298 results about "Dynamic planning" patented technology

The invention discloses a thermal power plant intelligent inspection path dynamic planning and anomaly recognition method, and particularly relates to the technical field of thermal power plants, and the method comprises the following steps: S1, constructing a dynamic three-dimensional risk thermodynamic diagram and an equipment health gene code; s2, multi-target dynamic path planning; s3, multi-modal data collaborative acquisition and anomaly detection are carried out; s4, performing abnormal root cause association and risk level judgment; s5, re-planning a closed-loop path of abnormal driving; and S6, executing and dynamically updating. According to the invention, through a core technology architecture of dynamic risk thermodynamic diagram-equipment health gene code-multi-modal anomaly detection-closed-loop path planning, full-process optimization of intelligent inspection of the thermal power plant from risk perception, path decision-making to anomaly handling is realized, and the inspection efficiency, the equipment reliability and the emergency response capability are significantly improved; meanwhile, risks of man-made misjudgment and system failure are reduced, and core technical support is provided for intelligent transformation of the thermal power plant.

The invention discloses a complex water area garbage intelligent detection and collection method based on multi-modal perception, and the method comprises the steps: obtaining image perception data and semantic environment data of a complex water area; the method comprises the following steps: performing preprocessing, performing feature extraction and semantic coding, and then performing multi-modal feature alignment fusion, feature extraction and multi-task detection to obtain a garbage detection result, thereby effectively realizing deep fusion of semantic priori and visual features, and improving the accuracy and robustness of garbage recognition in a complex water area environment; then task importance evaluation is carried out to construct a garbage disposal priority queue, a path dynamic planning strategy oriented to energy consumption constraint and task priority collaborative optimization is constructed to carry out path generation, navigation energy consumption is reduced on the premise that task benefits are considered, and garbage detection and collection tasks in a complex water area are achieved; the system adapts to various intelligent devices such as unmanned ships, unmanned aerial vehicles and shore-based platforms, and has high autonomy, environmental adaptability and engineering deployability.

The invention discloses an L-shaped conveyor belt path dynamic planning and congestion prediction system, which relates to the technical field of automatic control, and constructs a conveying path node diagram by collecting material weight, speed and spacing information of each node on a conveying path, and calculates a congestion index of each path section; the method comprises the following steps: combining a light-mass material jumping sensitivity model, identifying a high-risk node and marking the high-risk node as a congestion prediction area, constructing a dynamic optimization model based on path risk distribution and a material flow direction, generating and evaluating a plurality of path adjustment strategies, executing an optimal strategy according to priority, and realizing path speed adjustment, standby path switching and feeding rhythm control; through a feedback mechanism, a strategy effect is evaluated in real time, a path state is updated, operation data is synchronized to a historical database, a learnable conveying operation file is formed, the stability and the intelligent response capability of a conveying system can be effectively improved, and the method is suitable for high-speed sorting and automatic packaging scenes.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as personal intelligence, financial science and technology and medical health, and discloses a mechanical arm task planning and control method, device, equipment and medium. Encoding visual information and task description language information to obtain visual features and language features, generating a target action sequence by using a language model, generating a basic path through dynamic planning under an environment model, adjusting the basic path according to environment change in combination with reinforcement learning, generating a final path, and decoding the target action sequence into a joint control instruction. And generating a motion control signal based on the final path, and optimizing a joint control instruction in combination with environment feedback. According to the method, through multi-modal information fusion and mixed path planning, the control instruction is dynamically optimized in combination with environment feedback, and the operation efficiency and task generalization ability of the mechanical arm in a complex environment are improved.

The invention discloses a polar region sea area ice area route dynamic planning method based on multi-source data, and the method comprises the following steps: obtaining sea ice, ocean current and wind field historical data of a target channel sea area, and carrying out the preprocessing; inputting a preprocessing result into a constructed sea ice forecasting model to obtain a sea ice concentration and sea ice thickness distribution forecasting result of a planning day; combining the forecast result with ocean current and wind field forecast data, and utilizing a pre-established random forest navigational speed reasoning model to obtain target channel sea area planning daily navigational speed space distribution data; and carrying out route planning with minimum time cost through an improved A * algorithm to obtain a dynamic planning result of the target channel sea area route. The invention relates to the technical field of ship navigation in a sea ice area, and relates to the technical field of ship navigation in the sea ice area, a polar sea area navigation speed background field is deduced according to a fitting relationship through random forest modeling and fitting of the relationship between the navigation speed and sea ice, ocean current and a wind field, and dynamic route planning with minimum time cost is carried out.

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 relates to the technical field of unmanned aerial vehicle path planning, in particular to an unmanned aerial vehicle path planning method and system based on laser scanning. The method comprises the following steps: scanning a roadway environment in real time by using a laser scanner to obtain original radial section point cloud data; analyzing a structural stability index and a dangerous rock potential energy threat value based on the original radial section point cloud data to determine a risk attribution area of the current flight path of the unmanned aerial vehicle; judging whether a high-risk detour area exists or not according to the risk attribution area, and if yes, dynamically planning a risk detour path so as to adjust a single pre-scanning voyage and generate an intelligent safety execution route; and instructing the unmanned aerial vehicle to fly to the terminal point along the intelligent safety execution route, and taking the terminal point as the starting point of the next round of path planning until the iterative exploration of the target underground mine space is completed. According to the method, the structural stability and potential energy threat of the surrounding rock are quantified in real time to realize safe path planning for actively avoiding surrounding rock instability.

The invention discloses a dynamic planning method and system for an intelligent patrol point location of power transformation equipment, and belongs to the technical field of intelligent patrol of power systems, the dynamic planning method for the intelligent patrol point location of the power transformation equipment comprises the following steps: mapping dynamic features and static attributes to the same feature space and carrying out cross-modal association; constructing a defect severity model, and introducing defect severity in risk quantification calculation to generate a point location priority list; a transformer substation three-dimensional point cloud model is built, path nodes are initialized according to a point location priority list, and a greedy algorithm is utilized. A closed-loop decision-making system of multi-modal data fusion is constructed; according to the method, automatic planning driven by a risk quantification model based on defect history, generation of a three-dimensional space non-blind area coverage path, dynamic adjustment triggered by two factors of equipment change and inspection effect, deep collaborative analysis of machine account-defect-real-time data and continuous inspection of a complex scene are guaranteed by a semantic compensation mechanism.

The invention provides a dynamic planning method for a fire evacuation path of a deep subway station based on a cellular automaton, and the method comprises the steps: building a subway space topology through employing the cellular automaton, simulating a fire environment through employing an FDS tool, and obtaining environment data; constructing a multi-field model based on subway space topology and environment data; based on the multi-field model, the pedestrian transition probability is calculated, then the pedestrian transition probability is corrected in combination with the deep subway facility characteristics and the pedestrian consensus behaviors, and finally the corrected pedestrian transition probability is output; and optimizing a cost function of an A star algorithm based on the corrected pedestrian transition probability to obtain a dynamic cost function, further obtaining an improved A star algorithm, planning an initial path based on the improved A star algorithm, formulating a path updating rule, and implementing a smooth transition strategy to realize dynamic path planning. According to the invention, the evacuation safety and the scheduling efficiency under the fire situation can be improved.

The invention discloses a wafer carrying speed control method and system, and the method comprises the steps: obtaining the three-dimensional data of a carrying path, decomposing the data into a straight line and a bent segment, and obtaining geometric features; the curvature of the bent section is calculated and smoothly connected, and continuous curvature distribution is determined; dynamically constraining the speed upper limit of each point according to the curvature, and generating a preliminary speed curve; the speed of each axis is calculated through inverse kinematics, iterative optimization is carried out until the speed of each axis is coordinated, and a multi-axis cooperative speed sequence is generated; calculating attitude deviation based on real-time sensor data fusion; and dynamically adjusting the acceleration parameter, optimizing and reducing the deviation, and obtaining a final control instruction. According to the control method, through curvature dynamic planning, multi-axis collaborative optimization and real-time posture self-adaptive adjustment, synchronous improvement of the carrying speed and stability is systematically achieved.

The invention relates to the technical field of intelligent vocational ability analysis, and discloses a dynamic planning method for a vocational ability genetic map based on multi-modal data and federal learning. The method comprises the steps that multi-source heterogeneous data in occupational activities is collected, and a multi-modal data set is formed through data cleaning and standardization processing; a graph neural network technology is utilized to construct a correlation topology between capability elements, spatio-temporal context information is fused, a dynamically evolved vocational capability gene map is established, and the accuracy and interpretability of vocational capability analysis are improved. Multi-node cooperative training is realized by adopting a federated learning framework, data privacy security is ensured in combination with a differential privacy technology, and the problem of data islands is solved. A capability evolution path is simulated based on a three-dimensional visualization and digital twinning technology, an occupational development scheme is generated through an intelligent recommendation algorithm, and a real-time feedback optimization mechanism is established. According to the method, dynamic, visual and personalized vocational ability analysis is realized, and accurate decision support is provided for vocational planning.

The invention provides an emergency rescue path planning method and system for adaptive risk avoidance. The method comprises the following steps: establishing an emergency rescue road network structure; the method comprises the following steps: before an actual natural disaster comes, carrying out risk entropy driven adaptive Monte Carlo simulation analysis, carrying out path information statistical analysis on a Monte Carlo simulation result, and establishing a pre-planning path library; when an actual natural disaster comes, prior risk distribution of each planned path in the pre-planned path library is established; performing dynamic correction on the prior risk distribution of each planned path in the emergency rescue process by adopting sequential Bayesian reasoning; and carrying out emergency rescue path dynamic planning based on the path posterior risk mean value of each planning path after sequential Bayesian reasoning, selecting an optimal rescue path, and executing an emergency rescue task until the emergency rescue activity is finished. According to the invention, through a path dynamic adjustment strategy, a decentralized transportation strategy, a path pre-planning strategy and a network-free communication strategy, rescue resources are ensured to efficiently and safely arrive at a target location in natural disaster scenes such as flood and typhoon.

The invention provides an intelligent agent dynamic planning and regulation method and device, electronic equipment and a storage medium, and relates to the technical field of data processing, and the method comprises the steps: obtaining task request information and historical interaction context of a user, carrying out the semantic analysis and structural processing of the task request information and the historical interaction context, and obtaining the task request information and the historical interaction context; constructing a situation map representing the current task environment, wherein the situation map comprises nodes representing task elements and edges representing relationships among the elements; inputting the situation map into a pre-training language model, identifying a dominant target and a hidden demand of a user through chain reasoning, and generating a structured intention map; matching calculation is carried out based on the intention map and a preset agent capability map, a task execution path is generated according to the matching degree and the system resource state, and the task execution path defines an agent sequence needing to be called for completing the task and a cooperation mode of the agent sequence.

The invention relates to the technical field of evacuation intelligent simulation, in particular to a fire-fighting evacuation path dynamic planning method and system based on digital twinning. In the fire-fighting evacuation path dynamic planning method and system based on digital twinning, a digital twinning environment containing multi-dimensional real-time sensing data such as temperature, smoke and heat radiation intensity is constructed; the method can dynamically analyze space-time evolution characteristics such as fire spreading trend and heat accumulation, further carries out accurate quantitative evaluation on the danger level of each segment of an evacuation path, and meanwhile, integrates video monitoring and wireless positioning information to grasp the specific positions and moving tracks of people in a building in real time, thereby improving the safety and reliability of the evacuation path. The actual flow bottleneck is identified based on the flow direction consistency, the distribution density and the congestion condition of the crowd, and finally, the dynamic danger of the fire environment and the macroscopic flow trend of the crowd are combined, and the passage cost value of each path section is subjected to dual dynamic correction, so that the flow rate of the crowd is improved. And an evacuation path planning result giving consideration to fire hazard avoidance and crowd congestion evacuation is generated, so that the real-time performance and safety of evacuation decision making are effectively improved.

The invention discloses an unmanned aerial vehicle autonomous path dynamic planning control system based on fault analysis, relates to the technical field of unmanned aerial vehicle flight control, and aims to solve the technical problems that when an existing unmanned aerial vehicle encounters a fault in flight, a path cannot be rapidly adjusted, and task failure and even air crash are easily caused. The routing inspection task module is used for receiving mountain wind turbine generator blade routing inspection task information sent by a service object, and the path planning module is used for receiving the task information and dynamically planning the current path of the unmanned aerial vehicle through task data in combination with the influence of wind turbine generator blade rotation, air turbulence generated by blade rotation and environment airflow on path planning. According to the method, the association degree of each blade and the fault risk can be accurately quantified, the problem of fuzzy association of the fault risk and the blade in a multi-unit cluster scene is solved, the unmanned aerial vehicle can pertinently avoid the risk source blade, and data acquisition control of the unmanned aerial vehicle in the wind turbine unit inspection process is effectively completed.

The invention discloses a dynamic planning method and system for smart city traffic, and relates to the technical field of smart cities and intelligent traffic, and the method comprises the steps: collecting multi-source traffic data, and carrying out the fusion preprocessing; constructing a dynamic weight model to calculate a priority weight; a signal lamp timing scheme is generated based on the weight and is adjusted through a rolling optimization mechanism; outputting an optimization strategy by combining historical and real-time feedback evaluation effects; and a manager is notified through the acousto-optic prompt device and a report is generated. According to the invention, through the dynamic weight model and the rolling optimization mechanism, the overall delay time of the intersection is reduced, the traffic capacity of the traffic flow is improved, the Kalman filter is introduced to fuse multi-source data, and the data processing precision is improved; the LSTM network is adopted to predict the future traffic flow, a scientific basis is provided for a signal lamp timing scheme, and the workload of traffic management personnel is reduced through the acousto-optic prompt device and the automatic report generation module.

The invention relates to the technical field of path planning, in particular to an intelligent sorting path dynamic planning method based on digital twinning. The method comprises the following steps: constructing a digital twinborn body corresponding to a medical logistics transportation system, obtaining multi-source data corresponding to a transportation robot, carrying out space-time alignment and abnormity cleaning processing, and generating a standardized robot operation data set, a material attribute feature set, an environment state parameter set and an order priority matrix; motion feature parameters are extracted based on a standardized robot operation data set, and material sorting constraint parameters are analyzed in combination with a material attribute feature set; meanwhile, constructing an environment constraint matrix; and carrying out association analysis on the order priority matrix and the material sorting constraint parameters, constructing a multi-objective optimization function to carry out sorting path planning analysis and virtual simulation testing, and carrying out dynamic adjustment iteration updating at the same time to generate a corresponding medical material sorting dynamic optimization path. According to the invention, the medical logistics sorting path planning efficiency can be improved.

The invention belongs to the technical field of path planning, and particularly relates to a multi-AGV textile workshop material distribution path dynamic planning method and system, and the method comprises the following steps: S1, obtaining the position and speed of each AGV, and the Lidar and visual data of the environment, and recognizing and classifying the barriers, such as textile equipment, raw material stacks, workers and the like, in the environment through a semantic segmentation network; and fusing the position information of the Lidar point cloud and the semantic category information of the obstacle, constructing an asymmetric probabilistic risk potential field for different categories of obstacles, and ensuring that the risk potential field gradient of a region corresponding to a worker is the highest. According to the method, the AGV can accurately distinguish different obstacles such as working personnel and equipment by utilizing the semantic segmentation network and constructing the asymmetric risk potential field, so that the operation safety in a complex man-machine co-fusion environment is improved, and particularly, the safety of the personnel is guaranteed.

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 discloses an indoor barrier-free path dynamic planning and voice navigation system, and particularly relates to the technical field of path dynamic planning, and the system comprises a multi-source sensing fusion module, a motion mode modeling module, a path planning module and a voice navigation module. The system constructs a multi-modal intermediate representation tensor by fusing data of an RGB-D camera, a millimeter wave radar and a thermal infrared module, and realizes the recognition of the geometrical shape, the material type and the heat source state of a target based on a multi-task neural network model. In combination with track caching and state machine modeling methods, the system dynamically discriminates the motion mode of the obstacle target and constructs a state transition structure with time sequence consistency. The path planning module generates a path with fault tolerance and high accessibility according to the target semantic category and the motion state result in combination with map topology and navigation confidence constraints. And the voice navigation module generates a personalized voice instruction according to the path structure information and the current state of the user.

The invention relates to the technical field of logistics automation and intelligent scheduling, and discloses a multi-objective optimization logistics distribution path dynamic planning method, which comprises the following steps: S1, mapping a logistics distribution area and vehicles, clients and constraint entities in the area to a continuous coordinate space; s2, in the continuous coordinate space, optimizing cost, time, priority and constraints based on the mapped clients and constraint entities, and respectively constructing corresponding decision force fields; s3, based on a preset strategy adjustment matrix, carrying out linear transformation and combination on the plurality of constructed decision force fields so as to synthesize total decision force acting on the space; and S4, taking the synthesized total decision-making force as an external driving force. According to the method, through the strategy adjustment matrix and the path dynamic evolution equation, the high-level strategy and the path physical characteristics are deeply coupled, and refined, smooth and efficient regulation and control of multi-target dynamic path planning are realized.

The embodiment of the invention provides an intelligent prediction and decision-making method for military demand material guarantee, and the method comprises the steps: constructing a multi-source military demand data fusion framework system, and integrating multi-dimensional material demand feature information; on the basis of military rules and business knowledge, defining and quantifying feature vectors of different combat scenes, and establishing a scene classification model; constructing a military demand large model based on scene dynamic supply, and fusing scene information and material demand feature information by adopting a layered attention mechanism; implementing a multi-stage training strategy to complete military demand large model training; and based on a prediction result of the military demand large model, constructing a dynamic supply decision module based on reinforcement learning, and realizing real-time optimization of a supply scheme. The embodiment of the invention has the advantages of high prediction precision, strong adaptability, decision-making intelligence, high resource utilization rate and good expandability, and realizes intelligent prediction, dynamic planning and accurate supply of military supplies.

The invention provides a vaccine cold storage AGV operation navigation method and a cold storage AGV, and the operation navigation method comprises the following steps: S1, dividing cold storage regions by taking temperature collection points as boundaries, and determining the temperature distribution in each region; s2, obtaining AGV position data and temperature data of a current position in real time; and S3, determining a starting point and a target point, establishing a multi-objective function with the minimum temperature variation, the minimum path length and the minimum energy consumption, and solving the multi-objective function to obtain a dynamic planning optimal path. According to the invention, path planning is carried out through temperature distribution in each area, temperature shock areas are avoided, temperature fluctuation of vaccines in the transportation process is reduced, and the damage rate of goods is reduced; the adaptability of the distance, the temperature and the energy consumption is considered in the path planning of the multi-objective function, so that the operation efficiency is improved; the dynamic energy consumption distribution strategy reduces the energy loss in a low-temperature environment, prolongs the endurance time of the AGV, and reduces the charging frequency of the AGV entering and exiting the refrigeration house, thereby reducing the disturbance to the temperature field in the refrigeration house.

The invention provides a power distribution network comprehensive planning method and system in electric power engineering design. The method comprises the following steps: firstly, acquiring geographic space data and historical operation data in a planning area and cable vibration spectrum data monitored by an optical fiber sensing network; carrying out distributed storage and correlation analysis on the geographic space data and the cable vibration spectrum data through a mixed integer programming parallel acceleration algorithm, and generating a cable aging risk distribution diagram based on spatial topology constraints; performing multi-target collaborative optimization based on a second-order cone programming model, and generating a high-voltage distribution network line capacity increasing scheme and a cable replacement priority sequence; and finally, in combination with a partial discharge frequency domain characteristic change trend in the cable vibration spectrum data, correcting a line corridor extension parameter, and outputting a dynamic planning map including a cable life prediction node and a capacity increasing path. According to the technical scheme provided by the invention, precise planning and optimization of the high-voltage power distribution network are realized, and the accuracy and efficiency of cable life prediction and capacity increasing path planning are improved.

The invention provides a dynamic planning method and system for a low-altitude route of an unmanned aerial vehicle, and relates to the technical field of intelligent planning, and the method comprises the steps: receiving a new delivery point instruction, obtaining a three-dimensional coordinate, carrying out the task integration of a new delivery point and an existing delivery point, defining a geographic range through three objects, namely a communication base station, a wireless tower and a permanent landmark building, and carrying out the task integration. Performing space structure division on the geographic range, and disassembling the geographic range into uniformly distributed three-dimensional voxel grids; and generating an adjustment value according to the coordinate interval of the three-dimensional voxel grid, including the delivery point density, the linear distance with the landmark building and other structural attributes, and adjusting the task integration result by using the adjustment value to obtain the adjusted task integration result of all delivery points. According to the invention, safe, efficient and accurate route planning of a multi-delivery-point dynamic newly-added scene in a complex low-altitude environment is realized.

The invention discloses an intelligent flight path planning method for an unmanned aerial vehicle in a multi-type scene, and the method comprises the following steps: 1, building task input and task type recognition: setting the unmanned aerial vehicle to be in a fixed-height flight mode, and obtaining space information required by a task, including a take-off point position, a task point coordinate set and a task region boundary, the system identifies the task type through analysis; if the input is a discrete point set, the task is judged as a point task; if the structure is a broken line structure, judging the task as a line task; if the boundary is a task area boundary, the task is judged to be a surface task, and if the boundary width L is small, the task is still judged to be a line task. The method has the advantages that a fitness function is improved, the shortest total time of multiple unmanned aerial vehicle tasks is used as an optimization target, and task real-time weight parameters are introduced, so that a planning result meets task timeliness and energy consumption optimization at the same time; the coverage of planar tasks is realized in combination with a cattle farming algorithm, dynamic planning of electric quantity and a battery swap station is performed in combination with a greedy algorithm, and path optimization and improvement of energy management are realized.

The invention discloses a regenerative braking energy recovery method combining dynamic planning and model predictive control, which relates to the technical field of vehicle energy conservation, and comprises the following steps: establishing a dynamic model of a vehicle braking process, performing global energy recovery optimization based on dynamic planning, generating a global optimal reference trajectory, and adopting model predictive control rolling optimization. The reference trajectory is tracked to maximize energy recovery efficiency. According to the method, global optimum is guaranteed through dynamic planning, and meanwhile, the energy recovery efficiency of the regenerative braking process is improved by utilizing model predictive control.

The invention provides a hierarchical multi-agent bridge and tunnel group maintenance decision-making method and device based on GA-RL, and relates to the technical field of civil engineering. The method comprises the steps that an expert strategy library is constructed offline according to an expert strategy and a genetic algorithm, and an optimal strategy is selected online according to a network-level reinforcement learning agent; a near-end strategy optimization algorithm is adopted to train an exclusive bridge and tunnel level reinforcement learning agent for each bridge and tunnel type, and a maintenance application of the to-be-maintained bridge and tunnel is generated; and converting the optimal strategy into a computable priority evaluation model, generating a bridge and tunnel maintenance priority sequence, optimizing a budget allocation proportion according to a differential evolution algorithm, and generating a bridge and tunnel group network-level maintenance scheme. A hierarchical multi-agent decision framework based on a GA-RL hybrid algorithm is provided, the defect that static optimization lacks environmental adaptability is overcome, the calculation bottleneck of dynamic planning in a large-scale scene is broken through, and a more efficient and adaptive solution is provided for complex bridge and tunnel group network maintenance decision.

The invention discloses a robot intelligent management and control system for energy conservation and efficiency improvement of a cold end of an indirect cooling auxiliary unit, and belongs to the technical field of intelligent management and control, the robot intelligent management and control system comprises an initial evaluation module, a dynamic planning module and a feedback monitoring module, obtaining a scaling thickness distribution diagram and a scaling coverage rate, and generating a fin flow channel matrix model; the dynamic planning module is used for dividing virtual operation blocks, constructing a scaling planning path in combination with a cleaning index, carrying out rapid rough sweeping, carrying out cavitation early warning, and processing cavitation early warning through a hierarchical control architecture; and the feedback monitoring module is used for constructing a quality deviation set, screening out substandard virtual operation blocks, calculating and correcting priorities, sorting and grading, optimizing and correcting parameters by utilizing a genetic algorithm, and performing feedback regulation and control, so that the cold end heat exchange efficiency is improved to achieve an energy-saving target, a fin structure is protected, and the service life of equipment is prolonged.