4869 results about "Road condition" patented technology

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.

This application discloses a method, system, and related equipment for intelligent energy management of new energy vehicles. Based on the vehicle's starting and ending points, at least one candidate energy-saving path is determined. The predicted energy consumption of the vehicle along at least one candidate energy-saving path is lower than that of other paths. The total energy consumption is predicted based on road condition information and energy consumption impact information for each path. In response to the selection of at least one candidate energy-saving path, a preset travel route is determined. The preset travel route includes multiple road segments, and the total energy consumption includes the energy consumption of each road segment. With the goal of minimizing fuel consumption along the preset travel route, the engine's operating state is controlled based on the initial state of charge (SOC) of the power battery in each road segment, the energy consumption of the road segment, and the vehicle's actual overall demand, ensuring the engine operates within its high-efficiency range. Using this application can reduce fuel consumption for users and improve the driving experience.

A multi-degree-of-freedom drive-by-wire unmanned vehicle obstacle avoidance path planning method mainly comprises the following steps that based on environment perception information, surrounding obstacles are analyzed and processed, and an environment safety boundary is established; on the basis of an environment safety boundary, speed sampling is carried out based on dynamic window trajectory planning of hierarchical response, and an optimal local trajectory is calculated and selected; in the driving and obstacle avoidance process, according to obstacle distribution and road condition fluctuation real-time information, the posture of a vehicle body is dynamically adjusted, speed sampling is updated, and path planning is updated. According to the method, the optimal obstacle avoidance path can be planned in real time, so that the unmanned vehicle can quickly respond in a complex scene, and potential collision risks can be avoided in time; the characteristics that the multi-degree-of-freedom drive-by-wire unmanned vehicle is suspended and the vehicle body posture is adjustable are fully utilized, vehicle posture adjustment is fused into an obstacle avoidance strategy, and the feasibility of an obstacle avoidance planning track and the stability of vehicle execution are improved.

The invention relates to the technical field of intelligent transportation control, in particular to a tomato transportation speed self-adaptive adjustment method based on path road condition feedback, which comprises the following steps: acquiring road images, vibration waveforms and altitude data through a multi-source sensing unit, and constructing real-time road condition information; identifying a driving mode and extracting a corresponding bumping parameter; detecting the maturity grade of the tomato by combining multispectrum and thermal imaging, and querying a maturity-compressive strength corresponding table to calculate a cargo damage threshold value; constructing a dynamic mapping model under multiple working conditions, predicting vibration response and converting the vibration response into equivalent pressure; comparing the equivalent pressure with a damage threshold value to obtain a safety margin, constructing a speed adjustment decision tree and generating a maximum allowable speed value of each road section; and dynamically generating a segmented variable-speed control instruction based on the speed decision matrix, and controlling a throttle valve and a braking system to cooperatively change speed. The method has the advantages of accurate working condition identification, dynamic fruit adaptation, closed-loop speed regulation and control and the like, and is suitable for fine speed control of a high-sensitivity fruit and vegetable transportation scene.

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 end-cloud cooperative detection method and system for traffic anomalies, and relates to the technical field of intelligent traffic control. The method comprises the following steps: collecting traffic video streams through edge equipment, and identifying abnormal behaviors and generating structured event data by using a lightweight YOLOv3-tiny model; when the confidence exceeds a dynamic threshold and the event type is a high-risk type, uploading a video clip and data to a cloud; the cloud integrates a historical road condition map, meteorological data and a real-time traffic flow state, reconstructs a three-dimensional event scene by adopting a space-time attention pyramid network, and verifies the authenticity of an event in combination with a traffic flow sudden change detection algorithm; and generating a signal lamp forced switching instruction for the risk level overrun event, and issuing the signal lamp forced switching instruction to roadside equipment within 3 seconds to execute emergency response. According to the invention, full-link closed-loop control of traffic accidents from identification to response is realized, and the false alarm probability is greatly reduced while the identification accuracy is guaranteed.

The invention relates to the technical field of intelligent driving, and discloses an obstacle avoidance analysis system for intelligent driving. The system comprises a data acquisition module, a road feature extraction module, a lane type judgment module, a virtual marking generation module, an obstacle detection and trajectory prediction module, a dynamic obstacle avoidance decision module and a system verification and optimization module. The method comprises the following steps: acquiring data through a laser radar, a camera and the like; fusing point cloud and an image to extract the width and curvature of a road; judging the type of a lane according to the width; generating a virtual marking line on a marking-free road, fitting by using a B-spline curve when the curvature is large, fusing the point cloud and the image to detect an obstacle, and predicting a track by using Kalman filtering or a social force model. In combination with virtual marking and risk assessment, a safe trajectory is generated through an OccupanyGrid map, an A * algorithm and MPC, and a simulation and real road test optimization system is adopted, so that the problem of marking-free road trajectory planning deviation is solved, and the obstacle avoidance precision and the road condition adaptability are improved.

The invention belongs to the field of vehicle early warning, and particularly relates to a vehicle hidden danger recognition and safety early warning method and device, and the method comprises the following steps: collecting vehicle speed, tire pressure and road condition data, obtaining the states of key parts of an engine and a braking system through combining an OBD system, and achieving the dynamic perception of a whole vehicle; based on a deep learning algorithm, accurately identifying the too short distance of the front vehicle, fatigue driving, overspeed, area deviation, long-term left-occupying driving of a lane, vehicle retrograde driving and abnormal line pressing tracks; through vehicle-mounted OBD data and cloud large model analysis, potential mechanical faults of tire wear and brake pad aging are predicted, and maintenance suggestions are pushed in advance, so that a driver can be reminded of safety, the driving safety is improved, the hidden danger recognition capability is improved, safety preventive warning is performed on hidden dangers, the driving safety is ensured, and the driving safety is improved. Meanwhile, the vehicle can be guided to avoid high-risk or forbidden road sections, and the probability that the vehicle enters a dangerous or forbidden area is reduced.

The invention relates to the technical field of new energy vehicles, in particular to a multi-working-condition energy management method and system for a hybrid chariot, and the method comprises the steps: determining a corresponding working condition weight coefficient through obtaining the altitude, environment temperature and road condition data in real time, and the real-time working parameters of three power sources, namely an engine, a fuel cell and a lithium battery, so as to achieve the multi-working-condition energy management of the hybrid chariot; the system constructs a multi-objective optimization model based on the data to realize comprehensive fuel consumption minimization, lithium battery SOC balance and lithium battery life protection, and the model comprises establishment of a power source efficiency parameter mapping relation, determination of a corresponding relation between lithium battery power and cycle life, and construction of a membership function of an optimization objective. By adopting a method of combining Pareto local optimization and fuzzy evaluation, the optimal power distribution proportion among the engine, the fuel cell and the lithium battery is calculated, the overall efficiency of the hybrid chariot is effectively improved, and the service life of the hybrid chariot is effectively prolonged.

The invention discloses a tire wear diagnosis method, device and equipment and a storage medium, and the method comprises the steps: generating a tire wear calculation model based on obtained key factors affecting tire wear, wherein the key factors comprise vehicle driving mileage, historical driving road condition information, driving habits, vehicle tire pressure and four-wheel positioning information; and calculating a current tire wear coefficient according to the tire wear calculation model, and diagnosing tire wear by using the current tire wear coefficient. The abnormal condition of tire wear can be effectively identified, a terminal client is reminded of the tire wear state of the vehicle, and the use safety of the vehicle tire is ensured.

The invention provides a traffic control strategy self-adaption method and system based on simulation feedback, and belongs to the technical field of traffic prediction and control, and the method comprises the steps: firstly obtaining traffic control scene data containing traffic flow data and road condition information, then constructing a simulation evaluation environment, configuring scene parameters based on the traffic control scene data, and carrying out the simulation evaluation environment; the method comprises the following steps: simulating traffic operation states under different traffic control strategies, calling a pre-trained reinforcement learning model to perform simulation evaluation on each strategy in a traffic control strategy set, generating a strategy effect feedback set comprising a traffic operation efficiency index and a traffic order stability index, and according to the strategy effect feedback set, calculating the traffic order stability of the traffic control strategy. And performing parameter adjustment on the strategy in consideration of the index association relationship to obtain an adjusted strategy, and finally outputting the adjusted strategy to the traffic control system to realize strategy updating, thereby effectively improving traffic operation efficiency, ensuring traffic order stability, and realizing adaptive optimization of the traffic control strategy.

The invention relates to the field of real-time visualization of road conditions, in particular to a data simulation visualization rendering method and system for urban traffic road conditions. The method comprises the following steps: extracting a real-time satellite streetscape image based on urban satellite remote sensing scanning, and performing scene pixel-level segmentation to obtain scene texture rendering parameters; scene illumination visual identification is carried out according to the real-time satellite streetscape image, traffic scene background modeling is carried out based on scene texture rendering parameters, and a real-time scene background model is constructed; the method comprises the following steps: acquiring urban-level multi-source traffic monitoring data flow, performing vehicle state sensing, and constructing a multi-dimensional particle feature matrix; road network topological correlation analysis and global traffic network state perception are carried out according to the real-time satellite streetscape images, and a road network state perception model is constructed. According to the invention, a real real-time traffic environment is visualized, scene effects in different traffic states are presented, the current road condition can be rapidly evaluated, and the traffic control decision efficiency is improved.

The invention discloses a multi-mode energy recovery optimization control system of a hybrid electric vehicle, and relates to the technical field of energy recovery control. The state sensing module is integrated with a sensor to collect vehicle speed, acceleration, battery state and road condition signals, and data accuracy is ensured through Kalman filtering fusion; the driving mode recognition module is used for recognizing modes such as urban congestion and high-speed cruise through an algorithm based on driving behaviors and road conditions; the energy recovery strategy decision module is used for dynamically adjusting recovery priorities and parameters according to different modes, and balancing efficiency and smoothness; the power distribution execution module converts and stores energy through cooperative control of double clutches and a gearbox, and is linked with an ABS (Anti-lock Brake System) to guarantee safety; and the feedback module is optimized in real time, battery and motor states are monitored, strategies are dynamically corrected, and efficiency and component protection are both considered. The energy recovery efficiency is improved, the driving smoothness and the braking safety are improved, the system adapts to multiple scenes, the service life of the battery is prolonged, and the performance of the hybrid electric vehicle is comprehensively improved.

The invention discloses a supply chain collaborative management method and system for a whole-industry link platform, and the method comprises the steps: constructing a distributed supply chain data warehouse through employing a block chain and lightweight encryption; generating a multi-dimensional matching degree score table to recommend suppliers; decrypting and storing the encrypted bidding file, and calculating a comprehensive score according to a quantitative index and an expert qualitative index; constructing a hierarchical prediction model, and outputting a three-dimensional prediction result; calculating a supply-demand gap according to a supply-demand elastic coefficient coupling algorithm, automatically generating an order, and synchronizing the order to each end in real time; building a supply chain digital twinborn model, and dynamically planning an optimal transportation route through an intelligent fusion road condition algorithm; early warning information is automatically sent to generate a coping strategy, and full-link operation data are integrated for visual display; the objective of the invention is to solve the problems of existing supply chain data splitting, low supplier matching efficiency, low demand prediction precision, rigid logistics planning, risk monitoring lagging and poor collaboration of all links.

The invention relates to the technical field of information, and discloses an intelligent scheduling method and system for a vehicle-mounted mobile charging system, and the method comprises the steps: obtaining the emergency degree values, geographic positions and charging demand grade values of all charging demands in a current region, and building a demand priority list; acquiring real-time electric quantity states, position information and electric quantity consumption rates of all charging vehicles, and establishing a vehicle availability list; according to the two tables, combined with the target position distance, the road condition and the traffic condition, calculating the estimated time and the residual electric quantity of each charging vehicle arriving at the target position; when the estimated time is smaller than a preset time threshold value and the residual electric quantity meets the requirement, the vehicle is marked as an available vehicle; when the available vehicles are insufficient, judging whether cross-regional scheduling is carried out or not; and optimizing the global scheduling instruction by adopting a scheduling algorithm and dynamically scheduling the charging vehicles. The method can achieve the reasonable scheduling of the charging vehicles, and improves the overall efficiency.

The invention provides a multi-dimensional data fusion system for operating truck risk rating, and relates to the technical field of data processing, and the system comprises a data collection module which is used for obtaining the dynamic operation data and historical static information of a target operating truck; the data preprocessing module is used for performing field alignment, abnormal value elimination and format standardization; the feature analysis module is used for extracting dynamic behavior features of vehicle operation and performing statistical calculation in a preset time window; the event identification and risk assignment module is used for identifying a single event or a combined event, assigning an event risk weight to the single event or the combined event, and determining a corresponding road condition risk level in combination with the vehicle position information; the multi-dimensional fusion module is used for fusing features and generating time serialized multi-dimensional risk feature vectors; the risk rating module is used for scoring the multi-dimensional risk feature vector and outputting a corresponding risk rating result; according to the invention, the accuracy of the multi-dimensional data fusion system is improved.

The invention discloses an Internet of Vehicles network selection and switching decision system supporting access of multiple operators. According to the method and the device, the timeliness and the continuity of network switching are remarkably improved through prediction and planning in advance. The module depends on a trajectory-network matching prediction sub-module, combines a vehicle navigation trajectory and a global digital twinborn model, identifies a base station coverage area in a future driving path and the network service quality of each operator in advance, and avoids the lag problem that switching is triggered only after signals are weakened in traditional passive switching. The pre-switching resource reservation sub-module further sends a resource request to a target operator base station and confirms reservation before the vehicle enters a new road section, ensures that resources such as bandwidth and time slot required during switching are in place, reduces switching failure or interruption caused by resource competition, avoids common communication interruption after signal sudden drop in traditional switching, and improves the switching efficiency. And continuous transmission of key businesses such as automatic driving control instructions and real-time road conditions is ensured.

The invention discloses a new energy automobile hybrid power system cooperative control device and method.The new energy automobile hybrid power system cooperative control device comprises a multi-source data collection module, a cooperative control center and an execution unit, and the multi-source data collection module transmits collected information data to the cooperative control center; the cooperative control center comprises a data fusion unit, an energy management module, a heat management module and a cooperative decision-making unit, the data fusion unit receives data information of the multi-source data acquisition module, carries out fusion processing, and transmits the data information to the energy management module and the heat management module; the energy management module and the heat management module output a control strategy to the collaborative decision-making unit, and the collaborative decision-making unit judges a correction strategy and then sends a control instruction to the execution unit. The method has the advantages that cooperative control of energy management and heat management is achieved, various dimension data of road conditions, driving behaviors, power states and heat source states are fused, the problem of system unbalance caused by single module optimization is solved, and dynamic cooperation of energy management and heat management is achieved.

The invention provides a multi-dimensional driver capability evaluation and intelligent matching scheduling system, and the system comprises a data collection module which is used for obtaining driver driving behavior data, vehicle state data and environment data in real time; the preprocessing module is used for carrying out noise filtering, missing value filling and standardization on the acquired data; the multi-dimensional capability evaluation module is used for calculating a driving safety score, an efficiency score and an emergency response score of the driver through a dynamic weight distribution algorithm based on the preprocessed data; the demand analysis module is used for analyzing the route complexity, the time sensitivity and the special service demand of the passenger order; the matching scheduling module is used for generating a matching result according to the driver ability score and the passenger demand and outputting a scheduling instruction; the dynamic optimization module monitors the driver state and the road condition change in real time and adjusts the matching weight; and the interaction module is used for pushing real-time scheduling information and abnormal event early warning to the driver and the passenger. The scheduling efficiency and safety can be improved, and the passenger travel experience and the operation management level are improved.

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 relates to the technical field of intelligent traffic, in particular to an urban traffic flow prediction method fusing a dynamic graph convolutional network and a Transformer, which comprises the following steps: firstly, collecting traffic data in a road network to form a data set, then constructing a space-time dynamic GCN unit to capture dynamic evolution of road network topology, and extracting multi-scale space-time characteristics through expansion time convolution; then, constructing a Transform unit for modeling a global time sequence dependency relationship of the traffic flow; finally, in combination with a gating fusion prediction unit, spatial-temporal features are fused through a spatial-temporal cross attention mechanism, and the prediction precision and robustness are effectively improved. Through verification and evaluation of real data, the method is suitable for a traffic flow prediction scene in an urban road network dynamic environment, and especially has good performance in sudden road conditions and peak hours.

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 is applicable to the technical field of emergency rescue, and relates to an optimal path planning method based on a fire rescue road network directed graph, which comprises the following steps: reconstructing the road network directed graph according to a road priority principle by using multi-source data such as an open street map road network, road conditions, survey data, real-time road conditions and the like and fusing remote sensing image data; on the basis, based on a dynamic weight model, an optimal path planning algorithm based on a fire rescue road network directed graph is further constructed. The application of the method can effectively make up for the deficiency of online map path planning used in the existing research, so that the path planning better fits the path planning requirement in a real fire rescue scene, and the driving path of fire rescue can be accurately calculated, so that the path is more reasonably planned, and the efficiency of fire rescue is improved.

The invention discloses a road disease intelligent identification and evaluation method based on a multi-modal large model and an instance segmentation algorithm, and belongs to the field of road health monitoring. The method comprises the following steps: acquiring disease image data of road appearance, and constructing an original data set; training a road apparent disease recognition model based on an instance segmentation (Mask-RCNN and the like) deep learning method; constructing a multi-modal prompt engineering framework based on parameter state transition, and generating a field adaptive question and answer data set; historical disease data and professional knowledge and evaluation standards related to road diseases are collected, a professional knowledge compiling framework is constructed, and a multi-modal large model disease evaluation data set is generated; optimizing a training result based on a collaborative optimization method of a multi-modal large model fusion specification conformity function to form a large model special for the field; and constructing an end-to-end road disease intelligent identification and evaluation system based on the fine-tuned special large model. By means of the method, the problems that a traditional road appearance detection method depends on artificial experience and is low in efficiency can be solved, and meanwhile a professional road condition evaluation system is built through a large model.

The invention relates to the technical field of intelligent identification, in particular to a highway pavement damage intelligent identification and evaluation method, which comprises the following steps: detecting a structure continuity and texture density region based on a pavement image, extracting crack, pit slot and track contour marks, grouping crack, pit slot and track texture to generate an identification graph, and evaluating the pavement damage. And extracting a crack area texture and gradient change to generate a difference layer, judging trend offset to generate an evolution label, and adjusting a classification boundary to evaluate a damage level. According to the method, the structure continuity and the texture density area are detected, the positioning precision is improved, misrecognition is reduced, direction gradient and texture changes are collected under multiple scales, the crack directions are clustered and grouped, the texture recognition stability is enhanced, the difference of the crack area and the periphery is analyzed, boundary jump is recognized, difference perception is enhanced, and the dynamic recognition depth is improved. Evaluation fuzzy errors are reduced, the overall process improves morphological representation precision and trend tracking and classification boundary stability, and the method adapts to various road condition changes.

The invention relates to the technical field of vehicle instrument display, in particular to an oil mass display method and system fusing multi-source information and dynamic regulation, and the method comprises the steps: carrying out the fusion processing of multi-source data collected in real time, and training a pre-established oil mass consumption prediction model based on the multi-source data after the fusion processing; through the trained fuel consumption prediction model and in combination with a navigation planning route, predicting the remaining fuel quantity and the travelable mileage of the vehicle when the vehicle travels to the destination; according to the oil mass states of the vehicle under different working conditions, a graded oil mass display strategy is executed, and oil mass display under each working condition is dynamically regulated and controlled; wherein the vehicle running state comprises a normal running working condition, a congestion working condition, a high-speed running working condition, a bumpy road section working condition and a slope parking working condition. According to the scheme, accurate, stable and comprehensive fuel quantity information is provided for a driver, and the driving experience and the driving safety are improved.

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 discloses an asphalt pavement maintenance strategy intelligent generation method and system, and relates to the technical field of road engineering.The maintenance strategy generation method comprises the steps that traffic analysis data information and traffic load data information are extracted from a multi-source traffic monitoring system, and the traffic analysis data information and the traffic load data information are obtained through a laser scanning and image intelligent recognition algorithm; analyzing the pavement structural disease level in each grid unit; according to the road surface structural disease level, the road condition risk level of each grid unit under the traffic load and damage superimposed effect is analyzed, a risk response area is divided, and a service life loss analysis instruction is generated; analyzing an accelerated decline trend of pavement structure deterioration of each grid unit in the risk response area to determine a life reduction value of each grid unit in the risk response area; after comparison, generating a pavement maintenance strategy of a corresponding grade; the maintenance strategy generation method has the advantages of being high in real-time performance, fine in area recognition and dynamically adjustable in strategy response.

The invention relates to the technical field of pedestrian floor tile monitoring, and discloses a pedestrian floor tile road condition analysis method based on a knowledge graph. The method comprises the following steps: firstly, acquiring spatial topological data and historical maintenance records of road infrastructures, fusing real-time road condition monitoring data and environmental parameters acquired by a multi-source sensor, and constructing a pedestrian floor tile state knowledge graph structure; analyzing a data association relationship through a map entity alignment algorithm to generate road condition evaluation features, and updating map node weights by using a dynamic path optimization algorithm in combination with real-time data and environmental parameters; then, based on the updated node weight and evaluation features, an intelligent agent decision engine is operated to output a floor tile area risk evaluation result; and finally, collecting whole-process data, and optimizing a knowledge graph structure by using an incremental graph updating algorithm. According to the method, multi-source data fusion and dynamic analysis are realized, and delicacy management of pedestrian floor tile road conditions is assisted.

The invention provides an image scene understanding method and system applied to vehicle networking road condition analysis, and the method comprises the steps: firstly obtaining a multi-source road condition image set which is collected by a vehicle networking roadside sensing node in a continuous time period and comprises road scene image sequences in different shooting angles and different exposure modes, and carrying out the time-space consistency calibration processing of the multi-source road condition image set; the method comprises the following steps: generating a standardized image sequence, performing hierarchical feature analysis on the standardized image sequence to form a feature hierarchical chain from a bottom layer to a high layer, inputting the feature hierarchical chain into a pre-training scene semantic understanding model for cross-layer association reasoning, and generating scene semantic description containing road element type labels and dynamic relationships among elements; and finally, generating road condition analysis data containing road condition element positioning information and interaction trend prediction based on the scene semantic description, and transmitting the road condition analysis data to an Internet of Vehicles communication terminal to support driving decision optimization, thereby effectively improving the accuracy and decision support capability of Internet of Vehicles road condition analysis.