112 results about "Real-time path planning" patented technology

The invention relates to the technical field of unmanned aerial vehicle flight path planning, in particular to an unmanned aerial vehicle real-time path planning system and method based on dynamic weight distribution and multi-source data fusion. The system comprises a multi-source data fusion module, an integrated laser radar, a millimeter wave radar, a visual sensor and a Beidou positioning unit. The dynamic weight distribution module dynamically adjusts the weight coefficient of each sensor according to the environmental complexity, the threat level and the state of the unmanned aerial vehicle by adopting a mixed decision-making mechanism combining fuzzy logic and reinforcement learning; an improved RRT * algorithm and a Markov decision process are built in the real-time path planning module, and a global optimal path and a local obstacle avoidance track are generated by adopting a layered planning architecture; the unmanned aerial vehicle cooperative control module comprises a dual-redundancy flight control system and a dynamic obstacle avoidance unit; and the communication relay module supports 5G and low-orbit satellite dual-mode communication, updates an environment cognitive model of each unmanned aerial vehicle through federated learning, and realizes multi-source fusion real-time path planning based on dynamic weight distribution and the unmanned aerial vehicles.

The invention discloses a cargo transportation real-time path planning method based on artificial intelligence, and relates to the technical field of artificial intelligence and intelligent traffic, and the method comprises the following steps: collecting real-time data from a traffic monitoring system, a vehicle-mounted sensor, a road event platform and a weather information system in a transportation process, forming an original data set containing multi-source information; time synchronization and space alignment are carried out on source data in an original data set, and a standardized data frame is constructed to ensure the consistency of different source information in a space-time dimension. According to the method, the accuracy, the real-time performance and the intelligence of path planning are effectively improved through multi-source data consistency identification, weighted fusion and a dynamic re-planning mechanism. The system can timely identify data conflicts, dynamically adjust paths and enhance transportation safety and efficiency, has a self-learning capability, continuously optimizes path strategies and adapts to complex traffic environments.

The invention discloses an intelligent operation and maintenance scheduling and resource optimization method and system for a new energy power station, and belongs to the technical field of data processing and management.The method comprises the steps that real-time power generation data, equipment sensor data, weather forecast data, a power grid scheduling instruction and electricity market electricity price information of the new energy power station are obtained; a unified data matrix is generated through space-time alignment processing to execute bidirectional feedback prediction, and a corrected power generation plan is generated; a cooperative scheduling decision is generated through a multi-target dynamic balance algorithm, and a personnel dispatching scheme and a material allocation path are generated through real-time path planning; and generating a historical decision data set based on the execution result and the actual execution deviation, and adjusting prediction parameters and scheduling parameters through an incremental learning model. A closed-loop optimization method combining data fusion, collaborative prediction, multi-target scheduling and adaptive learning is adopted, global collaborative optimization of multiple factors such as power generation, operation and maintenance, energy storage and markets can be achieved, and the economic benefit and the intelligent level of overall operation of a power station are improved.

The invention relates to the technical field of path planning, and discloses a real-time path data planning processing method and system based on SLAM. The method comprises the following steps: performing semantic-geometric joint feature extraction through a laser radar and an RGB camera to obtain joint observation data; performing covariance propagation on the SLAM state vector based on semantic constraint Kalman filtering to obtain a positioning covariance and a covariance ellipse; performing constraint propagation on the semantic object set to generate virtual feature points to construct an enhanced feature map; environment grid safety scoring is carried out according to the semantic risk weight and the obstacle distance; semantic cost and SLAM uncertainty processing is carried out through bidirectional A * search, and a real-time navigation path is generated. According to the method, the technical problem of insufficient comprehensive consideration of semantic security constraint and positioning uncertainty in real-time path planning based on SLAM in a feature sparse environment is solved.

The invention discloses a sea condition perception and path prediction navigation method and system for a high-speed unmanned ship, and relates to the technical field of multi-source path decision optimization, and the method comprises the steps: collecting storm real-time perception data through a sensing device, carrying out the preprocessing, outputting a standardized sea condition state vector, and providing an experience reference for real-time path planning through ship-borne historical data; constructing a multi-target navigation prediction algorithm model to predict and generate a path candidate scheme set on the basis of the storm real-time sensing data and the ship-borne historical data, and performing evaluation through a conditional random field in combination with path candidate schemes generated by the storm real-time sensing data, the ship-borne historical data and target navigation prediction; and carrying out multi-dimensional safety risk scoring on an evaluation result through a fuzzy analytic hierarchy process by combining the wave height and direction influence, the hull stability and the obstacle avoidance complexity, and determining an optimal sailing path. According to the invention, fusion of multi-source sea condition perception and dynamic path optimization is realized, and the navigation stability, the adaptability and the risk control capability of the high-speed unmanned ship are improved.

The invention discloses an intelligent self-adaptive industrial raw material transportation system based on digital twinning, which comprises a multi-source positioning sensing module composed of a UWB positioning unit, an IMU compensation unit and an RFID verification unit, an edge calculation module and a dynamic actuator, the system integrates multi-sensor data through Kalman filtering to realize high-precision positioning, and based on a digital twinning model, the intelligent self-adaptive industrial raw material transportation system based on digital twinning is obtained. An improved deep reinforcement learning algorithm is adopted for dynamic obstacle prediction and real-time path planning, an optimal transportation path is generated, a dynamic actuator controls the transportation vehicle to move according to the planned path, and self-adaptive navigation and obstacle avoidance are achieved; through the improved deep reinforcement learning algorithm and in combination with dynamic obstacle prediction, the problem of path failure caused by a local optimal solution is effectively avoided, the path diversity exploration and dynamic obstacle avoidance capability in a complex environment is enhanced, and the planning robustness is improved.

The invention relates to the technical field of inter-city cargo splicing and loading, in particular to an inter-city cargo splicing and loading transportation path optimization algorithm based on large model optimization, and solves the problems that in the prior art, an optimization algorithm based on a large model generally relates to a complex calculation process and needs a large amount of calculation resources and time, and especially in a real-time path planning scene, the calculation process is complicated. And the calculation delay can influence the transportation efficiency. The invention discloses an inter-city cargo splicing transportation path optimization algorithm based on large model optimization. The algorithm comprises data collection and preprocessing, model construction and training, algorithm optimization and verification, path optimization calculation, result evaluation and feedback, and algorithm deployment and application. According to the method, various possible transportation paths are analyzed and compared through an algorithm, the optimal path is selected, unnecessary detour and waiting time is reduced, so that the transportation efficiency is improved, and the algorithm can perform intelligent scheduling and reasonable arrangement of transportation vehicles and routes according to real-time traffic conditions, cargo demands, vehicle states and other information.

The invention discloses a Web end real-time path planning method and system, a storage medium and equipment, and relates to the technical field of path planning, and the method comprises the steps: collecting traffic data and environment change information in a Web end application scene in real time in the driving process of a vehicle, and converting the traffic data and environment change information into a standard data format for algorithm processing; performing path initial planning by adopting a hybrid algorithm to obtain an initial path; acquiring actual path driving information fed back by the environment sensing device and the user, comparing the actual path driving information with the initial path, and judging whether an actual path in the actual path driving information is different from the initial path or not; if yes, revising the recommended path in the navigation system in real time according to a preset proofreading rule to obtain a target navigation path shared to other vehicles; wherein the hybrid algorithm is a combination algorithm of a genetic algorithm and an ant colony algorithm. According to the invention, real-time optimization of the distribution path is realized, various road condition changes are efficiently coped with, and individual requirements of clients are met.

The invention discloses an unmanned forklift indoor navigation system and method, and the method comprises the steps: obtaining navigation data of an unmanned forklift based on an indoor environment, and determining a current position and a target position of the unmanned forklift; performing dynamic path planning based on the current position and the target position to generate an optimal navigation path; according to the optimal navigation path, a control instruction is generated, and the unmanned forklift is commanded to execute corresponding navigation and operation. Through real-time path planning and dynamic adjustment, the unmanned forklift can quickly respond to environmental changes, the waiting time is shortened, and the logistics efficiency is improved.

The invention discloses an unmanned aerial vehicle photovoltaic inspection real-time path planning method and system, and relates to the technical field of control in a specific dimension. Comprising the steps of planning an initial route and setting inspection routes in different areas; an image is collected by adjusting the camera pitch angle of the second holder, and the distance is measured in real time through the laser range finder; uploading an image shot by the second pan-tilt camera, identifying the photovoltaic string, establishing a string position correction model, and calculating the three-dimensional coordinate of the center point of the string; implementing local route optimization according to the string center point coordinate data set and the initial route based on an ant colony algorithm; the unmanned aerial vehicle flies to a set waypoint along a flight route, the first pan-tilt camera shoots an image, identifies a picture center photovoltaic string, calculates a center point coordinate difference, and the unmanned aerial vehicle corrects the position and executes an inspection image acquisition task. The problems of insufficient real-time performance, poor adaptability to complex terrains and unstable image acquisition quality are solved, and the purposes of strong real-time performance, strong adaptability, high image quality, high recognition capability and high precision are achieved.

The invention discloses a multifunctional AI robot which comprises a robot body, a sensing module, a voice interaction module, an environment monitoring module, a smart home control module, a mobile chassis, a display screen, a data processing module, a control module, an execution module and a power management module. The beneficial effects of the invention are that 1, a multi-sensor fusion sensing system is arranged, a detection blind area of 0.1-5 meters is effectively covered, and 92% of speech recognition rate is still maintained in a 60dB noise environment; a self-adaptive motion control system is arranged, so that the attitude stability of + / -0.5 degrees during omni-directional movement is realized, and the system is suitable for real-time path planning in a dynamic environment; 2, a safety protection system is arranged, so that the collision accident rate can be reduced to 0.01 times per thousand hours, and uninterrupted power supply within 300ms during hot plug replacement is supported; 3, man-machine interaction upgrading is realized, and the system is more intelligent and multifunctional; and thirdly, a modularized quick-release structure is realized.

The invention discloses an unmanned aerial vehicle real-time path planning system based on digital twinning, relates to the technical field of unmanned aerial vehicles, and aims to realize efficient and real-time path planning of an unmanned aerial vehicle in a campus traffic road environment. The core architecture of the system comprises a traffic flow monitoring end, a data analysis processing end, a digital twin model construction end and an unmanned aerial vehicle regulation and control end. A campus traffic digital twinborn model is constructed by using the traffic flow and the campus traffic road network three-dimensional diagram, the campus traffic flow in a future period of time is predicted in real time, and a prediction result is transmitted to the unmanned aerial vehicle regulation and control end; the number and flight paths of the unmanned aerial vehicles corresponding to the traffic roads are locked according to the predicted flow, accurate and dynamic path planning of the unmanned aerial vehicles is achieved, and the flight efficiency and adaptability of the unmanned aerial vehicles in the complex traffic environment of the campus are improved.

The invention discloses an indoor robot path planning scheme, and belongs to the technical field of intelligent hardware, and the method comprises the steps: obtaining the current position information, target position information and indoor dynamic obstacle behavior information of a robot through a plurality of sensors disposed on the robot; predicting the behavior intention of the dynamic obstacle according to the dynamic obstacle behavior information; generating a plurality of candidate paths according to the behavior intention of the dynamic obstacle, the current position information of the robot and the target position information; selecting an optimal path from the plurality of candidate paths through a multi-objective optimization algorithm; and controlling the robot to operate according to the optimal path. According to the indoor robot path planning scheme provided by the invention, the real-time path planning efficiency, safety and flexibility of the robot in a complex dynamic environment can be improved.

The invention provides an intelligent tactile stick interaction method and system based on multi-modal perception and edge calculation, and relates to the technical field of intelligent auxiliary equipment. The method comprises the following steps: acquiring environment and position data through a camera, a GPS and an inertial measurement unit; performing multi-modal data fusion and deep learning identification by using an edge calculation module to generate a dynamic environment map; performing real-time path planning and obstacle avoidance based on a map and a destination; analyzing a user instruction through voice recognition and interactively adjusting a path; haptic and auditory bimodal feedback is provided through a vibration module and a voice synthesis module according to a navigation result; and when an emergency obstacle is detected or a user actively alarms, a local and remote alarm mechanism is triggered. The system correspondingly comprises an intelligent tactile stick terminal and a cloud server. According to the invention, accurate and real-time perception and intelligent navigation of the environment are realized, visual and natural interaction experience is provided, an effective emergency help channel is established, and the safety, independence and convenience of travel of visually impaired people are significantly improved.

The invention discloses an unmanned system navigation method based on an asymmetric privileged world model. The method comprises the following steps: constructing an asymmetric world model WM world by using sample data in an experience pool; a technical means of combining an asymmetric privileged world model and an intelligent decision algorithm is applied in a real-time path planning module, and an asymmetric world model WM world, a participant model Actor and a commentator model Critic are constructed. A WM participant model and a WM commentator model are respectively constructed in a WM world model. In an unmanned system navigation scene, a WM world model performs path training in a storage buffer area through interaction of an unmanned system and environment information, and a synchronous path training process is adopted to ensure that a WM participant model can access an observation result of the unmanned system. The WM commentator model utilizes privilege information to realize high performance and security of security reinforcement learning.

The invention discloses an unmanned aerial vehicle path planning method and system in an unknown obstacle environment, and particularly relates to the technical field of unmanned aerial vehicle autonomous navigation and path planning. Constructing an observation space containing a relative target position, a speed, an attitude angle, an angular speed and an obstacle; designing a continuous action space comprising total lift force, rolling moment, pitching moment and yawing moment; constructing a multi-dimensional award function fusing a task end award, a distance target award, a forward efficiency award and an obstacle avoidance safety award, and guiding an intelligent agent to optimize a path; on the basis of observation space and reward feedback, an intelligent agent control strategy is continuously optimized through exploration and development of a self-adaptive entropy regularization balance strategy in an SAC algorithm, and collision-free and short-path real-time path planning is achieved.

The invention relates to the technical field of military equipment maintenance support, in particular to a maintenance cabin intelligent system based on multi-modal data fusion and dynamic cooperative control, which comprises a multi-modal sensor module, a dynamic decision module and an actuator module, the multi-modal sensor module, the dynamic decision-making module and the actuator module are connected through a closed-loop control link, and original data of the multi-modal sensor module are input into the dynamic decision-making module; a control instruction output by the dynamic decision module drives the actuator module to act; and the execution state of the actuator module is fed back to the dynamic decision module to dynamically adjust the task priority. According to the method, a three-dimensional map is constructed through multi-modal sensor fusion, and task allocation is dynamically optimized through reinforcement learning and Nash equilibrium; the GAN generates adaptive camouflage, improves the YOLOv7, improves the damage detection precision, carries out real-time path planning and dynamic power management, and achieves the high environmental adaptability, efficient execution, and reliable and stable system performance.

According to the online path planning method and system for the automatic polishing robot, the one-to-one mapping relation between the plane and the curved surface is established through orthographic projection, the complex curved surface is locally segmented into the simple curved surface, the algorithm time complexity of searching the coordinates of the path points on the surface of the model is reduced, the running speed is increased, and the real-time path planning requirement is met; according to the method, path planning can be directly carried out on point cloud data, the requirements for reconstruction algorithm precision and model quality are low, three-dimensional path planning can be converted into two-dimensional plane path planning, a two-dimensional path in any shape can be generated, and automatic and efficient online path planning of a robot is ensured.

The invention discloses an unmanned aerial vehicle autonomous navigation method under a complex illumination condition, and the method comprises the steps: obtaining an image sequence and an inertial measurement value through the synchronous data collection of a binocular camera and an IMU (Inertial Measurement Unit); key point detection and matching in a dynamic environment are realized based on SuperPoint and LightGlue models, and pose estimation is optimized in combination with IMU pre-integration; constructing a dynamic voxel map based on binocular depth information and pose estimation, and performing real-time path planning of obstacle perception through a D * Lite algorithm, and taking the real-time path planning as a global path constraint; a discrete path generated by adopting MINCO trajectory representation and dynamic constraint optimization D * Lite is adopted, a smooth and executable unmanned aerial vehicle trajectory is generated, and finally autonomous obstacle avoidance flight is achieved through a control instruction. According to the invention, the accuracy and robustness of unmanned aerial vehicle positioning are improved; and local mapping is carried out by using camera depth data through a planner, and a traditional global planning algorithm and trajectory optimization are combined to find an optimal route, so that rapid autonomous navigation of the unmanned aerial vehicle is realized.

The invention discloses a dynamic obstacle avoidance and path optimization method based on a body intelligence robot, and relates to the technical field of robot path planning, and the method comprises the following steps: collecting map data and task constraint data; performing path planning analysis on the map data and the task constraint data based on a set path optimization algorithm to generate initial planning path data; according to the intelligent robot dynamic obstacle avoidance and path optimization method, a robot actual driving path prediction model is constructed, and a target function is constructed for the deviation between the robot actual driving path prediction model and the real-time path planning data of the next time period to carry out minimization analysis; and the real-time path planning data of the next time period is optimized, so that the robot runs according to the optimized real-time path planning data to obtain an actual running path which fits the original real-time path planning data, and the running error of the robot is reduced.

The invention discloses a real-time path planning method and system for fire scene evacuation. The method comprises the following steps: initializing a to-be-explored list FS, an explored list ES and a set P; judging whether the to-be-explored list FS is empty or not, and if yes, backtracking to find a father node and outputting a path; if not, setting the grid with the minimum cost as the current grid Gi, and adding the neighbor grid Gj into the to-be-explored list FS; judging whether the current grid Gj is equal to the target network Gt or not, and if so, backtracking to find a father node and outputting a path; otherwise, checking a neighbor grid Gj of the grid Gi; whether the neighbor grid Gj is not in the explored list ES or not is judged, if yes, the neighbor grid Gj is added into the to-be-searched list FS, and returning is continued to judge whether the to-be-searched list FS is empty or not; otherwise, neglecting the grid Gj, and continuously returning to judge whether the to-be-explored list FS is empty or not. According to the invention, more accurate, real-time and globally considered evacuation path planning can be provided so as to ensure safe and rapid evacuation of personnel in a fire scene.

The invention discloses an enhanced intelligent interaction-based self-adaptive pick-up and drop-off system for an autonomous vehicle. The system comprises a passenger intelligent interaction control module, a multi-modal large model optimization module, a real-time path planning and dynamic adjustment module, a passenger feedback and personalized recommendation module and an intelligent accident response and safety module. The passenger is allowed to autonomously regulate and control the pick-up point and adjust the path in real time through the mobile device, the pick-up scheme is intelligently optimized according to traffic conditions, road changes and emergencies under the support of the multi-mode large model, the method is particularly suitable for scenes where the passengers have flexible requirements for the pick-up point, and the method is suitable for popularization and application. The pick-up efficiency and the passenger experience can be improved in a dynamically changing environment.

The invention relates to a real-time path planning method and system based on conflict resolution, equipment and a medium. The method comprises the following steps: preprocessing original observation data to obtain an evidence unit of each sensor; calculating a total conflict amount and a sensor pair contribution matrix based on an evidence unit, updating the credibility of each sensor and selecting a fusion operator to obtain fusion evidence and a conflict degree after fusion; a short-term time sequence is generated according to the fusion evidence and the conflict degree after fusion, the expected occupation probability and the expected conflict trend of each grid unit are predicted, and a time sequence conflict map is obtained; according to the time sequence conflict map, conflict-level adjacent grid unit aggregation fuzziness and area-level conflict characteristics are carried out; and inputting the fusion evidence, the conflict fluctuation degree, the expected stable time and the region-level conflict feature as cost and constraint into a real-time local path planner to obtain a control decision. By adopting the method, the problem of path decision oscillation caused by multi-source sensor conflicts can be relieved.

The invention discloses a real-time processing method and system for robot dog environment perception and decision, and relates to the field of robot dogs. Carrying out multi-source data fusion and semantic segmentation based on deep learning; carrying out semantic enhanced 3D point cloud construction and target detection; real-time path planning and obstacle avoidance decision making in a dynamic environment; and carrying out adaptive motion control and system fault-tolerant processing. According to the invention, through four core technologies of multi-modal fusion, semantic enhancement, real-time calculation optimization and intelligent decision making, the robustness and real-time performance of multi-modal sensor fusion are significantly improved, and the precision of environmental perception is improved; calculation delay is greatly reduced, edge computing power distribution is optimized, and high-real-time decision making is achieved; the intelligent obstacle avoidance and path planning capability in the dynamic environment is enhanced, the intelligent decision-making capability in the dynamic environment is improved, the reliability and practicability of the system are improved, and an efficient, stable and low-cost solution is provided for large-scale application of the system in the fields of industry, security and logistics.

The invention belongs to the technical field of path planning, and discloses a path planning method and system based on roadside collaboration, and the method comprises the steps: coding the position of equipment in a planning region, recording the path information corresponding to the equipment according to a coding result, and generating a path information matrix based on the path information and a real-time updating mechanism; connecting the path information matrix to a data broadcast to generate a real-time path plan, and designing a path resource allocation scheme according to an equipment planning demand and a weight mechanism; and after receiving the path resource allocation scheme, the equipment implements return verification processing, executes a planning scheme according to a processing result, and monitors path information in a planning area in real time in the execution process to realize real-time update of allocation. According to the method, the path conflict is effectively avoided, and the efficient operation of the traffic flow is ensured.

The invention discloses a multi-sensor data fusion method for an intelligent transfer device of power equipment, and the method comprises the steps: carrying out the dynamic weight fusion of multi-source data through building a measurement model of a plurality of visual sensors, and generating a high-precision measurement sequence; in combination with an improved adaptive Kalman filtering algorithm, process noise and a measurement noise covariance matrix are estimated in real time, and the stability and the anti-interference capability of state estimation are optimized. And based on the fused high-precision state information, a real-time path planning instruction is generated, and the transfer device is controlled to complete obstacle avoidance and millimeter-level docking of the cabinet body. The problems that traditional manual transfer efficiency is low, the positioning error is large, and a single sensor is prone to electromagnetic interference are solved, the navigation precision and operation safety of heavy equipment in the complex electromagnetic environment are remarkably improved, and the method is suitable for intelligent transfer scenes of equipment such as transformer substation circuit breaker handcarts and the like.

The invention discloses an AI path planning method and system based on deep reinforcement imitation learning, and the method comprises the steps: carrying out the weighted fusion through introducing a state incidence matrix, and constructing an environment state space with time sequence correlation; on the basis of a generative adversarial imitation learning framework, adversarial training of a strategy generation network and a strategy discrimination network is carried out by utilizing a pre-trained expert strategy database; a master-slave distributed architecture is adopted to train a plurality of strategy network copies in parallel, and global parameters of the strategy generation network and the strategy discrimination network are optimized through an aggregation gradient and parameter soft update mechanism; and deploying the lightweight strategy generation network, outputting an action instruction according to a real-time environment state, and obtaining a real-time path planning result through a dynamic path correction mechanism and online performance fine adjustment. The problem that the robustness, the safety and the efficiency of path planning of the robot in a complex environment are difficult to meet actual requirements is solved.

The embodiment of the invention provides a ship surface equipment real-time path planning algorithm based on an adjustable fast step leveling method, and the algorithm comprises the steps: building a kinematics model of a rod traction system and an obstacle constraint model, and generating a planning space based on the models and global environment constraint information; obtaining the optimal cost value of each grid in the planning space in the whole planning space; obtaining an optimal path from the target point to the starting point by solving the optimal cost value; the safety margin diagram is changed by adjusting the extension distance, so that the generated planned path can be balanced between the collision risk degree and the path length according to specific task requirements. On the basis of the multi-wave-source expansion principle, the safety margin diagram is changed by adjusting the expansion distance, the balance of the path length and the risk degree is achieved, and the safety and rationality of the path are improved; by introducing an obstacle constraint model into a planning space, real-time correction of ship surface dispatching is realized, and the traceability and robustness of a path are improved.

The application discloses a real-time path planning method fusing improved locust-dynamic window method, and comprises the following steps: S1, generating an initial path according to an improved A* algorithm, performing global path planning on the path in a radiation environment through an improved locust optimization algorithm, and determining the path from the starting point to the ending point; S2, performing local path planning according to the path through an improved DWA algorithm, and generating a planning path. Through the introduction of a position updating strategy considering radiation distribution information and an improved adaptive comfort zone coefficient, the influence of the radiation risk on path selection is more truly reflected, and the position updating process of the GOA is optimized. This enables the algorithm to effectively guide the locust population to avoid high radiation areas in a nuclear environment, and significantly reduces the radiation exposure risk. Through the combination of the global optimization capability of the improved A* algorithm, the initial population distribution of the locust optimization algorithm is improved, and then the convergence speed of the locust optimization algorithm is improved.

The invention discloses a traffic dynamic shortest path distribution method and system based on a triangular fuzzy number weight, and relates to the technical field of intelligent traffic systems.Fuzzy information in a path evaluation attribute is represented through a triangular fuzzy number, a comprehensive weight is analyzed and calculated in combination with deviation maximization and a moisture value, and the decision reliability is effectively improved; a K-Means algorithm is adopted to perform dynamic path search, and through a representative vertex subset pre-calculation and increment updating strategy, the path retrieval efficiency is remarkably improved; a travel time prediction model related to departure time and real-time flow is established, and accurate response to accidental congestion is realized; and finally, outputting an optimal path scheme through a multi-objective decision-making method. Compared with a traditional algorithm, the speed is improved on a real road network data set, the path distribution accuracy is improved, meanwhile, the path safety, comfort and efficiency are effectively balanced, real-time path planning under a large-scale road network is supported, and an efficient solution is provided for intelligent traffic management.