2748 results about "Road condition" patented technology

The invention relates to the technical field of artificial intelligence, in particular to a driver fatigue state monitoring system and method based on multi-modal biological feature fusion, and the system comprises a sensing module, a data processing and analysis module, an environment risk assessment module, a personalized model module, a self-adaptive decision fusion module, and a vehicle interaction and vehicle control module. Compared with the prior art in which fatigue judgment is performed by adopting a fixed threshold value, the fatigue judgment cannot adapt to a complicated and changeable driving environment and may cause excessive alarm or missing alarm under a monotonous road condition in a dangerous road section, an environment perception and dynamic threshold value adjustment algorithm is introduced, so that the system can intelligently evaluate the environmental risk and monotonicity, and the fatigue judgment accuracy is improved. The alarm sensitivity is automatically adjusted, and the early warning accuracy and the scene adaptability are both improved.

The invention provides an intelligent suspension control system based on complex road condition recognition, and relates to the technical field of suspension control. The system comprises a data acquisition module, a theoretical adjustment calculation module, an adjustment map generation module, a dynamic adjustment generation module and a real-time monitoring module. The data acquisition module generates structured road condition suspension matching data. The theoretical adjustment calculation module calculates a theoretical suspension adjustment reference value. The adjustment map generation module generates an initial suspension adjustment map. The dynamic adjustment generation module obtains a dynamic suspension adjustment map of the driving task. And the real-time monitoring module monitors the intelligent suspension control in real time. Various complex road conditions are recognized and classified through semantic analysis, an accurate road condition basis is provided, a theoretical adjustment reference value is calculated, meanwhile, a self-adaptive correction factor matrix is introduced, the adjustment reference and the fault-tolerant bandwidth are dynamically adjusted according to the real-time posture of the vehicle, vehicle body vibration and suspension air pressure, stable control is guaranteed, and the control accuracy is improved. And dynamic balance between comfort and controllability is realized.

The invention relates to the technical field of automobile electric control, in particular to a new energy automobile electric control steering control method and system, and the method comprises the steps: obtaining and preprocessing a four-wheel vibration data sequence, carrying out the segmentation and consistency evaluation of the vibration data sequence of each wheel, and selecting a current data sequence in an optimal segment to calculate a road condition influence coefficient. And matching the current data sequence with the historical vibration data sequence, calculating the similarity to determine the influence weight, adjusting the influence coefficient in combination with the steering angle change rate of the historical vibration data sequence, inputting the adjusted pavement condition influence coefficient into the ECU, dynamically adjusting the steering auxiliary torque, and realizing accurate steering control. According to the method, the optimal segment is selected through segment processing and consistency evaluation to accurately reflect the current road condition, the steering auxiliary torque is dynamically adjusted in combination with historical vibration data sequence matching and the steering angle change rate, and the driving safety and comfort are improved.

The invention relates to an intelligent logistics scheduling and resource distribution system based on multi-source data fusion, and relates to the technical field of logistics management technologies, and the system comprises the steps: collecting vehicle operation state data, road condition data, logistics order dynamic data and warehouse inventory change data in real time based on a multi-source heterogeneous interaction platform; constructing a multi-modal traffic logistics time series data set; based on a maximum priority scheduling algorithm, giving priority weights to the logistics orders in combination with logistics order timeliness demands and cargo features; based on the logistics order priority weight, path optimization is carried out by using a space-time hierarchical planning algorithm, and an initial transportation path set is constructed; real-time monitoring of the transportation process is achieved based on the digital twinborn technology, the path condition is sensed dynamically, the optimal transportation path is obtained through continuous adjustment, and an intelligent logistics scheduling and resource allocation scheme based on multi-source data fusion is generated. The method has the advantages that the resource allocation efficiency is improved and the transportation cost is reduced by optimizing the priority path and continuously adjusting the transportation path.

The invention discloses an intelligent self-adaptive vehicle suspension system based on deep learning and a control method, and belongs to the technical field of vehicle suspensions.The system comprises a quantum state collaborative sensing module, a Lie group road condition prediction module, a topology invariant optimization module and a dual closed-loop control module; the quantum state collaborative perception module realizes multi-sensor deep fusion based on a quantum state entanglement theory, the fusion precision is improved by more than 40%, the Lie group road condition prediction module establishes a road surface dynamic evolution model by using a special Euclidean group SE (3), the prediction precision is improved by 45%, and the topology invariant optimization module extracts road surface topology invariants through continuous coherence, so that the road surface dynamic evolution model is optimized. The suspension parameters are optimized on the octyl manifold, the convergence speed is increased by 60%, the dual closed-loop control module constructs a forward prediction and backward correction double closed loop, the response time is shortened to 15 milliseconds, and the comfort and controllability of the vehicle are remarkably improved.

The invention discloses a power failure event cooperative processing method, system and device based on geographic information and a medium, and relates to the technical field of power system power distribution network fault processing and informatization. The method comprises the following steps: when a power failure event occurs, acquiring multi-source heterogeneous data from an associated system; performing fault analysis on the multi-source heterogeneous data through a fault diagnosis model, and outputting fault positioning information and a fault type; obtaining associated maintenance resource state data and real-time traffic road condition data according to the fault positioning information; the maintenance resource state data comprises a rush repair team position, a skill level and a vehicle state; and based on the fault positioning information, the fault type, the user attribute, the historical work order data, the maintenance resource state data and the real-time traffic road condition data, generating an optimal maintenance scheduling scheme through a reinforcement learning scheduling model. The cross-department and cross-business cooperative processing is realized, and the first-aid repair efficiency and the resource utilization rate are remarkably improved.

The invention relates to the technical field of intelligent driving, and discloses a vehicle control method and device, a vehicle and a storage medium, the method comprises the following steps: collecting current multi-source driving data, and fusing the multi-source driving data to obtain fused driving data; performing coupling processing on road section information of different dimensions in the fused driving data to obtain corresponding driving scene data under the current road condition; inputting the driving scene data and the fused driving data into a preset neural network model, and predicting an occurrence probability and a risk level of a preset risk driving scene in a future road section; and based on the occurrence probability and the risk level, generating a corresponding vehicle control strategy to control the vehicle. According to the technical scheme, the slope and curve coupling scene can be effectively handled, and the vehicle control precision and safety are improved.

The invention discloses an intelligent chassis preview control system and method based on a roadside sensor, and the system comprises a roadside sensing unit which is used for obtaining the road condition information of a line road, and transmitting the road condition information to a roadside communication base station; the roadside communication base station is used for preprocessing the road condition information and packaging the road condition information into a set format; the vehicle-mounted communication module is used for sending the road condition information with the set format to the vehicle-mounted controller; the vehicle-mounted controller is used for performing fusion processing on the road condition information and the vehicle state data, and analyzing the fused data by adopting a preview control algorithm model to generate a control instruction of the intelligent chassis executing mechanism; and the intelligent chassis executing mechanism is used for performing preview control on the vehicle chassis based on the control instruction. The method has the advantages that pre-judgment adjustment of the vehicle chassis is achieved, the safety and stability of vehicle driving are effectively improved, and the system reliability is enhanced especially under complex road conditions and severe weather conditions.

The present disclosure relates to a method and system for monitoring the road condition by a machine learning system and to a method for training the machine learning system. The methods include: providing or acquiring data by a sensor system of a vehicle, wherein the sensor system captures the surroundings of the vehicle (2) as training input data X; providing or acquiring data which characterize the road condition by means of a reference sensor fitted in or on the vehicle as training target values, and training the machine learning system. Training data, which include training input data X and training target values corresponding to these training input data X, are provided. The training data are used to adjust parameters of the machine learning system in such a manner that the machine learning system generates output data similar to the training target values when the training input data are input.

The invention relates to an ice and snow disaster variable speed limit control method and system based on a friction coefficient, and belongs to the field of intelligent traffic. According to the method, a set of variable speed limit control units are arranged on a road at intervals, and all the variable speed limit control units are in communication connection with a centralized decision-making unit through communication modules; each set of variable speed-limiting control unit detects traffic flow data, meteorological environment data and road condition data of a lane level in real time, uploads the data to the centralized decision-making unit, determines an optimal lane-level variable speed-limiting control scheme and an optimal deicing and snow-removing scheme through a decision-making optimization algorithm, and issues the optimal lane-level variable speed-limiting control scheme and the optimal deicing and snow-removing scheme to the variable speed release module; and the variable speed release module releases a lane level variable speed limit control scheme of a corresponding section, namely the lowest speed limit value, the highest speed limit value, the suggested speed and the like of each lane, and performs deicing and snow removal operation according to the deicing and snow removal scheme so as to ensure road traffic safety in ice and snow weather. The road traffic safety guarantee level is improved through variable speed limiting.

The embodiment of the invention relates to the technical field of intelligent driving, and discloses a motorcade passing control method, device and equipment based on global planning, and the method comprises the steps: obtaining the driving related information of vehicles in a motorcade; the motorcade comprises a plurality of vehicles, and the driving related information comprises at least one of road condition information, vehicle driving information and map navigation information; according to the driving related information of at least one vehicle in the motorcade, performing global planning by taking the vehicles in the motorcade not to fall behind or the fallen vehicles to get up to the motorcade as a control target, and determining a target state of a target vehicle in the motorcade; the target state indicates driving information needing to be executed by a target vehicle, and the target vehicle is any vehicle in a motorcade; and sending the target state of the target vehicle to the target vehicle to control the target vehicle to be switched from the current state to the target state. By applying the technical scheme of the invention, the current problems that the formation vehicles are easy to fall behind and are difficult to recover because of being cut off in the complex urban road can be solved.

The invention discloses a new energy automobile motor efficiency control method and system, and relates to the technical field of efficiency control, and the method comprises the steps: carrying out the data collection of the operation state of a new energy automobile motor, and constructing a motor digital twin efficiency model; performing dynamic coupling analysis based on the motor digital twin efficiency model to extract a multi-field coupling influence factor, predicting the degradation trend of the motor efficiency parameter of the new energy vehicle based on the multi-field coupling influence factor, and determining a degradation trend prediction result; the method comprises the steps of establishing an environment disturbance factor library, obtaining real-time road condition topological data and driving behavior characteristic data, constructing a dynamic disturbance scene set based on the environment disturbance factor library, the real-time road condition topological data and the driving behavior characteristic data, and confirming an optimization strategy based on the dynamic disturbance scene set; and generating a dynamic efficiency regulation and control instruction set based on a degradation trend prediction result and an optimization strategy, and realizing energy efficiency closed-loop optimization control of the driving motor. The method has the effect of improving the motor control efficiency of the new energy automobile.

The invention relates to the technical field of noise measurement systems, in particular to a noise measurement system for a new energy tire, which comprises a tire positioning and driving unit, a sensor unit, a data acquisition and synchronization unit, a data processing unit, a data analysis unit and a man-machine interaction and display unit, the tire positioning and driving unit simulates different loads, different speeds and different road conditions of a tire by using a rack type tire positioning device, and drives the tire to rotate by using a brushless mute motor, so that a high-speed rotating effect without introducing extra mechanical noise is realized; the sensor unit is arranged on the two sides of the tire and at the position close to the ground through an acoustic sensor free field type condenser microphone array, is fixed to the suspension support through an acceleration sensor and is used for collecting vibration signals; the running states of the tire under different loads, speeds and road conditions are accurately simulated, noise data of the tire under various actual working conditions are obtained, and the measurement result is closer to the real situation.

The invention relates to the technical field of intelligent switching, in particular to a multi-mode dynamic coupling intelligent switching method and system based on road condition recognition and a vehicle. The method comprises the steps that gradient, vehicle speed, accelerator and brake signals are collected in real time, and a multi-dimensional feature vector fusing driving habits is constructed after preprocessing; a real-time road condition label is automatically generated through unsupervised clustering, and the clustering effectiveness is verified online by using a contour coefficient. Enabling the road condition label and the current power mode to form a system state, and inputting the system state into a self-learning decision network based on reinforcement learning; and the network updates the state-action value matrix through a reward function in a time sequence difference mode, and outputs a target power mode with the highest accumulated reward. And calculating the motion confidence coefficient of the target mode based on the value matrix, if the motion confidence coefficient exceeds a threshold value, generating a switching instruction, and smoothly adjusting the torque by an execution mechanism through a torque transition algorithm to realize impact-free switching. Automatic road condition recognition, self-adaptive decision making and non-inductive execution are achieved, and smoothness and adaptability are improved.

The invention relates to the technical field of satellite communication, and discloses a vehicle-mounted station satellite dynamic tracking device and method.The technical scheme is characterized in that the theoretical satellite alignment angle pointed by an antenna is calculated, the antenna is driven to rotate to the angle for coarse alignment, and picture frame searching is started when effective satellite signals are not captured; a main tracking mode and an auxiliary tracking mode are switched in a self-adaptive mode according to the real-time vehicle speed, and servo driving parameters are dynamically adjusted based on the processed tracking deviation to drive an antenna to track a satellite in real time; compensating the tracking angle of the antenna in real time; when it is judged that the satellite is lost, the current angle to which the antenna should point is predicted, and conical scanning is executed with the predicted angle as the center to recapture satellite signals. According to the invention, the adaptive hybrid tracking mode switching, multi-dimensional error real-time compensation and'inertial navigation prediction + conical scanning 'rapid recovery cooperation mechanism is adopted, and high-precision, high-stability and rapid-recovery satellite dynamic tracking under complex road conditions and high-speed movement is realized.

The invention discloses a two-stage cascaded distributed optical fiber sound wave sensing vehicle trajectory reconstruction method and system, belongs to the technical field of intelligent traffic perception, and aims to solve the problem that geometric accuracy and topological integrity are difficult to consider under the conditions of low signal-to-noise ratio and complex road conditions in the conventional DAS vehicle trajectory reconstruction technology. The method comprises the following steps: in the first stage, carrying out nonlinear enhancement on original vibration data and converting the original vibration data into a two-dimensional space-time grayscale image, inputting the two-dimensional space-time grayscale image into a deep learning semantic segmentation network to generate a high-fidelity track mask, extracting a skeleton through morphological processing, and constructing an initial candidate topological graph; and in the second stage, multi-hop neighborhood kinematics constraint pruning is performed on the initial topological graph, false connection edges are eliminated, global optimization matching is performed by using a coupling cost function to repair fractures, then isolated points are recalled through local geometric scores, and finally a vehicle trajectory graph with complete topology is output. According to the method, geometric accuracy and topological integrity can be effectively considered in complex scenes such as speed change, lane change and multi-vehicle intersection, and the robustness of all-weather traffic flow monitoring is improved.

The invention provides a self-adaptive tire force calculation method and device, a program product and a vehicle, and relates to the technical field of vehicle tires, the method performs dynamic compensation on tire rigidity through real-time fusion of multi-source information such as a road adhesion coefficient, tire pressure, tire temperature and abrasion loss, the limitation of static fixation of traditional model parameters is broken through, and the tire force calculation accuracy is improved. And the tire force calculation can accurately reflect the physical state of the tire under the real working condition. According to the method, the calculation precision of the longitudinal force and the lateral force under different environmental conditions and tire aging degrees is improved, feedforward pre-judgment is achieved by introducing road surface information, a basis for advanced intervention is provided for a vehicle stability control system, and therefore the response speed and the control performance of a vehicle under the sudden change road condition are effectively improved, and the service life of the vehicle is prolonged. And the driving safety and the control stability are enhanced.

The invention discloses a full-automatic road marking spraying robot path planning system, and relates to the technical field of road construction. The system comprises a multi-source heterogeneous sensor fusion positioning module which realizes stable and high-precision positioning of a complex scene through cooperative acquisition of four sensors, a two-stage fusion algorithm and dynamic error compensation; the reinforcement learning dynamic re-planning module responds to dynamic obstacles in real time through obstacle classification dynamic modeling, a three-factor dynamic weight reward mechanism and a rapid re-planning technology; the path optimization module is used for reducing shutdown loss through consumable equipment state and path parameter coupling optimization and dual-prediction adjustment; the multi-machine collaboration module is used for realizing task balanced distribution and accurate splicing through digital twin modeling and distributed control; and the execution control module guarantees accurate path and uniform spraying through kinetic model trajectory tracking, PID parameter dynamic adjustment and spraying parameter matching. All the modules cooperate to achieve full-automatic high-precision efficient spraying under complex road conditions.

The invention relates to the technical field of call center control, and discloses an unmanned vehicle formation control and obstacle avoidance reconstruction system based on complex road conditions. The system call data acquisition module is used for acquiring multi-source interaction data in real time; the dynamic feature extraction module is used for performing semantic intention hierarchical analysis on the voice stream text conversion data, performing event fragment segmentation on the user behavior time sequence data, and generating an operation track feature vector; the scene clustering engine module is used for performing space-time alignment on the semantic intention hierarchical analysis result and the event fragment segmentation result to generate a multi-dimensional scene clustering tag; the strategy matching core module is used for retrieving an interaction strategy set matched with the current scene from a distributed strategy library according to the multi-dimensional scene clustering label and generating a dynamic control instruction sequence; and the instruction distribution execution module is used for distributing the dynamic control instruction sequences to corresponding seat terminals and voice routing equipment after sorting the dynamic control instruction sequences according to priorities.

The invention discloses a vehicle-road collaborative perception and fusion estimation vehicle stability control method for a beyond-visual-range scene, and belongs to the technical field of crossing of intelligent traffic, vehicle control engineering and an automobile safety technology. The method comprises the following steps: acquiring vehicle dynamic data through a vehicle end sensor, estimating a dynamic pavement adhesion coefficient based on a seven-degree-of-freedom model and unscented Kalman filtering, acquiring a pavement image by using a roadside camera, identifying a pavement state by using a MobileNetV3 + model, and mapping the pavement state into a visual pavement adhesion coefficient; a discrete time Kalman filter is adopted to fuse dynamics and visual estimation results, and the fusion weight is dynamically adjusted according to the confidence coefficient; and transmitting the fused road adhesion coefficient to a vehicle end controller, and designing an upper-layer controller and a lower-layer controller based on sliding mode control and road adhesion coefficient identification, thereby realizing stability control of the vehicle under complex road conditions. The accuracy and foresight of road adhesion coefficient estimation can be effectively improved, and the handling stability of the vehicle under the limit working condition is enhanced.

The invention discloses a hazardous chemical substance transportation early warning system based on a fuzzy neural network, and relates to the technical field of traffic transportation and road safety monitoring. According to the hazardous chemical substance transportation early warning system based on the fuzzy neural network, road geometric influence in original data of a sensor is accurately stripped from dynamic response of a vehicle; according to the method, false risk signals caused by road design can be eliminated, the method focuses on instability risks really caused by dynamic factors such as vehicle control and load transfer, and the rollover risk can be judged more essentially and more accurately; converting the preprocessed characteristic quantity into a fuzzy linguistic variable, reasoning by simulating a fuzzy rule of human expert experience, and finally outputting continuous risk indexes and levels; the processing mode not only better conforms to the imprecise characteristic of risk assessment, but also enhances the adaptability and robustness of the system to different vehicle types, loads and road conditions.

The invention provides a traffic real-time road condition prediction method and system based on knowledge engineering, and the method comprises the steps: collecting multi-source traffic data in real time, carrying out the missing data filling and data space-time alignment processing of the multi-source traffic data, and obtaining the processed multi-source traffic data; fusing the processed multi-source traffic data by adopting a method based on dynamic space-time tensor modeling to obtain fused traffic data; based on the fused traffic data, constructing a self-evolution traffic knowledge graph with time-space attributes; on the basis of the self-evolution traffic knowledge graph, constructing a road condition hybrid prediction model by adopting a knowledge-guided meta-learning framework; and performing traffic real-time road condition prediction based on the road condition hybrid prediction model. According to the invention, the robustness of the system is obviously enhanced, and more accurate road condition prediction is realized.

The application provides a highway charging load prediction method and control system based on traffic flow power. Through a second-by-second specific power model of different electric vehicle types on the highway, factors such as road slope, vehicle load, electric vehicle braking recovery characteristics, instantaneous speed and acceleration can be considered to achieve more accurate energy consumption modeling. By establishing a specific power operation mode distribution library of different vehicle types, using a shock wave model and a cellular transmission model to solve, quantifying the congestion propagation process, dynamically correcting the specific power calculation parameters, only the road section parameters and average speed are needed to calculate the energy consumption under complex road conditions. The driver's range anxiety is quantified as a dynamic variable, and charging decision rules are established for different scenarios. The charging probability is dynamically corrected based on the queue length, weather, and driver type, and an electric vehicle charging time prediction model is established based on different service area charging scenarios.

The invention provides an intelligent road management and maintenance system and method based on multi-model fusion, and relates to the technical field of road maintenance and intelligent traffic. The method comprises the following steps: acquiring road condition data and positioning data through a multi-source data acquisition module; the disease recognition module is combined with a neural network model to analyze the multi-modal data, so that the disease detection and recognition precision is improved; the big data processing early warning module mines a disease rule through a big data model, and realizes disease early warning and predictive maintenance; the digital twin modeling module is used for constructing a road digital twin model and realizing real-time monitoring and simulation analysis; and the decision scheduling module realizes automatic maintenance scheduling based on the output of the modules. Through multi-model deep fusion, the problems of high manual dependence, low precision, lack of predictability and scheduling lag in traditional road management and maintenance are solved, the road maintenance efficiency, quality and intelligent level are remarkably improved, and the maintenance cost is reduced.

The invention provides a new energy automobile endurance prediction method based on working condition identification and prediction, and belongs to the technical field of new energy automobile endurance mileage estimation. According to the method, sliding window statistical characteristics are constructed through speed, current, voltage and other signals, typical working conditions of cities / suburbs / highways and the like are identified, the working condition of a next time window is predicted, and energy consumption priori which can be updated in real time along with changes of road conditions is formed. Secondly, a rolling capacity estimation model is provided, the available capacity of the battery is continuously evaluated based on historical charging and discharging data, and dynamic changes of the SOH of the battery along with time and use conditions are recorded; according to the method, working condition prior, rolling SOH and time sequence energy consumption characteristics are fused, a TCN-BiLSTM multi-model cooperation framework is constructed, short-term dynamic and long-term attenuation trends are considered, high-precision and generalizable endurance mileage prediction under complex working conditions and battery state fluctuation is realized, reliable support is provided for intelligent energy consumption management and journey planning of vehicles, and the method is suitable for popularization and application. The method can also be applied to an intelligent power distribution system with a high reliability requirement, and provides a core solution for technology promotion service in related fields.

The present disclosure relates to the technical field of intelligent driving, in particular to a vehicle driving intelligent assistance system, which includes an electronic control unit and a Controller Area Network (CAN) module, wherein the CAN module is connected with a driving detection module, a vehicle body state detection module, an engine detection module and an environment detection module, wherein the vehicle body state detection module is configured to acquire a steering angle of a steering wheel and a suspension condition, the environment detection module is configured to acquire environmental information around the vehicle and road conditions; the electronic control unit is connected with a vehicle body stabilization system, a vehicle control system and a lane departure early warning module, and the vehicle control system is configured to control acceleration, steering or braking of a vehicle to achieve assisted driving.

According to the automatic driving questioning and answering method and device based on the knowledge graph and the multi-modal large model, the method comprises the steps of firstly responding to a questioning and answering triggering condition, obtaining a real-time road condition video and a traffic safety problem, further obtaining a multi-modal query problem, then performing analysis processing on the multi-modal query problem based on a visual language model, and finally obtaining an automatic driving questioning and answering result. According to the method, visual semantic keywords are obtained, a candidate entity node set related to the visual semantic keywords is determined from a plurality of entity nodes of the knowledge graph, core knowledge nodes related to query questions are accurately focused, the invalid search range is reduced, question retrieval and answer generation efficiency is improved, and user experience is improved. The comprehensive similarity between each candidate node in the candidate entity node set and the visual semantic keyword is calculated, the key entity node set is obtained, the accuracy of question retrieval and answer generation is improved, and finally the question and answer result corresponding to the traffic safety question is generated according to each key entity node in the key entity node set.

A method for virtual evaluation of on-road energy consumption of a new energy vehicle, comprising: constructing an evaluation route on the basis of cities where a vehicle is used and typical routes of said cities; generating an on-road working condition library on the basis of the evaluation route, a month, a weather condition, a road condition and a vehicle usage period; acquiring test data of the vehicle under different working conditions; determining evaluated energy consumption on the basis of the test data; determining a nominal energy consumption discrete coefficient on the basis of the evaluated energy consumption and nominal energy consumption; determining average comprehensive energy consumption on the basis of the evaluated energy consumption; and determining an energy consumption level of the vehicle on the basis of the nominal energy consumption discrete coefficient and the average comprehensive energy consumption. Also disclosed is an apparatus for virtual evaluation of on-road energy consumption of a new energy vehicle. Even without large-scale road testing, on-road energy consumption can still be efficiently and conveniently evaluated in numerous cities and diverse vehicle usage scenarios.

The invention discloses a long-distance maneuvering path planning method and system fused with DOP information, and belongs to the technical field of path planning and navigation. The method comprises the following steps: collecting special vehicle information, GNSS DOP information, geographic information and traffic information; based on the DOP value and the traffic condition, the roads are preliminarily screened, and the gradient, the slope direction, the height limit, the load bearing, the turning radius and DOP information are integrated; road condition information is converted into constraint conditions, and DOP information serves as an optimization target and is fused into a heuristic cost function of an improved algorithm; a path with the best navigation effect is obtained through path searching, visual display is carried out on a geographic information system map, and DOP information changes of all key points on the path are displayed in real time. According to the method, a GNSS signal blind area can be effectively avoided, the feasibility and navigation precision of path planning are improved in combination with vehicle dynamics constraints, and the method is suitable for long-distance maneuvering tasks of special vehicles.

The embodiment of the invention provides a vehicle track reconstruction correction method and device based on spatio-temporal data fusion, and the method and device achieve the unified processing of data through the innovative design of a multi-source data fusion model, coordinate transformation and time calibration. A trajectory completion system is constructed, and a reliable trajectory reconstruction mechanism is established in combination with road network features and real-time road conditions. Visual editing is introduced, and the accuracy of track correction is ensured through interactive operation and coordinate adjustment. According to the method, the defects of the traditional technology in the aspects of data fusion, trajectory completion, visual editing and the like are effectively overcome, and technical guarantee is provided for vehicle trajectory reconstruction.