1713 results about "Road traffic" patented technology

The invention discloses a road traffic flow prediction method based on a space-time mixed attention network, and the method breaks through the limitation of a conventional time sequence model and a single deep learning architecture based on the systematic analysis of urban road traffic flow space-time heterogeneity, periodic non-stationarity and road network topological relevance, constructs the space-time mixed attention network, and achieves the prediction of road traffic flow. Spatial heterogeneous correlation of road network nodes is captured through a graph convolution network, dynamic time sequence evolution characteristics of traffic flow are modeled by adopting a hybrid architecture, a residual attention mechanism is introduced to realize layer-by-layer refining of multi-scale spatio-temporal characteristics, and the overall architecture of the method has remarkable advantages in the aspects of spatial topology modeling and time dynamic capture compared with a traditional model. Feature decoupling learning is carried out on multi-source heterogeneous data such as weather and events, adaptive integration of environment sensitive features is realized through a parameterized gating fusion strategy, and the prediction error fluctuation amplitude in an extreme weather scene is reduced by 34.8%.

The invention provides a real-time map updating method and system based on multi-source geographic information data fusion. Wherein a three-dimensional model is constructed by integrating traffic flow data, remote sensing images and road network topology, the road traffic pressure is quantified, and a feature correlation index is established. And obtaining a vehicle displacement vector and a speed gradient in combination with laser point cloud and video monitoring data, generating a dynamic road feature data set, and establishing a mapping relationship with the index table. On the basis, a road state prediction model is constructed, and through correlation analysis of traffic pressure data and a real-time feature data set, deep correlation between vehicle motion features and road states is established. And dynamically adjusting a road network connection structure, synchronously updating mapping parameters and predicting model precision, and realizing real-time adaptive adjustment of the road network topology. According to the technical scheme provided by the invention, the real-time performance of map updating and the road condition prediction accuracy are remarkably improved, and the urban traffic congestion index can be reduced.

The invention relates to the field of road traffic, in particular to a multi-feature fusion fatigue driving detection method and a fatigue driving detection system. The method comprises the following steps: extracting facial features from a face image of a driver; extracting vehicle features from the vehicle driving parameters of the vehicle driven by the driver; and fusing the facial features and the vehicle features to judge whether the driver is in fatigue driving. Extracting facial features by designing a CNN model; extracting basic convolution features; extracting local convolution features; extracting global convolution features; performing pooling operation; aggregating global features; and carrying out dimensionality reduction mapping. A self-encoder is designed to extract vehicle characteristics; a symmetric deep neural network structure is adopted, and high-dimensional time sequence data is compressed to a low-dimensional potential space through nonlinear mapping; through combination and matching of the CNN model and the auto-encoder, the technical defects of feature redundancy, noise interference, information loss and suboptimal decision existing in an existing multi-feature fusion fatigue driving detection system are thoroughly solved.

The invention relates to the technical field of traffic flow regulation and control, and discloses an electronic information traffic flow automatic regulation and control system based on a wireless sensor network. The multi-source sensing module collects road network multi-dimensional traffic data through a wireless sensing network, and traffic situation characteristics are generated through a specific data fusion method. The spatio-temporal feature fusion module utilizes a spatio-temporal attention mechanism to associate cross-modal features, and the collaborative decision-making module inputs fusion features into a pre-trained distributed decision-making model to generate a regulation and control instruction set. The dynamic game optimization module constructs a multi-target game optimization model, and traffic signal parameters are optimized by adopting a dynamic game strategy decomposition algorithm. And the hierarchical execution module executes regulation and control instructions in a distributed manner through a three-level control architecture of a central decision-making layer, a regional coordination layer and an intersection execution layer. The system can comprehensively collect and fuse traffic data, realizes scientific decision making and accurate regulation and control, effectively improves the road passing efficiency, balances the road network load, and relieves traffic jam.

Provided are a method and an apparatus for constructing a road congestion prediction model, a device, a medium, and a product. A road traffic network is defined as a directed weighted graph. Historical dynamic traffic features of each road segment in the road traffic network are obtained as sample data, including recent dynamic traffic features and periodic dynamic traffic features. The sample data is input into a mixture of adaptive graph learners (MAGL) model for learning, and a probability prediction vector is output. The sample data is input into a trend expert model, and a trend distribution vector of a predicted probability of future traffic conditions is output. The periodic dynamic traffic features are fused to determine a periodicity prediction vector. An aggregated logit vector is obtained. An objective function is determined based on the aggregated logit vector. Congestion prediction training is performed to obtain a road congestion prediction model.

The invention relates to an urban road carbon emission prediction and optimization control method and system, and belongs to the technical field of road traffic carbon emission monitoring, and the method comprises the steps: obtaining real-time monitoring data of a to-be-detected road, and carrying out the preprocessing; performing space-time alignment on the preprocessed real-time monitoring data, extracting dynamic traffic features and static road features, and generating a space-time feature matrix; inputting the spatial-temporal characteristic matrix into a pre-trained carbon emission prediction model to obtain a prediction result matrix of the to-be-detected road; on the basis of the prediction result matrix, identifying a road grid of which the carbon emission intensity prediction value exceeds a preset threshold value as a high emission area; acquiring real-time traffic state data of the high emission area; and based on the real-time traffic state data and the prediction result matrix of the high emission area, constructing a multi-objective optimization function and carrying out solving to obtain an optimization control strategy of the high emission area. According to the invention, traffic carbon emission optimization control and real-time traffic condition synchronization can be realized.

The invention discloses a road intelligent induction and dynamic early warning method and system integrated with meteorological perception, and relates to the technical field of intelligent traffic and road safety. The method comprises the following steps: acquiring real-time weather, traffic and road data, performing multi-source data fusion by adopting an improved Kalman filtering and attention mechanism, and generating unified state estimation; dynamic risk assessment is carried out in combination with Bayesian reasoning and a Markov model, and speed-limiting adaptive adjustment is realized based on safety, traffic efficiency and energy consumption multi-objective optimization; and further calculating the length and position of the dynamic early warning area, and controlling devices such as intelligent spikes to issue induction information. The system comprises a data acquisition unit, a fusion estimation unit, a risk prediction unit, a speed adjustment unit, an early warning calculation unit and an induction unit. According to the invention, real-time monitoring, risk prediction and intelligent regulation and control of the road traffic environment in complex weather are realized, and the driving safety and the traffic efficiency are improved.

The invention provides a high-precision perception-driven intelligent road network collaborative optimization method and system, and relates to the technical field of intelligent traffic, and the method comprises the steps: collecting road sensing data through multi-sensor fusion, and obtaining multi-source perception data; performing dynamic traffic flow modeling according to the multi-source sensing data to obtain a traffic state prediction result; performing optimization decision on the traffic state prediction result to obtain an intelligent optimization decision; performing road network level cooperative control on the intelligent optimization decision to obtain a road network level cooperative control result; and performing real-time dynamic adjustment by using the road network level cooperative control result to obtain a road network scheduling result. According to the method and the device, the technical targets of global collaborative scheduling, improvement of traffic flow prediction precision, optimization of a real-time dynamic scheduling scheme and improvement of the intelligent level of urban traffic management can be realized, and the technical effects of reducing traffic congestion, improving road traffic efficiency, optimizing traffic resource allocation and improving emergency response capability are achieved.

The invention belongs to the technical field of intelligent monitoring, and particularly relates to a 5G Internet of Things fused video monitoring intelligent early warning method, which comprises the following steps of: acquiring vehicle attribute information and environment perception data, constructing a 5G Internet of Things perception network, fusing a vehicle movement track and road condition environment data through a cross-modal attention mechanism, generating an enhanced environment perception graph, and simultaneously, carrying out early warning on the vehicle movement track and the road condition environment data. Optimizing the transmission efficiency by adopting a dynamic resolution adjustment algorithm; the method comprises the following steps: constructing a dynamic behavior prediction model by using a space-time diagram convolutional network, predicting a vehicle abnormal behavior probability and an evolution trajectory, calculating an early warning level through an adaptive risk quantification algorithm, simulating a risk diffusion coefficient by using a dynamic risk propagation model according to the road section vehicle density, the average vehicle speed and the road traffic capacity, and correcting early warning sensitivity parameters in real time. And transmitting to a command platform, performing situation deduction, triggering a grading early warning instruction, and generating thermodynamic diagram warning information. Therefore, the problems of insufficient positioning precision, weak analysis capability, poor transmission efficiency and the like in the prior art are solved.

The invention discloses an urban road traffic entrance and exit influence evaluation method based on big data, and relates to the technical field of urban traffic management. Constructing an urban road traffic network topological graph based on the traffic feature vectors, proposing a dynamic weight graph embedding algorithm, and establishing a road network association mapping model; applying a graph neural network algorithm based on an attention mechanism to the road network association mapping model, performing road node influence factor evaluation, and quantitatively analyzing the road entrance and exit influence degree; fusing influence factor evaluation results, and constructing a multi-dimensional traffic influence evaluation model by adopting a cross-domain ensemble learning method; and according to a performance evaluation result of the multi-dimensional traffic influence evaluation model, generating urban road traffic entrance and exit optimization decision suggestions through an intelligent recommendation algorithm, and completing accurate scheduling of traffic network nodes. The intelligent recommendation algorithm is developed based on reinforcement learning, and reliable optimization suggestions are provided for traffic management decisions.

The invention relates to the technical field of data analysis, in particular to a road traffic intelligent operation and maintenance method and system based on multi-source data fusion, and the method comprises the steps: constructing a three-dimensional model through a credibility calculation module by integrating three types of factors, namely a road environment, an equipment state and historical verification, generating a dynamically updated credibility index, and correcting a weight in real time based on feedback; the problem that the reliability of the multi-source data fluctuates dynamically due to the influence of environmental interference, equipment aging and historical deviation is solved; the road feature conflict resolution module maps to feature dimensions corresponding to road topology conflict features through dynamic credibility indexes, constructs a road conflict decision matrix, and fuses and generates a road anomaly decision set with credible labels according to the feature dimensions; the resource scheduling module is used for dividing three-level response areas based on credible indexes, executing accurate scheduling of triple constraints in combination with device priorities and credible labels and generating a resource-decision association graph, and self-adaptive optimal configuration of limited maintenance resources according to data reliability is achieved.

The invention discloses a complex scene traffic sign detection method and system based on dynamic frequency band focusing and double-domain attention screening. The method comprises the following steps: carrying out data preprocessing and data enhancement on a collected road traffic sign image; a CADPCM module and a CHAttention cross coordination attention mechanism are used to construct a CACHNet backbone network; designing a DWMSN neck network, and establishing a dynamic fusion mechanism of multi-scale features; a CACHNet and a DWMSN neck network are used to construct a CDWN model, and a traffic sign enhancement data set is used to train the CDWN model to determine the optimal model weight thereof. Compared with the prior art, the method has the advantages that the average detection precision is improved by 3.3% while the light weight of the model is maintained by constructing a three-level framework of the feature extraction unit, the attention feature expression enhancement and the dynamic feature fusion, the complex scenes such as illumination variation and shielding can be effectively dealt with, and the method is suitable for popularization and application. And high-precision traffic sign detection support is provided for a vehicle-mounted intelligent auxiliary driving system.

The invention provides a road traffic space-time service platform and method based on multi-source heterogeneous data fusion. The platform comprises a data acquisition module; a data fusion management module; the base map making module is used for generating a road traffic space-time service base map taking a road intersection as a key node; the traffic situation analysis module is used for forming a traffic situation prediction map; and the decision support and personalized service module is used for providing real-time and visual traffic situation display, traffic guidance strategies and personalized travel services for traffic managers and traffic participating users based on the traffic situation prediction map. According to the invention, real-time and visual decision support is provided for traffic managers, and personalized travel schemes can be customized for traffic participating users, so that comprehensive perception and accurate regulation and control of road traffic operation states are realized, and the intelligent level of traffic operation management is greatly improved.

The invention discloses an intelligent traffic control system and method based on multi-agent near-end strategy optimization, and belongs to the field of intelligent traffic, Internet of Vehicles and deep reinforcement learning. The method comprises the following steps: firstly, constructing a fog-cloud collaborative three-layer architecture, and realizing real-time monitoring and dynamic regulation and control of traffic flow through cloud global decision and local sensing collaboration of a road side unit (RSU); secondly, designing indexes of'road section overlap ratio 'and'road section time overlap ratio', and solving the problem of secondary congestion caused by rerouting; then, a multi-agent near-end strategy optimization (MAPPO) algorithm is adopted, so that the traffic signal lamp is used as an autonomous agent to dynamically adjust the phase, and the limitation of single-point control is broken; and finally, through integrated optimization of rerouting and adaptive signal control, an original multi-objective optimization problem is converted into a layered multi-agent reinforcement learning problem. According to the invention, vehicle driving time and system energy consumption can be effectively reduced, road traffic efficiency is improved, and active avoidance and dynamic alleviation of urban traffic congestion are realized.

The invention discloses a hybrid electric vehicle predictive energy management system based on multi-layer cooperative control, and aims to improve the vehicle performance and energy efficiency. According to the method, a hierarchical cooperative control architecture is adopted, and the reinforcement learning, vehicle speed multi-step prediction and model prediction control technologies are combined, so that joint optimization control over vehicle acceleration and power distribution is achieved. The system obtains the vehicle running state and the surrounding environment information in real time through the state sensing module, and determines the expected acceleration by using a strategy network trained by a PPO algorithm. In addition, the future vehicle speed is predicted in combination with an XGBoost multi-step regression model, the optimization module is designed through an MPC strategy, oil consumption, the battery state and driving smoothness are comprehensively considered, and therefore efficient management of the power system is achieved. According to the scheme, the obvious energy-saving advantage is achieved, the driving smoothness and comfort are improved, the good self-adaptive capacity and a real-time feedback mechanism are achieved, and the method is suitable for the complex urban road traffic environment.

The invention relates to the technical field of traffic scheduling, in particular to a road traffic analysis scheduling method and system based on event driving. The method comprises the following steps: collecting traffic state data in real time, and generating an original traffic data flow; feature extraction is carried out on the original traffic data flow, a space-time traffic flow graph is constructed, and a dynamic traffic network graph is generated; performing analysis and space-time correlation analysis on the dynamic traffic network diagram to generate a traffic event list; triggering an intelligent scheduling response based on the traffic event list, calling a corresponding scheduling strategy template according to the event type, and generating a candidate scheme set; a multi-objective optimization model is constructed for optimization, and an optimal scheduling scheme is generated; issuing the optimal scheduling scheme to road control equipment, and executing traffic scheduling; and collecting traffic feedback data, evaluating a scheduling effect, and if an expected target is not reached, adjusting scheduling parameters and regenerating an optimization scheme until the traffic state is improved. According to the invention, accurate analysis and efficient scheduling of road traffic can be realized.

The invention discloses a road congestion prediction system and method based on spatial-temporal feature extraction, and belongs to the field of intelligent traffic. The system adopts a layered distributed architecture, and comprises a multi-source data acquisition module, a data preprocessing unit, a double-flow spatio-temporal feature extraction network, a two-stage spatio-temporal attention mechanism module, a congestion prediction model and a result feedback interface. Multi-modal data such as a vehicle-mounted GPS track, checkpoint flow, video monitoring and meteorological data are integrated, a double-flow feature extraction network is constructed by adopting a graph convolutional network and a bidirectional gating circulation unit, and a key space-time region is dynamically focused in combination with a multi-head self-attention and time weighted dot product attention mechanism; and finally, optimizing the generalization ability of the model through a composite loss function. According to the method, a dynamic adaptive learning framework and multi-source data combined modeling mode is adopted for urban road traffic flow characteristics, the space-time precision and the real-time response capability of road network congestion prediction are remarkably improved, and reliable decision support is provided for intelligent traffic control.

The invention discloses a collaborative optimization method of new energy facilities in an expressway carbon sink system. The method comprises the following steps: monitoring carbon emission data of a full life cycle of an expressway and carrying out data fusion; carrying out accounting and intelligent analysis processing on the carbon emission data of the whole life cycle of the expressway; according to the invention, based on a'vehicle-road-ring 'carbon emission multi-dimensional integrated monitoring system, intelligent fusion and parameterization analysis of highway and traffic flow carbon emission multi-source data are carried out; a highway full life cycle carbon emission accounting model is established, an associated activity level metering system is constructed, a highway full life cycle carbon emission digital twinborn management system based on big data is constructed, and a highway carbon emission evaluation system is constructed to comprehensively analyze low-carbon high-speed full life cycle carbon emission characteristics. The coupling digital twinning technology realizes comprehensive visualization of carbon emission data, and provides important support for green and low-carbon development of road traffic.

The invention relates to a traffic scene risk prediction method based on knowledge graph and multi-source knowledge fusion. The method comprises the following steps: S1, establishing a road traffic scene ontology reference model under the constraint of road design specifications; s2, acquiring a historical accident report, and extracting according to the historical accident report to obtain one or more first information groups; s3, after traversal of all historical accident reports is completed, risk values are calculated, multiple risk levels are obtained through clustering, risk nodes are generated according to the obtained risk levels, and a second information group is obtained through extraction; s4, acquiring a scene with an unknown risk level as a scene node to be reasoned, and constructing to obtain a security risk knowledge graph; s5, based on the security risk knowledge graph, aligning and fusing external risk knowledge to realize dynamic updating of the security risk knowledge graph; and S6, acquiring a to-be-assessed scene, matching the to-be-assessed scene with each scene node in the security risk knowledge graph, and outputting a risk level of the scene node obtained by matching as a risk detection result. Compared with the prior art, the method has the advantages of wide application range and the like.

The invention discloses an intelligent traffic light coordination control method and system for traffic flow optimization, and relates to the technical field of intelligent control. The method comprises the following steps: collecting traffic flow data of lanes in all directions in a target area in real time through heterogeneous sensor networks deployed at all intersections; identifying the traffic flow data to obtain tidal traffic flow features; performing dynamic intensity evaluation based on the tidal traffic flow characteristics to obtain a tidal direction intensity index; a multi-agent cooperative network is constructed, and a continuous intersection green light phase offset sequence is generated based on cooperative calculation of the multi-agent cooperative network; and when the tide direction intensity index is greater than a preset threshold value, inputting the continuous intersection green light phase offset sequence into a traffic signal lamp control system so as to start a tide green wave mode. The technical problems that in the prior art, traffic flow control is not flexible enough, and traffic lights cannot be intelligently adjusted according to the traffic flow condition are solved, and the technical effects of improving the road passing efficiency and reducing congestion are achieved.

A road traffic event analysis method and system, and a device and a medium. The traffic event analysis method comprises: decoding and coding the current monitoring video, so as to obtain a target video picture (S102); calibrating the target video picture, so as to obtain a calibrated video picture (S103); performing preliminary analysis on the calibrated video picture, so as to obtain a preliminary analysis result (S104); determining state change data on the basis of the preliminary analysis result (S105); compiling statistics on a traffic flow, so as to determine peak time periods and plateau time periods of a road traffic site (S106); performing preliminary detection on frame scenes on the basis of pre-determined detection parameters corresponding to the peak time periods and the plateau time periods, so as to obtain a preliminary detection result (S107); excluding congestion events from the preliminary detection result (S108); and tracking targets, and determining motion state features, so as to obtain a final detection result and report same (S109). The scale of a traffic accident event triggered in the detection result and the statistical result are used for adjusting the detection parameters corresponding to the peak time periods and the plateau time periods. The traffic event analysis method can improve the detection accuracy of road traffic events.

The invention discloses an energy-saving type rescue path dynamic planning system and method for an intelligent networked automobile, and relates to the technical field of intelligent rescue planning. The system comprises an information sensing module, an initial path planning module, a real-time monitoring module, a path adjusting module and a result display module, the method comprises the following steps: acquiring road, traffic, weather and vehicle information, constructing a multi-objective function containing energy-saving and rescue timeliness targets by an initial path planning module, planning an initial path by applying a multi-objective optimization algorithm, continuously monitoring information changes and vehicle states by a real-time monitoring module, calculating path and energy-saving deviation, and performing real-time monitoring on the path and the energy-saving deviation. The path adjusting module is used for triggering an adjusting mechanism according to deviation, searching an alternative path by adopting a local search algorithm and comprehensively evaluating and selecting an optimal path, the result display module is used for displaying the adjusted path and related information, and the system and the method realize energy conservation and high efficiency of a rescue path through multi-source information fusion and dynamic adjustment.

The invention provides a lane control strategy optimization method and system for a traffic congestion state, and the method comprises the steps: firstly obtaining a real-time traffic state data set of a target road section, including vehicle flow, driving speed and road space occupancy data, carrying out the traffic congestion feature analysis of the real-time traffic state data set, and carrying out the traffic congestion feature analysis of the real-time traffic state data set; the method comprises the following steps: obtaining congestion area distribution, congestion degree evaluation and congestion cause correlation characteristics, generating an initial lane control strategy containing variable lane direction setting, lane opening and closing states and lane passing priority distribution parameters based on the characteristics, sending the initial strategy to road control equipment for execution, and collecting and feeding back a traffic state data set; and adjusting strategy parameters according to a comparative analysis result of the feedback traffic state data set and the real-time traffic state data set, and generating an optimized lane control strategy, thereby improving the adaptability of the lane control strategy, and effectively improving the road traffic efficiency and the congestion relieving capability.

The invention relates to the technical field of automatic driving, in particular to an automatic driving obstacle intention prediction and avoidance method and system. The method comprises the following steps: acquiring image data and three-dimensional point cloud data in real time, respectively extracting static features and dynamic features, weighting by adopting an attention mechanism, fusing to form a fused feature vector, inputting the fused feature vector into a plurality of independently trained deep learning models, and obtaining a prediction intention by using a model difference weighted fusion output result. The method comprises the following steps of: setting an association constraint of an operation state and input control based on a prediction intention, establishing an avoidance strategy model by taking path length and time cost as double optimization targets, continuously updating input control in a prediction time domain until an optimization target value is iterated to be minimum, outputting a time sequence of an avoidance strategy, and generating a final execution instruction in combination with a real-time operation state. According to the invention, the bottleneck of a traditional system based on static characteristics and preset rules can be broken through, and the road traffic efficiency is obviously optimized while the traffic safety redundancy is improved.

The invention, which relates to the technical field of intelligent traffic and carbon emission monitoring, discloses a highway carbon emission energy consumption abnormity monitoring optimization system comprising a vehicle characteristic acquisition module, an environment dynamic sensing module, a carbon emission dynamic calculation module, an abnormity intelligent diagnosis module and an attribution optimization execution module. Calculating a real-time dynamic carbon emission value by combining environmental factor data such as vehicle characteristics, real-time driving speed and wind speed, road gradient and the like; setting a dynamic carbon emission reference value according to the traffic flow and environmental factor data of the current road section; and identifying an abnormal attribution type by associating data such as vehicle speed abrupt change, environmental factor abrupt change and road section traffic condition in the abnormal carbon emission time period, and generating a corresponding optimization adjustment instruction. The method improves the attribution accuracy of the carbon emission abnormity, supports the real-time optimization decision oriented to traffic management, and provides technical support for the operation of a green intelligent expressway.

The invention discloses an intelligent traffic flow statistics and prediction platform based on multi-source data fusion, and relates to the technical field related to traffic prediction, and the platform comprises a data collection layer which is used for collecting road traffic flow data, meteorological data, public traffic operation data and data including traffic condition description on a social media platform; a data preprocessing layer; a data fusion layer; a traffic flow calculation module; and the traffic flow prediction module constructs a prediction model framework based on the space-time convolutional neural network, optimizes hyper-parameters of the ST-CNN, and applies the optimized model to traffic flow prediction. According to the invention, data collected by the annular induction coil and the camera focuses on actual traffic conditions of specific roads and intersections at a microscopic level, and meteorological data, public traffic operation data and social media data reflect traffic influence factors of the whole city or the region from a more macroscopic perspective, so that macroscopic and microscopic aspects are combined, and the traffic influence factors of the whole city or the region are reflected. And the operation condition of the urban traffic system can be known more comprehensively.

A method and an apparatus for predicting a road traffic carbon emission based on panoramic images, a device and a medium are provided, relating to the technical field of data prediction. In the method, a historical street view image of an observation area is acquired from the Internet, feature analysis is performed on the historical street view image to obtain a historical feature vector, and a road traffic carbon emission predicted value is obtained based on the historical feature vector and a carbon emission prediction model. With the method or the apparatus, auxiliary explanations for carbon emission sources in cities can be provided based on features of street views.

The invention provides a settlement detection method and device based on machine vision, belongs to the technical field of underground engineering, and is applied to a settlement monitoring area, a measuring point target is arranged in the area, and a reference point target is arranged outside the area; comprising the steps that S1, a settlement monitoring area is continuously shot, a monitoring image set is obtained, and the monitoring image set covers a monitoring point target arranged in the settlement monitoring area and a reference point target arranged outside the settlement monitoring area; s2, processing the monitoring image set to obtain the relative elevation of each measuring point target; and S3, comparing the relative elevation of each measuring point target with the corresponding initial elevation to obtain the settlement amount of each measuring point target. The beneficial effects are that by arranging the measuring point target and the reference point target, the normal operation of road traffic is not interfered, the road traffic is not easily damaged by rolling of running vehicles, the problem of machine vision wide-area monitoring is solved, and the settlement monitoring area can be monitored more comprehensively and accurately.

The invention discloses a county and rural traffic planning decision-making method and system based on a road traffic index. The method comprises the following steps: acquiring navigation API data, social economic data and POI information related to county and rural traffic planning; the method comprises the following steps: acquiring data, processing the data, then calculating a route growth index, a per-hour time index, a holiday influence index, a broken road index and a planning simulation index, carrying out weighted fusion to obtain a road traffic index, when the road traffic index is calculated, distributing an initial weight for each index, and adopting a self-adaptive weight adjustment algorithm to calculate the road traffic index. Dynamically adjusting the weight of each index according to the real-time traffic data and the simulation result; county and rural traffic planning is carried out by taking reduction of the road traffic index as a target, and a road traffic index difference value before and after planning is calculated as a decision basis. According to the invention, the problem of asymmetry of basic traffic management information is effectively solved, and the connection accuracy of the road network is improved.

The invention provides an intelligent traffic jam early warning method and platform driven by a high-precision perception technology, and relates to the technical field of traffic early warning, and the method comprises the steps: carrying out the real-time data collection of a preset road region through a traffic perception sensing module, and generating a multi-source sensing data set; performing multi-source fusion on the multi-source sensing data set to generate global traffic state information; carrying out traffic flow change analysis according to the global traffic state information, and identifying a traffic congestion point which is a road position with a congestion risk greater than or equal to a preset risk index; and carrying out traffic jam early warning according to the traffic jam point. The technical problem that in the prior art, due to the fact that the advanced high-precision sensing technology, big data analysis and an intelligent decision algorithm are not fully utilized, traffic jam cannot be effectively prevented or relieved is solved, and the technical effects of improving the accuracy and response speed of traffic flow prediction, reducing traffic jam and improving the road traffic efficiency are achieved.