348 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 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 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 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 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.

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 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 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.

The invention relates to the field of intelligent security and path planning, and discloses a weapon escorting route dynamic optimization and risk early warning platform, which comprises a multi-modal data acquisition module, integrates traffic flow, geographic elevation and threat public opinion data through space-time alignment and tensor filling, and constructs a multi-channel space-time feature tensor; the dynamic risk assessment module is used for solving and generating a dynamic risk field based on an adversarial game model and Nash equilibrium, and quantifying a risk gradient under an attacker strategy; the quantum collaborative optimization module is used for generating quantum parameters through tensor hidden variable decomposition and solving a multi-target optimal route in combination with a quantum annealing algorithm; the hypergraph emergency response module is used for reconstructing a mutual exclusion redundant path and feeding back and updating a strategy space when a threshold exceeding risk is detected; and the strategy collaborative updating module fuses the risk gradient and the game utility to generate a joint reward signal, and drives the reinforcement learning model to dynamically optimize the decision strategy. The safety, efficiency and reliability of the escorting task are improved.

The invention discloses a multi-constraint emergency material vehicle path optimization method and system, and belongs to the technical field of vehicle path planning. Comprising the following steps: acquiring input data from a data source, and constructing a multi-objective optimization model containing an objective function and constraint conditions based on the input data; based on the input data and the constructed multi-objective optimization model, generating an initial path scheme meeting all constraint conditions by adopting a heuristic algorithm combining nearest distance distribution and an earliest time window priority rule; and constructing a hierarchical collaborative optimization architecture consisting of an ALNS execution layer and a PPO decision layer, carrying out iterative optimization by taking the initial path scheme as a starting point, terminating the iterative optimization when a preset termination condition is met, and finally outputting an optimal path scheme. Compared with the prior art, the method has the beneficial effects that the key indexes are collaboratively optimized through the multi-objective optimization function, and the global solution quality and the scheduling precision are remarkably improved.

An optimal route can be determined for delivering an item through a logistics system where vehicles have multiple stops when traveling along routes. The optimal route is determined using a hybrid system employing a classical computing device and a quantum annealer. The classical device reduces the search space that allows the quantum annealer to determine a more optimal solution. In an aspect, a routing graph comprising nodes and edges is populated from routes of vehicles, where the nodes identify locations and the edges represent routing data. At least a portion of the nodes and edges is removed to form a refined routing graph. For an origin-destination input, the refined routing graph can be filtered according to a first set of routing constraints to form a reduced route search space. A quantum annealer is invoked according to an objective and a different second set of routing constraints.

The invention discloses a large transport intelligent line selection implementation method fusing a graph neural network and a large language model, and the method comprises the following steps: S1, constructing multi-source features to a unified high-dimensional vector space according to large transport historical data and real-time environment data; s2, constructing a structured prompt template through multi-modal input; s3, based on the structured prompt template, generating a candidate route set in combination with a large language model; s4, dynamically optimizing the candidate route set through a reinforcement learning framework, and outputting an optimal route. Compared with the prior art, the method has the advantages that intelligent generation and dynamic decision making of a large transportation route are realized through multi-modal feature fusion, graph structure semantic compression and large model reasoning optimization, and the transportation reliability, safety and economical efficiency can be remarkably improved; traffic accidents can be responded in real time to realize route adjustment, routes are dynamically optimized to reduce passage cost, planning quality is continuously improved through historical case learning, and safe and punctual delivery of overrun goods is guaranteed.

The invention discloses an intelligent public transportation transfer recommendation system based on multi-source data fusion, and relates to the technical field of intelligent transportation systems, and the system comprises a transfer recommendation management center which is in communication connection with the following modules: a data acquisition module which is used for collecting and preprocessing traffic data from a plurality of traffic data sources, and transmitting the traffic data to a data processing module; various traffic modes of metros and buses are covered; and the multi-source data fusion analysis module is used for carrying out fusion processing on the collected multi-source heterogeneous data. By fusing multi-source traffic data and combining simulation analysis and real-time traffic states, the transfer path is dynamically optimized, the transfer waiting time and total consumed time of the user are remarkably shortened, the user can obtain optimal path recommendation based on real-time traffic information, long-time waiting caused by sudden delay is avoided, the travel efficiency and experience are improved, and meanwhile, the user experience is improved. Double constraints of walking distance and transfer waiting time are supported, it is ensured that path planning meets actual requirements of users, and travel experience is further optimized.

The invention relates to a retrieval routing optimization method and device based on a distributed retrieval system, and relates to the field of computer technology distributed retrieval, the method comprises the following steps: obtaining inverted metadata of a document, including routing information, fragmentation information and a document storage identifier, judging whether the document is a new document, and combining the resource state and availability of each node to optimize the retrieval routing optimization of the document. And determining an optimal routing path by adopting an optimal execution fragmentation formula. A retrieval request is split into a plurality of sub-requests and is directionally issued to a target fragment, and the coordination node integrates return results of the fragments and then returns the return results to the client. The device comprises an information integration module, an inverted metadata recorder and a retrieval routing control module, and supports a dynamic configuration routing strategy, a document state refreshing mechanism and performance monitoring parameters. By introducing the document state identifier, the dynamic routing algorithm and the node resource monitoring mechanism, accurate routing and efficient processing of the retrieval request are realized, so that the retrieval performance and the resource utilization rate of the system are improved.

The invention relates to the technical field of maritime navigation and intelligent shipping, in particular to an electronic chart shipping relation network construction and dynamic updating method and system based on a chart database, and the method comprises the steps: obtaining electronic chart static data and shipping dynamic information, and carrying out standardized preprocessing to unify a space reference and an attribute format; defining node types and edge relation types of channel space elements based on the graph database, and constructing a shipping relation network; shipping dynamic information is accessed in real time, and the weight and the attribute of the side relation are dynamically updated to reflect the real-time state of the navigation channel; and receiving a user path planning query request, executing a path planning algorithm fused with the navigation constraint condition based on the updated network, and generating and outputting an optimal route, thereby effectively solving the problems of weak data association, difficult cross-scale fusion and poor dynamic adaptability of a traditional electronic chart, realizing intelligent management and accurate path planning of a channel relationship, and improving the user experience. And the shipping efficiency and safety are improved.

Each of virtualized base stations is configured by connecting respective units of CU, DU, and RUs. An ACC is installed at least a part of computers that virtualize the respective units. The respective units notify the RAN controller of ACC information of computers and traffic information. The RAN controller comprises a route calculation unit 101 that calculates an optimal route based on the ACC information and traffic information of the respective units, and a route notification unit 102 that notifies the respective units of information on the optimal route, and the respective units switch the route between the respective units based on the notified route information.

The invention discloses an underwater multi-target routing method based on an improved non-dominated sorting genetic algorithm. The method comprises the following steps: firstly, arranging sensor nodes in a target water area, networking, and encoding a feasible path from a source node to a target node by adopting a segmented structure; secondly, constructing a routing multi-target fitness function, and introducing a dynamic weighting function to form a comprehensive fitness index; and finally, grading the population by using an improved non-dominated sorting genetic algorithm, and exploring a better path through crossover and mutation operations. And designing a reference point elite selection strategy, improving the coverage of the solution set in the target space, and outputting an optimal routing path until the algorithm converges. According to the method, the dynamic balance among different optimization targets can be realized, and the link quality is considered while the energy consumption and the time delay are reduced, so that the network routing performance is improved.

The invention provides a wiring design method and device of a printed circuit board, equipment and a storage medium. The method comprises the following steps: constructing a geometric grid diagram of the printed circuit board according to a device position and obstacle information of the printed circuit board; performing surface merging and edge merging on the geometric grid diagram to generate a merged grid resource diagram; constructing a weighted directed graph corresponding to the available wiring channel on the printed circuit board according to the merging edge of the grid resource graph and the regional adjacency relation; according to the weighted directed graph, determining a geometrical relationship between the path guidance and the merging edge, and according to the geometrical relationship, updating the weight of the merging edge; and based on the weighted directed graph and the updated weight, determining an optimal routing path of the printed circuit board through a genetic algorithm. A grid resource map is generated through surface merging and edge merging, path planning is optimized in combination with a genetic algorithm, the optimal routing path can be automatically generated according to the device layout and obstacle information of the printed circuit board, and the wiring efficiency can be remarkably improved.

The invention discloses a ship maintenance inspection robot and a use method, and relates to the technical field of ship maintenance inspection robots. Through mutual cooperation of the telescopic springs and the anti-collision plates, it is conveniently guaranteed that in the collision process of the robot, impact on a robot body is reduced, the use safety of the robot is improved, and then the function of protecting the robot and ship equipment is achieved; a linear relation model of obstacle length, width, walkway width and passing time is constructed to quantify the influence of the obstacle on the passing efficiency, and the limitation that only the route length is used as a single optimization index is avoided; the actual passing time of each route is calculated based on historical inspection data, the consumed time of the optimal route and the consumed time of the to-be-selected route are dynamically compared, real-time adjustment of the route priority is achieved, and the inspection efficiency in a complex environment is improved; and the distribution rule of abnormal positions is judged by using a linear correlation coefficient, and if abnormal points are linearly arranged, the corrosion possibility is eliminated, so that the misjudgment rate is reduced.

The invention discloses a taxi route recommendation method based on a FastDTW time sequence clustering algorithm. The method comprises the following steps: firstly, acquiring original GPS track data of a taxi from a public data platform, and preprocessing the data; thirdly, calculating the similarity between the trajectories by using a FastDTW algorithm, and constructing a distance matrix between every two trajectories; then, the distance matrix is applied to a hierarchical clustering algorithm, a high-frequency destination hot spot area is mined, and a high-probability passenger-carrying destination is obtained; and finally, in combination with factors such as the driving distance, the driving time and the driving cost, calling an Amod map # imgabs0 # to obtain detailed route information, and calculating and recommending an optimal route through comprehensive scores. According to the invention, based on hot destination prediction and multi-target route optimization of data mining, the optimal passenger searching route is recommended for the unloaded taxi, the unloaded rate of the taxi is reduced, the passenger loading probability of the unloaded taxi is improved, and time and resource consumption are reduced, so that the vehicle scheduling efficiency and the driver operation efficiency are improved.