752 results about "Weather data" patented technology

The invention discloses an offshore platform anti-collision device and method based on multi-mode BEV perception, and the device comprises a three-dimensional fusion distance measurement module, a UWB distance measurement module, a ship navigation state monitoring module, a platform marine environment monitoring module, and a central processing unit. The three-dimensional fusion ranging module comprises a holder, a laser radar and a camera, and is used for performing 360-degree three-dimensional scanning on the periphery of the offshore platform; the UWB distance measuring modules are installed on the periphery of a ship and pile legs of an offshore platform. The ship navigation state monitoring module is used for acquiring ship position, navigational speed and course information in real time; the platform marine environment monitoring module collects wind wave data and weather data near the offshore platform and route information of passing ships through network communication. The invention discloses an offshore platform anti-collision device with real-time monitoring and collision risk assessment functions, the anti-collision safety of an offshore platform is improved, and the occurrence rate of collision accidents is reduced.

According to the permanent basic farmland quality intelligent monitoring system and method provided by the invention, accurate monitoring and dynamic management are realized through multi-module cooperation, the terrain gradient and canopy height are calculated based on LiDAR point cloud, the complexity index is generated in combination with meteorological data, and the A * algorithm is improved to optimize the unmanned aerial vehicle inspection path. The method comprises the following steps: extracting hyperspectral data to analyze soil and crop states, calibrating an electrode response curve, calculating a bio-availability index in combination with temperature and humidity, dynamically adjusting an early warning threshold value, identifying abnormal data by adopting an isolated forest algorithm and carrying out grading processing, and constructing a space-time cube by adopting space-time Kriging interpolation and CNMF algorithm and fusing multi-source data. According to the method, indexes such as ecological protection grades and economic values are integrated to generate a graded soil quality index model, a disease thermodynamic diagram is generated, key data are stored in a chaining mode, and farmland quality real-time monitoring, risk early warning and ecological economic balance decision making are achieved through multi-source heterogeneous data fusion, dynamic weight optimization and block chain technologies.

The invention belongs to the technical field of intelligent energy and power system automatic control, and particularly relates to a cross-level collaborative regulation and control system integrating meteorological prediction, power market and equipment state, which comprises a central collaborative controller, a data acquisition unit, a communication network and an execution terminal, the data acquisition unit is used for acquiring meteorological data, power load data, electric energy consumption cost data and equipment operation state data in real time; the execution terminal at least comprises a photovoltaic inverter, an energy storage converter and a charging pile controller; the central cooperative controller is configured to execute three cooperative optimization closed loops, namely, a data fusion and prediction closed loop, a multi-target dynamic optimization closed loop and a multi-time scale control closed loop. According to the system and the method provided by the invention, the problems of real-time performance and accuracy of multi-source heterogeneous data fusion are solved; the balance optimization bottleneck of multiple targets such as economy, stability and environmental protection in the dynamic process is broken through; full-time-scale seamless cooperative control from second-level emergency response to hour-level economic dispatching is realized; and the self-adaptive capability and the overall performance of the system in different application scenes are improved.

The invention relates to the technical field of intelligent supply chain management and multi-modal artificial intelligence crossing, and discloses a multi-modal large model-based supply chain toughness evaluation system, which comprises a data acquisition module, a multi-modal processing module, a toughness evaluation module, a cloud edge collaboration module and a decision support module, the data acquisition module is used for acquiring supply chain texts, images and time sequence heterogeneous data; the multi-modal processing module is used for realizing dynamic feature fusion through term matrix enhanced Transform; the toughness evaluation module quantifies toughness scores based on a three-level index system and a fuzzy matter element calculation engine; the cloud edge collaboration module realizes edge end real-time reasoning through a lightweight model; the decision support module optimizes node reinforcement and path planning based on network topology and real-time weather data. According to the method, the problems of insufficient data isomerism processing, single evaluation dimension and lagging real-time response in the prior art are solved.

The invention discloses a meteorological data assimilation method fusing topographic features, and relates to the technical field of meteorological data assimilation, and the method comprises the steps: combining a meteorological variable-topographic factor correlation map with an atmospheric state numerical field, and obtaining a multivariable initial meteorological field model fusing topographic factors; identifying space-time dynamic deviation distribution characteristics of the multivariable initial meteorological field model by using real-time multi-source environment observation data, and generating a terrain disturbance response matrix; carrying out dynamic correction and space-time error reconstruction based on a terrain disturbance sensitive weight on the terrain disturbance response matrix and the multivariable initial meteorological field model through an assimilation optimization algorithm to obtain a meteorological assimilation result; according to the method, dynamic correction and space-time error reconstruction are carried out on the initial meteorological field through the terrain disturbance sensitive weight, directional perception and intelligent correction of observation deviation are realized, and the response capability and the error control level of the model in a dynamic change scene are effectively enhanced.

The invention relates to the technical field of meteorological data processing, in particular to a training data acquisition method and system for a power transmission line fault prediction model. The method comprises the following steps: constructing a multi-source heterogeneous original data pool containing severe convection meteorological data, power transmission line operation and fault data and geographic space data; performing time-space reference unified processing and grid division on the data; mapping the line paragraph to a grid unit, identifying a severe convection event and dividing an influence area through a density clustering algorithm based on a multi-parameter collaborative threshold value, calculating a grid exposure degree, and combining a strength grade weight and a terrain correction weight to calculate a space correlation strength; setting a differentiation time association window to calculate a time factor according to the severe convection dominant type, calculating association credibility based on the space association strength and the time factor, and screening high-credibility association results; and performing attribution labeling on the fault information to obtain a training data set. The problem of low training data quality is solved.

The invention relates to the technical field of power grid dispatching, in particular to a power grid dispatching operation ticket intelligent generation and risk pre-judgment system based on multi-modal data fusion. The method comprises the following steps: a weather-time window mapping unit associates and identifies weather data on that day, and judges the rationality of planned operation time and steps by establishing a mapping relationship between a meteorological condition and an operation time window and utilizing a meteorological influence factor matrix; after the historical operation ticket analysis unit confirms meteorological condition operation, information is extracted from the meteorological condition operation through the NLP technology, and a set of standard operation ticket template library is constructed; the four-dimensional risk association unit establishes a four-dimensional risk association model based on power safety regulations, historical accident cases and equipment defect data. According to the design of the invention, through multi-dimensional data fusion technologies such as meteorological-time window mapping, four-dimensional risk association analysis and dynamic topology simulation, safety risks existing in the operation process can be comprehensively identified and evaluated.

The invention discloses a long-tail data generation method based on feed-forward stylized reconstruction. The method comprises the following steps: constructing multi-modal geometric perception input and feature codes; reconstructing a semantic guidance scene based on a feature-level linear modulation mechanism; the invention relates to 3D consistency controllable style migration based on a feedforward reconstruction model. According to the invention, a feedforward reconstruction network based on DINOv2 improvement is constructed. Multi-modal adaptation is performed on a backbone network input layer, and decoupled high-level semantic features are explicitly injected by using a feature-level linear modulation mechanism, so that the geometric accuracy of automatic driving scene reconstruction is improved. On the basis of a three-dimensional scene of feedforward reconstruction, a distillation strategy is adopted, and the generation capability of a 2D diffusion model is migrated to a 3D Gaussian field. According to the method, the phenomena of object deformation, disappearance and the like in the generated data are effectively avoided, and long-tail weather data with geometric consistency of rain, snow and the like can be generated through the text instruction.

The invention provides a power supply strategy optimization method for condensation control and a ring main unit, and belongs to the technical field of power supply, and the method specifically comprises the steps: determining the coincidence probability of the ring main unit and humidity intervals of different fluctuation types in a future preset time period according to the humidity monitoring data of the ring main unit, when the coincidence probability is utilized to determine that the distribution data of the coincidence risk time period in the future preset time period meets the requirement, the temperature change condition of the ring main unit under the weather data of the current date is taken as the basis, and the temperature change condition of the ring main unit is determined on the basis of the coincidence probability of the coincidence risk time period and humidity intervals of different fluctuation types; according to the method, the adjustment demand type of the humidity adjustment device of the ring main unit is determined, and the power supply processing strategy of the temperature adjustment device of the photovoltaic device at different monitoring temperatures is determined by using the adjustment demand type and the humidity monitoring data, so that the reliability of the control processing of the condensation risk is improved, and the use of the commercial power is reduced.

The invention discloses a 5G signal-based repeater signal processing system and method, and belongs to the technical field of communication, and the method comprises the steps: obtaining the signal strength of a 5G signal received by a repeater, a transmission distance target value, a topographic sign value of a transmission destination, weather data of a transmission link, and state data of a signal transmission candidate frequency band; generating a signal transmission loss value according to the transmission distance target value of the 5G signal received by the repeater, the topographic sign value of the transmission destination and the weather data of the transmission link; generating a final gain demand prediction value of the 5G signal according to the signal transmission loss value and the signal strength of the 5G signal received by the repeater, and judging whether the 5G signal transmission needs frequency band switching; according to the state data of the signal transmission candidate frequency band, generating an optimal candidate frequency band, and processing the 5G signal; according to the invention, the gain compensation precision is improved, dynamic frequency band switching can be realized, the signal processing accuracy of the repeater is improved, and the poor user experience is optimized.

The invention relates to an artificial intelligence technology, in particular to a task adaptive parameter adjusting method and system for a weather and climate basic model. The adjusting method comprises the steps that a weather and climate basic model is initialized, the weather and climate basic model comprises an encoder, a main body network and a decoder, and the main body network comprises a parameter efficient fine adjustment framework model provided with a task self-adaptive dynamic prompt module and a random snow-consuming guided self-adaptive selection module which work cooperatively; performing task self-adaptive dynamic prompt through a task self-adaptive dynamic prompt module, generating soft prompt lexical elements, and fusing the input weather data with the soft prompt lexical elements to obtain an enhanced lexical element sequence; and through a random snow-consuming guided adaptive selection module, random snow-consuming guided adaptive selection is carried out, and parameter fine tuning of the weather and climate basic model is realized. Model parameters can be dynamically, selectively and finely adjusted according to downstream tasks, a very small number of trainable parameters are used, and the calculation and storage cost is remarkably reduced.

The invention discloses an intelligent rainfall forecasting method and system based on real-time assimilation driving of sky-air-ground multi-mode sensing data, and relates to the field of machine learning, and the method comprises the steps: carrying out the coordinate alignment of an obtained standardized multi-source meteorological data set, distributing a weight according to the time-space credibility of a data source, and carrying out the calculation of the time-space credibility of the data source; generating an enhanced radar image feature set implying multi-modal information, and inputting the enhanced radar image feature set into the double-branch-UNet-GAN collaborative optimization model for training to obtain an optimal model adaptive to multi-modal data; based on severe convective weather characteristics and business requirements, precision correction and lightweight processing are performed on a forecast result after test set adversarial optimization through a regional attention mechanism, visual display is realized through an APP and a webpage terminal, positioning query and early warning push functions are synchronously provided, and a closed loop from data to service is completed. According to the method, full-chain innovation is formed from data fusion, model performance, aging precision, resource adaptation to service landing, and the scientificity and application value of intelligent rainfall forecasting are remarkably improved.

The invention discloses a photovoltaic power ultra-short-term prediction method, system and device and a storage medium, and relates to the technical field of new energy prediction. The method comprises the following steps: constructing an improved ensemble empirical mode decomposition algorithm to decompose original photovoltaic power data, and distinguishing and reconstructing a plurality of decomposed intrinsic mode functions to obtain reconstruction sequences of high, medium and low frequency components; for high-frequency and intermediate-frequency components, carrying out dynamic denoising by adopting an extended Kalman filtering algorithm; and inputting the original data, the low-frequency component, the denoised high-frequency component and the denoised intermediate-frequency component into a WTConv1d module, extracting multi-scale features through a wavelet filter and convolution operation, and inputting a multi-dimensional input matrix constructed by the multi-scale features and meteorological data into a Transform model for prediction. According to the method, noise interference can be effectively suppressed, multi-time scale feature expression is enhanced, and a self-attention mechanism is fully utilized to capture a cross-scale dependency relationship, so that the prediction precision and stability are improved.

The invention relates to a multi-modal meteorological data downscaling generation method and device and computer equipment. The method comprises the following steps: acquiring topographic data and meteorological data in a to-be-measured area; inputting the topographic data and the meteorological data into a pre-trained deep learning model, carrying out feature coding on the topographic data and the meteorological data through the deep learning model to obtain a topographic feature vector and a meteorological feature vector, carrying out feature interaction on the topographic feature vector and the meteorological feature vector to obtain interaction features, and carrying out feature extraction on the interaction features; and carrying out downscaling decoding on the interaction features to obtain downscaling meteorological data. By adopting the method, the downscaling generation efficiency and the physical rationality of the meteorological data can be considered.

The invention relates to the technical field of intelligent wearable equipment, and discloses an intelligent wearable system integrating multi-modal physiological monitoring and active intervention, which comprises a ring end acquisition module, a mobile end cache module and a cloud intelligent analysis module, and the cloud intelligent analysis module comprises a multi-modal fusion analysis unit, an active intervention decision unit and a user digital twinborn model construction unit, and is used for calculating a sleep score, a readiness score, a pressure index, a blood oxygen trend and a menstrual period prediction result according to the multi-source physiological characteristics, and executing an active intervention decision based on a health state. The sensing module is integrated in the ring body, the light-weight design is suitable for long-term wearing, all-weather data acquisition is guaranteed, compared with a bracelet or a watch, the ring is comfortable and hidden to wear, the continuous use willingness of a user is enhanced, meanwhile, the ring is suitable for high-voltage office or sports scenes, and non-inductive health monitoring is achieved.

The invention belongs to the technical field of wind power generation, and relates to a fan tower reliability evaluation method and system, electronic equipment and a medium, and the method comprises the steps: obtaining a weather fatigue accumulated damage coefficient according to different weather data information; obtaining a load coefficient based on the load stress and a preset allowable stress; the inclination is collected in real time through an inclination angle sensor, and according to the inclination and a preset inclination threshold value, an inclination coefficient is obtained through normalization processing; obtaining the service time and the design life of the fan tower, and obtaining a life attenuation coefficient based on the service time and the design life; and obtaining the reliability according to the weather fatigue accumulated damage coefficient, the load coefficient, the inclination coefficient, the life attenuation coefficient, a preset weather fatigue accumulated damage weight, a load weight, an inclination weight and a life attenuation weight. According to the method, the reliability is obtained through the weather fatigue accumulated damage coefficient, the load coefficient, the inclination coefficient and the life attenuation coefficient, and the problem of low reliability evaluation accuracy of the existing fan tower is solved.

The invention discloses a vehicle transport capacity scheduling system based on a network order, which comprises a data acquisition module, a data processing unit, a core scheduling unit, a data processing unit, a core scheduling unit, a data processing module, a data storage module, a data processing module, a data processing module and a data processing module, and is characterized in that the data acquisition module is used for acquiring network order data, vehicle real-time state data, real-time traffic data, weather data and driver biological characteristic data; the data processing unit is connected with the data acquisition module and used for cleaning, fusing and analyzing acquired data, the core scheduling unit is connected with the data processing unit, and the core scheduling unit comprises a dynamic adaptive scheduling module, an order allocation module, a path planning module, a dynamic adjustment module and a driver state adaptation module; by continuously receiving the environment state and optimizing the scheduling strategy based on the reward mechanism, the system can adapt to complex and changeable orders and traffic conditions, dynamic optimization of vehicle transport capacity scheduling is achieved, and the overall performance of the system is improved.

The invention relates to a UTE-LSTM-SA-based community user charging load prediction method, an electronic device and a medium, and the method comprises the steps: obtaining multi-source data, the multi-source data comprises community user behavior data, environment data and power data, the community user behavior data comprises commuting distance, travel time and user type labels, and the environment data comprises the environment data and the power data; the environment data comprises temperature data and weather data, and the power data comprises historical charging load and time-of-use electricity price; constructing multi-dimensional features based on the multi-source data, wherein the multi-dimensional features comprise commuting association features, time scene features, external influence features and abnormal value correction features; constructing a community user charging load prediction model based on a UTE-LSTM-SA prediction framework and the multi-dimensional features; and solving the community user charging load prediction model based on constraint conditions to obtain a community user charging load prediction result. According to the method, the commuting distance, the environment temperature and the time-of-use electricity price characteristics are fused, the charging load in the next 24 hours is predicted through the customized deep learning model, and the prediction precision is improved.

The invention discloses an energy storage system capacity configuration optimization method and device and a storage medium, and relates to the technical field of energy storage system capacity configuration.The energy storage system capacity configuration optimization method comprises the following steps that daily total electricity utilization power information of a factory is continuously collected, corresponding weather information is obtained, and factory power weather data is obtained; denoising processing is carried out, an electricity consumption power prediction model is constructed, and the daily total electricity consumption power of the factory is predicted; calculating a prediction error, carrying out error rule analysis, and correcting the predicted total power consumption power to obtain corrected power prediction data; calculating predicted electricity consumption, and performing configuration optimization on the capacity of the energy storage system of the factory according to the predicted electricity consumption; the method is used for solving the problems that when an existing energy storage system capacity configuration technology configures the capacity of an energy storage system of a factory through a power consumption prediction model, the rule of prediction deviation of the model cannot be analyzed, meanwhile, a prediction result cannot be corrected, and the accuracy of capacity configuration of the energy storage system cannot be improved.

The invention provides an intelligent heat supply control method based on load prediction and related equipment thereof, and the method comprises the steps: obtaining environment data and real-time operation data of heat supply equipment, and the environment data comprises indoor environment data of the heat supply equipment and outdoor meteorological data; processing the indoor environment data, the outdoor meteorological data and the operation data through a preset prediction model, and determining target load prediction data; based on the target load prediction data and the current time-of-use electricity price data, constructing an optimization control target function; through the optimization control objective function, performing optimization processing on initial output parameters of the heat supply equipment, and determining target adjustment parameters of the heat supply equipment; and the output of the heat supply equipment is regulated and controlled through the target adjustment parameters. Through the steps of the method, cooperative adjustment of the multi-heat-source equipment can be achieved, and the operation cost is remarkably reduced while stable and comfortable heat supply is guaranteed.

The invention provides a polar region power load prediction method, and relates to the technical field of power system load prediction, and the method comprises the steps: carrying out the preprocessing of collected Antarctic power load data and weather data, arranging and integrating the load data according to a time sequence, and forming a load sequence; decomposing the load sequence into K sub-modes through a metamorphosis decomposition method, wherein each sub-mode represents components of different frequencies in the load sequence; and constructing a CNN-BILSTM model on the basis of taking the sub-modes as input, outputting prediction subsequences corresponding to the sub-modes, adding the K prediction subsequences to obtain a load prediction result, and performing global optimization on network hyper-parameters of the CNN and BILSTM modules by adopting a sparrow search algorithm.

The invention provides a forest fire fighting and rescue fire scene low-altitude meteorological monitoring service system and method. The system comprises a forest fire scene meteorological environment monitoring system, a data fusion analysis system and a data distribution service system. The forest fire scene meteorological environment monitoring system comprises an air-ground collaborative meteorological observation system and a forest fire scene space information remote sensing system, and is used for acquiring low-altitude meteorological environment, vegetation and terrain situation information in a forest fire scene boundary layer; the data fusion analysis system is used for observation data space-time normalization processing, observation data space-time scale expansion, three-dimensional fire scene situation construction based on terrain situation information, meteorological data fusion analysis and information visualization display; and the data distribution service system comprises a data link and data distribution service software and is used for distributing data of the forest fire scene meteorological environment monitoring system and the data fusion analysis system to different forest fire fighting rescue forces and hardware terminals. The overall efficiency and safety of forest fire scene fighting work can be effectively improved.

The application discloses a kind of electric power time sequence prediction dataset construction method and system, the method includes obtaining electric power load and weather data, constitutes electric power dataset and carries out cleaning alignment;While obtaining energy, meteorological and other multi-source public time sequence data;Two kinds of data are uniformly formatted and normalized, extract time-frequency characteristics and calculate similarity between sequences, filter out the public data significantly related to electric power load;Dynamic sampling weighting method is used to assign weight to each data, set sampling frequency, finally build heterogeneous electric power time sequence dataset for large model pre-training and fine-tuning;The application integrates multi-source heterogeneous data, constructs a unified standardized data format, and aligns and fuses multiple sources of data based on timestamp, significantly improves the consistency and availability of data, improves data fusion efficiency and model generalization ability.

The invention provides an airplane ice accretion model training method, a prediction method, a product, equipment and a medium. The method comprises the following steps: acquiring airplane ice accretion observation data and historical weather data; based on the space information and the time information of the airplane icing observation data and the horizontal grid, the vertical layering and the time dimension of the grid points, space-time matching is carried out on the airplane icing observation data and the grid points, and the space-time matched grid points are obtained; calculating the feature data of the space-time matched grid points by using an ice accumulation scheme to obtain ice accumulation indexes of the space-time matched grid points; constructing a training data set according to the feature data corresponding to the plurality of spatio-temporal matched grid points, the icing indexes and the observation labels of the matched airplane icing observation data; and performing model training by using the training data set to obtain an airplane icing model. The training data set provides high-order features with more physical significance for machine learning, the interpretability of the model is improved, and the prediction accuracy of the airplane ice accretion is improved.

A system for generating customized alerts for a user may (1) receive weather data from a database; (2) determine, from the weather data, weather conditions indicative of a predicted weather event beginning within a predefined time period; (3) receive a location of the user; (4) receive information relating to one or more protection policies of the user; (5) determine that the predicted weather event is relevant to the user based upon the location of the user and the information relating to the one or more protection policies of the user for protecting at least one of the user or property of the user; and (6) automatically initiate an action to mitigate a potential impact of the predicted weather event on the user responsive to the determination that the predicted weather event is relevant to the user.

The invention discloses a local autonomous charging scheduling method and device for a charging station, and belongs to the technical field of charging scheduling, and the method comprises the steps: building a fixed user file and an unfixed user file according to the historical charging record of the charging station; according to the prediction time period, regular charging data of a fixed user is obtained in a fixed user file, and a fixed demand is calculated; obtaining predicted charging data of the unfixed user in the unfixed user file, and calculating an unfixed demand; determining the sum of the fixed demand and the unfixed demand as a basic load; weather data, time types, adjacent power grid load data and electricity price data of the prediction time period are obtained respectively, and corresponding influence coefficients are obtained through calculation; and calculating a predicted load of the charging station based on the basic load and each influence coefficient, and carrying out charging scheduling on the charging station in the prediction time period according to the predicted load. Therefore, by implementing the charging scheduling method and device, the problem of low charging scheduling accuracy in the prior art can be solved.

A weather data assimilation assistance device includes a wet delay calculator and a wet delay variance calculator. The wet delay calculator calculates a wet delay in a satellite line-of-sight based on observable data indicating an observable of a radio wave from a positioning satellite measured by a receiver to receive the radio wave from the positioning satellite and based on atmospheric delay data indicating an atmospheric delay in the satellite line-of-sight calculated based on a correction value for at least one of a satellite clock or a satellite orbit of the positioning satellite, and outputs data indicating the wet delay. The wet delay variance calculator calculates a variance of the wet delay based on the wet delay and a covariance matrix of the correction value for at least one of the satellite clock or the satellite orbit, and outputs data indicating the variance of the wet delay.

The invention relates to the technical field of ice prevention and disaster reduction of high-voltage power transmission lines, and discloses a cascaded direct-current ice melting system of a high-voltage power transmission line, a control method and a device. The method comprises the following steps: inputting a test signal to the cascaded direct-current deicing system, and detecting the voltage and current of each node; calculating line impedance distribution based on the voltage and current of each node, and constructing a line equivalent model; calculating the minimum ice melting current based on the ice thickness monitoring data and the weather data, and calculating the output current of each ice melting device based on the minimum ice melting current and the line equivalent model; monitoring circulating current between systems, and calculating virtual compensation voltage based on the circulating current; and adjusting the output voltage of each ice melting device based on the virtual compensation voltage. The ice melting success rate, the safety of the system and the service life of the system can be improved.

The invention provides an electric heavy truck lease risk assessment method, device and equipment, and relates to the technical field of data processing, and the method comprises the steps: obtaining real-time vehicle operation data, real-time battery state data, energy complementation behavior data, battery replacement behavior data and external application data of a target electric heavy truck vehicle, the external application data at least comprises a regional peak and valley electricity price table, weather data, maintenance data and freight service data; performing feature extraction on the real-time vehicle operation data, the real-time battery state data, the energy complementation behavior data and the battery replacement behavior data to obtain multi-class feature data of the target electric heavy truck; and according to the multiple types of feature data, performing lease risk assessment on the target electric heavy truck to obtain a target risk level of the target electric heavy truck. According to the method, comprehensive data dimensions are ensured, the evaluation result is more reliable, the early warning capability is improved, and the asset loss is reduced.

The invention relates to a ship route automatic recommendation and matching method and system based on big data, and the method comprises the steps: receiving a starting point position and an end point position selected by a user, and automatically matching a corresponding nearest port as an actual starting point port and an actual end point port; generating a plurality of candidate routes in combination with historical navigation data, real-time meteorological data and ship performance parameters in the route database; carrying out adaptive analysis on the candidate routes based on the ship type and the cargo capacity, and excluding candidate routes which do not conform to ship navigation conditions; performing multi-dimensional evaluation on the candidate airlines through a preset optimization algorithm, generating an airline recommendation list, and sending the airline recommendation list to a user interface; and continuously monitoring the real-time state data of the route selected by the user, and triggering an automatic re-planning process if detecting that the channel is abnormal or the weather is suddenly changed. According to the method, big data analysis, intelligent matching and dynamic optimization are integrated, and automatic, multi-dimensional and high-reliability route recommendation and collaborative management are realized.