444 results about "Optimal route" patented technology

The invention relates to the field of communication, in particular to a method for protocol optimization and dynamic routing for a network communication system. According to the invention, based on deep learning and big data analysis technologies, protocols in network communication are intelligently optimized. Through analysis of historical communication data, information such as a flow mode and a congestion condition in network communication is learned and predicted, so that communication protocol parameters are dynamically adjusted, and the efficiency and the stability of the network communication are improved. And meanwhile, a multi-objective optimization algorithm and a real-time network monitoring technology are adopted to realize dynamic routing selection. By monitoring network states such as bandwidth, delay, packet loss rate and the like in real time, an optimal routing path is calculated in combination with an objective function, and a data packet is transmitted along the path. By optimizing a data packet structure and a transmission mechanism of a protocol, redundant data is reduced, and the data transmission efficiency is improved. The method can effectively improve the transmission efficiency of network communication, reduce the time delay and packet loss rate, and improve the network stability and reliability.

The invention provides an unmanned aerial vehicle optimal route planning method and system fusing image recognition and M-RRT and APF algorithms. The point cloud and visual data of a fan are obtained in real time through a multi-mode sensor, key parts of blades are recognized based on deep learning, a geometric mapping model is constructed, the blade tip points are corrected in a clustering mode in combination with DBSCAN, and the shutdown posture is predicted. A global path adopts an M-RRT algorithm improved by a direction heuristic factor to guide and search a key area of a blade; a local track is optimized through a dynamic weight APF algorithm, and a repulsive force field is adjusted in real time to cope with attitude changes. An online re-planning mechanism is introduced, NSGA-III multi-target optimization is triggered when the environment suddenly changes or the tracking error exceeds a threshold value, and the optimal track is generated by integrating energy consumption, time and safety. The system integrates high-precision sensing, self-adaptive planning and dynamic optimization, and the inspection coverage rate and the track safety under the complex shutdown attitude are remarkably improved.

The invention discloses a maritime traffic flow analysis method based on big data, and relates to the technical field of information, and the method comprises the steps: collecting data related to maritime traffic, and carrying out the preprocessing; performing adaptive dynamic division on the sea area by using the processed data to generate a real-time updated water area boundary; based on the self-adaptive division result, analyzing a ship behavior mode to obtain an anomaly detection result; analyzing interaction among the ship groups according to an abnormal detection result, and predicting potential congestion and conflicts; based on the group behavior prediction result, ship path planning is optimized, and an optimal route suggestion is generated; by constructing a space-time diagram convolutional network model and comprehensively considering relative positions, speeds and rendezvous frequencies of ships, potential navigation conflicts can be accurately predicted; according to the method, the optimal route suggestion based on multi-objective optimization is provided for the ship by optimizing path planning, so that the sailing efficiency and the economical efficiency are improved to the maximum extent on the premise of ensuring the safety.

The invention discloses a multi-agent collaborative data exchange dynamic routing optimization method and system, and relates to the technical field of network communication, and the method comprises the steps: obtaining the real-time state information of each node in a network, and outputting a node state data set; constructing a historical state sequence of each node, calculating a load change trend, and outputting a routing adjustment signal when the load change trend reaches a load critical value; calculating a collaborative weight according to the network contribution degree of each agent, establishing a task allocation mapping relationship among the agents based on the collaborative weight, and outputting a collaborative scheduling result; determining a candidate path set, performing index evaluation on paths in the candidate path set, and outputting an optimal routing path; and executing data transmission, obtaining a quality difference between an actual transmission effect and an expected effect, and performing parameter correction. According to the invention, active optimization and multi-target balance of network routing are realized, and routing efficiency and system stability in a dynamic network environment are improved.

The invention discloses an unmanned aerial vehicle path planning method based on multi-objective optimization and improved particle swarm optimization. The method comprises the steps of 1, constructing a three-dimensional space map model; 2, introducing a multi-objective optimization strategy, and designing an objective function by adopting a weighted objective optimization method for evaluating the advantages and disadvantages of each path; 3, initializing particles by adopting an improved RRT algorithm in combination with a Sobol low-difference sequence, calculating a fitness value of each unmanned aerial vehicle path, and recording an optimal solution; 4, introducing a dynamic inertia weight adjustment strategy, and dynamically adjusting the inertia weight according to the number of iterations; a dive search mechanism in an eagle search algorithm is fused, and a particle restart mechanism is introduced to avoid falling into local optimum; 5, judging whether the set number of iterations is reached or not; if yes, iteration is stopped, and the optimal route of the unmanned aerial vehicle is returned to the environment model; if not, iteration is continued, and the optimal air route is searched. The invention aims to improve the path planning efficiency and robustness of the unmanned aerial vehicle in a complex environment.

The invention discloses a ship navigation decision-making method based on a large model technology, and the method comprises the following steps: S1, obtaining ship-end perception information and shore-end auxiliary information, and creating a ship feature data set comprising various types and magnitudes; s2, constructing a ship behavior research and judgment model on the basis of a Transform model framework and in combination with the ship feature data set; s3, performing ship behavior study and judgment model training optimization by adopting a loss function, performing error measurement on prediction results of future ship navigation behavior information, performing summation to obtain a total loss function, minimizing the loss function through an optimization algorithm, continuously updating and adjusting model parameters, and gradually reducing the loss function; and S4, processing local and global features based on a multi-layer sensing mechanism and a Transform architecture large model to construct a ship behavior decision model, comprehensively considering the local and global features, comprehensively analyzing a ship navigation environment, and making an optimal route plan and speed optimization decision.

The invention discloses an aerospace network hierarchical cooperation routing optimization method and system, and relates to the field of aerospace information network communication, and the method comprises the steps: constructing a system model comprising an unmanned plane, satellite nodes and a communication link, defining link attributes, building a routing optimization objective function, introducing a reputation value-based multi-agent cooperation mechanism, and carrying out the routing optimization of the unmanned plane, the satellite nodes and the communication link. A hierarchical reinforcement learning framework is designed, inter-cluster decision and intra-cluster decision are divided, an upper layer selects an inter-cluster path according to global information, a lower layer plans an unmanned aerial vehicle path and defines a state, an action space and a reward function according to local information, an intelligent agent interacts with an environment to collect empirical data, and optimal routing communication is selected in combination with a real-time reputation value after training. Adjustment can be carried out according to network changes; according to the invention, the cooperative reliability is improved through a reputation value mechanism, the decision complexity is reduced through hierarchical learning, the routing performance is optimized, the network robustness, expandability and adaptability are enhanced, and the cooperative communication efficiency of the unmanned aerial vehicle and the satellite and the system stability are improved.

The invention relates to the technical field of computers, discloses an intelligent scheduling method and system for server cluster load balancing, and aims to solve the defects of the existing server cluster load balancing technology in response lag, non-uniform resource utilization rate, service quality guarantee, global optimization capability, fine-grained state perception and scheduling decision. The method comprises the following steps: collecting server state and request feature data, constructing a cluster state and service capability model, and predicting a load trend; and generating an optimal routing strategy by using deep reinforcement learning and multi-objective optimization, and issuing adjustment request distribution. The system comprises a data acquisition module, an application request feature acquisition module, a state sensing and modeling module, a load prediction module, an intelligent scheduling decision module and an instruction execution module. By adopting the technical scheme, the resource utilization rate can be improved, the response time can be reduced, the throughput can be improved, the system stability and elasticity can be enhanced, and the operation cost and energy consumption can be reduced.

The invention discloses a surface mine transportation real-time intelligent scheduling method and system based on multi-objective optimization. The method comprises the steps that S1, an initial data loading module initializes the system; s2, the algorithm module verifies the vehicle state after receiving the scheduling request; s3, executing a path generation strategy according to states, wherein a normal vehicle generates all feasible routes based on the global path network; when a user appoints a starting point and a terminal point, a point-to-point optimal route is intelligently generated; s4, performing dynamic optimization scheduling based on the multi-source data; optimal task allocation is solved through a multi-objective optimization model, and a vehicle and route mapping relation is output; and S5, a scheduling result is issued to the vehicle-mounted terminal through the service layer, and a terminal interface displays a recommended route and operation guidance. According to the invention, a dynamic event-driven mechanism and a multi-source data collaborative optimization intelligent scheduling framework are fused, so that efficient distribution of vehicle tasks, real-time generation of paths and quick response to abnormal conditions are realized.

The invention provides a key flow routing optimization method based on deep reinforcement learning, and belongs to the field of flow engineering. Under the constraint of routing overhead, a part of the total flow is selected as a key flow and rerouting is carried out, so that the purposes of minimizing the maximum link utilization rate and reducing the time delay are achieved. And splicing the traffic matrix, the topological matrix and the feature matrix as input, and carrying out feature extraction with higher dimension through a graph neural network containing a K-hop peripheral sub-graph. For the discrete action space domain and maximum entropy problem, the optimal selection of the key flow is obtained by using a discrete soft behavior person-reviewer, and finally, the optimal routing scheme is obtained by solving through linear programming constraint. Simulation results show that compared with a traditional algorithm and other reinforcement learning algorithms, the provided algorithm has better time delay performance and ability to adapt to dynamic network topology.

The invention discloses a truck-unmanned aerial vehicle cooperative path optimization method and program product for a heterogeneity inspection task, and the method comprises the following steps: (1) setting a target function of a truck-unmanned aerial vehicle cooperative path optimization model as the minimum total cost, (2) adding routing constraints, heterogeneity constraints of different types of inspection tasks on equipment, unmanned aerial vehicle energy consumption constraints and time constraints to the truck-unmanned aerial vehicle cooperative path optimization model; and (3) solving the truck-unmanned aerial vehicle cooperative path optimization model to obtain an optimized decision variable so as to obtain an optimal path and an optimal unmanned aerial vehicle-truck configuration scheme for a heterogeneity inspection task. According to the invention, heterogeneity of inspection tasks is considered, and the planned path scheme is higher in inspection efficiency.

The present application relates to the technical field of network routing, and discloses a method and system for adaptively adjusting network routing, the method comprising: performing detection data packet transmission and hierarchical clustering on a network interface to obtain a path cluster set; performing double-circular matrix encoding on the path cluster set to obtain a routing path encoding set; constructing a spatiotemporal feature tensor based on the routing path encoding set, and performing tensor decomposition and feature pattern extraction to obtain a time-varying weight coefficient; performing sparse-aware proportional iterative optimization calculation based on the spatiotemporal feature tensor and the time-varying weight coefficient to obtain an optimal routing configuration scheme, thereby achieving differentiated processing of different business flows and improving network resource utilization efficiency.

The invention provides a multi-unmanned-ship cooperative control propulsion system and method based on Roland-C positioning, and relates to the field of unmanned ship control, the system comprises a Roland-C positioning system, a data processing module, a path planning module, a cooperative control module and a propulsion module, the Roland-C positioning system is used for providing real-time position information and course data of multiple unmanned ships, and the Roland-C positioning system is used for providing real-time course data of the unmanned ships; and the data processing module is used for transmitting the processed position information and environment data to the path planning module and the cooperative control module, dynamically adjusting the tracking propulsion path of the unmanned ship according to the optimal route, and generating a control instruction to enable the propulsion module to control the unmanned ships to keep formation and cooperatively work. Through a cooperative control strategy with strong robustness, formation stability and safe navigation of the unmanned ship are ensured so as to realize high-precision positioning, intelligent path planning and robustness cooperative control in a complex water area environment.

The invention discloses an e-commerce storage order real-time picking path planning method and system, and relates to the technical field of intelligent storage and logistics automation, and the method comprises the following steps: training a behavior prediction model based on historical picking behavior data, and outputting a path candidate region of a picker; constructing a path game model according to the path candidate area in combination with the current order task; based on a Stackelberg mechanism, solving a game equilibrium path distribution result; according to a solving result, evaluating the candidate path scheme through a multi-objective optimization algorithm, and outputting an optimal path scheduling strategy; according to the method, by introducing path candidate region extraction based on behavior prediction, fusion graph construction, a Stackelberg hierarchical game solving mechanism and multi-target optimization strategy output, real-time performance and global collaborative optimization of order picking path selection are realized.

A network device (ND) receives a first media access control (MAC) / Internet Protocol (IP) route for a customer ND. The ND installs the first MAC / IP route in a MAC / IP routing table and transmits the first MAC / IP route to a first subset of NDs causing the NDs to install the first route. The ND receives a second MAC / IP route for the customer ND and a sequence number indicative of an order of advertisement of the second MAC / IP route. The ND determines, based on the sequence number, that the second MAC / IP route is a best route for the customer ND. The ND installs the second MAC / IP route as the best route for the customer ND; and transmits the second MAC / IP route to a second subset of NDs including a first ND from which the first MAC / IP route originated, causing the first ND to request withdrawal of the first MAC / IP route.

The invention provides an unmanned aerial vehicle air route visual management system, and belongs to the technical field of unmanned aerial vehicle visual management, and the system comprises a collection module which obtains the model of a target unmanned aerial vehicle, determines a corresponding sensor, and collects the flight data of the unmanned aerial vehicle in real time; the first determination module is used for processing the flight data and determining the optimal route of the unmanned aerial vehicle based on a path planning algorithm; the dynamic adjustment module is used for acquiring surrounding environment parameters of the unmanned aerial vehicle and dynamically adjusting the optimal route of the unmanned aerial vehicle; the display module is used for visually displaying the flight path and the meteorological condition of the unmanned aerial vehicle according to the GIS technology, and obtaining the dynamic flight situation monitoring of the unmanned aerial vehicle; and the triggering module is used for setting a safety threshold according to the performance of the unmanned aerial vehicle and the real-time environment condition, triggering an alarm according to the safety threshold, and realizing visual management of the air route of the unmanned aerial vehicle according to the alarm information. The problems that the route planning is inaccurate, the visualization effect is poor and the real-time performance of acquiring the flight data cannot be ensured due to manual operation are solved.

The invention relates to the technical field of ship navigation, and discloses a ship navigation route optimization method and system. The method comprises a meteorological change sequence, an ocean current fluctuation sequence and channel obstacle distribution data. Then carrying out two-dimensional route topology construction processing to generate route distribution characteristics; calling a pre-trained path optimization model to carry out nonlinear path analysis processing to obtain an optimal route prediction value and a key risk area identifier; performing navigation environment compensation correction processing on the optimal route prediction value based on the association relationship between the meteorological change sequence and the ship control sensitivity to generate a corrected optimal route prediction value; and finally, generating a course maintenance strategy set including a course adjustment path optimization scheme and a speed dynamic regulation and control processing scheme according to the key risk area identifier. According to the method, dynamic environmental factors can be comprehensively processed, the accuracy and adaptability of route planning are improved, risks are effectively identified and avoided, and high efficiency and safety of ship navigation are guaranteed.

The invention relates to an optical network routing intelligent planning method and system based on multi-objective optimization. The method comprises the following steps: performing recursive partitioning on a target optical network to obtain a plurality of sub-network units and a boundary node set; inputting the plurality of sub-network units into a three-layer deep learning model for processing to obtain a routing decision matrix and a wavelength allocation vector; inputting the plurality of sub-network units and the boundary node set into a distributed controller for processing to obtain an error compensation matrix; based on the routing decision matrix and the wavelength allocation vector, performing dual strategy processing of time delay priority and hop count priority to obtain a time delay weighted evaluation function and a hop count weighted evaluation function; performing multi-target joint optimization to obtain an optimal routing scheme; and performing reconstruction cost calculation on the optical path set according to the error compensation matrix and the optimal routing scheme, and outputting a resource reconfiguration scheme. According to the implementation of the invention, the resource overhead in the network reconstruction process is reduced, and the overall operation efficiency of the network is improved.

The invention provides an Internet of Things cooperative routing method based on multi-agent deep reinforcement learning. The method comprises the following steps: constructing a wireless sensor network model; constructing an energy consumption model; establishing an optimization problem of network routing according to the constructed wireless sensor network model and the energy consumption model; modeling the optimization problem into a multi-agent reinforcement learning problem, and selecting an optimal routing path according to the multi-agent reinforcement learning problem; wherein in the multi-agent reinforcement learning problem, each sensor node is regarded as an independent agent, and each agent learns a strategy for optimizing the overall network performance according to a local state and a reward mechanism. According to the invention, the energy utilization efficiency of the routing protocol is improved, routing with balanced energy is realized, and the network life cycle is effectively prolonged.

The invention provides a stacked chip architecture modeling method and a stacked chip architecture modeling system. The method comprises the following steps: dividing an on-chip chip to be modeled into a plurality of core particles; the method comprises the following steps of: modeling a chip by adopting a three-stage joint modeling mode, and respectively executing core size level modeling, medium level modeling and substrate level modeling; arranging the placement positions of the core particles according to the factors of the time sequence of the key path, the bus length and the super-long path; according to the connection relation between the interior and the exterior of the core particle, and according to the weights of the internal netlist and the external netlist of the core particle or user input information, optimizing the positions of the internal elements of the core particle, and adjusting the positions of the micro convex points mapped by the elements; and carrying out automatic wiring based on the arrangement position of each core particle, removing a network with conflicts in wiring, carrying out labyrinth wiring on an area in which wiring is removed, and searching an optimal wiring path. The system is used for implementing the method. The invention provides a one-stop chip design EDA tool, and the design precision of the chip can be improved.

The invention discloses a real-time dynamic ship route induction method for a ship in an inland river route network. The method comprises the following steps: collecting information; calculating the real-time dynamic speed of the ship and the interpenetration clearance of the ship; establishing a ship route network weighting topological graph; establishing a generalized path resistance function; establishing an induction path optimization model and solving an optimal route; and outputting a real-time dynamic ship induction route. The real-time dynamic induction route of the ship is calculated by establishing the ship route induction model on the basis of the real-time dynamic ship traffic flow prediction result in combination with the ship route network topological structure in the inland river route network, so that the method has the characteristics of high universality and wide coverage; the LSTM network is adopted to predict the ship navigational speed, the prediction precision is high, convergence is fast, and the workload is small; according to the invention, accurate real-time dynamic induction route information of the ship can be provided, and the reliability is good; due to the fact that the ant colony optimization algorithm of operational research is used for achieving real-time dynamic route planning of the ships in the inland river route network, economical efficiency is good.

The invention discloses a data processing system for processing mass data and a processing method thereof, and relates to the technical field of data processing, the system comprises the following components: a data receiving module for monitoring a data access port, receiving data from an external data source, and performing primary processing on the received data for subsequent processing, according to the invention, the intelligent data routing module utilizes a reinforcement learning model to make an optimal routing decision based on data characteristics and real-time load conditions so as to ensure that data can be efficiently processed by specified nodes in a processed node cluster, and the load balancing module monitors and processes the load conditions of the node cluster in real time, so that the load balancing efficiency of the node cluster is improved. According to the method and the system, the future load peak is predicted, and the data routing strategy is intelligently adjusted according to the analysis result, so that the performance bottleneck and the fault risk caused by overload are effectively avoided, and the intelligent data routing and load balancing mechanism enables the system to keep efficient performance and stable operation when processing a large amount of data.

The invention discloses a rugged environment improved A * algorithm path planning method based on an elevation map, and relates to the technical field of path planning. The method comprises the following steps: establishing a ground trafficability model and a ground rugged model which conform to the characteristics of a rugged environment terrain based on an elevation map, further identifying the passing difficulty and wave degree of the ground, and then designing an elevation cost function capable of obtaining an optimal path according to the two types of models in combination with the main process of an A * algorithm. And finally, a continuous cost function is established in combination with an original distance cost function, the path optimality is improved, and an optimal route is selected. The improved algorithm enhances the stability and optimality of the planned path of the mobile robot in the rugged environment, and improves the autonomous navigation performance of the mobile robot in the rugged environment.

The invention provides a dynamic environment adaptive low-altitude route planning method and system, and the method comprises the steps: collecting multi-source environment information in real time, and constructing a flight environment model; according to flight historical data, in combination with the flight environment model, carrying out route planning; based on the battery capacity of the unmanned aerial vehicle, the endurance demand of the air route is checked, and the optimal air route is output; verifying whether the optimal route meets the requirements of safety and tasks or not, and controlling the unmanned aerial vehicle to fly according to the optimal route after verification is passed; and the unmanned aerial vehicle feeds back the flight state in real time, and dynamically optimizes the air route in combination with the flight environment model. The problems that in the prior art, the environment perception capability is insufficient, situation perception is missing, the optimization target is single, and the algorithm practicability is poor are solved. The problem of route planning of the unmanned aerial vehicle in a dynamic environment is solved.

The invention discloses a satellite-based routing decision making method and device and a storage medium. The method comprises the following steps: determining a plurality of reference curves in each first sampling period; determining a first distribution curve in the current time period, and broadcasting the reference curve of the current time period, the first distribution curve and the channel capacity of each area in the current time period to each satellite; determining a reference curve of the next time period, calculating a deformation relation between the reference curve of the second sampling time period and the reference curve of the third sampling time period, and performing deformation processing on the first distribution curve to obtain a plurality of second distribution curves of the next time period; predicting the satellite congestion degree of each region in the next time period according to the channel capacity of each region in the current time period on the basis of the plurality of second distribution curves; and based on each region, constructing a plurality of corresponding routing paths, and determining an optimal routing path in the plurality of routing paths by using a genetic algorithm. Therefore, the technical effect of reliably transmitting data is achieved.

The invention relates to the technical field of intelligent traffic systems, in particular to a personalized traffic travel route recommendation method based on multi-source data. The method mainly solves the problem that in the prior art, real-time collaborative optimization of a dynamic traffic environment and user personalized requirements is difficult. Real-time information is collected through a multi-source heterogeneous traffic data acquisition module, and a standardized data set is generated through data fusion processing; a dynamic user portrait is constructed in combination with machine learning modeling; adopting a multi-target constraint path to search and calculate personalized candidate routes; dynamically re-planning an optimal route based on the real-time traffic flow; and finally, continuously updating the user portrait through an interactive feedback closed loop. The collaborative optimization of traffic state perception and user preference dynamic matching is realized, and the accuracy and timeliness of route recommendation are remarkably improved.

The invention discloses a dynamic planning method and system for a photovoltaic multi-unmanned aerial vehicle cooperative inspection route, and belongs to the technical field of photovoltaic power station inspection, and the method comprises the steps: carrying out the three-dimensional modeling, dividing a no-fly area and a flight area, and building a multi-unmanned aerial vehicle ad hoc network communication network; analyzing parameters of the intruding aircraft based on a prediction algorithm, and predicting a future path of the intruding aircraft; the ground control center receives and shares flight state information of each unmanned aerial vehicle in real time, allocates inspection layers of different heights according to parameters of the unmanned aerial vehicles, introduces an avoidance mechanism to adjust flight parameters, generates a global optimal route based on a dynamic planning algorithm of heuristic search, and performs routing inspection on the unmanned aerial vehicles. And the safe distance model converts unmanned aerial vehicle flight limitation, environmental factors and anti-collision constraints into a cost function, and dynamically adjusts an inspection route. According to the invention, the limitation of the unmanned aerial vehicle in a complex environment can be effectively improved, the adaptability of the photovoltaic power station unmanned aerial vehicle to environment change in patrol inspection is improved, and the safety of the whole patrol inspection process is ensured while the patrol inspection efficiency is ensured.

The invention relates to an edge computing network-oriented micro-service and distributed database collaborative deployment system. The system comprises a database copy number dynamic optimization unit which obtains a dynamic optimization strategy of the copy number of a database based on a queuing gradient projection elastic scaling algorithm; a deployment strategy dynamic adjustment unit calculates end-to-end time delay based on the micro-service routing path and the database routing path; calculating a data inconsistency measurement index based on the number of each copy of the database; dynamically adjusting the deployment strategy based on the resource constraint between the end-to-end time delay and the data inconsistency measurement index and the dynamic optimization strategy of the number of copies of the database; the deployment strategy comprises micro-service instance deployment, database copy deployment and optimal routing path selection; according to the MEC scene-oriented distributed database design and fine-grained collaborative deployment optimization method, a message queue, a queuing theory and a Canal database incremental updating mechanism are integrated, and the micro-service application performance and the data query efficiency / reliability are improved.

The invention relates to the field of modern intelligent manufacturing and automation, and discloses a method for planning a path of an intelligent robot with a body based on visual inspection, which comprises the following steps: acquiring real-time image data, preprocessing and extracting position coordinates of an obstacle, and generating an obstacle distribution diagram; an A star algorithm is adopted to generate a preliminary optimal route sequence, and map information is updated; uncertain interference in the path is judged, and a path alternative set is generated through coordinate calibration; a time-varying parameter and a security constraint optimization path strategy are integrated through adaptive path optimization; key node coordinates are extracted and are fused with obstacle boundary identification to obtain a path execution sequence; real-time performance is checked, if calculation is overtime, a rollback mechanism is triggered, and an initial path is loaded and fused with real-time obstacle information to generate a final feasible route; a control instruction sequence is generated according to the final route and transmitted to the robot for execution. According to the method, efficient and adaptive path planning in a dynamic environment can be realized, and the real-time response capability and task execution reliability of the robot are improved.

The invention discloses a route determination method, system and device and a storage medium. The method comprises the steps of obtaining route notification data and a candidate route set; constructing a knowledge graph based on the route notification data, and calculating a reputation value of each autonomous domain node based on the knowledge graph; and based on the reputation value of each autonomous domain node, screening out a credible route set from the candidate route set, performing reputation weighted scoring on paths in the credible route set, and selecting the path with the highest score as the optimal route. Semantic organization is carried out on the autonomous domain, the routing prefix and the strategy relation through the knowledge graph, the data availability and analysis efficiency are improved, dynamic evaluation of the autonomous domain reputation is achieved in combination with neighbor recommendation, and the accuracy of credibility judgment is improved; a reputation evaluation result is directly applied to path selection, untrusted nodes and paths are effectively filtered, potential attack risks are reduced, and the overall security and stability of a routing system are improved.