Road intelligent induction and dynamic early warning method and system integrated with meteorological perception

Abstract

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.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of intelligent transportation systems and road safety control, and particularly relates to a road intelligent induction and dynamic warning method and system fusing meteorological perception. BACKGROUND

[0002] Under complex meteorological conditions such as dense fog, heavy rain, and snow, the road traffic environment changes dramatically, with significant shortening of the driver's visibility, decrease in road adhesion performance, and increase in vehicle braking distance, greatly increasing the probability of traffic accidents. In particular, at special road sections such as highways, bridges, and tunnels, due to complex terrain and weather conditions, sudden weather may cause more serious traffic safety hazards.

[0003] Currently, traditional traffic induction and warning systems mainly rely on cloud platforms or traffic management centers to provide static or periodically updated traffic information, and issue weather conditions and traffic tips to drivers through variable information display screens, broadcasts, and other means. However, such systems have obvious limitations in dealing with complex and changing weather environments, and are difficult to meet the requirements of real-time and precision, thereby affecting the effective guidance and safety control of driving behavior.

[0004] Specifically, the existing technology has the following main problems: 1. Response lag, poor timeliness: the frequency of meteorological and traffic data collection is low, and the processing and transmission process is cumbersome, making it difficult for the system to quickly respond to changes in sudden weather events.

[0005] 2. Limited coverage, insufficient refinement: most systems only deploy warning devices at key road sections or critical nodes, lacking dynamic monitoring of the entire road network and fine management of local areas.

[0006] 3. Information transmission delay, missing linkage mechanism: the information transmission link from meteorological monitoring to road warning devices is long, and the system is difficult to achieve efficient coordination and rapid linkage between the cloud and the road terminal.

[0007] 4. Fixed strategy, poor adaptability: existing induction systems usually operate based on preset rules, and fail to develop differentiated warning and induction strategies according to different weather types such as heavy fog, heavy rain, and snow, and road section characteristics, resulting in poor induction effect in changing environments.

[0008] 5. Low level of intelligence, weak decision-making ability: most systems lack deep analysis capabilities for multi-source heterogeneous data, and have not introduced intelligent algorithms such as machine learning and Bayesian inference for risk prediction and adaptive optimization, making it difficult to achieve early judgment and dynamic regulation of traffic conditions under adverse weather conditions.

[0009] In summary, the existing traffic guidance and warning system faces the problems of slow response speed, incomplete coverage, single strategy and low intelligence level under complex weather conditions, and an intelligent traffic solution that can realize weather perception, risk assessment, speed limit regulation, dynamic warning and path guidance is urgently needed. SUMMARY

[0010] To solve the above problems, the application proposes a road intelligent guidance and dynamic warning method and system integrating weather perception; the method realizes real-time monitoring, risk prediction and adaptive regulation of road traffic environment under complex weather conditions by integrating weather monitoring, traffic state perception, dynamic risk assessment and intelligent guidance control functions, thereby improving road traffic safety and operation efficiency.

[0011] The technical scheme adopted by the application is: A road intelligent guidance and dynamic warning method integrating weather perception, comprising the following steps: S101 acquiring real-time data: collecting multi-source heterogeneous data of weather, traffic and road environment to provide basic input for intelligent analysis; S102 data fusion estimation: based on multi-source heterogeneous data, using improved Kalman filtering combined with attention mechanism for data fusion to generate unified state estimation results; S103 risk prediction and evaluation: based on the fused state estimation results, the current road section is dynamically evaluated and trend predicted by Bayesian inference and Markov chain model; S104 real-time speed limit adjustment: based on the risk evaluation and trend prediction results, the speed limit value of the current road section is dynamically optimized with safety, traffic efficiency and energy consumption as objective functions; S105 dynamic warning area calculation: according to the vehicle operating state and weather conditions, the appropriate dynamic warning area length and position are calculated; S106 macro guidance information release: according to the results, the macro road network warning information is generated, the corresponding guidance scheme and information are released, and the studs are controlled for guidance to guide the driving vehicles.

[0012] Further, in S101 acquiring real-time data, the weather data, traffic data and road data are collected based on weather sensors, cameras, radars, floating car GPS and V2X communication; The weather data includes visibility, precipitation intensity, wind speed and air temperature; The traffic data includes speed, traffic flow, traffic density and acceleration; The road data includes geometric characteristics, slope, curvature and historical accident rate.

[0013] Further, in the data fusion estimation of S102, the improved Kalman filter and attention mechanism are adopted to fuse the feature vectors of meteorological data, traffic data and road data, and a unified data group containing space-time information is generated, represented as: ; In the formula, is the predicted state value, used to construct the prior state of the filter, combined with to update the fusion; is the state transition matrix, representing the change rule of the state over time; is the state value at the previous time; is the control input matrix, representing the influence degree of the control input on the state; is the system control input of speed limit and induction control; is the noise of the state process and the measurement process; is the real-time measurement value of meteorological data, road data and traffic data; is the observation matrix, representing the relationship between the true state and the observation value; is the attention weight, representing the contribution degree of different data sources to the fusion result; is the weight matrix in the attention mechanism; is the road feature vector, including road geometric parameters; is the state estimation value at time t after fusion, i.e. the state estimation result; is the gain matrix of Kalman filter, dynamically adjusting the filter weight.

[0014] Further, in the risk prediction evaluation of S103, the Bayesian inference model is used to output the risk posterior probability at the current time as the basis for risk level division; Prior probability: represents the historical probability of traffic accidents or congestion on a certain road section under specific weather conditions; obtained based on historical data analysis, reflecting the influence of weather on risk; Likelihood probability: ; represents the possibility of traffic state under known weather conditions; Input from the real-time traffic information extracted from the state estimation result after fusion; Posterior probability: calculated by Bayes formula: ; In the formula, is the conditional probability of traffic state under known current weather conditions, obtained from the state estimation result The posterior probability is evaluated by extracting the input item; The prior probability of weather represents the possibility of a certain weather appearing in a specific time period; The marginal probability of traffic state represents the possibility of a specific traffic state appearing.

[0015] Further, in the risk prediction evaluation of S103, the risk evolution trend at the future time is predicted by Markov chain state transition modeling; First, the risk state definition is performed, and the road segment risk is divided into three levels: Low risk, Medium risk, High risk; Then, the state transition probability calculation is performed: ; In the formula, is the probability of state transitioning to state ; is the fuzzy membership degree of state at time ; is the fuzzy membership degree of state at time ; , is the observed data of two consecutive times, which is the feature value combination extracted in the state estimation result ; is the time step.

[0016] Further, in the risk prediction evaluation of S103, the steady-state risk probability of the road segment is also calculated by the fuzzy membership function , which is used as an input parameter for real-time speed limit adjustment; First, the steady-state distribution is solved: through the state transition matrix of the Markov chain , the steady-state distribution vector is solved: ; In the formula, represents the long-term stable probability of state , corresponding to low, medium, and high risk levels, respectively; Then, the fuzzy membership function is constructed: ; is the fuzzy membership function considering the visibility , precipitation intensity , and traffic density variables, where visibility , precipitation intensity , and traffic density is the state estimation result decomposition quantity; Finally, the steady-state risk probability is calculated, and the calculation formula is: ; In the formula, is the steady-state risk probability, which represents the overall risk level of the road section after comprehensively considering the historical trend and the current environmental factors.

[0017] Further, in the real-time speed limit adjustment S104, based on the risk assessment and trend prediction results, i.e., the steady-state risk probability , a multi-objective optimization function is constructed to calculate and issue control of the dynamic speed limit value for the current road section; The multi-objective optimization function is as follows: Objective function minimization: ; Constraint conditions: ; ; Weight dynamic adjustment mechanism: ; In the formula, is the target speed to be optimized; is the dynamic weight, which is dynamically adjusted with the steady-state risk probability ; is the maximum traffic flow of the road; is the actual traffic flow of the road under the current speed; is the actual energy consumption of the vehicle under the current speed; is the standard energy consumption under the reference speed; , is the upper and lower limit of the allowed speed; is a key variable, representing the rate of change of road traffic flow with time; is the road traffic flow, i.e., the number of vehicles passing through a certain section per unit time; is time; is the sensitivity coefficient of traffic flow to speed difference change, i.e., the speed response coefficient; is the current average speed of the road; is the traffic density; is the risk sensitivity adjustment coefficient, which determines the sensitivity degree of the weight change with the risk, i.e., the risk sensitivity coefficient; is the summation index variable, =1 to 3 represent three different target items, =1 corresponds to the target safety, =2 corresponds to the target traffic efficiency, =3 corresponds to the target energy consumption control; The optimal vehicle speed is output by minimizing the objective function ; based on the optimal vehicle speed and the steady-state risk probability obtained by risk prediction evaluation Generate control instructions for intelligent stud light color adjustment.

[0018] Further, in the S105 dynamic warning area calculation, the suitable warning area length and position are dynamically calculated according to the vehicle operating state after speed limit adjustment and the current meteorological environment; The warning area length calculation formula is: ; In the formula, is the suitable dynamic warning area length; is the weather adjustment factor, which dynamically adjusts the warning length under different weather conditions; is the number of vehicles in the vehicle group; is the real-time speed of the first vehicle after speed limit adjustment, ; is the maximum safe braking deceleration of the vehicle; is the safety factor; is the average reaction time of the driver; is the amplification factor of vehicle speed fluctuation on the warning area; is the standard deviation of the vehicle speed in the vehicle group; is the amplification factor of acceleration fluctuation on the warning area; is the standard deviation of acceleration in the vehicle group; The warning area position calculation formula is: The warning start point : ; The warning end point : ; In the formula, is the real-time position of the vehicle; is the environmental influence factor; is the driver compensation factor.

[0019] A vehicle tracking system based on an intelligent stud array is used for the above-mentioned road intelligent induction and dynamic warning method based on meteorological perception, which comprises: Data acquisition unit: acquire real-time meteorological data, traffic data and road data; Data fusion estimation unit: fuse meteorological data, traffic data and road data through fusion algorithm to generate fusion state estimation data; Risk prediction unit: according to the fusion state data, the risk state under the current condition is calculated by the Bayesian model, and the dynamic risk is predicted according to the Markov model; Speed adjustment unit: based on the risk assessment result, the road speed limit is adjusted in real time, and safety, traffic efficiency and energy consumption are optimized as the optimization target; Warning area calculation unit: according to the real-time vehicle speed after speed limit, the dynamic warning area length of the corresponding section is calculated, and the warning start and end positions are calculated; Induction unit: according to the result, the macroscopic road network warning information is generated, the studs are controlled to guide the driving vehicles.

[0020] An electronic device, the electronic device includes a memory, a processor, and a computer program stored on the memory and capable of running on the processor; wherein the processor implements the method steps of the above-mentioned fusion weather perception road intelligent induction and dynamic warning method when executing the computer program.

[0021] A computer readable storage medium, the computer readable storage medium stores a computer program, the computer program is executed by the processor to realize the method steps of the above-mentioned fusion weather perception road intelligent induction and dynamic warning method.

[0022] The present application provides a kind of fusion weather perception road intelligent induction and dynamic warning method and system, by integrating weather monitoring, traffic state perception, dynamic risk assessment and intelligent induction control function, real-time monitoring, risk prediction and self-adaptive regulation to the road traffic environment under complex weather conditions are realized, the road traffic safety and operation efficiency are significantly improved. The following are the main beneficial effects: 1. Realize multi-source data fusion, improve the perception accuracy: use improved Kalman filter and attention mechanism, unify modeling for three types of heterogeneous data of weather, traffic and road; Introduce road feature vector to participate in the fusion process, enhance the adaptability of the model to special sections; The accuracy and robustness of state estimation are improved, which provides reliable data support for subsequent risk assessment.

[0023] 2. Build dynamic risk assessment model, realize accurate risk identification: based on Bayesian inference model, calculate the risk posterior probability of current section; Combined with Markov chain model, predict the evolution trend of future risk level; Introduce fuzzy membership function and steady-state distribution analysis, output comprehensive risk index; Support different risk modeling under different weather conditions.

[0024] 3. Introduce multi-objective speed limit optimization mechanism, consider safety, efficiency and energy consumption: build a speed limit adjustment model with safety, traffic efficiency and energy consumption control as objective function; Dynamic weight mechanism automatically adjusts the priority of each sub-target according to the current risk level; Realize the upgrade from fixed speed limit to adaptive speed limit, improve the ability to respond to sudden weather.

[0025] 4. Establish a dynamic early warning area model to improve the accuracy of induction: dynamically calculate the length of the early warning area according to vehicle speed, acceleration, driver reaction time and other parameters; introduce a fluctuation compensation term and a weather correction factor to enhance the robustness of the model; accurately calculate the starting point and endpoint of the early warning area for the starting control of intelligent studs and other devices; realize the transition from fixed warning to dynamic guidance.

[0026] 5. Support macroscopic road network induction to improve overall traffic efficiency: based on single-point early warning, generate a macroscopic induction scheme for the entire road network; publish path recommendations through variable message boards, electronic signs, V2X communication and other means; support a variety of induction strategies such as detour, split flow, lane guidance, etc.; effectively alleviate local congestion under adverse weather conditions and improve overall traffic flow stability.

[0027] 6. Good engineering adaptability and expansion potential: can be deployed on highway, bridge, tunnel, mountainous area and other complex terrain sections; supports edge computing architecture to meet low latency and high concurrency scenario requirements; modular design facilitates access to urban traffic brain, autonomous driving system and other platforms; can further improve the level of intelligence in combination with reinforcement learning and large models in the future. BRIEF DESCRIPTION OF DRAWINGS

[0028] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments described in the present application, and other drawings can also be obtained by those skilled in the art based on these drawings.

[0029] Figure 1 Flowchart of the road intelligent induction and dynamic early warning method of the present application combined with meteorological perception; Figure 2 Flowchart of risk prediction and evaluation of the present application; Figure 3 Flowchart of dynamic early warning area calculation of the present application; Figure 4 Framework diagram of the vehicle tracking system based on intelligent stud array of the present application; Figure 5 Embodiment schematic diagram of the electronic device of the present application; Figure 6 Embodiment schematic diagram of the computer readable storage medium of the present application. DETAILED DESCRIPTION

[0030] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings of the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative effort belong to the protection scope of the present application.

[0031] For improving the road passing safety and operation efficiency under complex weather conditions, the embodiment provides a road intelligent induction and dynamic warning method fusing weather perception; as shown in the figure, Figure 1 The road intelligent induction and dynamic warning method fusing weather perception comprises the following steps: S101 acquiring real-time data: Multi-source heterogeneous data of weather, traffic and road environment are collected to provide basic input for intelligent analysis. Among them, weather data, traffic data and road data are collected based on weather sensors, cameras, radars, floating car GPS and V2X communication.

[0032] The weather data includes visibility, precipitation intensity, wind speed and air temperature.

[0033] The traffic data includes vehicle speed, traffic flow, traffic density and acceleration.

[0034] The road data includes geometric characteristics, slope, curvature and historical accident rate.

[0035] S102 data fusion estimation: Based on multi-source heterogeneous data, improved Kalman filtering combined with attention mechanism is used for data fusion to generate a unified state estimation result. By fusing data from different sources, a unified state estimation data with time and space characteristics is generated, so as to improve the perception accuracy.

[0036] Specifically, the improved Kalman filtering and attention mechanism are used to fuse the feature vectors of weather data, traffic data and road data to generate a unified data group containing time and space information, and the expression is as follows: ; In the formula, is a predicted state value, which is used to construct the prior state of the filter, and is combined with to update the fusion; is a state transition matrix, which represents the change rule of the state over time; is the state value at the previous moment; is a control input matrix, which represents the influence degree of the control input on the state; is the system control input of speed limit and induction control; is the noise of the state process and the measurement process; are real-time measurements of meteorological data, road data and traffic data; is an observation matrix representing the relationship between the true state and the observation value; is an attention weight representing the contribution of different data sources to the fusion result; is a weight matrix in the attention mechanism; is a road feature vector, including road geometric parameters; is the state estimation value after fusion at time t, i.e. the state estimation result; is the gain matrix of Kalman filtering, dynamically adjusting the filtering weight.

[0037] wherein the road feature vector contains: the reciprocal of the radius of the road curve, which affects the stability of the vehicle driving; the road slope, which affects the acceleration and deceleration ability of the vehicle; the road surface type, which affects the friction coefficient; the accident-prone point identifier, which indicates whether it is a historical accident-prone section; the speed limit sign, which describes the original speed limit value of the current section; the number of lanes, which affects the traffic capacity and traffic density distribution.

[0038] The fusion process mainly includes: prediction stage, observation update, attention calculation and state correction. Among them, the prediction stage uses the state value at the previous time and the system control input of speed limit and induction control to predict the predicted state value ; the observation update stage obtains real-time measurements of meteorological data, road data and traffic data ; the attention calculation stage combines the real-time measurements of meteorological data, road data and traffic data with the road feature vector to calculate the attention weight ; the state correction stage uses the gain matrix of Kalman filtering and the attention weighted residual error to correct the predicted state, obtaining the state estimation value after fusion at time t .

[0039] The fusion algorithm improves the Kalman filtering framework by introducing the attention mechanism, realizes the efficient fusion of multi-source heterogeneous data such as meteorological, traffic and road data, and improves the state estimation accuracy and system robustness. Among them, Kalman filtering is used to model the state evolution and observation relationship; the attention mechanism enhances the model's attention ability to key data sources; the road feature vector as context information participates in the fusion, improving the algorithm's adaptability to complex environments.

[0040] S103 risk prediction evaluation: For example Figure 2As shown, based on the fused state estimation results, the current road section is dynamically evaluated and trended for risk level by Bayesian inference and Markov chain model. Through dynamic risk level evaluation and trend prediction of the current road section, decision basis can be provided for subsequent speed limit adjustment and guidance control.

[0041] Specifically, the Bayesian inference model in the embodiment is as follows: Prior probability: represents the historical probability of traffic accidents or congestion of a road section under specific weather conditions such as heavy fog, heavy rain, etc. Based on historical data analysis, it reflects the influence of weather on risk.

[0042] Likelihood probability: represents the possibility of traffic state under known weather conditions; Input from the fused state estimation results extracted real-time traffic information.

[0043] Posterior probability: calculated by Bayesian formula: ; In the formula, is the conditional probability of traffic state under known current weather conditions, extracted from the state estimation results , which is the input item for evaluating the posterior probability; is the prior probability of weather, which represents the possibility of a certain weather in a certain time period; is the marginal probability of traffic state, which represents the possibility of a certain traffic state.

[0044] Finally, the risk posterior probability at the current time is output by the Bayesian inference model as the basis for risk level division.

[0045] The Markov chain state transition modeling in the embodiment is as follows: First, risk state definition is performed, and the road section risk is divided into three levels: low risk, medium risk, high risk.

[0046] Then, the state transition probability is calculated: ; In the formula, is the probability of state transitioning to state ; is the probability of state at time​ fuzzy membership of state is state at time fuzzy membership of state , is observation data of continuous two time, is state estimation result feature value combination extracted in is time step.

[0047] The risk evolution trend at future time is calculated by the above state transition probability.

[0048] Further, steady-state risk probability calculation is carried out: The steady-state risk probability of the road section is calculated by the fuzzy membership function as the input parameter of real-time speed limit adjustment.

[0049] First, solve the steady-state distribution: through the state transition matrix of Markov chain , solve its steady-state distribution vector: ; In the formula, represents the long-term stable probability of state , low, medium and high correspond to three risk levels respectively. Then build the fuzzy membership function:

[0050] ; is the fuzzy membership function after considering visibility , precipitation intensity , traffic density variables, in which visibility , precipitation intensity , traffic density are the decomposition quantities of state estimation result .

[0051] Finally, the steady-state risk probability is calculated, and the calculation formula is: ; In the formula, is the steady-state risk probability, which represents the overall risk level of the road section after considering the historical trend and the current environmental factors.

[0052] ​The risk prediction evaluation realizes quantitative evaluation and trend prediction of road risk under complex weather conditions by combining Bayesian inference with Markov chain modeling. The Bayesian model is used to fuse weather and traffic state to generate the current risk posterior probability. The Markov chain can capture the evolution law of the risk state and support the prediction of future risk. The fuzzy membership function enhances the adaptability of the model to continuous variables and uncertain environment. The final output of the steady-state risk probability provides a scientific basis for subsequent speed limit regulation and path induction.

[0053] S104 real-time speed limit adjustment: Based on the risk evaluation and trend prediction results, i.e., the steady-state risk probability The speed limit value of the current road section is dynamically optimized with safety, traffic efficiency and energy consumption as objective functions, so as to improve road traffic safety, improve traffic efficiency and reduce vehicle energy consumption.

[0054] Specifically, the multi-objective optimization function is as follows: Objective function minimization: ; Constraint conditions: ; ; Weight dynamic adjustment mechanism: ; In the formula, is the target vehicle speed to be optimized; is the dynamic weight, which is adjusted dynamically with the steady-state risk probability ; is the maximum traffic flow of the road; is the actual traffic flow of the road under the current speed; is the actual energy consumption of the vehicle under the current speed; is the standard energy consumption under the reference speed; , is the upper and lower limit of the allowed speed; is a key variable, representing the rate of change of road traffic flow with time; is the road traffic flow, i.e., the number of vehicles passing through a certain section per unit time; is time; is the sensitivity coefficient of traffic flow to speed difference change, i.e., the speed response coefficient; is the current average speed of the road; is the traffic density; is the risk sensitivity adjustment coefficient, which determines the sensitivity degree of the weight to the risk, i.e., the risk sensitivity coefficient; is the summation index variable, =1 to 3 represent three different target items, =1 corresponds to the target safety, =2 corresponds to the target traffic efficiency, =3 corresponds to target energy consumption control.

[0055] Among them, the three optimization goals are: For safety, the lower the speed, the higher the safety factor, and the larger the value of this item; the weight increases when the risk is high, prompting the speed limit to be lowered; Traffic efficiency is used to measure the road utilization rate at the current vehicle speed. The smaller the value, the higher the efficiency. It is used for energy consumption control, measuring the unit energy consumption level at the current vehicle speed, and for energy conservation and emission reduction optimization.

[0056] Use Softmax form according to the current risk level Automatically adjust the priorities of the three goals; when the risk is high, If the risk increases significantly, the speed limit will be automatically lowered; when the risk is low, and Dominant, more emphasis on traffic efficiency and energy saving; risk sensitivity adjustment coefficient The sensitivity of the control weight change can be set according to the actual scenario.

[0057] Real-time speed limit adjustment outputs the optimal vehicle speed by minimizing the objective function ; Based on optimal speed and the steady-state risk probability obtained from risk prediction assessment Generate control instructions for adjusting the color of smart road stud lights. The specific mapping process is as follows:

[0058] Assume that after optimization calculation, we get: optimal speed limit ; Current steady-state risk probability ; The control instructions sent to the smart road studs are as follows:

[0059] Real-time speed limit adjustment is achieved by building a multi-objective optimization model and combining the risk assessment results of the current road section , realizing dynamic adaptive adjustment of speed limit value. Its core advantages are: multi-objective collaborative optimization of safety, efficiency and energy consumption, automatic adjustment of weights according to risk level, enhanced system robustness, and can be integrated into intelligent transportation systems. It supports edge computing and cloud linkage, realizing a complete closed-loop process from data perception to control execution.

[0060] S105 dynamic warning area calculation: like Figure 3As shown, based on the vehicle speed, acceleration and other operating conditions after the speed limit adjustment and the current weather environment, the appropriate warning area length and position are dynamically calculated to ensure that the driver has sufficient reaction time and distance in complex weather conditions, thereby effectively avoiding traffic accidents such as rear-end collisions and skidding.

[0061] Specifically, the appropriate warning area length and location are dynamically calculated based on the vehicle operating status after the speed limit adjustment and the current meteorological environment; The calculation formula for the warning area length is: ; Where, is the appropriate length of the dynamic warning area; It is a weather adjustment factor, which dynamically adjusts the warning length under different weather conditions; is the number of vehicles in the vehicle group; For the The real-time speed of the vehicle after the speed limit, ; The maximum safe braking deceleration of the vehicle; is the safety factor; is the average driver reaction time; is the amplification factor of vehicle speed fluctuation on the warning area; is the standard deviation of vehicle speed within the vehicle group; is the amplification factor of acceleration fluctuation on the warning area; is the standard deviation of acceleration within the vehicle group.

[0062] By calculating the length of the warning area, the appropriate dynamic warning area length is output ; Then, calculate the warning area location: The calculation formula for the warning area location is: Warning starting point : ; Warning endpoint : ; Where, The real-time location of the vehicle; is the environmental impact factor; is the driver compensation factor.

[0063] Output the warning starting point by calculating the warning area location and warning endpoints Dynamic Warning Area Calculation: By establishing a mathematical model that includes braking distance, fluctuation compensation, and weather correction, combined with the vehicle's real-time status and environmental factors, the length and location of the warning area are dynamically calculated, providing precise control instructions for road guidance equipment, significantly improving driving safety and guidance efficiency in complex weather conditions.

[0064] S106 Macroscopic Induction Information Publishing: According to the results, macroscopic road network early warning information is generated, and the corresponding induction scheme and information publishing are carried out; the studs are controlled to guide the driving vehicles. The macroscopic road network early warning information includes dynamic adjustment of stud color and information screen prompt.

[0065] In summary, the road intelligent induction and dynamic early warning method fusing meteorological perception acquires real-time meteorological data, traffic data and road data; fuses meteorological data, traffic data and road data through a fusion algorithm to generate fusion state estimation data; calculates the risk state under the current condition through a Bayesian model according to the fusion state data and predicts the dynamic risk according to a Markov model; adjusts the road speed limit in real time based on the risk assessment result, taking safety, traffic efficiency and energy consumption as optimization objectives; calculates the dynamic early warning area length of the corresponding road section according to the real-time vehicle speed after the speed limit, and calculates the early warning start and end positions; generates macroscopic road network early warning information according to the results, and the corresponding induction scheme and information publishing are carried out; the studs are controlled to guide the driving vehicles. The present application proposes a meteorological environment perception and road intelligent induction and warning method, which can realize real-time monitoring and early warning of severe weather in a wide range and precise warning of key road sections through integration of meteorological monitoring, real-time data transmission and intelligent induction functions.

[0066] On the other hand, to cooperate with the implementation of the road intelligent induction and dynamic early warning method fusing meteorological perception, the present embodiment provides a vehicle tracking system based on an intelligent stud array. As shown in Figure 4 The vehicle tracking system based on the intelligent stud array includes: The data acquisition unit 201 acquires real-time meteorological data, traffic data and road data; the data fusion estimation unit 202 fuses meteorological data, traffic data and road data through a fusion algorithm to generate fusion state estimation data; the risk prediction unit 203 calculates the risk state under the current condition through a Bayesian model according to the fusion state data and predicts the dynamic risk according to a Markov model; the speed adjustment unit 204 adjusts the road speed limit in real time based on the risk assessment result, taking safety, traffic efficiency and energy consumption as optimization objectives; the early warning area calculation unit 205 calculates the dynamic early warning area length of the corresponding road section according to the real-time vehicle speed after the speed limit, and calculates the early warning start and end positions; the induction unit 206 generates macroscopic road network early warning information according to the results, and controls the studs to guide the driving vehicles.

[0067] In addition, the road intelligent induction and dynamic early warning method fusing meteorological perception can also be implemented through an electronic device or a computer readable storage medium; specifically, as shown in Figure 5 and Figure 6As shown, the electronic device 300 includes a memory 310, a processor 320, and a computer program 311 stored on the memory and executable on the processor; wherein the processor 320 implements the method steps of the above-mentioned road intelligent induction and dynamic early warning method fusing weather perception when executing the computer program 311. The computer program 411 is stored in the computer readable storage medium 400, and the computer program 411 implements the method steps of the above-mentioned road intelligent induction and dynamic early warning method fusing weather perception when executed by the processor.

[0068] The basic principles, main features and advantages of the present application are shown and described above. It should be understood by those skilled in the art that the present application is not limited by the above-mentioned embodiments, and the above-mentioned embodiments and descriptions in the specification are only to illustrate the principles of the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the claimed present application. The scope of protection of the present application is defined by the appended claims and their equivalents.

Claims

1. A method for intelligent road guidance and dynamic warning integrating meteorological perception, characterized in that: The following steps are involved: S101 acquires real-time data: It collects multi-source heterogeneous data on meteorology, traffic, and road conditions to provide basic input for intelligent analysis; S102 Data Fusion Estimation: Based on multi-source heterogeneous data, an improved Kalman filter combined with an attention mechanism is used to perform data fusion to generate a unified state estimation result; S103 Risk Prediction and Assessment: Based on the fused state estimation results, dynamic risk level assessment and trend prediction are performed on the current road section through Bayesian reasoning and Markov chain models; S104 Real-time Speed ​​Limit Adjustment: Based on risk assessment and trend prediction results, the speed limit of the current road section is dynamically optimized with safety, traffic efficiency, and energy consumption as the objective functions; S105 Dynamic Warning Area Calculation: Calculates the appropriate dynamic warning area length and location based on vehicle operating status and weather conditions; S106 Macro-guidance information release: Generate macro-road network warning information based on the results, implement corresponding guidance plans and information release; control road spikes to guide driving vehicles.

2. The method for intelligent road guidance and dynamic warning integrated with meteorological perception according to claim 1 is characterized by: In S101, the meteorological data, traffic data, and road data are collected based on meteorological sensors, cameras, radars, floating vehicle GPS, and V2X communication. Meteorological data include: visibility, precipitation intensity, wind speed, and temperature; Traffic data includes vehicle speed, traffic volume, traffic density, and acceleration; Road data includes: geometric characteristics, slope, curvature, and historical accident rates; In the S102 data fusion estimation, an improved Kalman filter and attention mechanism are used to fuse the feature vectors of meteorological data, traffic data, and road data to generate a unified data set containing spatiotemporal information, which is expressed as follows: ； Where, To predict the state value, it is used to construct the prior state of the filter, and then combined with Perform integration updates; is the state transfer matrix, which represents the change of state over time; is the state value at the previous moment; is the control input matrix, which indicates the influence of the control input on the state; System control input for speed limit and induction control; is the noise of the state process and the measurement process; Real-time measurements of weather data, road data, and traffic data; is the observation matrix, which represents the relationship between the true state and the observed value; is the attention weight, which indicates the contribution of different data sources to the fusion result; is the weight matrix in the attention mechanism; is the road feature vector, including road geometric parameters; is the state estimation value at time t after fusion, that is, the state estimation result; is the gain matrix of the Kalman filter, which dynamically adjusts the filter weights.

3. The method for intelligent road guidance and dynamic warning integrated with meteorological perception according to claim 1 is characterized by: In the S103 risk prediction and assessment, the Bayesian inference model is used to output the posterior probability of risk at the current moment, which serves as the basis for risk level classification; Prior probability: It represents the historical probability of traffic accidents or congestion on a certain road section under specific meteorological conditions; Obtained based on historical data analysis, reflecting the impact of weather on risks; Likelihood probability: ; Indicates the probability of traffic conditions occurring under known weather conditions; Input comes from the fused state estimation result Real-time traffic information extracted from Posterior probability: calculated using the Bayesian formula: ； Where, It is the conditional probability of traffic status under the current weather conditions, which is determined by the state estimation result Extracted from, as input, used to evaluate the posterior probability; is the prior probability of the weather, which indicates the possibility of a certain weather occurring in a specific time period; is the marginal probability of the traffic state, which indicates the possibility of a specific traffic state occurring.

4. The method for intelligent road guidance and dynamic warning integrated with weather perception according to claim 1 is characterized by: In the S103 risk prediction and assessment, the risk evolution trend at future moments is predicted through Markov chain state transition modeling; First, define the risk status and divide the road section risk into three levels: For low risk, For medium risk, is high risk; Then, the state transition probability is calculated: ； Where, For status Transfer to state probability; Status At the moment The fuzzy membership of Status At the moment The fuzzy membership of , is the observation data of two consecutive moments, which is the state estimation result The combination of eigenvalues ​​extracted from is the time step.

5. The method for intelligent road guidance and dynamic warning integrated with meteorological perception according to claim 1 is characterized by: In the S103 risk prediction assessment, the steady-state risk probability of the road section is calculated using the fuzzy membership function. , as input parameter for real-time speed limit adjustment; First solve the steady-state distribution: through the state transition matrix of the Markov chain , solve its steady-state distribution vector: ; Where, Indicates status The long-term stability probability of Corresponding to three risk levels: low, medium and high; Then construct the fuzzy membership function: ; To comprehensively consider visibility , precipitation intensity , traffic density The fuzzy membership function after the variable, where visibility , precipitation intensity , traffic density is the state estimation result The amount of decomposition; Finally, the steady-state risk probability is calculated using the following formula: ； Where, is the steady-state risk probability, which represents the overall risk level of the road section after comprehensively considering historical trends and current environmental factors.

6. The method for intelligent road guidance and dynamic warning integrated with weather perception according to claim 1 is characterized by: S104 real-time speed limit adjustment, based on risk assessment and trend prediction results, i.e. steady-state risk probability ,By constructing a multi-objective optimization function, the dynamic speed limit value of the current road section is calculated and issued; The multi-objective optimization function is as follows: The objective function is minimized: ; Constraints: ; ； Dynamic weight adjustment mechanism: ; Where, is the target vehicle speed to be optimized; is the dynamic weight, which changes with the steady-state risk probability Dynamic adjustment; is the maximum traffic flow of the road; is the actual traffic volume of the road at the current speed; is the actual energy consumption of the vehicle at the current speed; is the standard energy consumption at the reference speed; , The upper and lower limits of the permitted speed; is a key variable, indicating the rate of change of road traffic flow over time; is the road traffic flow, that is, the number of vehicles passing through a certain section per unit time; For time; is the sensitivity coefficient of traffic flow to changes in vehicle speed difference, that is, the vehicle speed response coefficient; is the current average road speed; is the traffic density; The risk sensitivity adjustment coefficient determines the sensitivity of the weight to changes in risk, that is, the risk sensitivity coefficient; To sum index variables, =1 to 3 means three different target items, =1 corresponds to target security, =2 corresponds to the target traffic efficiency, =3 corresponds to target energy consumption control; Output the optimal vehicle speed by minimizing the objective function ; Based on optimal speed and the steady-state risk probability obtained from risk prediction assessment Generate control instructions for adjusting the color of intelligent road stud lights.

7. The method for intelligent road guidance and dynamic warning integrated with weather perception according to claim 1 is characterized by: In the S105 dynamic warning area calculation, the appropriate warning area length and location are dynamically calculated based on the vehicle operating status after the speed limit adjustment and the current weather conditions; The calculation formula for the warning area length is: ； Where, is the appropriate length of the dynamic warning area; The weather adjustment factor dynamically adjusts the warning length under different weather conditions; is the number of vehicles in the vehicle group; For the The real-time speed of the vehicle after the speed limit, ; The maximum safe braking deceleration of the vehicle; is the safety factor; is the average driver reaction time; is the amplification factor of vehicle speed fluctuation on the warning area; is the standard deviation of vehicle speed within the vehicle group; is the amplification factor of acceleration fluctuation on the warning area; is the standard deviation of acceleration within the vehicle group; The calculation formula for the warning area location is: Warning starting point : ; Warning endpoint : ; Where, The real-time location of the vehicle; is the environmental impact factor; is the driver compensation factor.

8. A vehicle tracking system based on an intelligent road stud array, wherein the vehicle tracking system based on the intelligent road stud array is used in the road intelligent guidance and dynamic warning method integrated with meteorological perception according to any one of claims 1 to 8, characterized in that: The vehicle tracking system based on the intelligent road stud array includes: Data acquisition unit: acquire real-time weather data, traffic data and road data; Data fusion estimation unit: fuses meteorological data, traffic data and road data through fusion algorithm to generate fusion state estimation data; Risk prediction unit: Based on the fusion state data, the risk state under the current conditions is calculated using the Bayesian model and the dynamic risk is predicted using the Markov model; Speed ​​adjustment unit: Based on risk assessment results, it adjusts road speed limits in real time, optimizing safety, traffic efficiency, and energy consumption; Warning area calculation unit: calculates the length of the dynamic warning area of ​​the corresponding road section according to the real-time vehicle speed after the speed limit, and calculates the warning starting and ending points; Guidance unit: Generates macro-road network warning information based on the results, controls road spikes for guidance, and directs driving vehicles.

9. An electronic device, characterized in that: The electronic device includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor; wherein, when the processor executes the computer program, the method steps of the road intelligent guidance and dynamic warning method integrating meteorological perception as described in any one of claims 1-8 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the method steps of the road intelligent guidance and dynamic warning method integrating meteorological perception as described in any one of claims 1-8.

Images