multi-dimensional state space and a layered action space, generating and optimizing an action strategy based on an Actor-Critic network, executing a scheduling instruction through a layered multi-agent structure and realizing conflict consensus, and dynamically adapting to state space change in combination with incremental learning and meta-learning mechanisms. The system and the method have the advantages of fine state modeling, efficient action response, adaptive strategy updating, stable agent coordination and the like, and can keep the continuity, the stability and the optimality of a scheduling strategy in an operation environment in which multi-source heterogeneous power resources participate in scheduling cooperatively, market rules change frequently and load fluctuation is violent.

The invention relates to the field of mechanical equipment health management, in particular to a large sliding bearing fault detection and evaluation method, device and system. Comprising the following steps: collecting multi-source sensing data, and constructing a comprehensive data set; constructing a state space model based on a sliding bearing physical mechanism; the multi-source sensing data and the state space model are fused through Bayesian filtering, and hidden state parameter posterior distribution is dynamically estimated; generating a virtual fault sample by using a generative adversarial network in combination with a physical rule base; designing a Bayesian space-time sequence diagnosis model based on an attention mechanism, and generating fusion health state features; processing and fusing the health state features by using a degradation process model, and predicting the remaining service life of the bearing; and based on the health state, the fault probability and the remaining service life, setting multi-stage early warning threshold values, and triggering intelligent early warning. According to the method, the defect that a single model is insufficient in adaptability and generalization ability under complex working conditions is overcome, and the accuracy and reliability of fault detection are remarkably improved.

The invention discloses a power system load dynamic optimization method based on reinforcement learning. The method comprises the following steps: S1, collecting power system data to construct a state space; s2, constructing a hierarchical reinforcement learning model based on the state space, and dividing a high-level decision and a low-level execution task; s3, training a high-level decision model, and outputting a scheduling task target category instruction in a high-level state; s4, training a low-layer execution model, and outputting a control action in combination with a current node state and a high-layer instruction; s5, introducing an evolutionary mechanism to generate a strategy population and optimizing a low-layer execution model; s6, fusing an evolutionary mechanism and a strategy gradient to synchronously optimize individuals with excellent performance; s7, deploying the trained model to a power dispatching system; s8, performing model parameter fine tuning based on scheduling feedback; and S9, continuously applying the fine-tuned model to load scheduling control. According to the invention, power load accurate scheduling and strategy efficient adaptive optimization are realized, and system responsiveness and operation stability are improved.

The invention discloses a multi-mode reinforcement learning driven PM2.5 chemical component vertical profile inversion system and method. The system comprises a deep reinforcement learning model; the deep reinforcement learning model comprises a state space, an Action action space, a reward function, an Actor network and a Critic network; the deep reinforcement learning model is used for calculating a reward value at the current moment in combination with a reward function according to the difference between a vertical profile predicted value of the concentration of each PM2.5 chemical component output by the Action action space and an actual monitoring value; and according to an evaluation result of the strategy evaluation, continuously performing strategy iteration by using a gradient descent method until the deep reinforcement learning model is converged, and obtaining an optimal target deep reinforcement learning model. An Actor-Critic network dynamic optimization strategy is adopted, minute-level model updating is achieved in combination with edge calculation, and the problems of high-altitude blind areas, insufficient real-time performance and the like of a traditional model are solved.

The invention discloses a multi-parameter MPC two-phase liquid cooling energy efficiency optimization distribution control system and method, and relates to the technical field of liquid cooling energy efficiency optimization. The multi-parameter MPC two-phase liquid cooling energy efficiency optimization distribution control system and method comprises the following steps: S1, collecting and preprocessing two-phase liquid cooling control data; s2, the thermal load level of each area is evaluated periodically, a discrete time linear state space model is constructed, and the thermal load change rate is extracted; s3, the cold distribution amount of each area is quantified periodically, the pump speed and the valve opening target value are calculated, and an adjustment control instruction is generated; and S4, high heat risk and low heat stability areas are recognized, the cooling capacity compensation requirement is evaluated, a cooling capacity enhancement and recovery distribution value is output, and a corresponding control instruction is generated. The problems that an existing two-phase liquid cooling system generally only depends on temperature feedback for adjustment, so that cooling capacity distribution response lags behind, a short-time overheating phenomenon is prone to occurring in a high-heat area, and cooling capacity waste exists in a low-heat area at the same time are solved.

The invention provides an elevator energy recovery application method and system based on a super capacitor, and relates to the technical field of energy recovery, and the method comprises the steps: collecting elevator operation data and super capacitor state data, constructing a state space and an action space, and constructing a reward function according to peak-valley electricity price economic benefits, super capacitor life loss and energy utilization efficiency; training a deep reinforcement learning model based on the double-Q network to obtain an energy recovery optimization strategy; when the elevator is braked, regenerative braking energy is stored in the super capacitor; when the elevator is driven, the stored energy is released for traction; the optimal storage allocation scheme of the multiple elevator rooms is calculated through the virtual energy storage unit, hierarchical control is implemented based on an optimization strategy and the allocation scheme, the upper layer adopts fuzzy self-adaptive weight to determine the energy storage priority, and the lower layer adopts a distributed algorithm to calculate the energy allocation coefficient and the target power; and the charge-discharge rate is updated according to the health state of the supercapacitor.

The invention discloses a ship inclination measurement method and system based on Kalman filtering fusion, and relates to the technical field of ship stability measurement, and the method comprises the steps: multi-sensor synchronous data collection, state space modeling and Kalman filtering fusion, adaptive dynamic adjustment of process noise, drift compensation, and fault tolerance and output. When the method and the system provided by the invention are used for measuring inclination, inclination angle error accumulation caused by gyroscope zero offset integral error, accelerometer dynamic interference, sensor noise and temperature drift, GNSS dependency and the like can be effectively avoided, available precision can still be maintained through multi-sensor depth coupling and an intelligent filtering algorithm, and the accuracy of inclination measurement is improved. The method is simple in operation, independent of satellite data, higher in stability, capable of greatly reducing hardware cost compared with GNSS and IMU schemes, and especially has the advantages of being simple in cost and installation configuration in ship refitting; and the method is also suitable for GNSS rejection or cost sensitive scenes such as inland ships, unmanned ships, offshore buoys and the like.

The invention relates to the technical field of heat dissipation control, and discloses a heat dissipation fan multi-order filtering control method and device, and the method comprises the steps: collecting the operation parameters of a multi-fan heat dissipation system, and carrying out the construction of a dynamic state space model, and obtaining a system operation state equation and a temperature observation equation; inputting the system operation state equation and the temperature observation equation into an expectation maximization sliding window iteration extended Kalman filter to obtain a system state estimation value and a prediction error covariance matrix; performing three-stage series design on the cut-off frequency of the low-pass filter, the passband range of the band-pass filter and the coefficient of the adaptive filter to obtain a system multi-order filtering control model; based on a system multi-order filtering control model, an airflow coupling influence coefficient matrix among multiple fans is calculated, the total energy consumption, the noise level and the heat dissipation efficiency are subjected to optimization solution, a fan control optimization parameter set is obtained, the heat dissipation efficiency is guaranteed while the energy consumption is reduced, and dynamic balance among all control units is achieved.

The invention relates to the technical field of clean environment control, in particular to a dynamic pipe network and model predictive control clean room energy-saving pressure control method and system. Obtaining current pipe network impedance according to the pipe network impedance curve; calculating the current valve impedance according to the valve opening; calculating the total pressure drop of the current pipe network; constructing a fan dynamic model based on a fan similarity law to calculate the air volume at the next moment; a clean room pressure difference dynamic model is constructed, and the clean room pressure difference at the next moment is calculated; constructing a state space model and a target function of a model prediction controller; and when the pressure difference of the clean room is unstable, predicting the change trend of the pressure difference of the clean room in the next time period, and reversely solving the optimal fan frequency and valve opening of the discretization state space model by using the model prediction controller so as to achieve the lowest pipe network impedance and obtain the optimal fan frequency. The control precision and robustness of the pressure difference of the clean room are remarkably improved, and meanwhile energy consumption is reduced.

The invention discloses an intelligent logistics system based on optimal scheduling, and relates to the field of logistics management, and the system comprises the steps: arranging environment sensors at key nodes of a logistics network, and transmitting collected real-time data to edge computing equipment for preprocessing; analyzing the preprocessed data by using a deep learning model, and predicting an environment change trend in a non-period of time; dynamically adjusting a logistics scheduling strategy based on the prediction result; after receiving the adjusted scheduling strategy, defining a state space, an action space and a reward function of the logistics system; training a reinforcement learning algorithm model, and continuously optimizing a decision strategy through interaction with the environment; and calling the reinforcement learning model through an API interface to make a real-time decision, and generating an optimal scheduling scheme. According to the invention, by integrating environment perception, deep learning prediction, adaptive scheduling and reinforcement learning, the defects of an existing intelligent logistics system in the aspects of environment perception, prediction and dynamic scheduling optimization are overcome, and the intelligent level and operation efficiency of logistics management are significantly improved.

The invention provides an integrated energy system dynamic scheduling method based on multi-agent deep reinforcement learning, and the method comprises the steps: constructing an agent for the scheduling of an integrated energy system based on a Markov decision interaction architecture, and the agent comprises a state space, an action space and a reward function; dividing the integrated energy system into multiple environments with different characteristics in different time periods by using K-means clustering; capturing environmental characteristics of the integrated energy system in a scheduling period by using a prediction model, and matching an intelligent agent of a corresponding environment; setting an economical depth deterministic strategy gradient model for each environment to carry out interactive learning and decision making, and forming a distributed learning-distributed execution architecture; and carrying out dynamic scheduling on the integrated energy system by utilizing the distributed learning-distributed execution architecture. Compared with other deep reinforcement learning methods and traditional optimization methods, the method provided by the invention can obtain a more economic and stable scheduling result.

The invention relates to the technical field of artificial intelligence computer vision, in particular to an event camera pedestrian detection method based on a space-time state space model, and the method comprises the steps: collecting a pedestrian detection data set based on an event camera, obtaining original event data, carrying out the preprocessing, determining an event tensor, and obtaining an event frame sequence; modeling is carried out through combination of a state space and an event tensor, an event-driven recursive space-time state space module is defined as a core unit, and an isomorphic deep neural network architecture is constructed; training the isomorphic deep neural network architecture by adopting the training set, and verifying through the verification set; inputting the test set into the verified isomorphic deep neural network architecture for detection, and generating a pedestrian detection result; the collaborative optimization of sparse adaptation-dynamic capture-noise suppression is realized in a unified framework, the essential characteristics of the event camera triggered based on brightness change are theoretically fit, and higher robustness and generalization ability are shown in an actual complex scene.

The invention provides a heat exchanger intelligent design method based on deep reinforcement learning, and relates to the technical field of data processing. Establishing a flow model for simulating the flow path, speed distribution and pressure change of the fluid in the heat exchanger; according to the geometrical shape of the heat exchanger, the physical property of a heat exchanger material and fluid flowing in the heat exchanger, a heat exchange model is established by combining the thermodynamic principle, and the heat exchange efficiency under different fluid flowing conditions is calculated; the flow model and the heat exchange model are coupled, so that a coupling performance evaluation model for evaluating the overall performance of the heat exchanger under the specific working condition is obtained; and determining a state space, an action space and a reward function according to the flow heat exchange intensity coupling performance evaluation model. The design efficiency is improved.

The invention relates to the technical field of new energy operation, and discloses a distributed new energy settlement and control system based on a block chain, and the system collects new energy power generation, energy storage and user power utilization data through an Internet of Things collector, and builds a network model comprising new energy production, energy storage, consumption and power grid nodes. And a Markov decision process is adopted for modeling. The system further defines a state space, an action space and a reward function, and a deep reinforcement learning algorithm optimization strategy is applied to maximize long-term rewards. Besides, a block chain technology is introduced, transparent and safe recording of new energy production, transaction, consumption and settlement processes is realized, transaction settlement is automatically executed through an intelligent contract, and the efficiency and reliability of energy transaction are improved.