2063 results about "Weather condition" patented technology

A system and method of calculating an estimated time of arrival for a traveler is provided, along with a notification of this estimated time that is sent to a third party. The traveler is equipped with a navigation unit having a global positioning sensor and a wireless communications device. The navigation unit communicates with a service center equipped with a server capable of generating a preferred navigational route for the traveler. The service center also includes a traffic database for maintaining current traffic information. Once the traveler has selected a predetermined time and frequency of notifications based upon a selected criterion, the navigation unit transmits the traveler's position and velocity to the service center. The service center then calculates the estimated time of arrival based upon the traveler's position, velocity, distance to be traveled and traffic and weather conditions. Once this time is calculated, the service center sends a notification to the third party.

Dynamic geofences may be generated and updated based on locations of related assets. For example, locations of a group of related assets, including a primary asset and one or more secondary assets, may be determined. A geofence associated with the primary asset may then be analyzed to identify any geofences of secondary assets that overlap. This process may be repeated for each overlapping geofence to identify additional overlap and expand or move a combined dynamic geofence. Environmental aspects such as time of day, weather conditions, and risk levels may be considered for dynamic geofence adjustments. The combined geofence and asset locations may be displayed on a user interface.

The invention relates to a low-altitude logistics unmanned aerial vehicle path planning method, which comprises the following steps of 1, discretizing an urban low-altitude region into computable grid units, a dynamic-static obstacle classification modeling technology is combined to endow a static obstacle with a basic risk degree, a height attenuation factor is introduced to quantify the influence of a high-altitude obstacle on a low-altitude path, and a collision risk of a dynamic obstacle is dynamically evaluated through a time dimension risk degree prediction model; 2, based on an improved genetic algorithm, comprehensively considering flight time, height change, risk degree and dynamic risk cost through a multi-objective optimization objective function, and generating a globally optimal task allocation scheme; 3, generating a local optimal three-dimensional path considering meteorological conditions and dynamic obstacle influence by adopting an improved particle swarm algorithm; and 4, performing path planning to feed back actual flight time and energy consumption to task allocation for optimization.

The invention relates to the technical field of environment perception, and discloses an optimization method and device for a multi-climate self-adaptive environment perception cooling fan system, and the method comprises the steps: collecting multi-point temperature, humidity and air pressure data, and carrying out the filtering processing, and obtaining an environment state vector; executing mathematical modeling of the multi-degree-of-freedom cooling system according to the environment state vector to obtain a linearized state space model; performing temperature gradient estimation and humidity compensation based on the linearized state space model to obtain an adaptive control law; inputting the adaptive control law into a model-free adaptive prediction controller to obtain a rolling optimization control sequence; weight self-adaptive adjustment and heat dissipation efficiency hierarchical prediction control are carried out on the rolling optimization control sequence based on climate conditions, fan rotating speed control and wind direction adjustment execution signals are generated, the problem that a traditional heat dissipation system is not sensitive to space temperature distribution and humidity changes is solved, and the heat dissipation control precision under different humidity conditions is improved.

The invention relates to a multi-scene self-adaptive high-speed obstacle early warning method and device, equipment and a medium. The method comprises the following steps: firstly, acquiring multi-source data of traffic sensing equipment, and carrying out space-time calibration and semantic analysis on the data to obtain a vehicle track feature vector set; thirdly, calculating real-time traffic flow parameters of the gridding area according to the feature vector set, and further generating a traffic feature database; secondly, modeling is carried out on the traffic characteristic database according to time periods and weather, and theoretical speed baseline parameters of all scenes are obtained; and finally, constructing a multi-dimensional feature matrix according to the theoretical speed baseline parameters, inputting the multi-dimensional feature matrix into the classification model to calculate an abnormal probability value, and if it is detected that a continuous abnormal probability value exceeds a dynamic threshold value in a preset time window, generating an early warning signal. By adopting the method, real-time monitoring and early warning of obstacles in multiple scenes of the expressway can be realized, and more effective guarantee is provided for driving safety of the expressway.

The invention discloses a rainy day target detection method based on paired image data generation and knowledge distillation, and belongs to the technical field of automatic driving target detection. The method comprises the following steps: acquiring a real image of a road scene and a semantic segmentation mask; inputting the semantic segmentation mask and the weather condition text information into a trained improved diffusion model to obtain a road scene generation image under a rainy day condition; introducing a CoT module and an ODConv module into the target detection model for optimization, and then improving the target detection model by adopting a dynamic weighted aggregation loss function to obtain an optimized target detection model; performing knowledge distillation to obtain a rainy day target detection enhancement model; and performing target detection by using the rainy day target detection enhancement model. The method provided by the invention is excellent in performance in a rainy day scene, has good generalization ability, can provide higher-precision target detection in various complex scenes, and effectively improves the detection accuracy and stability.

The invention provides an aircraft digital twinning situation deduction and risk assessment method in a complex meteorological environment, and belongs to the technical field of data processing, the aircraft digital twinning situation deduction and risk assessment method in the complex meteorological environment comprises the following steps: collecting and processing weather data covering parameters such as wind direction and wind speed; obtaining aircraft performance parameters and building a database; and inputting and analyzing the flight plan. By establishing a three-dimensional deduction model including dynamics, weather influence and environment models, aircraft situations under different weathers are deduced, and flight safety, performance and task risks are evaluated. The method has the advantages that weather factors are comprehensively and accurately analyzed, and influences are quantified through data acquisition and processing and a meteorological model; the system is suitable for various aircrafts, can integrate navigation and control systems, and realizes remote monitoring management; risk assessment is scientific and reliable, multiple factors are integrated, powerful support is provided for flight decision making, and aircraft safety and task success rate in complex weather are effectively improved.

The invention discloses an unmanned aerial vehicle three-dimensional path planning method based on an improved artificial travel mouse optimization algorithm, belongs to the technical field of unmanned aerial vehicle path planning methods, and aims at enabling an unmanned aerial vehicle to autonomously navigate, avoid obstacles, find an optimal path and improve task execution efficiency and safety in a complex environment. A constraint condition and a target function of unmanned aerial vehicle three-dimensional path planning are mapped into a search space and a fitness function of an optimization algorithm, an optimal flight path is searched by adopting an artificial travel mouse optimization algorithm, and optimization calculation is performed on the path in combination with unmanned aerial vehicle performance constraints, topographic features and meteorological conditions; aiming at a three-dimensional path planning problem of an unmanned aerial vehicle under a complex condition, an artificial travel mouse algorithm is adaptively improved, and algorithm parameters and structures are respectively adjusted according to mathematical characteristics of a problem model in the aspects of initialization, exploration and development; the flight path is adjusted based on environment change and self state information sensed by a sensor in real time, and the safety and task requirements are met.

The invention discloses a tree lodging detection method and system combined with reference object image analysis. The method comprises the steps that based on an initial image shot by a low-altitude camera, key point positions are determined through a tree and reference object key point detection model, two straight lines are formed through fitting, and the current weather condition is judged. And if the weather is not the extreme weather, evaluating the state of the tree through the two fitting straight lines, and outputting the image and position information of tree inclination or lodging. If the weather is extreme weather, further judging whether the tree is submerged by accumulated water or not. If not, monitoring the change of the included angle between the fitting straight lines to detect whether the tree locates or not; if so, outputting the lodging position and direction. And if the tree is submerged by the accumulated water, calculating the height of the submerged part by adopting a reference object and accumulated water relation model, updating the key points of the tree, finally determining the state of the tree by comparing the fitting straight line, and outputting a corresponding evaluation result. By implementing the method provided by the invention, the precision and reliability of tree lodging monitoring can be improved.

The invention discloses a ship management method and system based on digital twinborn scene construction. The method comprises the following steps: acquiring multi-source scene data of a target water area in real time, and constructing a corresponding ship management digital twin model; performing integrity evaluation on the constructed digital twinborn model, and performing defect compensation according to an evaluation result to obtain an optimized digital twinborn model; further collecting ship density and weather condition data of each sub-region of the target water area, constructing a network transmission delay prediction model, analyzing related data based on the model, and generating a network delay coefficient distribution diagram of the target water area; and according to the delay distribution diagram, determining the real-time updating lag amount of each subunit of the optimization model, and correcting the ship position information in the digital twin model. According to the method, the real-time performance and accuracy of the digital twin model can be remarkably improved, and the ship operation management efficiency and safety in a complex water area environment are enhanced.

The invention discloses a civil aviation wind shear and visibility early warning method, which comprises the following steps of: acquiring an echo signal by using a high-precision and high-temporal-spatial-resolution coherent laser radar, and simultaneously acquiring civil aviation meteorological conventional observation data; calculating wind field distribution through a wind field inversion algorithm, and calculating visibility distribution through a visibility inversion algorithm; positioning a wind shear region by using a wind shear recognition algorithm, analyzing a visibility space-time evolution trend by combining a space-time deduction algorithm, and generating a visibility profile; inputting the wind shear identification data, the visibility profile data and the meteorological routine observation data into a machine learning model for modeling training; generating a wind shear and visibility early warning scheme over the airport based on the trained prediction model; and generating a wind shear alarm signal and visibility early warning information based on the early warning scheme, and outputting the signals to a civil aviation meteorological early warning system. According to the invention, high-precision wind shear and visibility combined early warning can be realized, and the early warning accuracy under complex weather conditions is improved.

The invention provides a meteorological-power collaborative prediction method and device, and belongs to the field of new energy power systems. The method provided by the invention comprises the steps of determining condition parameters of a prediction task and a prediction site, wherein the condition parameters at least comprise multi-source meteorological observation data and new energy station operation data; according to the multi-source meteorological observation data and a pre-trained meteorological prediction large model, generating a future time sequence meteorological prediction result of the prediction site in a future time period; according to the time scale of the prediction task and historical weather conditions, adaptively matching a corresponding parameter set of the pre-trained power prediction large model; and inputting the future time sequence weather prediction result into the large power prediction model to generate a new energy power prediction result of the new energy station. According to the meteorological-power collaborative prediction method and device provided by the invention, the technical problems that the collaboration of meteorological prediction and power prediction in a new energy power system is insufficient, the precision is limited, and a complex scene is difficult to adapt can be solved.

The invention discloses an offshore wind turbine blade online monitoring method based on voiceprint recognition, which belongs to the field of wind turbine blade acoustic signal analysis, and comprises the following steps: acquiring wind turbine voiceprint data, weather condition data and wind turbine generating capacity data, and constructing a signal processing model; the collected voiceprint signals are subjected to denoising, amplification and feature extraction through a signal amplifier, a filter and a feature extraction unit, a deep learning model is constructed, voiceprint signal parameters, weather data and wind turbine generating capacity are imported into the deep learning model to evaluate future wind turbine blade cracks, a crack evaluation model is constructed, and the wind turbine blade cracks are evaluated. And importing the crack sound source position, the crack depth, the crack length and the estimated damage score into a crack evaluation model to evaluate the crack degree of the fan blade under the future weather condition, grading the fan cracks according to the crack damage score, and giving an alarm. The early warning of the future crack condition of the fan blade is realized by combining the voiceprint signal of the offshore fan blade, the weather condition and the generating capacity.

According to several aspects, a method for path planning for a vehicle includes determining a predicted trajectory of a remote vehicle. The predicted trajectory of the remote vehicle includes a plurality of predicted trajectory nodes. The method further includes determining a plurality of possible trajectories for the vehicle. The plurality of possible trajectories includes a plurality of possible trajectory nodes. The method further includes determining one or more evaluation metrics of each of the plurality of possible trajectory nodes based at least in part on a weather condition in an environment surrounding the vehicle. The method further includes selecting an optimal trajectory for the vehicle from the plurality of possible trajectories based at least in part on the one or more evaluation metrics of each of the plurality of possible trajectory nodes. The method further includes performing a first action based at least in part on the optimal trajectory.

The invention belongs to the field of road vehicle control systems, and relates to a method for predicting the risk of collision between an electric bicycle and a vehicle based on the multi-modal behavior of the electric bicycle, which utilizes physical modeling and takes the influence of the braking distance and the transverse and longitudinal stability of the electric bicycle on the vehicle as a basic risk; automobile risks caused by the environment are further considered, and multi-dimensional environment risk assessment modeling is carried out by integrating four dimensions of dynamic distance-speed, adjacent vehicle types, traffic flow density and weather conditions (precipitation, temperature, wind speed and visibility); the risk of electric bicycle operation behaviors (acceleration change and steering angle and frequency) to the automobile is combined; the complex relation between the environment change and the whole vehicle risk is captured in real time by using the environment sensitivity coefficient, and finally a set of comprehensive, accurate and real-time multi-dimensional comprehensive risk assessment method is provided for the vehicle for the multi-mode riding behavior of the electric bicycle, so that the collision risk in the complex traffic flow environment is reduced.

The invention relates to the technical field of intelligent traffic, and discloses an expressway parking risk dynamic early warning method and system based on spatial-temporal characteristics and an adaptive threshold, and the method comprises the steps: collecting vehicle driving data through a multi-type sensor, obtaining vehicle internal data through 5G communication, and employing a D-S evidence theory for fusion; performing spatio-temporal feature processing on the fused data, constructing a spatio-temporal matrix, and extracting features by using a spatio-temporal convolutional neural network; constructing a parking risk assessment model by combining a gating circulation unit of an attention mechanism based on the extracted features, and outputting a parking risk probability in a period of time in the future; according to road sections, time periods and weather conditions, an early warning threshold value is dynamically adjusted by using a reinforcement learning algorithm, multi-channel early warning is triggered when the threshold value is exceeded, and the system comprises a data acquisition and transmission subsystem, a data processing and analysis subsystem and an early warning decision and release subsystem. The objective of the invention is to realize prediction and timely and effective early warning of the parking risk of the high-speed vehicle.

The invention discloses an unmanned aerial vehicle obstacle avoidance method based on sensing fusion and reinforcement learning, and belongs to the technical field of aviation flight control, and the method comprises the following steps: S1, sensor fusion time-space synchronization; s2, sensing fusion obstacle recognition and positioning; s3, obstacle avoidance route planning based on reinforcement learning; the low-resolution global map and a local occupation grid map with the unmanned aerial vehicle as the center are superposed to serve as multi-channel input, and the current state and the historical action sequence of the unmanned aerial vehicle are combined to be input into an intelligent agent model fused by a trained convolutional neural network and a long-short-term memory network; and an obstacle avoidance decision is output through a reward function of the agent model, and the airborne computer executes the obstacle avoidance decision to control the unmanned aerial vehicle to complete an obstacle avoidance action. Through sensor fusion, the obstacle sensing capability under different weather conditions and different flight environments is enhanced, the sensing distance is also increased, and the flight safety under extreme conditions can be ensured.

The invention discloses a rainfall prediction method and system for the Beijing, Tianjin and Hebei region, and relates to the technical field of rainfall prediction, and the method comprises the steps: obtaining multi-source data, carrying out the preprocessing of the data, extracting the core characteristics of the data, carrying out the dynamic rainfall modeling according to the core characteristics of the data, and simulating a rainfall evolution path; and constructing a rainfall prediction model, performing rainfall intensity prediction based on the rainfall evolution path, performing extreme weather early warning based on a prediction result, and generating a rainfall distribution diagram of a high-risk area. Through fusion of multi-source data, the generative adversarial network and spectral analysis, the spatial resolution of the data and the rainfall evolution prediction capability are improved, the real-time performance and the accuracy of rainfall prediction are improved in combination with dynamic rainfall modeling, a fractional order differential equation and a multiple regression model, and the prediction accuracy is improved. The rainfall prediction challenge under complex weather conditions can be effectively dealt with, and accurate prediction of rainfall intensity and extreme weather events is realized.

The invention discloses a road tunnel lighting system and method based on traffic flow and external environment. Relates to the technical field of highway tunnel lighting. At present, a traditional road tunnel lighting system mode neglects factors such as tunnel external environment change and traffic flow density, so that energy waste and poor lighting effect are caused, especially under day and night alternation, weather change or special traffic conditions; according to the system, information such as traffic flow, environment illumination intensity and meteorological conditions inside and outside a tunnel is collected in real time through multiple sensors, and the illumination brightness inside the tunnel is dynamically adjusted by combining calculation and dimming strategies of a central control unit so as to cope with different traffic and weather conditions; according to the road tunnel lighting system and method, through dimming control, the energy utilization efficiency can be remarkably improved while traffic safety is guaranteed, and high economic benefits and social benefits are achieved.

The invention relates to the technical field of safe flight of unmanned aerial vehicles, in particular to an air-ground multi-mode bird monitoring and early warning method and system for guaranteeing low-altitude safety, and the method comprises the steps: obtaining radar, visual, infrared and other multi-sensor data, and determining a first bird recognition probability, a second bird recognition probability and a third bird recognition probability through the data; and determining a final bird recognition probability based on the basic weight matrix, when the final bird recognition probability is greater than a preset threshold value, obtaining a relative distance and an included angle between the unmanned aerial vehicle and the bird, and establishing a multi-stage early warning mechanism according to the relative distance and the included angle to perform bird early warning. According to the invention, the contradiction between load limitation of small and medium-sized unmanned aerial vehicles and wide-area monitoring requirements is solved, all-weather, all-weather, all-direction, non-blind-area, all-route and real-time bird monitoring, recognition and tracking under a complex urban environment are realized, the real-time monitoring accuracy of birds is improved, early warning is performed in combination with distance and included angle information, and the real-time monitoring accuracy of the birds is improved. The collision between the unmanned aerial vehicle and birds can be more accurately and effectively avoided, and the low-altitude flight safety is guaranteed.

The invention relates to a new energy automobile energy consumption evaluation method based on typical climate characteristics. The method comprises the following steps: step 1, establishing a typical climate region model; 2, establishing a typical test scene in combination with typical climate conditions; step 3, collecting power consumption data of the vehicle in different test scenes; and 4, energy consumption analysis and evaluation: establishing an energy consumption model, and analyzing the energy consumption condition in each typical test scene. According to the method, the energy consumption of the new energy vehicle in different use scenes can be comprehensively evaluated based on the typical climate characteristics, so that the evaluation is more objective and closer to the real use feeling of a user.

The invention discloses a geological disaster monitoring and early warning method and system based on a digital twinborn technology, and relates to the technical field of data monitoring. The method comprises the following steps: acquiring satellite image information, meteorological information and geological detection information of a mountain area to be monitored in a preset sampling period; based on the satellite image information, the meteorological information and the geological detection information, constructing a three-dimensional digital model of the mountain area to be monitored; receiving current mountain data, geological data and weather conditions of the mountain area to be monitored, and determining a plurality of risk sources of the mountain area to be monitored under the weather conditions and positions corresponding to the risk sources in the three-dimensional digital model in combination with the mountain data and the geological data; and for each risk source, when the change rate of any monitoring index in the mountain data or geological data of the risk source exceeds a change rate threshold value, performing disaster early warning based on the position of the risk source. By implementing the technical scheme provided by the invention, the effect of improving the accuracy of geological disaster monitoring and early warning is achieved.

The invention discloses a foggy day driving augmented reality auxiliary method and system based on multi-mode perception, and relates to the technical field of intelligent driving. The method comprises the following steps: constructing a three-dimensional environment map to carry out real-time driving risk prediction, and carrying out preliminary adjustment on data source weight distribution in the three-dimensional environment map according to a set rule; the PPO reinforcement learning algorithm is adopted to readjust data source weight distribution, a new three-dimensional environment map is reconstructed based on the readjusted data source weight, and the risk probability, predicted in real time, of sudden deceleration of the front vehicle and the reconstructed three-dimensional environment map are projected to a windshield through a layered projection strategy to prompt a driver. According to the method, accurate and comprehensive multi-mode sensing data can be obtained in a foggy day environment, the accuracy of decision making and risk prediction is ensured, quick response can be made according to environment changes, the safety of a driver under complex weather conditions is improved, and the method can adapt to more changing environments in practical application.

The invention provides an intelligent driving self-adaptive control method which comprises the following steps: S1) starting a vehicle, and judging whether an intelligent driving self-adaptive control system is activated or not; s2) if the self-adaptive control system is activated, a fusion sensing module collects local data and online networking information of a vehicle end in real time, multi-source information is fused, and road, traffic and weather conditions are accurately recognized; s3) the calculation processing module makes rapid comprehensive judgment and analysis processing on the current environment through the fusion information, makes an optimal decision according to the current scene, and sends a targeted adaptive control adjustment instruction to an adjustment control module; s4) the adjustment control module executes a corresponding control response after receiving the self-adaptive control adjustment instruction sent by the calculation processing module; and S5) quitting intelligent driving adaptive control. The invention further provides an intelligent driving self-adaptive control system which comprises a fusion sensing module, a calculation processing module and an adjustment control module.

The invention discloses a severe weather image sharpening method based on continuous learning, and the method comprises the steps: 1, carrying out the preprocessing of an image data set under various severe weather conditions, including image pairing and multi-task division; 2, dividing the image restoration network into two parts of feature projection and output projection, designing a degradation sensing module for extracting degradation specific information output by the feature projection under different weather conditions, and establishing background supervision and degradation supervision by adopting a multi-teacher model for guiding learning of a student model; and 3, establishing respective optimization targets for the student model and the teacher model, inputting pairing data of different weather removal tasks, and optimizing the student network by a multilateral distillation normal form, thereby obtaining an optimal model with integrated image severe weather removal capability. According to the method, different types of severe weather images continuously acquired in a real scene can be dealt with, and the implementation effect of an image restoration network in a resource-limited scene is improved.

The present invention relates to a pole-mounted anchoring device. The anchoring device is equipped with various adjustable components to insure a stable foundation. The device has a cone-shaped, threaded base component of plastic or metal that screws the device into the ground. The device also has a set of bags that hangs from the device to add weight to the overall unit. The number of weighted bags, amount of sand, or other material in the bags can be altered depending on the weather conditions and size of the item to be supported. Further, the device may be disassembled and transported via a carrying bag.

The invention belongs to the technical field of aviation meteorological monitoring, and relates to an airport dominant visibility automatic measurement method and system based on a three-dimensional laser radar, and the method comprises the steps: carrying out the full-space scanning of an airport through the laser radar, so as to collect echo signals; preprocessing and calibrating the echo signal; performing atmospheric extinction coefficient inversion on the preprocessed echo signal based on an inversion algorithm to obtain an atmospheric extinction coefficient, and then converting the atmospheric extinction coefficient into meteorological visibility; carrying out quantile statistics on all meteorological visibility in a period, judging a dominant visibility value, and carrying out adaptive weighted fusion on the dominant visibility value, the visibility value measured by the point-type visibility meter and the visibility value observed manually to obtain a fused dominant visibility value. The system can break through the limitation of traditional manual and point type visibility meter observation, and realizes accurate and automatic measurement of all-region, all-weather and unattended dominant visibility of an airport.

The invention discloses a vehicle door and window state monitoring and early warning method and system, and belongs to the technical field of vehicle monitoring. The method comprises the steps that when doors and windows are not closed and a vehicle locking signal is received, the parking environment of a vehicle is graded, and an environment risk index is determined; historical behavior data of a vehicle owner is obtained, behavior modeling is carried out, a parking duration level is determined, screening is carried out in a preset parking duration level table, and a duration risk index is determined; acquiring weather information, and determining a weather risk index based on a screening result of the weather information in a preset weather condition grading table; determining a comprehensive risk value according to the environment risk index, the duration risk index and the weather risk index; and according to the comprehensive risk value, determining an early warning level and pushing early warning information to the user side in a corresponding early warning form. More flexible, accurate and humanized prompts can be provided according to different scenes, user responses and personalized requirements; the problem of low interaction efficiency in the prior art is solved.

The invention provides an intelligent path planning method and system based on dynamic road condition prediction. The method comprises the steps of firstly obtaining multi-source dynamic data of a target area; then, constructing a weather influence prediction model to predict weather influence parameters in a future time period; secondly, constructing a weather-traffic coupling model, and respectively establishing correlation models of corresponding precipitation, traffic flow density and average vehicle speed according to road types through historical data analysis, so as to estimate the traffic efficiency of each road section under a dynamic weather condition; and finally, generating a plurality of candidate paths according to the passing efficiency, screening out an alternative path set meeting a multi-target optimization condition from the candidate paths, performing simulation evaluation on the alternative path set, and determining an optimal path according to a simulation result. Compared with a traditional static path planning method, the method has the advantages that the responsiveness of a traffic system to meteorological disasters is remarkably improved, and predictable navigation service is provided for intelligent network connection vehicles.

The invention discloses a logistics transportation platform monitoring system and method based on big data, and relates to the technical field of logistics monitoring. Based on historical road conditions, traffic flow, accident distribution and high-risk areas, combined with physicochemical properties of dangerous goods and real-time weather conditions, comprehensive risk values and adaptive values of paths are calculated; the route safety is guaranteed, and meanwhile, the transportation environment is adapted; meanwhile, based on the GPS signal intensity, the interruption area is marked, and a standby path is planned, so that the path continuity is improved; in the transportation process, a vehicle sensing module collects position information, environmental parameters and cargo state data in real time, integrates a GPS, inertial navigation and a historical track prediction model, dynamically corrects the position of a vehicle, verifies the accuracy of the position in combination with road signs, and enhances the monitoring precision; in addition, based on multi-source data, abnormal conditions are judged in real time, grading processing is carried out according to the severity of anomalies, and transportation safety is effectively guaranteed.