94 results about "Distributed model predictive control" patented technology

The invention discloses a path planning and hierarchical cooperative control method and system for an unmanned aerial vehicle cluster. The system comprises a path planning module and a formation motion control module. The method comprises the steps that firstly, an improved RRT * algorithm is adopted by a path planning module, through a multi-strategy heuristic node expansion mechanism fusing target bias and artificial potential field guidance and comprehensively considering path length, channel volume and Z-axis height change, a center path and a three-dimensional safe channel which take into account safety and smoothness are planned for an unmanned aerial vehicle cluster; and then, based on the central path, the formation motion control module adopts a distributed model prediction control framework, designs different optimization targets for a navigator and a follower, and solves an optimal control instruction on line, so that a cluster is guided to complete trajectory tracking, collision avoidance among individuals and self-adaptive formation reconstruction in a secure channel. According to the invention, the navigation problem of the unmanned aerial vehicle cluster in a complex obstacle environment is solved, and the path planning efficiency and the robustness of cooperative control are improved.

The invention relates to the field of robot control, and discloses a robot control method and system, and the method comprises the steps: carrying out the fusion sensing of environment data through a multi-modal sensor, and constructing a dynamic environment model through the combination of the clustering segmentation of a dynamic obstacle and the geometric matching of a target cart; generating a Nash equilibrium obstacle avoidance path based on a non-cooperative game framework, and coordinating motion tracks of multiple robots by using distributed model predictive control and an alternating direction multiplier method; a heterogeneous grabbing pose generation network is designed, simulation and real scene feature distribution are aligned through domain adversarial training, and online optimization of a grabbing strategy is achieved in combination with incremental learning; newly-added obstacles and cart deviation are monitored in real time to trigger dynamic re-planning, and the path and the grabbing pose are updated through a closed-loop feedback mechanism. According to the method, the cooperative control robustness in a dynamic environment is improved, the obstacle avoidance efficiency and the grabbing stability are both considered, and the method is suitable for automatic operation in complex scenes such as airports.

The invention provides an energy storage power station and power grid collaborative complementary regulation method and system, and relates to the technical field of smart power grids, comprising the steps of obtaining disturbance components by performing multi-scale decomposition on power grid frequency and voltage data, and constructing a distributed model prediction control framework to optimize energy storage resources in groups; and designing a self-adaptive dynamic programming controller to evaluate the system state and output the optimal charging and discharging power, and responding to the topological change of the power grid in real time. According to the invention, collaborative optimization configuration of energy storage resources can be realized, the power grid stability is improved, the service life of energy storage equipment is prolonged, and the system operation cost is reduced.

The invention relates to the technical field of power supply, and particularly discloses a peak and frequency regulation heat supply method for a multi-element energy storage coupling coal power unit, which is used for solving the problems that only active frequency is optimized, electric heating coordination and charge state constraint are lacked and time delay compensation is insufficient in the prior art. The method provided by the invention comprises the steps of system modeling, controller design, coupling constraint, adaptive parameter adjustment, delay compensation and distributed consensus. According to the method, a dual-domain electric power-thermal interconnection model is constructed, a cooperative controller based on delay compensation distributed model prediction control is designed, electric-thermal cooperative penalty and charge state constraint are introduced into a target function, a prediction window and weight parameters are adaptively adjusted, and network delay is identified and compensated online. And a consensus algorithm is adopted to realize multi-region power-heat collaborative optimization.

The invention relates to the technical field of on-load voltage regulation, in particular to a transformer on-load voltage regulation control method. According to the technical scheme, the transformer on-load voltage regulation control method comprises the following steps that S1, a transformer cluster running in parallel is decomposed into distributed intelligent nodes, and each node executes local voltage prediction and constraint condition management; s2, adopting a distributed model prediction control framework, and cooperatively solving a voltage regulation optimization target through an edge calculation unit; s3, a harmonic disturbance signal with a preset amplitude is injected into the transformer, equivalent impedance is dynamically identified based on response data, and virtual impedance compensation is triggered; and S4, establishing a hierarchical cooperative control architecture, and realizing clock synchronization, dynamic weight distribution and multi-objective optimization decision among nodes. Through a cooperative mechanism of distributed predictive control and dynamic impedance compensation, the response speed and the regulation precision of the on-load voltage regulation system to the power grid voltage fluctuation are remarkably improved, and the voltage out-of-limit problem under the complex working conditions of new energy grid connection, load sudden change and the like is effectively solved.

A self-triggering unmanned vehicle distributed formation control method based on a double-triggering mechanism belongs to the field of unmanned vehicle distributed cooperative formation control, and comprises the following steps: S1, constructing a multi-agent consistency cooperative control framework; s2, constructing an intelligent vehicle cooperative control system driven by a dynamic situation field; s3, carrying out communication topology modeling between the vehicles; s4, a self-triggering mechanism is introduced, and correct triggering of communication is ensured by designing a double-triggering threshold function; s5, establishing a prediction model of the vehicle formation; and S6, the distributed model prediction controller controls accurate implementation of a formation transformation strategy. According to the invention, through combination of a self-triggering mechanism and distributed model prediction control, the control efficiency and precision of the unmanned vehicle formation are significantly improved; compared with the traditional method, the communication frequency is reduced, and the communication burden is reduced by 5%; when the vehicle runs on a curve, the vehicle track error is reduced and is 4% more accurate than that of a traditional method.

The invention belongs to the technical field of unmanned aerial vehicle cooperative crossing, and relates to a multi-unmanned aerial vehicle cooperative crossing method and device. The method comprises the following steps: acquiring scene information of cooperative crossing of multiple unmanned aerial vehicles, and obtaining a data set, a model of a crossing frame and a dynamic model of each unmanned aerial vehicle; according to the dynamic model of each unmanned aerial vehicle, constructing a model prediction control framework of each unmanned aerial vehicle; acquiring a historical trajectory sequence of each unmanned aerial vehicle to obtain a trained neural network, and embedding the trained neural network into a model prediction control framework to form a distributed model prediction control framework of multiple unmanned aerial vehicles; and performing path prediction planning on the current unmanned aerial vehicle and other unmanned aerial vehicles according to the distributed model prediction control framework of the current unmanned aerial vehicle, the model of the crossing frame and the kinetic model of the current unmanned aerial vehicle to obtain the real-time position of each unmanned aerial vehicle so as to perform multi-unmanned aerial vehicle cooperative crossing. According to the invention, cooperative crossing can be realized under the condition that multiple unmanned aerial vehicles do not communicate with each other.

The invention relates to the technical field of robot welding, in particular to an intelligent path planning system and optimization method for large structure robot welding, by collecting three-dimensional point cloud data of a workpiece and temperature field and stress field data in the welding process, a geometry-heat-force multi-field coupling model is established, and a machine learning algorithm is adopted for parameter correction; based on the model, a deep learning network is adopted to identify welding seam topological features and generate a path constraint parameter set; welding path nodes are optimized by using a hybrid optimization strategy of reinforcement learning and a multi-target genetic algorithm; monitoring the shape of a molten pool through a visual sensing system, and dynamically adjusting welding parameters in combination with a model predictive control algorithm; a distributed model predictive control algorithm is adopted to coordinate the motion trails of the multiple robots; and finally, constructing a welding process knowledge graph to realize continuous optimization of the system. The problems of inaccurate welding deformation prediction and poor path planning adaptability in the prior art are solved, and the welding quality and the production efficiency of the large structural part are remarkably improved.

The invention discloses a multi-unmanned aerial vehicle navigation method, system and device based on distributed model predictive control and a virtual pipeline and a medium, and the method comprises the steps: constructing the virtual pipeline, and forming a flight area of an unmanned aerial vehicle group; in each sampling period, each unmanned aerial vehicle generates an assumed trajectory in a prediction time domain by using a kinetic model of the unmanned aerial vehicle based on the current state of the unmanned aerial vehicle, and broadcasts the assumed trajectory to the neighbor unmanned aerial vehicle; obstacle intrusion judgment and virtual pipeline dynamic processing are carried out, a linear separation hyperplane is constructed, and local convex safety sub-pipeline constraints are generated; carrying out inter-machine collision risk judgment and dynamically activating inter-machine collision avoidance constraints; setting a target cost function, and constructing an optimization problem in combination with the constraints; solving to obtain an optimal predictive control input sequence at the current moment, and executing a first control instruction of the optimal predictive control input sequence; and the process is repeatedly executed until all the unmanned aerial vehicles pass through the virtual pipeline. According to the method, multiple challenges of dynamic environment adaptability, calculation real-time performance, track smoothness and the like can be solved at the same time.

The invention relates to an airport snow sweeper cooperative formation control method, electronic equipment and a medium, and the method comprises the steps: building a vehicle transverse and longitudinal coupling kinematic model in an airport snow removal environment based on environmental parameters, the vehicle transverse and longitudinal coupling kinematic model comprises transverse and longitudinal physical quantity cross terms, enabling the longitudinal speed to affect the transverse posture change, and enabling the longitudinal speed to affect the transverse posture change; the transverse path characteristics and the control input restrict the longitudinal motion; constructing a formation constraint model based on the vehicle transverse and longitudinal coupling kinematics model; constructing a distributed communication topology model based on a directed graph and an adjacent matrix; constructing a single vehicle local optimization control problem model; and iteratively solving the single vehicle local optimization control problem model based on a path guiding type distributed model predictive control algorithm to obtain the optimal control input of each vehicle. According to the method, control input can be optimized while accurate path tracking of the snow sweeper is ensured, energy consumption and cost are reduced, and collaboration, safety and efficiency of snow removal operation of an airport can be improved.

The invention discloses a multi-unmanned aerial vehicle integrated planning and control method based on distributed model predictive control. The method comprises the following steps: S1, judging an unmanned aerial vehicle communication topology mechanism in an offline stage, determining an expected target point of each unmanned aerial vehicle, initializing a predictive state value of each unmanned aerial vehicle, and setting controller parameters; s2, the front end performs unmanned aerial vehicle positioning and local map construction according to the sensor data, and searches a reference trajectory according to the local map; s3, the rear end uses an IDMPC controller to solve according to the error between the measured value and the predicted value, and an unmanned aerial vehicle input optimal value is obtained; s4, the solved optimal input is converted into unmanned aerial vehicle attitude control quantity input through differential flatness, and unmanned aerial vehicle formation trajectory tracking is achieved. According to the method, quick response and anti-disturbance capability under the high frequency of 100Hz are realized, and the superiority of the method in the aspects of success rate, calculation efficiency and anti-disturbance performance is verified through a simulation experiment.

The present disclosure provides a cooperative distributed model predictive control (Co-DMPC)-based chassis multi-agent system (MAS) cooperative control method for autonomous vehicles, a controller, and a storage medium. A distributed state-space equation with state coupling and control input coupling characteristics is established. Meanings and transformation methods of predicted trajectories, assumed trajectories, and optimal trajectories of the states and control inputs are designed, providing a communication basis for information exchange between the agents. In order to coordinate the global performance indexes of a vehicle, a local agent optimization problem considering cost coupling is established, and the influence of the cooperative relationship on the control effect is quantitatively analyzed through adaptive weight coefficients. A method of performing a plurality of iterations within a unit sampling time is adopted, and iteration errors are utilized to enable the controller to achieve a balance between solution accuracy and efficiency.

The invention discloses a multi-underwater robot dynamic formation cooperation method based on distributed model prediction control. The method comprises the following steps: establishing an underwater robot dynamic model; designing a distributed model predictive control architecture; making a dynamic formation strategy; an information interaction mechanism based on communication constraints; performing interference compensation and robust control; and setting an objective function and constraint conditions. According to the invention, efficient and stable dynamic formation collaborative operation of multiple underwater robots can be realized in a complex underwater environment. The method has a good application prospect in tasks such as marine environment monitoring, resource exploration, underwater rescue and the like.

The invention discloses an unmanned aerial vehicle base station trajectory and virtual network function collaborative optimization method based on model prediction control, and belongs to the field of unmanned aerial vehicle mobile network and service quality optimization. Firstly, a utility-driven ABS track and service quality coupling framework is provided, the relationship between the ABS position and the user service quality is quantified, and a decision basis is provided for track optimization. A rapid evaluation module for online VNF arrangement and resource allocation is introduced, service delays under different deployment strategies are accurately estimated, and key input is provided for an RDT model and trajectory optimization. And a closed-loop adaptive optimization and cooperation mechanism is constructed: through an ABS trajectory adaptive optimization module based on model prediction control, the unmanned aerial vehicle can prospectively plan and adjust the flight trajectory so as to maximize the average communication rate of the user. For a multi-ABS scene, a distributed model predictive control and conflict resolution mechanism based on a virtual token is provided, so that a plurality of ABSs can effectively avoid conflicts and carry out cooperative services while optimizing own service quality.

The invention provides an urban power distribution network probabilistic load flow calculation method for large-scale access of demand side resources, and belongs to the technical field of power system power distribution network analysis and optimization. The method comprises the following steps: constructing a dynamic Copula model in combination with space-time sequence analysis, introducing a Markov chain, and carrying out multi-dimensional uncertainty coupling modeling; a probabilistic power flow-multi-objective optimization model is constructed, and energy router cooperative control is carried out based on distributed model predictive control; sparse expansion is carried out by adopting error feedback adaptive sampling and sparse Bayesian learning in combination with polynomial chaos expansion, and probability power flow is calculated through OpenDSS; establishing a three-phase probabilistic power flow model and a DG-EV-DR collaborative probability model; and evaluating the extreme scene risk, and visualizing the result through a digital twin platform. According to the method, the accuracy and practicability of distribution network probabilistic load flow calculation in a demand side resource large-scale access scene are effectively improved, and a risk quantification basis is provided for power grid dispatching.

The invention discloses an automobile queue control method based on pipeline distributed model predictive control, and belongs to the field of intelligent automobiles and intelligent traffic. The method comprises the following steps: constructing a vehicle discrete dynamic model; establishing a queue cooperative control framework through vehicle-vehicle communication; introducing a wheel slip rate into the queue performance index function, and setting a longitudinal slip rate as a constraint; on the basis of nominal model predictive control, a nominal optimal control input sequence and an optimal state track are generated through decision making; and then the state of the disturbed vehicle is corrected in real time through an auxiliary control law, so that the disturbed vehicle is restrained near the optimal track. In the queue car-following control process, an interference suppression mechanism and tire nonlinear modeling are introduced into the system, and the limitation that the node vehicle stability and the full-queue control performance are not sufficiently considered in a traditional method is broken through. Even under the limiting working conditions of high-speed driving, low adhesion and the like, the stability and the good all-working-condition control performance of the automobile queue can be guaranteed.

The invention provides a tunnel robot self-adaptive action control method and system of a multi-level collaborative architecture, and relates to the technical field of tunnel construction.The method comprises the steps that real-time three-dimensional force / torque data of an end effector of a mechanical arm is obtained, denoising and preprocessing are conducted, environment contact rigidity is calculated, and a fractional order impedance model is established; inputting the model to a sliding mode control layer to construct a self-adaptive sliding mode surface and generate a compensation torque; the compensation torque and the real-time contact force are input into a distributed model predictive control layer to dynamically optimize impedance parameters; the optimized parameters and historical data are input to a reinforcement learning layer, global optimization is carried out by using a near-end strategy optimization algorithm, and target impedance parameter correction is generated; and generating a speed control instruction based on the correction, and driving the mechanical arm to complete operation. According to the method, rapid suppression of high-frequency disturbance, accurate adjustment of impedance parameters and long-term adaptive optimization are achieved, and the flexibility, stability and operation efficiency of the mechanical arm in the dynamic tunnel environment are remarkably improved.

The invention discloses a multi-unmanned aerial vehicle formation control method based on distributed model predictive control. The method comprises the following steps: describing a motion law of each unmanned aerial vehicle by adopting a state-space equation; modeling a static obstacle as a geometric body, and setting a minimum safe distance; considering position tracking cost, control energy cost, input change cost and formation cost, and establishing an objective function for each unmanned aerial vehicle; adding a CBF security constraint according to whether the security domain has an obstacle avoidance demand or not; constructing an optimal control problem for each unmanned aerial vehicle; in each control period, the optimal control problem is solved; only the first item in the control sequence is adopted as an actual control instruction; and then the system state is updated based on the state-space equation, and prediction and optimization are carried out again in the next period. According to the invention, the problems of obstacle avoidance and formation keeping when the multi-unmanned aerial vehicle formation passes through the obstacle in a complex environment are effectively solved. The method can be widely applied to the field of unmanned aerial vehicle control.

The invention relates to a multi-unmanned aerial vehicle (UAV) search strategy optimization method based on genetic programming (GP), which is characterized in that under a distributed model predictive control (DMPC) framework, each UAV is guided by a target function to realize a search trajectory optimization process, and essentially, a search strategy is converted into a mathematical logic mapping relation. In a traditional method, objective function construction mainly depends on a design thought dominated by artificial experience, and the fixed search mode based on subjectivity has the limitations of insufficient environmental adaptability, weak strategy generalization ability and the like in a complex dynamic environment. In order to solve the problem, the invention provides a search strategy optimization method based on a genetic programming algorithm, and online dynamic evolution and parameter adaptive adjustment of a search strategy are realized by establishing a dynamic mapping model between a strategy parameter space and a search performance index.

The invention discloses a distributed model prediction control method for an offshore front-end speed regulation type wind turbine generator, and belongs to the technical field of new energy power generation. According to the method, an overall control task is decomposed into three parallel subsystems including a variable pitch subsystem, a hydraulic variable torque speed regulation subsystem and an excitation subsystem, and local discrete prediction models are established respectively. And in each sampling period, rolling optimization of each subsystem is executed in parallel, and future prediction tracks of the rotating speed and the terminal voltage of the generator are introduced as global consistency variables. And by constructing an augmented Lagrangian cost function based on an alternating direction multiplier method and performing iterative solution, driving each local prediction trajectory to converge to a globally consistent optimal trajectory. And finally, outputting a first instruction of an optimal control sequence obtained after iterative convergence to each execution mechanism, and realizing cooperative control of the three subsystems, thereby rapidly and smoothly satisfying frequency and voltage synchronization conditions at the same time before grid connection, effectively inhibiting coupling interference and wind speed fluctuation, and improving grid connection quality and operation reliability.

The invention discloses an unmanned aerial vehicle cluster collaborative obstacle avoidance method based on distributed model predictive control, and belongs to the technical field of unmanned aerial vehicle cluster collaborative navigation. The method aims to solve the problem that the decision is easy to oscillate due to non-convex obstacle avoidance constraint, difficult solution and information asynchronization of the cluster in a dynamic environment. According to the scheme, each unmanned aerial vehicle constructs a local optimization problem containing a non-convex constraint and a cost function based on the unmanned aerial vehicle and neighbor information in each period; taking the optimal trajectory of the previous period as a reference, and converting constraint linearization and cost function second-order approximation into convex quadratic programming sub-problems; solving and outputting a control sequence within the time constraint; and executing the current control quantity and broadcasting a new predicted trajectory. The method is used for real-time collaborative obstacle avoidance of the unmanned aerial vehicle cluster in a communication limited environment.

The application belongs to the technical field of unmanned aerial vehicle cooperative crossing, and relates to a multi-unmanned aerial vehicle cooperative crossing method and device. The method comprises the following steps: acquiring scene information of multi-unmanned aerial vehicle cooperative crossing, obtaining a data set, a model of a crossing frame and a dynamic model of each unmanned aerial vehicle; constructing a model prediction control framework of each unmanned aerial vehicle according to the dynamic model of each unmanned aerial vehicle; acquiring a historical trajectory sequence of each unmanned aerial vehicle, obtaining a trained neural network and embedding the trained neural network into the model prediction control framework to form a distributed model prediction control framework of the multi-unmanned aerial vehicle; and performing path prediction planning on the current unmanned aerial vehicle and other unmanned aerial vehicles according to the distributed model prediction control framework of the current unmanned aerial vehicle, the model of the crossing frame and the dynamic model of the current unmanned aerial vehicle to obtain real-time positions of each unmanned aerial vehicle for multi-unmanned aerial vehicle cooperative crossing. The application can enable multi-unmanned aerial vehicles to realize cooperative crossing without communication with each other.

The present application relates to a kind of based on distributed model predictive control's electric precipitator cluster energy-saving optimization operation control method and control system, based on electric precipitator cluster establishment distributed linear dynamic model, describe the dynamic response relationship between the dust concentration in each electric chamber in electric precipitator cluster and high voltage power supply secondary voltage;According to the outlet concentration information of electric precipitator cluster, design state observer, estimate the dust concentration in each electric chamber;Based on the estimated state of distributed linear dynamic model and state observer, design the controller based on distributed model predictive control, obtain optimal control strategy;Based on method implementation control system.The present application is convenient for control implementation, get rid of the dependence on historical data;Real-time estimation of unobservable electric chamber dust concentration can be implemented in electric precipitator cluster engineering application;Method ensures accurate regulation for each electric chamber, realizes multi-field collaborative dust removal, efficiently and reasonably allocates energy, saves electric precipitator cluster operating cost.

The invention relates to the field of power systems, and particularly discloses a source network load storage dynamic balancing system and method for a high-proportion new energy power grid. The basic principle of the scheme of the invention is as follows: a regional collaborative optimization layer performs rolling optimization by using a distributed model predictive control algorithm based on wide-area measurement and ultra-short-term predictive data to generate a regional collaborative control target in the form of voltage or reactive power demand; and after receiving the target, each intelligent power converter in the distributed equipment execution layer performs real-time negotiation with adjacent equipment by running a distributed consistency algorithm, autonomously determines respective virtual admittance regulation quantity, and finally quickly adjusts the equivalent admittance of the grid-connected point through a virtual admittance control technology, and outputs the required reactive power. The most core technical effect of the scheme is that the second-level real-time dynamic balancing capability and the safe and stable operation level of the power grid in the high-proportion random new energy access environment are improved.

The application particularly relates to a cooperative string stability model predictive control method based on unmanned aerial vehicles, and belongs to the field of unmanned aerial vehicle control, and comprises the following steps: establishing a discrete time state space model of a following unmanned aerial vehicle; introducing a step factor beta, and establishing a determination relationship between control increments at each step in a future prediction time domain and a current time control increment; based on the state space model and the determination relationship, establishing a local optimization problem of a distributed model predictive controller for the following unmanned aerial vehicle; in a control period, the following unmanned aerial vehicle solves the local optimization problem, obtains an optimal current control increment, updates a current control quantity and outputs the current control quantity to an executing mechanism, and cooperative control of the unmanned aerial vehicle is realized. The application also relates to a cooperative string stability model predictive control system based on unmanned aerial vehicles, an electronic device and a computer readable storage medium. The application adopts a distributed efficient model predictive control strategy, and ensures that different types of unmanned aerial vehicles can maintain the stability and string stability of a queue when flying in formation.

This invention relates to the field of power system operation and control technology, and particularly to a method for enhancing grid frequency resilience and providing power support considering inertia spatial distribution. The method includes: acquiring grid operation information, determining the equivalent inertia index of each node or region and generating an inertia spatial distribution heatmap, identifying local inertia-weak areas and frequency-vulnerable areas; after detecting a disturbance event, combining grid operation information, the inertia spatial distribution heatmap, and grid topology relationships to perform spatiotemporal joint prediction of the post-disturbance frequency evolution process, obtaining the frequency change trend and disturbance propagation path; screening target support resources, and forming a power support strategy through distributed model predictive control, implementing emergency power support, and dynamically adjusting the exit rate and power recovery rate of the target support resources during the recovery phase. This method achieves visualized characterization and weak link location of the uneven spatial distribution of grid inertia, solving the problem of difficulty in detecting local inertia depressions in frequency control.

The application belongs to the field of robots, in order to solve the problems of motion planning scalability, navigation flexibility and system safety of large-scale robot cluster, the application provides a collision risk awareness and high scalability large-scale cluster robot motion planning method and device, comprising: in the macro stage, using a Gaussian mixture model to represent the macro state of the cluster, and planning the transport trajectory of the Gaussian mixture model through model predictive control; in the micro stage, using an artificial potential field method to assign target positions to the robots, and combining a distributed model predictive control to realize the tracking and dynamic obstacle avoidance of the individual to the transport trajectory of the Gaussian mixture model. The application can effectively improve the motion planning performance of the large-scale cluster robot system in a complex environment. In the application, by adjusting the risk acceptance degree, the distance between the robot cluster and the obstacle can be flexibly adjusted, and the balance between safety and motion efficiency is realized.

The invention provides a multi-mode collaborative lithium battery protection circuit control method. The method comprises the following steps: acquiring voltage, current and surface temperature of a battery pack and a relay state signal in real time, and constructing a standardized time sequence tensor through preprocessing; inverting the core temperature of each single battery based on an electricity-heat-aging coupling model; the aging rate index is dynamically calibrated by combining the joint extended Kalman filtering and the real-time core temperature; a dynamic adjacency matrix is constructed through voltage correlation and temperature gradient, and a topological weakness risk is identified by using a lightweight graph convolutional network; according to the core temperature, the aging index, the risk score and the SOC difference, a protection mode is dynamically switched in a multi-mode logic state machine, and a control instruction is generated; and finally, the time sequence of a power tube is adjusted through PWM phase compensation, and a distributed model prediction control algorithm is adopted to drive an equalization circuit, so that lithium battery system protection with safety, service life and performance collaborative optimization is realized.

The invention discloses a longitudinal-transverse coupling distributed model prediction control method for a multi-queue vehicle system, and the method comprises the steps: constructing an eight-dimensional state space model containing longitudinal and transverse motion parameters, and building an in-queue forward chain type and inter-queue multi-strategy communication topological structure; comprising three strategies of pilot car-tripper car communication, pilot car-pilot car communication and hybrid communication; coupling constraints are defined based on a communication topological structure, and a longitudinal-transverse coupling distributed model is constructed to predict control optimization problems, including local optimization of queues and following vehicles and longitudinal-transverse collaborative optimization of cut-in vehicles; terminal state constraint is introduced to ensure system stability; and solving an optimization control problem for each vehicle node, and adjusting a longitudinal traction torque and a transverse steering angle at the same time to realize cooperative updating of vehicle states. According to the method, through a unified longitudinal-transverse optimization framework and a distributed solution mechanism, vehicle following and lane changing behaviors can be processed at the same time, and the safety, comfort and fuel economy of a multi-queue system are improved.

A kind of comprehensive energy system optimization method and system giving consideration to cost and new energy consumption, comprising: comprehensively considering the characteristics of multi-energy equipment and spot market electricity price mechanism, constructing regional comprehensive energy system model;Based on the regional comprehensive energy system model, a multi-objective optimization scheduling model containing day-ahead, intra-day, real-time multi-time scale and minimizing system total cost and maximizing new energy consumption is established;The day-ahead and intra-day multi-objective optimization scheduling model is solved;Based on the day-ahead and intra-day optimization results, the real-time multi-objective optimization scheduling model is solved by designing a distributed intelligent agent architecture and using a distributed model predictive control algorithm;According to the solving result, the output plan of each unit, the charging and discharging plan of energy storage and the system energy purchase plan are determined to realize optimal scheduling.The present application comprehensively considers operation cost and new energy consumption, and realizes economic and low-carbon operation of regional comprehensive energy system.