807 results about "Dynamic programming" patented technology

The invention relates to a remote-control system of a stratospheric airship and a control method thereof. The control system is divided to four levels: a decision-making level for executing the selection and switch of a remote-control flight mode according to the actual working state data, and sending a corresponding mode command; a planning level for dynamically planning and generating an airship motor drive command according to the mode command sent by the decision-making level; an executing level for realizing the drive control to an airship motor according to the motor drive command of the planning level; and a perceiving level for finishing the real-time monitoring and data collection of the airship working state, and providing the original data of the airship working state to the decision-making level. The four-level control system architecture design of the control system is capable of simplifying the control device, improving the control efficiency, and fundamentally solving the problems of the function, efficiency and reliability of the near space airship ground and airborne control system.

The invention discloses an Android system user interface interaction method and device based on a large language model, and relates to the technical field of intelligent man-machine interaction, and the method comprises the steps: receiving a natural language instruction of a user, and synchronously capturing a real-time interface state of a current Android screen to generate a structured context containing semantic information of interactive elements; inputting the instruction and the context into a large language model, and generating a hierarchical task plan formed by a plurality of atomic operations with a logic dependency relationship; based on a dynamic adaptation mechanism, converting the atomic operation sequence into an operation instruction which can be executed by the Android system; receiving interface change information after instruction execution, and feeding back the interface change information as a new round of context to the large language model to determine a task execution state; and finally generating natural language feedback information to the user according to the execution state. By means of the mode, complex cross-application tasks can be understood and executed, and interaction intelligence of the Android system is remarkably improved through real-time interface perception and dynamic planning adjustment.

The invention relates to the technical field of aerospace, in particular to low-altitude traffic flow airspace-oriented real-time planning, and provides a centimeter-level precision detection network covering a low-altitude airspace by integrating multi-dimensional data sources such as radar electromagnetic feature recognition, ADS-B (Automatic Dependent Surveillance-Broadcast) automatic monitoring, Beidou or GPS (Global Positioning System) space-time reference positioning and the like. The three-dimensional trajectory and motion situation of the aircraft are solved in real time, intelligent reconstruction of an airspace sector and adaptive optimization of a flight corridor are realized by adopting a dynamic programming algorithm driven by reinforcement learning based on the real-time pose data of the aircraft, and a flight conflict prediction model constructed by combining a space-time convolutional neural network is used for predicting the flight conflict. According to the method, potential risks can be pre-judged, an optimal avoidance path can be generated, full-process digital management and control from identity verification to airspace authorization can be realized by constructing an aircraft digital identity authentication system and a dynamic access control mechanism, and modules such as a three-dimensional navigation information service module, a low-altitude digital communication private network module and an intelligent early warning and warning system module are integrated. And the guarantee of full-life-cycle service is provided for the low-altitude aircraft.

The invention provides a computing power center resource centralized self-adaptive control scheduling system and method, and the method comprises the steps: S1, a user submits a computing power training task through an API, and sends the task to a DAG task composer, and forms a task list to be scheduled; s2, the intelligent monitoring analysis module is used for continuously collecting node data and calculating real-time CS and HS scores of each node; s3, analyzing the dependency relationship type data by a task composer, and calling a dynamic programming algorithm in an adaptive scheduler to make a decision; s4, the adaptive scheduler performs dynamic task distribution according to the task list of the DAG task orchestrator and the intelligent monitoring analysis module, and S5, the distributed runtime and environment management module is used for uploading the prepared Docker mirror image to the distributed storage; and S6, through the unified communication adaptation layer, the mirror image is pulled from the distributed storage on the target node and the container is started, the user does not need to concern the difference of the operating system in the whole process, and efficient and intelligent adaptive scheduling is realized.

The invention discloses a generative dialogue optimization method and system based on multi-step intensified simulation, and the method comprises the steps: generating a plurality of candidate replies based on the current dialogue history through a basic dialogue model; using the user simulator to generate a dialogue chain for each candidate reply; calculating a reward for each dialogue chain, selecting K dialogue chains with the highest reward value as high-quality samples, and taking the dialogue chain with the lowest reward value as a negative sample; updating parameters of the basic dialogue model by using the high-quality sample and the negative sample through a contrast learning and reinforcement learning method to obtain a trained basic dialogue model as a customer service model; and the customer service model generates a reply according to a statement input by the user. According to the conversation generation method, multi-round conversation dynamic planning, medical scene adaptability optimization and efficient strategy exploration can be fused, and double optimization of user trust establishment and business goal achievement is achieved while compliance is ensured.

The invention provides an unmanned aerial vehicle flight path dynamic planning method and system, and relates to the technical field of flight path planning, and the method comprises the steps: obtaining the starting and ending points of the flight of an unmanned aerial vehicle, and planning an initial unmanned aerial vehicle flight path based on the starting and ending points of the flight of the unmanned aerial vehicle and a preset constraint; constructing an unmanned aerial vehicle flight environment model based on a preset flight area map, the elevation information and the obstacle information; route optimization is carried out based on the initial unmanned aerial vehicle flight route and a flight environment model, and an unmanned aerial vehicle initial obstacle avoidance route is generated; the unmanned aerial vehicle initial obstacle avoidance route is optimized based on a preset iterative optimization model, and an optimized unmanned aerial vehicle obstacle avoidance route is obtained; and updating the optimized unmanned aerial vehicle obstacle avoidance route to the initial unmanned aerial vehicle flight route to generate a final unmanned aerial vehicle flight route. According to the invention, the flight time and cost are saved, the adaptability to the environment, especially the urban complex environment, is enhanced, and the safety is guaranteed.

The invention discloses an intelligent scheduling method based on task optimization. According to the method, firstly, the task load of each time period is predicted through historical data, and then an M / M / c queuing model is established to quantify a task queuing duration expected value as an optimization target. A two-stage optimization strategy is adopted: in the first stage, the optimal number of service desks in each time period is determined by applying dynamic planning, and accurate distribution of human resources is realized; in the second stage, specific employees are distributed to shifts through constraint planning, and meanwhile, complex constraints such as total working time fixing, continuous working duration, shift intervals and skill matching are met. According to the system, a queuing theory, constraint optimization and a prediction model can be combined, and on the premise of ensuring the feasibility of a scheduling scheme, the task processing waiting time is effectively shortened, and the service quality and the human resource utilization rate are improved.

The invention discloses a ground wire adaptive visual trajectory tracking control method for unmanned aerial vehicle inspection, relates to the technical field of unmanned aerial vehicle inspection, and solves the problem that it is difficult to effectively fuse geometric, texture and multispectral features to construct a ground wire lightweight model. And a three-dimensional coupling constraint system is difficult to be combined to adapt to a dynamic scene of ground wire inspection. Comprising the following steps: collecting an initial visual image and multispectral data of a ground wire, extracting and splicing geometric, texture and state features, and constructing a ground wire lightweight model based on knowledge distillation to output a state evaluation result; the flight controller combines the model and geographic information to construct a three-dimensional coupling constraint system, calls a dynamic programming algorithm to generate an initial trajectory and sets a constraint deviation real-time monitoring mechanism; multi-dimensional deviation is calculated, and comprehensive deviation is obtained through weighting of the incidence matrix; and if the deviation exceeds a threshold value, starting a lightweight prediction controller to solve an optimal control problem and adjust flight parameters to generate a new trajectory, otherwise, flying along an initial trajectory.

The invention provides a multi-database automatic synchronization method under an AI platform, and belongs to the technical field of database automatic synchronization. The captured change records are converted into synchronous messages in a unified format, the synchronous messages are input into an intelligent scheduling optimization model for dependency analysis and transaction reordering based on dynamic programming, data conversion rules are dynamically generated according to mode version differences, and data consistency is verified through a Merkle tree hierarchical structure; the multi-stage self-adaptive compression transmission and data writing are completed after the target node executes conflict detection and circular dependency processing, and finally the final consistency among the multiple databases is guaranteed through a periodic full-amount account checking task, so that the technical problem of synchronization performance bottleneck caused by low transaction scheduling efficiency in the automatic synchronization process of the multiple databases is solved.

The invention discloses a ship route planning method and system in a polar region sea ice environment and a program product. The ship route planning method comprises the steps of preprocessing satellite remote sensing radiation intensity data, performing image reconstruction of inter-ice water channel detection through an improved Vision Transform model, planning and visualizing a ship route and the like. The deep features of the image blocks are extracted through a Transformer encoder which comprises a multi-head self-attention mechanism and is processed by a multi-layer perceptron; and the Transform encoder firstly performs layer normalization on the input, and then performs multi-head attention mechanism and multiple linear transformation and regularization processing. And realizing path planning in an inter-ice water channel environment by fusing a dynamic planning cost function through a fast marching method. Compared with a traditional algorithm, the method has the advantages that the calculation precision is higher in the same scene, the calculation efficiency is greatly improved, and the planning result obtained after multiple costs are comprehensively considered is given instead of pursuing the shortest distance.

The invention relates to a multi-round compression super-long text summarization system and method based on a large language model, and belongs to the technical field of natural language processing. The system comprises a text partitioning module, a semantic map construction module, a multi-round compression controller, a hierarchical abstract generator and an abstract fusion optimization module, a global semantic structure is extracted through semantic partitioning and map construction, a compression strategy is dynamically planned in the multi-round compression process, and a local abstract is generated. And finally fusing, optimizing and outputting a consecutive and redundancy-removed summary text. According to the scheme, the context length limitation of a large language model is broken through, and the information integrity and the processing efficiency of the super-long text abstract are improved.

The invention provides a voice synthesis method and system based on VITS improvement, and the method comprises the steps: optimizing a text encoder of a VITS model, introducing a large language model, and enabling the emotion, intention and speaking style of an input text to be captured when the text is encoded; a random disturbance item is introduced when the Q value is dynamically planned and solved, the alignment flexibility in the initial training stage is improved, meanwhile, monotonicity constraint is strictly kept, and it is avoided that a suboptimal solution is obtained through convergence too early; a ConvNeXt module is used as a basic backbone network of a decoder, and ISTFT is utilized to efficiently reconstruct a time domain signal, so that waveform up-sampling is realized, redundant calculation of traditional transpose convolution is avoided, and reasoning is accelerated. According to the method, the reasoning speed, the emotion expression ability and the style control flexibility of speech synthesis can be effectively improved, a new solution is provided for cross-language diversified speech synthesis, and a reference is provided for the more efficient and more intelligent development of the speech synthesis technology.

The invention discloses a mechanical arm autonomous grabbing system and method based on visual guidance and visual touch fusion. The mechanical arm autonomous grabbing system at least comprises a visual semantic guidance and grabbing pose generation module, a smooth grabbing control module based on visual touch fusion and a dynamic planning module based on model prediction path integration. The visual module efficiently generates a six-degree-of-freedom grabbing pose from the point cloud data by using a deep learning network; the smooth grabbing control module monitors object slippage in real time and adaptively adjusts clamping force by fusing visual and tactile information, and stable grabbing of fragile or slippery objects is achieved. And the dynamic planning module adopts a model prediction path integral algorithm MPPI to realize collision-free path planning of the mechanical arm in a complex dynamic environment. The problems that an existing mechanical arm is insufficient in sensing capacity, poor in grabbing robustness and low in path planning efficiency in an unstructured scene are solved, and the autonomous operation capacity and safety of the mechanical arm in the scenes of accompanying, intelligent sorting and the like are remarkably improved.

The invention provides a power distribution network fault section rapid calibration method based on a feeder terminal. According to the method, power distribution network line electrical quantity and distributed power supply output information are collected in real time through a feeder terminal and preprocessed, then fault transient signals are processed through complete set empirical mode decomposition of mean value self-adaptive noise, and intrinsic mode components are obtained to judge fault types. Thirdly, fault section preliminary positioning is carried out, and an approximate area is determined according to a positive sequence voltage vector for a symmetric short-circuit fault, a negative sequence voltage vector for an asymmetric short-circuit fault and a zero sequence current amplitude for a single-phase earth fault; then accurate calibration is carried out, a single-phase earth fault is judged through a filter and correlation coefficients, and short circuit and broken line faults are calibrated through a model or an intelligent algorithm in combination with line topology and electrical parameters. And finally, establishing a target function containing a penalty term, converting the target function into a Markov decision process, solving through an approximate dynamic programming algorithm, realizing rapid and accurate calibration of a fault section, and improving the power supply reliability of the power distribution network.

The invention discloses an underwater acoustic processing CPU-GPU dynamic load balancing method based on a task flow model, and belongs to the technical field of underwater acoustic processing. The method comprises the following steps: deconstructing an underwater acoustic processing application into a task flow model represented by a directed acyclic graph; establishing a feature portrait including calculation complexity, parallelism and data throughput for each task node, and constructing a cost prediction model; the CPU / GPU utilization rate and the data transmission performance are monitored in real time; a processor is distributed to each task node by using a dynamic programming algorithm in combination with a cost prediction model and a real-time system state with the goal of minimizing the total task flow completion time; and dynamically scheduling tasks through a central scheduler according to a decision result, and periodically updating a strategy. According to the method, the defects that static task division lacks adaptability and neglects task dependence and communication overhead are overcome, dynamic and efficient utilization of CPU and GPU resources is achieved, and the efficiency and real-time performance of underwater sound processing are remarkably improved.

The invention discloses a global scanning image splicing and defect positioning system for an ultra-large-diameter quartz crucible, and relates to the technical field of quartz crucibles, and the system comprises a scanning control module which constructs a fusion model through multiple sensors, evaluates a risk coefficient, obtains the fusion model, dynamically plans a multi-arm path of a robot based on reinforcement learning, and sends the multi-arm path of the robot; adaptively adjusting the acquisition frequency of the corresponding area based on the risk coefficient, and outputting multi-modal data through a multi-round feedback iteration optimization acquisition process; the image splicing module is used for acquiring multi-modal data for local splicing, and generating a panoramic image in combination with a multi-arm path of the robot; the defect positioning module is used for constructing a quartz crucible model through the panoramic image, detecting defects in a layered manner by utilizing a YOLOv5 model, tracking the defects through extended state Kalman filtering, quantifying the defect volume and the hazard level by combining point cloud semantic segmentation, and performing 3D positioning and evaluation on the defects; the output data is ensured to always meet the subsequent image splicing and defect positioning requirements, and the system stability is guaranteed.

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 a material scheduling optimization method based on a dynamic programming and genetic algorithm hybrid strategy. The method comprises the following steps: establishing a material scheduling task model; constructing a dynamic planning state space and a state transition relation; a dynamic planning method is adopted to solve the scheduling problem of the material scheduling task model in stages, and a plurality of initial feasible solutions are obtained; constructing a genetic algorithm initial population on the basis of an initial feasible solution obtained by dynamic programming; executing genetic algorithm evolution operation based on the genetic algorithm initial population; a local dynamic planning mechanism is embedded in the genetic algorithm evolution process, and fine adjustment repair is carried out on part of individuals so as to improve the individual fitness; judging whether a preset convergence condition is met or not; and outputting an optimal or approximately optimal scheduling result. According to the method, the local optimization capability of dynamic planning and the global search capability of the genetic algorithm are fused, the quality and the solving efficiency of the scheduling optimization result are remarkably improved, and the method is suitable for various material scheduling scenes such as manufacturing, logistics and construction.

The invention discloses a multi-mode urban traffic prediction system, belongs to the technical field of traffic, and solves the problems that a tunnel structure is not early warned in time due to hidden damage under the action of multiple coupling, and finally, a river-crossing tunnel bursts water suddenly and is forced to be closed under the triggering of peak traffic flow vibration, so that the tunnel structure cannot be early warned. Therefore, the problem of chain paralysis of the traffic system of the whole city is solved. Comprising a multi-modal data acquisition module, a damage evolution modeling module, a traffic influence analysis module, a collaborative optimization control module and a dynamic plan generation module. According to the method, the sensing capability is constructed by fusing multi-source monitoring data, hidden structure damage is identified and predicted by means of a multi-physics field coupling model, a traffic collaborative optimization strategy is generated based on adaptive dynamic planning, and then whole-process prevention and control from risk to emergency are realized through a dynamic plan and meta-learning. Therefore, the vicious circle of structural damage-traffic jam-rescue blocking is blocked, and regional paralysis is avoided.

The invention discloses a dynamic obstacle avoidance method and system for a substation inspection robot. The position of the robot is positioned based on laser point cloud data, and static and dynamic obstacle features are extracted at the same time. An environment association graph is constructed through perception information in the advancing process, and feature information of each node serves as an attribute of the environment association graph; calculating the distance, the relative speed and the relative angle between the edge weights of the nodes according to the nodes; on the basis of the environment association graph, aggregating all-graph obstacle node information to generate real-time feature representation for robot nodes, and correcting a calculation mode of attention weight according to edge weight between the nodes; and finally outputting a real-time feature vector of the robot node. And defining a reinforcement learning framework and executing path planning. According to the invention, through combination of GAT and dynamic planning, efficient obstacle avoidance and path planning of the robot in a substation environment are realized, and the decision and execution capability of the robot in a dynamic complex scene can be improved.

The invention relates to the technical field of medical decision assistance, and particularly discloses a doctor-patient co-decision multi-issue negotiation method and system and a readable storage medium, and the method comprises the following steps: firstly collecting preference information and behavior characteristics of a doctor and a patient for a plurality of negotiation issues; constructing a fuzzy constraint and satisfaction function based on preference information and behavior characteristics; constructing a proxy behavior model based on the fuzzy constraint and the satisfaction function, and forming a distributed negotiation problem; and dynamically optimizing the negotiation strategy through a value function by adopting an ADP (adaptive dynamic programming) algorithm until the negotiation is consistent to generate a common negotiation result or the negotiation fails and terminates the negotiation, and sending a recommendation scheme to the doctor agent and the patient agent. According to the doctor-patient co-decision multi-issue negotiation method and system and the readable storage medium, the negotiation success rate and efficiency are improved, the medical specialty and patient individuation requirements are balanced, the doctor-patient satisfaction is remarkably improved, and the doctor-patient co-decision multi-issue negotiation method and system are suitable for a multi-issue medical decision scene.

The invention relates to the technical field of visual health management, in particular to a ciliary muscle adjustment training system and method based on eye movement tracking, and the method comprises the steps: collecting eye movement data of a fixation point track, an adjustment amplitude change rate and pupil response time in real time, and combining personalized parameters such as the age of a user, diopter and a training target; inputting a ciliary muscle training dynamic planning algorithm module, dynamically generating a stage scheme containing a training action type, a single-group duration, a stimulation interval and a focal length adjustment strategy, and adaptively adjusting the strategy according to real-time adjustment stability and a historical ability curve; through multi-dimensional matching analysis of a pre-stored standard eye movement template, a training specification score is calculated from the track goodness of fit, speed uniformity, amplitude standard-reaching rate and binocular coordination, visual fatigue parameters are obtained based on continuous training duration, pupil fluctuation and retina reflex change, a scoring result is dynamically corrected, and a visual fatigue evaluation result is obtained. Personalized, self-adaptive and quantitative safety control of the training process is realized.

The invention relates to the technical field of agent path optimization, in particular to a multi-agent dynamic high-dimensional manifold planning and conflict-free control system and method, and the method comprises the steps: obtaining the multi-dimensional dynamic state information and motion intention of an agent, carrying out the analysis processing, generating the state and intention data flow of the agent, and storing the state and intention data flow of the agent; constructing on a dynamic high-dimensional manifold in combination with a motion and dynamic model of the intelligent agent, and generating a high-dimensional space-time pipeline of the intelligent agent; detecting whether the high-dimensional space-time pipelines of any multiple agents have geometric overlap in the dynamic high-dimensional manifold or the probability of geometric overlap in a future preset time period is greater than a preset probability, generating manifold crossing information, performing collaborative planning, generating a high-dimensional space-time pipeline without geometric overlap, and converting to generate a control instruction sequence; and sending to the intelligent agent. According to the scheme, predictive and collaborative dynamic planning and optimization are carried out on behaviors of multiple agents, and the operation efficiency of the whole system and the operation efficiency of the individual agents are maximized on the premise of safety.

The invention belongs to the technical field of energy system planning, and particularly relates to a park integrated energy system stochastic planning method and system based on multiple uncertainties, and the planning method comprises the steps: building a probability model of a multi-energy load growth rate based on park industrial planning and historical data; utilizing Monte Carlo simulation and K-means clustering to generate a representative load scene tree; establishing an upper and lower boundary prediction model of the energy price and the equipment cost by adopting a quantile regression forest method; constructing a multi-stage collaborative optimization model taking the minimum comprehensive cost expectation as a target, and considering constraint conditions such as power flow, operation, time sequence and space; and carrying out reverse recursion solution by utilizing a dynamic programming algorithm, and outputting an optimal equipment configuration and construction scheme of each stage. According to the method, the problems of load increase unpredictability and energy market price fluctuation risk in different development stages of the park energy system are solved by combining scene analysis, data-driven modeling and a dynamic optimization mechanism.

The invention belongs to the field of offshore wind power, and discloses a single-pile offshore wind turbine power regulation and load relief method, which comprises the following steps of: constructing a thrust system model of wind to a rotor and a tower; constructing a dual-mass transmission system model of the wind turbine generator; constructing a tower top front-back displacement model and a pile soil front-back displacement model; constructing a rotor speed tracking error model based on an adjustment error method; constructing a tracking error model of the change rate of the front-back displacement of the tower top; constructing an optimal control strategy based on an infinite vision field optimal cost function; and designing an optimal pitch control scheme based on a robust adaptive dynamic programming algorithm. According to the design of the RADP-based propeller pitch controller provided by the invention, the MOWT model does not need to be completely known, so that the calculation cost of the controller is reduced, and the calculation efficiency is improved.

According to the intelligent navigation method and device for the inspection robot, a map fusion system is innovatively designed, and the environment is effectively expressed through multi-source data and rasterization processing. And an environment perception mechanism is constructed, and a reliable obstacle recognition model is established in combination with semantic analysis and trajectory prediction. Dynamic planning is introduced, and the accuracy of obstacle avoidance is ensured through interference analysis and state optimization. According to the method, the defects of the traditional technology in the aspects of map fusion, environment perception, dynamic planning and the like are effectively overcome, and technical guarantee is provided for the inspection robot.

The invention relates to the field of remote communication network optimization, and discloses a predictive remote communication method and system. The method comprises the following steps: generating a time sequence sample structure based on historical communication data, and constructing a deployable model through LSTM model training and Bayesian optimization; receiving a communication request in real time, predicting a demand probability, and triggering a dynamic handshake protocol to be connected with the security encryption; generating optimal path configuration by combining network topology analysis and a dynamic programming algorithm; and finally, online updating of the model is realized through performance monitoring and incremental learning. According to the method, time sequence prediction and dynamic path optimization are fused, the problems that model training and network topology are disjointed, dynamic optimization lags and the like in a traditional method are solved, the connection establishment speed and the transmission stability in a high-delay scene are remarkably improved, and meanwhile the self-adaptive capacity of the system is enhanced through embedded packaging and a hot updating mechanism.

The invention relates to the field of port energy management, and provides a bulk cargo wharf production operation process-based energy consumption prediction method and system, and the method comprises the steps: obtaining a cargo attribute information set and an equipment parameter set, and constructing a linkage database through context fusion, time sequence storage and data cleaning; based on a cargo attribute information set splitting process, performing verification and collaborative division in combination with an equipment archive and an operation emergency degree coefficient, and performing risk assessment according to theoretical and actual parking duration to generate a link list; according to the link list and the real-time working condition, a candidate device set or a compensation scheme is output in combination with the mapping table, a collaborative operation trigger signal is generated, and a device allocation list is generated through resource allocation when a collaborative scheme is started; constructing an initial energy consumption prediction model, and carrying out online iterative optimization; and calculating each operation link and a final energy consumption prediction result, and generating a final energy consumption prediction report. According to the method, dynamic planning of a collaborative operation process and adaptive prediction of energy consumption are realized, and the method is suitable for port smart energy management and automatic scheduling.

The invention relates to the technical field of medical information processing, in particular to a path planning device of a bronchoscope. Firstly, multi-modal medical image data of a plurality of breathing phases of a patient are collected through a construction module, and a three-dimensional model containing a focus, a bronchus and an accompanying blood vessel is generated. Then, the three-dimensional model is loaded to AR display equipment, AR virtual dissection is carried out on the three-dimensional model through a virtual dissection module based on the target operation instruction until the transparency of the focus reaches the set transparency, and a dissection model corresponding to the three-dimensional model is obtained. An initial path is then generated in the anatomical model based on a preset security constraint. Finally, the initial path is dynamically updated through a dynamic planning module in combination with the real-time breathing phase, and a target path is obtained. According to the invention, a focus shielding blind area is broken through, the target fine bronchial segment shielded by the focus is visually presented, and the operation precision and safety of the bronchoscope are improved.

The invention provides a ramp energy-saving control method based on off-line speed planning and dual-mode model prediction tracking, and aims to solve the problems of insufficient precision of ramp driving energy consumption and safety cooperative control and frequent mode switching of an automatic driving vehicle. The method comprises the following steps: firstly, identifying a key road section based on high-precision road gradient data, and generating an optimal energy-saving reference speed matched with gradient characteristics in an off-line manner by adopting a dynamic planning and sequential quadratic planning hybrid optimization strategy; secondly, when the vehicle runs, relevant information is loaded after confirmation of a driver, the system takes over the longitudinal control right, two control modes are dynamically switched based on the front vehicle distance, and frequent mode switching is avoided through a hysteresis threshold value; and finally, taking the slope-fused vehicle longitudinal dynamics model as a prediction model, solving an optimal control instruction online by adopting a model prediction control algorithm, performing closed-loop execution, monitoring intervention operation of a driver, and immediately returning the driving right after the intervention operation is detected. According to the method, offline planning and online dual-mode tracking technologies are integrated, the method has the advantages of high energy consumption optimization precision, stable mode switching and good safety adaptability, and energy-saving and safe driving of the automatic driving vehicle in a ramp scene is realized.